United States
Environmental Protection
Agency
Administration And
Resources Management
(PM-273)
21M-3002
March 1991
Indoor Air Quality
And Work Environment Study
EPA Headquarters' Building
Volume 3
Relating Employee Responses
To The Fpllow-Up Questionnaire
With Environmental Measurements
Of Indoor Air Quality
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INDOOR AIR QUALITY AND WORK ENVIRONMENT STUDY:
EPA HEADQUARTERS BUILDINGS
VOLUME III:
Relating Employee Responses to the Follow-Up Questionnaire
with Environmental Measurements of Indoor Air Quality
Atmospheric Research and Exposure Assessment Laboratory
Office of Research and Development
U. S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
January 1991
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DISCLAIMER
Although the research described in this document has been funded wholly or in
part by the United States Environmental Protection Agency, it has not been
subjected to Agency review and therefore does not necessarily reflect the views
of the Agency, and no official endorsement should be inferred. Mention of trade
names or commercial products does not constitute endorsement or recommendation
for use.
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ACKNOWLEDGEMENT
We wish to thank all those whose efforts contributed to the success of this
study.
We thank Jan Parsons for her editorial review and many helpful suggestions
as to format and style.
We appreciate the EPA management support of Gary Foley, Hugh McKinnon, Jack
Puzak, Dale Pahl, David Weitzman, Julius Jimeno, Kevin Teichman, Jeff Davidson,
and David Smith. They have worked closely with the project team to ensure the
success of this study.
We appreciate the review of this report and the helpful comments provided
by William Hirzy, Hale Vandermer, and Myra Cypser of the National Federation of
Federal Employees. We also appreciate the review and helpful comments by Kirby
Biggs of the American Federation of Government Employees.
We appreciate the review of the data analysis plan and this report provided
by the Peer Review Panel: Steven D. Colome, Claudia S. Miller, and Peder Skov.
Their thoughtful suggestions helped in making this a better report.
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TECHNICAL TEAMS
This study of indoor air quality and work environment was conducted by
three technical teams representing multiple organizations. It was jointly
developed and carried out at EPA headquarters and the Library of Congress Madison
Building under the auspices of these teams working independently of both
management and unions at both EPA and the Library of Congress.
Overall project coordination was provided by two technical team leaders:
Kevin Teichman at EPA and Lawrence Fine at NIOSH.
EPA
NIOSH
REPORTING AND ANALYSIS TEAM
C. J. Nelson, Statistician
Mel Kollander, Survey Stat
Lance Wallace, Envir. Sci.
Brian Leaderer, Envir. Sci.a
Rebecca Bascom, M.D.e
Andy Clayton, Statistician*
George Dunteman, Statistician*
Anne Fidler, Epidemiologist
Joseph Hurrel, Psychologist
Brian Leaderer, Envir. Scientist'
John Selfridge, Envir. Scientist'
MONITORING TEAM
Ross Highsmith, Chemist
Lance Wallace, Envir. Sci.
Tom Lumpkin, Chemist
Steve Hern, Biologist
Vinson Thompson, Chemist
Ken McLauchlan, Prof. Engineer1*
Linda Stetzenbach, Microbiologistc
Richard Gorman, Industrial Hygienist
Michael Crandall, Industrial Hygien.
Rebecca Stanevich, Ind. Hygienist
Brian Leaderer, Envir. Scientist*
John Selfridge, Envir. Scientist'
Mel Kollander, Survey Stat.
Lance Wallace, Envir. Sci.
Cecil Brenner, Stat.
Robert P. Clickner, Stat.d
Stephen K. Dietz, Stat.d
SURVEY DESIGN TEAM
Anne Fidler, Epidemiologist
Thomas Wilcox, Physician
Joseph Hurrell, Psychologist
Richard Hornung, Statistician
Brian Leaderer, Envir. Scientist"
John Selfridge, Envir. Scientist'
b
c
d
e
f
John B. Pierce Foundation, Yale University.
Kansas State University.
Consultant.
University of Nevada, Las Vegas.
Westat.
University of Maryland, Baltimore.
Research Triangle Institute.
Dr. Self ridge is now at
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CONTENTS
Chapter
LIST OF TABLES AND FIGURES vii
EXECUTIVE SUMMARY ES-1
1. INTRODUCTION 1-1
1.1 Background 1-1
1.2 Study Objectives 1-1
1.3 Study Design and Limitations 1-2
1.4 Organization of Report 1-4
2. BACKGROUND 2-1
2.1 Previous Indoor Air Quality Studies/Hypotheses 2-1
2.2 Description of the Environmental Protection Agency
Headquarters Buildings 2-2
3. EPA HEADQUARTERS BUILDINGS ENVIRONMENTAL MONITORING AND
FOLLOW-UP SURVEY DESIGN 3-1
3.1 Selection of Environmental Monitoring Sites 3-1
3.2 Environmental Monitoring Study Design 3-3
3.3 Follow-Up Survey Design 3-5
4. FOLLOW-UP SURVEY RESULTS 4-1
4.1 Data Sources and Merging of Data Files 4-1
4.2 Outcome Variables 4-4
4.3 Potential Confounding Variables 4-14
5. ENVIRONMENTAL MONITORING RESULTS FOR RESPONDENTS TO THE
FOLLOW-UP SURVEY 5-1
5.1 Temporal Data -. 5-1
5.2 Volatile Organic Compound Data 5-4
5.3 Microbiological Data 5-7
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CONTENTS (continued)
Chapter page
6. RESULTS RELATING SURVEY DATA TO ENVIRONMENTAL MONITORING DATA .... 6-1
6.1 Analytical Objectives 6-1
6.2 Analytical Approach 6-2
6.3 Summary of Modeling Results 6-9
6.4 Discussion of Modeling Results: Health Symptoms 6-14
6.5 Discussion of Modeling Results: Thermal Comfort 6-27
6.6 Discussion of Modeling Results: Odors 6-28
6.7 Discussion of Modeling Results: Air Quality
Acceptability 6-29
6.8 Discussion of Modeling Results: Mood State Scales 6-31
7. CONCLUSIONS AND RECOMMENDATIONS 7-1
7.1 Conclusions 7-1
7.2 Recommendations 7-4
8. REFERENCES 8-1
APPENDICES
A. Indoor Air Quality and Work Environment Survey: EPA Headquarters
B. Indoor Air Quality and Work Environment Follow-Up Survey: EPA
Headquarters
C. Tabulations of Responses to the Indoor Air Quality and Work
Environment Follow-Up Survey: EPA Headquarters
D. Summary of Modeling Results
E. Detailed Modeling Results for Model A
F. Detailed Modeling Results for Model B
G. Detailed Modeling Results for Model C
H. Detailed Modeling Results for Model D'
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LIST OF TABLES AND FIGURES
ES-1 Dependent Variables Associated with Temporally Measured Variables
in Model A (0.01 level of significance) ............. ES-13
ES-2 Dependent Variables Associated with Volatile Organic Compounds,
Integrated RSP, and Microbiological Variables in Model A (0.01
level of significance) ..................... ES-13
ES-3 Dependent Variables Associated with Self -reported Comfort and
Odor Concerns in Model C (0.01 level of significance) ...... ES-14
ES-4 Dependent Variables Associated with Workstation Variables and
Personal Variables in Model B (0.01 level of significance) ... ES-15
4-1 Distribution of Questionnaire 2 Respondents by Building and Sector 4-21
4-2 Percentage of Responding Employees Reporting Health Symptoms
That Began at Work on the Day of Environmental Monitoring,
by Gender and Workstation Location ................ 4-22
4-3 Percentage of Responding Employees Reporting Comfort Concerns
on the Day of Environmental Monitoring, by Gender and
Workstation Location ....................... 4-24
4-4 Percentage of Responding Employees Reporting Odors on the Day of
Environmental Monitoring, by Gender and Workstation Location . . .4-25
4-5 Percentage of Responding Employees Reporting Air Quality Concerns
on the Day of Environmental Monitoring, by Gender and Workstation
Location ........................... 4-26
4-6 Summary of Distributions of Mood-State Variables, by Gender .... 4-27
4-7 Means of Mood-State Scales, by Gender and Workstation Location . . 4-27
4-8 Definitions of Work Station Variables Used as Independent
Variables for Modeling Health Symptoms, Comfort, Odor, and
Mood-State Variables ....................... 4-28
4-9 Definitions of Personal/Medical Variables Used as Independent
Variables for Modeling Health Symptoms, Comfort Concerns, Odors,
and Mood-State Variables ..................... 4-29
4-10 Distribution of Dichotomous Variables Used as Potential
Confounding Variables, by Gender and Workstation Location ..... 4-31
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LIST OF TABLES AND FIGURES (continued)
Table Page
4-11 Summary of Distributions of Continuous Potential Confounding
Variables, by Gender 4-33
4-12 Mean Values for Continuous Potential Confounding Variables,
by Gender and Workstation Location 4-34
5-1 Temporal Variables 5-11
5-2 Summary of Overall Distributions of Temporally Measured Variables . 5-11
5-3 Means of Temporally Measured Variables, by Gender and Workstation
Location 5-12
5-4 Summary of Overall Distributions of Variables in VOC Data File . . 5-13
5-5 Means of Variables in VOC Data File, by Gender and Workstation
Location 5-14
5-6 Volatile Organic and Microbiological Variables 5-15
5-7 Summary of Overall Distributions of Variables in Microbiological
Data File 5-16
5-8 Means of Variables in Microbiological Data File, by Gender and
Workstation Location 5-17
6-1 Listing of Major Analytic Objectives 6-34
6-2 Summary of Stepwise Regression Results 6-35
6-3 Summary of Hypothesis Testing Results 6-36
6-4 Summary of Model B Results for Potential Confounders 6-37
Fi g:ure Page
6-1 Modeling Strategy for Health Symptom Outcomes 6-33
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EXECUTIVE SUMMARY
Background
In rec?r* years, employees in the three Headquarters building complexes
occupied by the U.S. Environmental Protection Agency (EPA) have expressed
concerns about indoor air pollution and work environment discomforts. Some of
these concerns arose from incidents in which EPA employees became ill shortly
after building renovations. In response to these continuing concerns, EPA
decided to undertake a systematic study of the nature and spatial distribution
of employees' health symptoms and comfort concerns and to attempt to determine
if associations exist between employee responses and specific workplace
conditions.
This research effort was integrated with a parallel study at the Library
of Congress Madison Building, where employees were also reporting health symptoms
and discomfort that they attributed to the building. The study team consisted
of researchers from EPA, the National Institute of Occupational Safety and Health
(NIOSH), the John B. Pierce Foundation at Yale University, and Westat, Inc., a
health statistics consulting firm. The National Institute of Standards and
Technology (NIST, formerly the National Bureau of Standards, NBS) was engaged to
study the Madison Building's ventilation system. Both the EPA and the Library
of Congress surveys made use of similar study designs and survey instruments;
however, separate reports are being prepared for each agency.
Objectives
Specific objectives of the study were the following:
1. Survey 'Ae nature, magnitude, and spatial distribution of health
symptoms and comfort concerns.
2. Characterize selected physical, chemical, and microbiological
aspects of the building in selected locations during the survey period.
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3. Generate hypotheses from any associations observed between health
and comfort effects and environmental factors while taking into
account factors that would confound or modify such associations.
4. Identify areas not in compliance with standards or guidelines.
This report, the third in a series of reports documenting the EPA portion of the
study, addresses Objective 3.
Description of Buildings
The EPA Headquarters is housed in three separate office complexes located
within a several mile radius in the Washington, DC, area: the Waterside Mall
complex, the Fairchild Building, and the Crystal Mall Building. The Waterside
Mall complex includes a central four-story shopping mall and two 12-story towers
(East and West). It is located at 401 M Street, S.W. The original structure was
built in 1970, and EPA took occupancy in 1971-1972. Three additional structures
(Northeast mall, Southeast mall, and Southwest mall) were added during the 1980s.
At the time of the study, EPA leased 1,004,450 ft2 of office space, which was
assigned to approximately 3700 EPA personnel. The Fairchild Building, a
nine-story office building located at 499 South Capitol Street, S.W., near
downtown Washington, DC, was first occupied by EPA during the 1979-1980 time
frame. At the time of the study, four floors (121,015 ft2) were leased to EPA,
housing approximately 850 EPA employees. The Crystal Mall is a 14-floor office
building located at 1921-31-41 Jefferson Davis Highway, Arlington, VA. At the
time of the study, four floors (103,019 ft2) of office space, leased initially
to EPA during 1971-1972, were assigned to approximately 560 persons.
Study Design
An extensive questionnaire, the Employee Survey Questionnaire, was
administered to all employees (approximately 5000) working in the three EPA
complexes. Responses were obtained for 3955 employees. This questionnaire,
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administered in February 1989, asked about health symptoms present within the
previous year and last week and their relationship to time at work. Also asked
were extensive questions about demographic and personal factors, as well as
descriptions of the work environment. The first report (Volume I) summarized the
design, conduct, and descriptive statistics of this initial cross-sectional
study.
The responses to the Employee Survey Questionnaire were used in selecting
a set of environmental monitoring sites (rooms/areas). Monitoring was performed
during normal working hours during the week of March 6-10, 1989. The monitoring
results were presented in Volume II. In conjunction with the monitoring, a
second survey, called the supplemental or follow-up survey, was administered to
EPA employees in the vicinity of the monitoring sites. This follow-up survey
asked about health symptoms on the day the questionnaire was administered, and
the relationship of symptoms to the workday. The primary intent of the follow-up
survey was to estimate the prevalence of work-related health symptoms in areas
where environmental monitoring was being performed. The design, conduct, and
results of the follow-up survey are presented in this report. The statistical
analysis results are of three major types:
1. descriptive statistics characterizing the information reported by
respondents to the follow-up questionnaire
2. descriptive statistics characterizing the environmental monitoring
information obtained in offices of these respondents
3. statistical modeling results that relate the questionnaire response
data to the environmental data.
The third item -- determining the association between the environmental factors
measured at the EPA Headquarters Buildings and the reported health symptoms,
perceived indoor air quality (IAQ), comfort concerns, mood states, and odors
noticed -- is the main focus of this report.
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Variables Used In the Statistical Analyses
The statistical analyses focus on those 384 individuals in the study who
responded to both the first questionnaire and the follow-up questionnaire. The
dependent (outcome) variables on each individual's data record were developed
from the health, comfort, odor, overall air quality, and mood-state responses in
the follow-up questionnaire. In some cases, the outcome variables for the
analyses were formed by combining responses of several similar questionnaire
items (e.g., several similar health symptoms) from the follow-up survey. The
particular groupings were largely determined by examining results of principal
components analyses that were applied to similar items from the first employee
survey. Confounding variables, both personal (age, gender, smoking, etc.) and
workstation (type of office, carpet in office, etc.), were taken from both the
first and follow-up questionnaires. The choice of initial workstation and
personal/medical variables to be included as potential confounders was primarily
based upon results of prior studies (Surge et ml.. 1990; Skov and Valbjorn,
1987). Detailed definitions and summary statistics for the dependent and
confounding variables are found in Chapter 4.
Also associated with each individual were various environmental variables
that were measured in his/her workstation area on the same day the questionnaire
was filled out. All individuals used in the analyses had temporal variables
measured in their area (morning, midday, and afternoon measurements of
temperature, humidity, carbon dioxide, and instantaneous respirable suspended
particulates [RSP]), These data were available for 100 monitoring sites (383
employees). Approximately half of these employees (56 sites) also had
microbiological and volatile organic compound (VOC) concentrations measured in
their vicinity. Detailed summaries of the environmental variables are found in
Chapter 5,
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Study Limitations
Observational studies of this type have certain limitations that can affect
the interpretation of results. Several such limitations specific to this study
should be recognized. First, it Is clear that inferences cannot be made to any
buildings other than the three EPA buildings included in the study. In fact,
with the exception of the data obtained solely by responses to the first
questionnaire (approximately 4000 respondents among the 5000 employees),
inferences cannot be extended beyond those areas of the buildings that were
actually selected for monitoring, because the process used to select the
monitoring sites was purposeful, rather than random (see Chapter 3). Second,
inferences to other points in time are not possible. No sampling over time was
used; rather the study provides simply a "snapshot" of the monitoring sites at
the given point in time (essentially a single workday) that monitoring took
place. Third, budget limitations mandated that monitoring be performed in the
middle of a room occupied by perhaps several employees, rather than in the
"breathing zone" of each individual. While this is a commonly used
approximation, it is recognized that results from such indirect estimates of an
individual's exposure may differ from measurements taken in a discrete breathing
zones. Finally, the ability to detect associations was hampered by the small
sample size, especially for those analyses relating to microbiological and VOC
measurements.
In the case of persons with sensitivities to low levels of chemicals, it
may be that different individuals experience different symptoms, even when
exposed to the same substances (Ashford and Miller, 1989). This observation
corresponds with the experiences reported by the most affected individuals who
left EPA, If thi» was the case, attempts to correlate single symptoms or even
clusters of symptoms with exposure measurements may be thwarted. The present
report does not focus on the most affected Individuals and was not statistically
designed to address this problem.
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In retrospect, it is unlikely that health effects would be detected with
the limited data collected in this study. This is due to several factors: the
study design, the limited variability of environmental pollutant concentrations
(e.g., geometric standard deviations ranged between 1.6 and 2.1 |ig/m3), and low
values of the environmental measures (e.g., geometric means ranged from 0.9 to
11.0 |ig/m3). The limited variability in the dependent variables that were
considered also contributed to the problem. However, the study design did meet
the stated study objectives utilizing the available time and resources. The
limited variability of the results could not have been predicted before the study
was conducted.
Statistical Analysis Methods
To determine whether there were associations between the outcome measures
(the self-reported health symptoms [that began while at work], thermal comfort
concerns, odors, mood-state measures, and air quality measures) and the
environmental monitoring results, a series of (logistic) regression analyses was
performed. The basic strategy consisted of five steps:
Step 1. Stepwise linear regression was used to select meaningful
confounding variables from among the initially specified set. Separate
models for each outcome measure were estimated for males and females.
Step 2. Using those confounders identified at step 1, regression models
(Model A) were estimated and statistical tests were performed to identify
those temporal variables associated with the self- reported outcome
measures. All outcome variables were binary variables except the
mood-state variables. Hence, all regression models were logistic, except
for the mood states, for which ordinary linear regression was used.
Step 3. Interim models (Model B) were then fit for each outcome. These
contained as independent variables the temporal variables and the
workplace and personal confounders that were statistically significant in
Model A. These variables were used as a set of core variables for
subsequent models.
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Step 4. Variables reflecting respondents' reported perceptions of thermal
comfort and odor concerns were then added as independent variables to
those of Model B (to form Model C) in order to test if these were
associated with the outcome measures (self-reported health symptoms and
perceived indoor air quality [IAQ]). Since some of the rooms were
selected by the frequency with which high- and low-complaint health
symptoms and high- and low-complaint comfort concerns occurred, these
associations (or the strength of these associations) may have been unduly
influenced by the monitoring-study design.
Step 5. The final set of models attempted to determine the association
between microbiological (bacteria, fungi, and thermophiles) and VOC
variables and the health symptoms, perceived IAQ, odors noticed, and mood
state variables (Model D). These models contained the independent
variables of Model B and 14 other independent variables (four VOC- related
variables, integrated RSP concentration [log scale], and nine
microbiological measures). Because of the number of variables and the
fact that these environmental measurements were made at fewer sites,
estimation of many of the models was not possible. Six of the nine
microbiological variables were therefore dropped. The resultant model,
referred to as Model D', was thus estimated. Because of the smaller
sample size, less power for detecting associations is achievable for these
models than for Models A and C.
Conclusions
The major findings are summarized below. A 0.01 level of statistical
significance was used as a basis for judging the significance of the various
associations. Use of a 0.01 level was judged appropriate because of the large
number of statistical tests being performed. More false positive tests would be
expected if a higher significance level such as 0.05 were used. However, results
using both the 0.01 and 0.05 confidence level are shown in Tables 6-2 through
6-4. Complete results of the logistic regressions.on symptom clusters, found in
Appendices E through H, show the parameter estimates, the probability that the
estimate is different from zero, the odds ratio, and the 99% confidence level.
(Some of the health symptoms had low prevalences reported for both males and
females; models ten-Jed to overfit in these cases. None of the tables in this
section present results for these symptoms, which included chest symptoms
[variable H8] , chills and fever symptoms [H12], dizziness/lightheadedness [HIS],
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and dry/itchy skin [H16]. Also, since variable H3 is a combination of variables
HI [nonspecific IAQ symptoms) and H2 [mucous membrane symptoms], results for H3
are also omitted.)
Model A (as described in Chapter 6) was used to test the temporal
variables (temperature, relative humidity, carbon dioxide concentration,
integrated RSP concentration, and temperature change) for significant
associations with the employee-reported health, comfort, odor, perceived air
quality, and mood-state variables. Statistically significant (0.01 level)
results are summarized in Table ES-1. In areas that had higher measured C02
levels, males reported a significantly higher prevalence of nasal/cough symptoms.
However, in this same model, temperature showed a negative association (at the
0.05 level) with the nasal/cough symptom prevalence; because the C02 and
temperature variables are highly correlated with one another, it is unclear as
to what extent either of these associations should be considered real. Both
males and females more often reported too cool and/or too drafty conditions in
areas that had lower temperatures measured. The sparseness of significant
relationships among the outcome measures and the temporal measurements may be due
to the limited degree of variability in the latter; for example, the humidity
ranged from 18 to 38%, (see Table 5.2).
Model D' tested whether levels of chemicals (VOCs), aerosols (RSP), or
micrcbiologicals could be associated with the health symptoms, mood states,
odors, and general perceptions of air quality reported by the participants.
Statistically significant results are summarized in Table ES-2. Because of the
small number of sites at which the measurements were made, this model has a
reduced number of observations (about half as many as in Models A, B, and C) and
correspondingly reduced power to detect associations. In fact, no strong
(p<0.01) associations of VOC or RSP levels with any of the outcomes occurred
simultaneously for both men and women.
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For men, only one strong relationship with VOCs was observed: Men in areas
with higher levels of aromatic compounds (e.g., toluene and xylene) were
significantly more likely to complain of cosmetic and other odors. These
chemicals are, in fact, used heavily in cosmetics and many other consumer
products; however, the concentrations measured are hundreds of times below the
known odor thresholds of these chemicals. It is possible that an accompanying
highly odorous chemical (such as acetone or butyl acetate) was responsible for
the odors (Fanger, 1988; Otto, et al.. 1990).
For women, a strong relationship with RSP was observed: Indoor air quality
was more often perceived as fair or poor by women in areas with lower levels of
RSP. This result may be spurious, since the observed levels of RSP were
extremely low, and no observable effects would be expected; also, the direction
of the effect is counterintuitive. A strong negative association between
thermophile levels and prevalence of mucous membrane symptoms was observed for
women. It is possible that this association is fortuitous. However, one
speculation is that the relationship between the thermophile concentration and
mucous membrane irritation is an indirect measure of long term-local humidity.
Thermophiles are known to increase in warm moist conditions. While the humidity
measured during the study was uniformly low and varied very little, it is
possible that greater variation occurs in the building over time. If this is so,
the concentration of thermophiles may reflect variations in average local
humidity. Areas which had consistently lower humidity may have lower thermophile
concentrations. Similarly, areas with consistently lower humidity might be
associated with increased mucosal symptoms.
We conclude that because of the relatively small number of sites monitored
for VOCs, integrated RSP, and bioaerosols, the development of models that allowed
testing of relationships between these measures and the various outcome measures
was hampered (i.e., there was limited power to detect such effects). This
limitation was compounded by the fact that the observed levels of the VOCs,
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integrated RSP, and microbiologicals were uniformly low across the monitoring
sites compared to published American Society of Heating, Refrigerating, and Air-
conditioning Engineers guidelines (ASHRAE 62-89) and the 10 public-access
building study (Wallace et al.. 1987).
This study was unable to establish consistent relationships between
measured environmental parameters and self-reported health and thermal comfort
perceptions among the sampled employees. (Some of the employees represented
areas having high and low rates of health and comfort complaints, as established
from a questionnaire administered a few weeks earlier.) This inability to find
relationships does not preclude the possibility that such relationships might,
in general, exist. It should be remembered, for instance, that measurements at
a given office were made on only one day and that that day may have been atypical
(for a number of reasons). For example, verbal reports of the unusually high
airflow during the monitoring week were heard from many employees.
This study in general demonstrated a stronger association between
employees' reported health symptoms and their perceived thermal comfort measures
(including cosmetic/body odors) than between the reported health symptoms and the
environmental measurements. Specifically, in Model C, females who reported
cosmetic/body odors and hot/stuffy air tended to report health symptoms
previously associated with poor indoor air quality (see Table ES-3). Employee-
reported central nervous system symptoms were significantly associated with the
u.se of chemicals at the workstation (p<0.05) and increased reports of cosmetic
odors (p<0.05). Males' reporting of these same types of symptoms were more
generally associated with complaints of dry air. There are several possible
explanations for these interesting findings. First is the likelihood that the
observed associations are partly due to the site selection procedure (i.e., since
rooms were ranked on the basis of both health and thermal comfort indices, rooms
having high values of both indices and rooms having low values of both indices
were overrepresented in the monitoring study). Second is the possibility that
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human "sensors" of thermal comfort, with a great capacity for memory, are better
"instruments" than mechanical/chemical sensors placed in fixed locations for
short periods of time. A third explanation is that common psychological factors
similarly influence perception of thermal comfort and the reporting of health
symptom occurrences. According to this explanation, some people will report
concerns whether the issue is air quality or health. A fourth possible
explanation is that differential susceptibility exists among the employees.
People's perception of thermal comfort may be affected by health symptoms that
they are experiencing while at work (e.g., people who develop a headache in a
room may be more likely to describe that room as being uncomfortable). That is,
the perception of the environment reflects the risk of that environment to the
individual. It is not clear which of these various explanations is most
plausible.
In developing the above-described models, a number of personal and
workstation variables were found to be significantly related to the health
symptoms, perceived IAQ rating, comfort concerns, odors noticed, and mood states.
Hundreds of tests were performed, and Table ES-4 summarizes only those that were
significant at the 0.01 level (Model B).
Recommendations
Based on the results of the tests conducted here and the results from both
Volumes I and Volume II, the following recommendations are made. Since
measurements were made only in the winter while the humidity was low, mechanisms
for humidifying the indoor air during the winter heating season should be
considered. However, this recommendation should be carefully studied prior to
implementation. Humidification of the supply air to any office building can
increase the potential for additional airborne microbiological agents, which
might increase the risk of injury to employees.
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Because the effects of cosmetics, body, and non-fish foods odors on health
symptoms are significant, the employees should be informed of these findings and
encouraged to be sensitive to the concerns of their fellow employees regarding
the use of scented cosmetics, etc.
Providing employees a way to have more control over their work areas may
improve their perception of indoor comfort and air quality. For example, lack
of privacy, meeting areas, furniture arrangement, wall decoration, and other
basic design factors influence a worker's sense of autonomy and productivity.
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Dependent Variables Associated with Temporally Measured Variables
in Model A (0.01 level of significance)
Increased prevalence of
was reported by who worked in areas having
Nasal, Cough (H7) symptoms males
increased Carbon Dioxide (T3)
Too Drafty/Too Cold (C4)
males
females
decreased Temperature (Tl)
decreased Temperature (Tl)
TABLE ES-2. Dependent Variables Associated with Volatile Organic Compounds,
Integrated RSP, and Microbiological Variables in Model D' (0.01
level of significance)
Increased prevalence of
was reported by who worked in areas having
Mucous Membrane (H2)
symptoms
females
decreased Thermophiles (V8)
Fair/Poor IAQ (Al)
females
decreased RSP (V5)
Body, cosmetic or non-
fish food odors (02)
males
higher aromatic levels (V2)
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TABLE ES-3.
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Dependent Variables Associated with Self-reported Comfort and Odor
Concerns in Model C (0.01 level of significance)
Increased prevalence of
Mucous Membrane (H2)
symptoms
Ergonomic (H5) symptoms
Eye Irritation (H9)
symptoms
Throat (H10) symptoms
Tiredness (Hll) symptoms
Fair/Poor IAQ (Al)
Poor IAQ (A2)
Non- Specific IAQ (HI)
symptoms
Mucous Membrane (H2)
symptoms
Headache & Nausea (H6)
symptoms
was reported by
males
males
males
males
males
males
males
males
females
females
females
females
females
who also reported
Air Too Dry (C2)
Air Too Dry (C2)
Air Too Hot/Stuffy (Cl)
Air Too Dry (C2)
Air Too Dry (C2)
Air Too Cool/Drafty (C4)
Air Too Hot/Stuffy (Cl)
Air Too Dry (C2)
Cosmetic Odors (02)
Air Too Hot/Stuffy (Cl)
Air Too Hot/Stuffy (Cl)
Cosmetic Odors (02)
Air Too Hot/Stuffy (Cl)
Nasal, Cough (H7) symptoms females
Eye Irritation (H9) females
symptoms
Tiredness (Hll) symptoms females
Nervous System (H14) females
symptoms
Air Too Hot/Stuffy (Cl)
Air Too Hot/Stuffy (Cl)
Air Too Hot/Stuffy (Cl)
Cosmetic Odors (02)
Fair/Poor IAQ (Al)
females
females
Air Too Hot/Stuffy (Cl)
Air Too Dry (C2)
* These symptoms include headache, unusual fatigue or tiredness, and sleepiness
or drowsiness.
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Dependent Variables Associated with Workstation Variables and
Personal Variables in Model B* (0.01 level of significance)
Increased prevalence of
Non-specific IAQ (HI)
symptoms"
Flu-like (H4) symptoms
Nasal, Cough (H7) symptoms
Eye Irritation (H9) sympt.
Too Drafty/Too Cold (C4)
Cosmetic Odor (O2)
Poor Air Quality (A2)
was reported by
males
males
males
males
males
males
males
males
males
males
males
who
were younger (age=Pl)
wore Contacts/Glasses (P12A)
were diag. Asthmatics (P13)
had increased VDT use (W6)
had more External Stress (P10)
had more External Stress (P10)
had incr. Role Clarity (P9)
had more External Stress (P10)
were Heavy Smokers (PI IB)
were in Glued Carpet (W8) offices
wore Contact Lenses (P12B)
High Fatigue (Ml) scores males
High Vigor (M2) scores males
High Tension (M3) scores males
wore Contact Lenses (P12B)
used Chemicals at Work (W5)
had more Role Conflict (P5)
(continued)
Results were generally similar for other models.
These symptoms include headache, unusual fatigue, and sleepiness.
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Increased prevalence of
was reported by who
Eye Irritation (H9) sympt. females
Ergonomic (H13) symptoms females
worked in Encl. Offices (W2B)
worked in Encl. Offices (W2B)
Cosmetic Odor (O2)
females
females
worked in Open Offices (W2B)
had high VDT Use (W6)
Fair/Poor IAQ (Al)
Poor IAQ (A2)
females
females
had lower Role Conflict (P9)
had lower Job Satisf. (P4)
High Fatigue (Ml) scores females
High Vigor (M2) scores females
High Tension (M3) scores females
had increased Workload (P7)
were older (age=Pl)
had increased Workload (P7)
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1. INTRODUCTION
1.1 Background
»
In recent years, employees in the three Headquarters building complexes
occupied by the U.S. Environmental Protection Agency (EPA) have expressed their
concerns about indoor air pollution and work environment discomforts. Some of
these concerns arose from incidents in which EPA employees became ill shortly
after building renovations. In response to these continuing concerns, EPA
decided to undertake a systematic study of the nature and spatial distribution
of the employees' health symptoms and comfort concerns and to attempt to
determine if associations exist between employee responses and specific workplace
conditions.
1.2 Study Objectives
The goal of this study was to characterize the extent of building-related
health, coirfort, and environmental problems at the three EPA Headquarters
buildings and, where possible, to suggest remedies.
The four specific objectives of the study were as follows:
1. Survey the nature, magnitude, and spatial distribution of health
symptoms and comfort concerns.
2. Characterize selected physical, chemical, and microbiological
aspects of the building in selected locations during the survey
period.
3. Generate hypotheses from any associations observed between health
and comfort effects and environmental factors while taking into
account factors that would confound or modify such associations.
4. Identify areas not in compliance with standards or guidelines.
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This is the third report documenting the study and addresses Objective 3. Volume
IV will report the analyses of the employee responses of the last-year portion
of the first questionnaire. The responses to the last-week portion of the first
questionnaire were not analyzed. Volume III documents the results of a
statistical investigation of the interrelationships among employees' responses,
the environmental monitoring data, identified risk factors, and confounding
factors. Two prior reports, Volumes I and II (U.S. Environmental Protection
Agency, 1989a, 1990), addressed Objectives 1 and 2, respectively. Objective 4
was addressed by bringing to the attention of the Environmental Health and Safety
Division (EHSD) the two rooms that had high environmental measures. One room had
carbon dioxide measurements of 1350 and 1150 ppm, concentrations greater than the
1000-ppm maximum recommended by the American Society of Heating, Refrigerating,
and Air-Conditioning Engineers (ASHRAE #62, 1989). One room had fungi measured
at 883 colony-forming units (CPUs), which was considered high in relation to the
outdoor and other indoor fungi concentrations. However, there are no standards
set for microbiological measures.
1.3 Study Design and Limitations
The basic study design consisted of an extensive initial questionnaire,
followed by environmental monitoring and concomitant follow-up survey. The first
questionnaire, the Employee Survey Questionnaire, was administered to all
employees working in the three EPA complexes: the Waterside Mall complex and the
Fairchild Building in Washington, DC, and Crystal Mall in Arlington, VA. This
questionnaire, administered in February 1989. asked about health symptoms present
within the previous year and last week and their relationship to time at work.
The analysis discussed in Volume IV deals only with the previous year response.
Also asked were extensive questions about demographic and personal factors, as
well as descriptions of the work environment. The first report (Volume I)
summarized the design, conduct, and descriptive statistics of this initial
cross-sectional study. Appendix A provides a copy of the questionnaire.
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Environmental monitoring was performed at selected sites during normal
working hours during the week of March 6-10, 1989. The monitoring results were
presented in Volume II. Simultaneously with the monitoring study, a second
survey questionnaire, the follow-up, was administered to selected EPA employees
working in clcio proximity to the monitoring sites. The follow-up survey asked
about health symptoms on the day the questionnaire was administered and about the
relationship of symptoms to that workday. The questions on the follow-up survey
were nearly identical to comparable questions on the first questionnaire. The
primary intent of the follow-up survey was to estimate the prevalence of
work-related health, comfort, and odor concerns in areas where environmental
monitoring was being performed. The design, conduct, and results of the follow-
up survey are presented in this report. The questionnaire is shown in Appendix
B.
Observational studies of this type have certain limitations that can affect
the interpretation of results. Several such limitations specific to this study
should be recognized. First, it is clear that inferences cannot be made about
any buildings other than the three EPA buildings included in the study. In fact,
with the exception of the data obtained solely by responses to the first
questionnaire (approximately 4000 respondents among the 5000 employees),
inferences cannot be extended beyond those areas of the buildings that were
actually selected for environmental monitoring. This is because a purposeful,
rather than a random, process was used to select the monitoring sites. A second
limitation is that inferences to other points in time are not possible.
Longitudinal sampling was not conducted. Rather, the study provides a "snapshot"
of the monitoring sites at the given point in time (essentially a single workday)
that monitoring took place. A third limitation was that the monitoring was not
carried out in the breathing zones of individuals. Rather, stationary sites were
used. Because the tollow-up questionnaires were administered to individuals in
the room within approximately 30 ft of the monitoring location, the measured
"exposure" is thus implicitly assumed to be applicable to all such employees.
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It is recognized that different results might occur if breathing zone
measurements had been obtained. Such differences have been noted in various
other Total Exposure Assessment Methodology (TEAM) studies (e.g., Wallace, 1987).
1.4 Organization of Report
This report is organized as follows. Chapter 2 presents background
information and a description of the EPA Headquarters buildings. Chapter 3
explains the monitoring and follow-up survey design. The next three chapters
present results of statistical analyses. These are of three major types:
1. descriptive statistics characterizing the information reported by
respondents to the follow-up survey (Chapter 4);
2. descriptive statistics characterizing the environmental monitoring
information obtained in offices of these respondents (Chapter 5);
3. statistical modeling results that relate the questionnaire response
data to the environmental data (Chapter 6).
The third item listed above is the main focus of this report. Chapter 7 gives
the conclusions and recommendations for improvement of the indoor air quality in
the buildings studied. A series of appendices contain the Employee Survey
Questionnaire, the follow-up survey questionnaire, tabulations of responses to
the follow-up questionnaire, and detailed modeling results.
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2. BACKGROUND
2.1 Previous Indoor Air Quality Studies/Hypotheses
The quality of the air and the work environment in office buildings has
become an increasingly important issue. Workers in numerous modern, apparently
well-designed office buildings have raised concerns about their health. Concerns
of workers in office buildings fall into several categories, including health
symptoms associated with indoor air quality, comfort concerns, and ergonomic
symptoms. Indoor air quality symptoms refer to a complex mix of occupant
reported symptoms associated with acute discomfort (e.g., headache, fatigue,
stuffy nose, sinus congestion, eye irritation, sore throat) that improve while
away from work. Comfort issues include concerns about air movement, temperature,
humidity, odors, and other physical comfort considerations (e.g., lighting,
noise). Back pain/stiffness or pain/numbness in shoulders or hands are examples
of symptoms associated with ergonomic stresses (repetitive motion or awkward
postures).
Building-related illnesses, another important potential health problem
among office workers, are diseases that are caused by specific building-related
etiologic factors. For example, hypersensitivity pneumonitis can be caused by
bioaerosols produced by microbial contamination of ventilation systems,
water-damaged rugs, furniture, or ceilings. This respiratory illness is
characterized by infiltrates seen on chest X-rays and nonspecific symptoms
(fever, muscle aches, cough, and shortness of breath). Other building-related
illnesses include toxic effects of overexposure to chemical agents such as carbon
monoxide (initial symptoms of headache and nausea) and dermatitis caused by
fibrous glass from ventilation duct linings. These symptoms can, of course,
often occur for reasons unrelated to working in the building. Essential to the
proper diagnosis of individuals with building-related illnesses are a physician's
evaluation and measurement of environmental contaminants.
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Information continues to be obtained by both labor and management on the
health symptoms of EPA employees and the quality of indoor air at the EPA
Headquarters. For example, both the National Federation of Federal Employees
Local 2050 and the American Federation of Government Employees Local 3331 have
accumulated information on the illnesses experienced by EPA employees. This
information is provided in a supplement to Volume I (U.S. Environmental
Protection Agency, 1989b).
This research effort was conducted concurrently with a parallel study at
the Library of Congress Madison Building, where employees were also reporting
health symptoms and discomfort concerns that they attributed to the building
indoor air quality. The study team consisted of researchers from EPA, the
National Institute of Occupational Safety and Health (NIOSH), the John B. Pierce
Foundation at Yale University, and Westat, Inc., a health statistics consulting
firm. At the time of the study, the National Institute of Standards and
Technology (NIST, formerly the National Bureau of Standards, NBS) was conducting
a long-term study of ventilation and air quality at the Madison Building, under
contract to the Department of Energy. Both the EPA and the Library of Congress
surveys made use of similar study designs and survey instruments, although
separate reports are being prepared for each agency.
2.2 Description of the Environmental Protection Agency Headquarters Buildings
The EPA Headquarters is housed in three separate office complexes located
within a several mile radius in the Washington, DC, area: the Waterside Mall
complex, the Fairchild Building, and the Crystal Mall Building.
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2.2.1 Building Description
2.2.1.1 Waterside Mall Complex
The Waterside Mall complex includes a central four-story shopping mall and
two 12-story towers (East and West). It is located at 401 M Street, S.W. The
original structure was built in 1970, and EPA took occupancy in 1971-1972. Three
additional structures (Northeast mall, Southeast mall, and Southwest mall) were
added during the 1980s. At the time of the study, EPA leased 1,004,450 ft2 of
office space, which was assigned to approximately 3700 EPA staff members. An
underground parking garage (approximately 750-vehicle capacity) is located
immediately below the Waterside Mall ground floor. The first floor of Waterside
Mall is predominantly occupied by light commercial establishments such as
restaurants, gift shops, and convenience stores. The second floor of the mall,
originally designed for small shops and business, has been renovated (with 10-ft
walls added) to accommodate offices. The second floor central area office
ceilings are open bay. exposed to the communal space resulting from the original
mall design. The third floor was originally designed for offices and has
standard 8-ft enclosed ceilings. The mall is served by four pairs of elevators
and stairways, one pair in each corner.
The East Tower and West Tower 12-story structures are nearly identical,
each being designed for general office occupancy. Four elevator shafts are
located in the center of each tower. Figure-8 hallways service the half-height
windowed exterior offices and the enclosed interior offices. The third floor
mall is connected to the fourth floor West Tower and East Tower by the 3100
hallway. All three buildings are connected by a hallway in the basement that
runs beside the parking garage. The only other access among these three
structures is via outdoor entrances.
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Three- to five-story structures were added to three corners of the
Waterside Mall complex over the years: Northeast (NE), Southwest. (SW), and
Southeast (SE) malls. The first floor of the three-story SE mall is a large
grocery store, and several small businesses occupy the first floor of the
three-story SW mall. The five-story NE mall (two underground floors) is occupied
EPA office space.
A diversity of office designs exists in the second and third floors of the
Waterside Mall, especially the second floor. The office design generally
accommodates 6-12 workers and is centered around a single, large, administrative
area occupied by one or more persons. Additional single-worker or two-person
offices, accessible only through the central office area, complete the office
design. In most cases, the attached office includes a privacy door. "Hallway"
office designs include an initial reception area leading to a hall that services
the individual office areas. Several of these "hallway" complexes are
interlinked with similar office areas, which complicates the physical
distinction between the end of one office area and the beginning of another. One
hallway, about 100 ft long, intertwines through distinctively different renovated
areas. Some small single or dual office spaces are also present. With the
exception of the few offices on the exterior north and south section, the offices
do not have individual windows.
The SW mall offices are similar in complexity to the second floor mall
offices. NE and SE mall office areas are less complex, with small central
offices serving two to six individual office areas. Full or half-height windows
are included in the exterior SW, SE, and NE mall areas.
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2.2.1.2 Fairchild Building
The Fairchild Building, a nine-story office building located at 499 South
Capitol Street, S.W., near downtown Washington, DC, was first occupied by EPA
during the 197°. 1.980 time frame. Four floors (121,015 ft2) were leased to EPA
housing approximately 850 EPA employees. The building offers no underground
parking. One floor (the basement) houses an underground snack bar. The building
is served by a central core elevator system. Figure-eight hallways provide
access to the half-height windowed exterior and windowless interior offices
located on each of the four EPA-leased floors. The majority of offices in the
Fairchild building are large, multiple-occupancy, open-bay office areas. Half
or three-quarter partitions separate work areas. A few individual or two-person
offices exist along the exterior walls.
2.2.1.3 Crystal Mall
The Crystal Mall is a 14-floor office building located at 1921-31-41
Jefferson Davis Highway, Arlington, VA. Four floors (103,019 ft2) of office
space, leased initially to EPA during 1971-1972, were assigned to approximately
560 persons. The Crystal Mall building is part of a building complex that
includes an underground interconnecting shopping area and a subsequently lower
subground multilevel parking garage that can house in excess of 1000 vehicles.
Central core elevators service the squared hallways that serve the exterior and
interior offices. Interior offices are generally small and have only one to two
occupants. Two types of exterior office areas exist: single or double-occupant
offices and central office areas that include a reception area interior to and
servicing multiple individual offices located on the exterior wall. Offices with
exterior walls have half-height windows.
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2.2.2 Ventilation System Description and Evaluation
The Waterside Mall complex ventilation systems includes 119 known air-
handling units [AHUs], which are serviced by more than one contractor. Outside
air, controlled by a mechanical damper at the central unit, mixes with the return
indoor air drawn through the overhead plenum in each zone to make up the supply
air. A constant volume of supply air is then provided to the individual offices.
Thirty-six of the 119 AHUs supplying air to the monitoring locations were
examined on the same day the heating, ventilating, and air conditioning [HVAC]
system was providing supply air to one or more monitoring sites. The AHU data
was not examined in this report but will be analyzed in Volume IV. The larger
population responding to the first questionnaire being considered in the Volume
IV analysis provides more power for testing the relationship between the AHUs and
the employee health and comfort concerns.
The ventilation system evaluation performed during the environmental
monitoring period was a component of an ongoing building ventilation system
analysis of the Waterside Mall HVAC systems. The specific objective of the
ventilation system evaluation was to determine the AHU ventilation rates.
Ventilation parameters were measured at AHUs serving Waterside Mall environmental
monitoring sites. This information could be compared to the ventilation rates
prescribed by the American Society of Heating, Refrigerating and Air-Conditioning
Engineers (ASHRAE, 1989).
No attempt was made to determine the instantaneous Waterside Mall total
building ventilation rate, either in total outdoor cubic feet per minute (CFM)
or in air changes per hour (ACH). This decision was based on the logistical
problems associated with simultaneous airflow measurements at the multiple
Waterside Mall complex AHUs with outdoor intakes located throughout the structure
of the Waterside Mall. Also time and resources were not available to do tracer
gas studies.
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Resources necessary to conduct similar evaluations of the Fairchild and
Crystal Mall buildings were not available. Therefore, no measurements or
evaluations of either the Fairchild or Crystal Mall buildings' AHUs were
conducted during the environmental monitoring study.
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3. EPA HEADQUARTERS BUILDINGS ENVIRONMENTAL MONITORING AND FOLLOW-UP SURVEY
DESIGN
Environmental monitoring was performed and a follow-up survey administered
during a one-week period, March 6-10, 1989. Environmental monitoring sites were
chosen according to the selection criteria outlined below. The follow-up survey
was then administered to occupants in close proximity to those sites. Detailed
descriptions of the site selection process, including algorithms used in the
ranking and selection process, are provided in Volume I (U.S. Environmental
Protection Agency, 1989a) . The following subsections provide a brief summary.
3.1 Selection of Environmental Monitoring Sites
A health symptom index was computed for each employee from responses to the
first questionnaire (Appendix A), and a standardized mean symptom score was
computed for each room in the building. Similarly, a comfort index was computed
for each employee from the questionnaire responses, and a standardized mean
comfort score was computed for each room in the building. Rooms were
independently ranked according to the standardized health and comfort indices.
Rooms were selected by Yale University and Westat for environmental monitoring;
the first rooms chosen were the rooms with the highest values for both indices
(designated as high- complaint areas) and with the lowest values for both indices
(designated as low-complaint areas). Results of these rankings were not revealed
to the monitoring team or EPA management to avoid possible selection bias. In
the selection of rooms, greater priority was given to the health symptom index
than to the comfort index; and less priority was given to rooms with only one
occupant.
Although the first questionnaire had been administered to the Fairchild and
Crystal Mall EPA employees, the data for these two buildings had not been
statistically evaluated, and the health symptom and comfort indexes had not been
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calculated prior to the initiation of the environmental monitoring program.
Therefore, the site selection process for these two buildings differed from the
site selection process at the Waterside Mall complex. A list of potential sites
was provided by the EHSD and EPA unions. This list included those locations
where the employees had reported concerns about the indoor air environment and
locations where no employee concerns had been reported. Potential sampling
locations were identified for each floor having EPA employees.
Each potential site was visited and evaluated for number of workers,
suitability regarding electrical and space requirements, and the presence of
obvious indoor pollutant sources. At the Fairchild and Crystal Mall buildings,
the potential sites were also evaluated to ensure that they represented the
typical EPA work areas available in the two buildings. In support of the overall
study design criteria, rooms having obvious indoor emission sources (Xerox rooms,
print shops, etc.) were deemed ineligible for selection as a site for
environmental monitoring.
One of the survey-identified indoor locations at the Waterside Mall was
selected for monitoring throughout the entire five-day sampling period to assess
possible changes over the week. In addition, an outdoor location in the middle
of the Waterside Mall 3 roof was selected for monitoring on each of the five days
to assess the influence of outdoor contaminants on the indoor environment. The
site was located as far as possible from the building exhaust vents.
In addition to the sites chosen in the manner described above, some special
study sites were selected to be responsive to management and union requests.
These sites are not considered in the analyses described in this report because
no follow-up questionnaires were administered to employees at those sites.
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3.2 Environmental Monitoring Study Design
Comfort and environmental parameters were monitored at the selected
locations during routine employee working hours (between 7:00 a.m. and 5:00 p.m.)
during the week of March 6-10, 1989. Four categories of monitoring locations
were identified: primary, secondary, fixed, and special. Except where noted,
monitoring was conducted on only one day at each primary, secondary, and special
study location. Samples were collected during all five daytime sampling periods
at the fixed indoor and fixed outdoor monitoring locations.
3.2.1 Primary Sites
Extensive monitoring was conducted at each primary site to characterize the
magnitude and spatial differences of the comfort and environmental parameters.
The following measurements were made.
Temperature (T), relative humidity (RH), carbon monoxide (CO),
carbon dioxide (C02), and respirable suspended particulate (RSP)
measurements three times per day during the monitoring period:
morning, midday, and afternoon
Viable and nonviable microbiological samples
Integrated 9-h RSP, volatile organic compound (VOC), and passive
device formaldehyde samples
Nicotine measurement by passive badges installed over the 5-day
study period
Integrated 9-h aldehyde and pesticide samples at selected sites
daily
3.2.2 Secondary Sites
Measurements of T, RH, CO, 2, and RSP were taken three times (morning,
midday, and afternoon) at each secondary site.
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3.2.3 Fixed Indoor and Outdoor Sites
Samples were collected daily to determine daily changes in comfort and
environmental parameters and the influence of the outside air on the indoor air
quality. Protocols and types of samples were identical to those described above
for the primary sites.
3.2.4 Number of Environmental Monitoring Sites and Monitoring Schedule
Environmental monitoring was conducted according to the following schedule:
the Waterside Mall 3 (i.e., third floor) locations on Monday. March 6; half of
the Waterside Mall 2 (i.e., second floor) locations and the Crystal Mall on
Tuesday; the remaining half of the Waterside Mall 2 locations and the Fairchild
Building on Wednesday; the West Tower on Thursday; and the East Tower on Friday,
March 10. With the exception of the microbiological contaminants, environmental
monitoring was conducted at 56 primary, 61 secondary, and 70 special sites, in
addition to one fixed indoor site and one fixed outdoor site. The distribution
of indoor environmental monitoring locations is shown below.
FACILITY
Waterside Mall Complex
Fairchild Building
Crystal Mall
PRIMARY
47"
5
5
SECONDARY
38
12
11
SPECIAL
67
2
1
TOTAL
152
19
17
8 Includes the fixed indoor monitoring location.
The large number of Waterside Mall 2 monitoring locations necessitated that some
environmental monitoring locations be sampled on both of the days when sample
collection was scheduled for Waterside Mall 2.
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A total of 79 viable airborne microbiological samples were collected.
Fifty-three indoor and six outdoor microbiological samples were collected at the
Waterside Mall. Five indoor samples and one outdoor microbiological sample were
collected at both the Fairchild and Crystal Mall buildings. Eight quality
control samples were collected at the Waterside Mall. Fourteen indoor and three
outdoor fungal spore samples were collected at Waterside Mall. One indoor air
fungal spore sample was collected at both the Fairchild and Crystal Mall
buildings.
3.3 Follow-up Survey Design
The follow-up survey instrument was designed to acquire information about
the activities and perceptions of the employees on the day of environmental
sampling. The questions were nearly identical to comparable questions on the
first questionnaire. The first part of the follow-up questionnaire asked about
time spent at activities. The second part asked about environmental conditions
(air movement, temperature, humidity, etc.) and odors noticed. The third part
inquired about the same symptoms as in the initial questionnaire plus burning
lungs. The fourth part inquired about feelings (worn out, listless, lively,
etc.).
The follow-up survey was administered to employees at the same time as
environmental monitoring was conducted. Resources were available for
environmental sampling devices in approximately 100 locations (20 per day) for
the temporal variables and about 50 locations (10 per day) for the continuously
monitored variables. All employees within approximately 30 ft of a sampling site
were assumed to be represented by the measurements at that site.
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4. FOLLOW-UP SURVEY RESULTS
This chapter provides a tabulation of results for the follow-up survey
(Appendix B), and describes the data processing needed to create the data files
for these tabulations and subsequently described data analyses. Because data
from both the first survey (Appendix A) and the follow-up survey are used, we
refer to the former as Questionnaire 1 (or Ql) and the latter as Questionnaire
2 (or Q2). The emphasis of this chapter is on the health, comfort, odor, and
mood state data provided by responses to Questionnaire 2 (parts III, II, II, and
IV, respectively) . These data were used to construct the main outcome
(dependent) variables of the models. Summaries of data from other questionnaire
items are of interest because such items represent potential confounders in the
models that relate the outcomes to the exposure measures.
Because of the manner in which the employees for the monitoring study were
selected, no inferences from the results presented herein can be made concerning
the health and comfort concerns of the general population of EPA employees.
4.1 Data Sources and Merging of Data Files
Five major data files furnished information for the data analysis.
Ql Data = one data record per respondent (3955 records)
Q2 Data = one data record per respondent (515 records, with 384 matching
respondents to Ql)
El = temporal data (up to three measurements per day per monitoring site
— for temperature, humidity, CO, CO2, and integrated RSP)
E2 = VOC data (one integrated 9-h measurement per day — for nine VOCs,
total VOC, and RSP)
E3 = microbiological data
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As indicated above, 515 employees at the EPA Headquarters buildings
completed Questionnaire 2. Of these, 384 (75%) matched with an employee -
completed first questionnaire. Most of the remaining 131 employees had no
matching first questionnaire and could not be used in these analyses.
Environmental data can be associated with most of the 515 persons who completed
the second questionnaire. However, since several key variables were available
only from data arising from Questionnaire 1 (e.g., age, sex, etc.), we restricted
the statistical analysis efforts to the other 384 respondents.1 Hence, a first
step in the data processing involved a merging of the Ql and Q2 data files. This
combined questionnaire file is referred to as the Q12 file.
The monitoring data are associated with locations (building, sector, room)
and dates of sampling, whereas the questionnaire data are associated with
employees in these locations on the day of sampling. Hence, prior to analysis,
it was necessary to merge the data files containing these component types of data
to form a single file containing one record per responding employee. This was
accomplished by first developing a unique identification code (UIC) that
identified monitoring locations and dates. Each record in the Q12, El, E2, and
E3 files was assigned a UIC based upon the available information. The
development of the UIC was required because of the lack of consistency in the
originally coded dates and locations (e.g. , room numbers were not always recorded
in a consistent manner). The contents and development of the El, E2, and E3 data
files are described in Chapter 5.
1Response distributions of the 384 respondents and of the 515 respondents
were tabulated for comparison and were found to be similar. These results are
given in Appendix C.
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Available data are summarized below.
Source of Data No. of Respondents No. of UICs
Q2 515
Q2 and Ql 384
Q2, Ql, and El 383 100
Q2, Ql, El, E2, and E3 218 56
Table 4-1 presents the distribution of respondents and UICs by building and
sector.
In addition to the major data files, several other types of information
were available:
Health Status Indicators. These indicators, which were based on
responses to the health and comfort questions in Questionnaire 1,
identify "low complaint" and "high complaint" locations (rooms)
within the Waterside Mall complex. Low- and high- health-complaint
areas within the complex are used in some of the tabulations of this
chapter. The various sectors are identified as CC (Crystal Mall),
FC (Fairchild), WCJHIGH (Waterside Complex, high-health complaint
areas), and WC_LOW (Waterside Complex, low-health complaint areas).
See Section 3.1 and Volume I for additional details.
Carpet Data. Installation of the carpet data began in October 1987.
These data, derived from information provided by William Hirzy,
President of the National Federation of Federal Employees Local 2050
(Chamberlain memo, 1988), were added to the basic data file. A
single variable was defined for analyzing the new carpet data (0=no
new carpet; l=new carpet, tacked down; 2=new carpet, glued down).
From this variable, we created two binary variables for analysis: a
carpet age indicator (l=new carpet, 0=otherwise), and a new-
carpet-with-glue indicator (l=glued-down new carpet, 0=otherwise).
Air Handling Unit Data. These data were reviewed, but were not
included in the data files constructed for analysis. Accurate
information on characteristics of the AHUs was not available within
the time frame required for producing the final statistical
analyses.
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Other Environmental Monitoring Data:
aldehyde data (available for only 19 monitoring sites)
nicotine data (detected values at only six monitoring sites)
The aldehyde and nicotine data were not used in the statistical
analyses because of the sparseness of the data as noted above.
4.2 Outcome Variables
This section describes the various types of outcome measures used in the
statistical models and indicates how they were developed from the specific
questionnaire items. Some summary statistics are also presented and discussed.
For instance, means or proportions, reported separately by workstation location
and by gender. All such statistics are presented purely for descriptive
purposes. Since the sample drawn was not a random sample, this precludes the
development of interferences to other areas or to employees not sampled. In
addition, inferences cannot be made to other periods in time outside the period
of the environmental monitoring study. Separate subsections are presented for
health symptoms, thermal comfort, odors, air quality ratings, and mood states.
4.2.1 Employee-Reported Health Symptoms
Part III of Questionnaire 2 (see Appendix B) furnished information on the
33 individual health symptoms listed below:
a. headache r. unusual fatigue or tiredness
b. nausea s. sleepiness or drowsiness
c. runny nose t. chills
d. stuffy nose/sinus congestion u. fever
e. sneezing v. aching muscles or joints
f. cough w. problems with contact lenses
g. wheezing/whistling in chest x. difficulty remembering things
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h. shortness of breath y. dizziness/lightheadedness
i. chest tightness z. feeling depressed
j. burning lungs aa. tension or nervousness
k. dry/itching/tearing eyes bb. difficulty concentrating
1. sore/strained eyes cc. dry or itchy skin
m. blurry/double vision dd. upper back pain or stiffness
n. burning eyes ee. lower back pain or stiffness
o. sore throat ff. shoulder/neck pain/numbness
p. hoarseness gg. hand/wrist pain/numbness
q. dry throat
Initially, two binary variables were constructed to indicate the presence or
absence of each of the symptoms:
Yl = 1 if "yes" response to symptom (first part of question) and began
"this morning or afternoon at work;" Yl=0 otherwise.
Y2 = 1 if "yes" response to symptom (first part of question) and began
"this morning at work;" Y2=0 otherwise.
One option for data analysis would have been to analyze each of these 66
variables separately. However, for most of the individual items, the prevalence
of the symptom was relatively rare, thereby hindering the development of
meaningful models.
Therefore, a method of grouping or clustering health symptoms was needed.
Using only the data from the 384 respondents that had answered both the first and
the follow-up questionnaire, a system of classification into clusters was
developed. Binary variables associated with health symptom clusters (defined
below) were then formed in the following generic way:
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Y1_CLUS = 1 if any Yl variable (i.e., the symptom began in the morning
or afternoon at work) in the cluster is equal to 1; Y1_CLUS=0
otherwise.
Y2_CLUS = 1 if any Y2 variable (i.e., the symptom began in the morning
at work) in the cluster is equal to 1; Y2_CLUS=0 otherwise.
Several ways for forming clusters were considered; the following two
schemes were considered most meaningful (letters shown below refer to the
specific symptoms listed above and in part III of Questionnaire 2). Scheme 1
grouped the health symptoms into five clusters. These were consistent with the
scheme used in Volume I by both the EPA and NIOSH (U.S. Environmental Protection
Agency, 1989a):
HI) Nonspecific indoor air quality (IAQ) symptoms (symptoms a, r, and s)
H2) Mucous membrane symptoms (symptoms c, d, k, n, and q)
H3) Combination of cluster 1 and 2
H4) Flu-like symptoms (symptoms f, g, h, i, u, and v)
K5) Ergonomic symptoms (symptoms dd, ee, ff, and gg).
Scheme 2 grouped the health symptoms into 11 clusters formed on the basis
of a principal components analysis (PCA) that was applied to the corresponding
health symptom data of Questionnaire 1 (Appendix A). In particular, a varimax
rotation was used to perform a PCA on the five-point scales (from part II,
question 7) through which respondents indicated the frequency of experiencing the
various symptoms during the prior year.2 All Ql respondents with nonmissing data
were included. The PCA used will be discussed more thoroughly in Volume IV. (A
symptoms "problems with contact lenses" and "burning lungs" were
omitted. The former symptom applies to a very small subset of individuals; the
latter symptom was not asked for in Questionnaire 1.
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(A PCA analysis was performed on the binary responses of the 384 respondents of
Q2. Similar results were obtained, though the clusters were less well defined
because of the smaller sample sizes.) The clusters developed from the PCA were
as follows:
H6) Headache or nausea (symptoms a and b)
H7) Nasal and cough symptoms (symptoms c, d, e, and f)
H8) Chest-related symptoms (symptoms g, h, and i)
H9) Eye-related symptoms (symptoms k, 1, m, and n)
H10) Throat-related symptoms (symptoms o, p, and q)
Hll) Tiredness (symptoms r and s)
H12) Chills or fever (symptoms t and u)
H13) Ergonomic (symptoms v, dd, ee, ff, and gg)
H14) Mental or nerve symptoms (symptoms x, z, aa, and bb)
H15) Dizziness/lightheadedness (symptom y)
H16) Dry or itchy skin (symptom cc)
The Y1_CLUS and Y2_CLUS variables generated for each of the 16
health-symptom clusters were then correlated with one another. Because those
health symptoms reported as starting at work were usually reported as beginning
"this morning at work" rather than "this afternoon at work," the two variables
for each cluster were found to be closely related (e.g., in the health symptom
results presented in Appendix C, the "started in afternoon" percentage for
headache was 6.5% and tended to be about half the corresponding "started in the
morning" percentage of 12.0.) The Y2_CLUS variables (i.e., those relying only
on the reporting of symptoms starting in the morning) were thus dropped from
further consideration; the 16 Y1_CLUS variables were retained for further
analysis.
Table 4-2 summarizes the distributions of responses to the 16 health
symptom cluster variates. The percentages of positive responses are shown
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separately for males and females and for each of the four work-station locations
(CC, FC, WC HIGH, and WC_LOW) . For all symptom clusters, the overall percentage
of female employees reporting work-associated health symptoms was greater than
the percentage of male employees reporting the same symptom. The largest gender
differences (female/male) were seen in headache/nausea (27.6/12.0%), nasal
symptoms (44.3/28.3%), fatigue/tiredness (31.8/18.8%), and nervous system
symptoms (31.8/17.8%). However, health symptom complaints have previously been
reported as typically higher for women than men. A consistently higher
percentage of health symptoms was reported by the sampled employees in the
WC_HIGH areas, as compared to the WC_LOW areas. This suggests that the
employees' responses were very similar from the first questionnaire to the second
questionnaire. Among the H6 through H16 symptoms, the following symptoms were
reported to be more than two times as prevalent in the high-complaint versus the
low-complaint areas of Waterside Hall complex: headache/nausea, chest symptoms,
eye symptoms, throat symptoms, nervous system symptoms, musculoskeletal symptoms
(females only), chills/fever (females only), tiredness (males only), dizziness
or lightheadedness (males only), and dry/itchy skin (males only). Recall that
these two areas were previously selected on the basis of high and low rates of
health symptom reporting in the first questionnaire. This suggests that the
selection criteria were appropriate. Several of the symptoms were uncommon:
chills and fever (0% in Fairchild Building), chest symptoms (less than 10% in
Fairchild, Crystal Mall, and WCJUOW), and dizziness/lightheadedness (less than
10% at all locations). The low prevalence of these symptoms limited the
development of subsequent models (i.e., developing meaningful models for these
symptoms is hindered by the small sample size).
The population surveyed in this report is small (384). Therefore,
comparisons of the response rates for the whole EPA Headquarters population being
conducted in Volume IV will be more meaningful.
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4. 2.2 Perceived Thermal Comfort
Employee-reported thermal comfort experienced over the past year was
ascertained in Questionnaire 1 (part III, questions la-lj). Respondents reported
the level of acceptability of air movement, temperature, humidity, and stuffiness
on a five-point scale ranging from never acceptable to always acceptable. To
reduce the number of thermal parameters and to account for those that may be
highly related to one another, a PCA was performed on these thermal comfort
questions. The results of this analysis suggested the following four thermal
comfort clusters:
1) Cl - too little air movement, too hot, too stuffy
2) C2 - too dry
3) C3 - too humid
4) C4 - too much air movement, too cold.
These clusters are consistent with data reported from chamber studies of
occupant-reported assessments of thermal comfort under & range of thermal
conditions found in buildings (Berglund et_al., 1990).
PCA-developed thermal clusters were used to derive corresponding thermal
comfort outcome measures from Questionnaire 2, part II, questions 1, 2, 3, and
5. Binary variables reflecting the thermal clustering were constructed. For
example, the first cluster variable was assigned a value of 1 if the employee
indicated that there was too little air movement, that it was too hot, and/or
that it was too stuffy in either the morning or the afternoon.
The percentage of respondents to the follow-up questionnaire for which each
thermal cluster variate was assigned a value of 1 is shown in Table 4-3. The
percentages are presented by gender, by building, and by high- and low-complaint
sectors within the Waterside Mall complex. Overall, only about 5% of the
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respondents reported the air too humid. Hence, this cluster (C3) was dropped
from any additional statistical modeling analysis (Chapter 6) because of the
small number of positive responses. The hot/stuffy air concerns (Cl) were most
frequently reported (about 50% overall), and the frequency for reporting dry air
(C2) was second (about 45%). A marked difference in frequency of reporting
hot/stuffy air, dry air, and cool/drafty air by designated high- and low-
complaint areas in the Waterside Mall complex is evident in Table 4-3. Frequency
of thermal clusters for the Crystal Mall and Fairchild Building were generally
between levels observed for the high- and low-complaint sectors in the Waterside
Mall complex. Except for the WC_LOW area, a higher percent of females generally
reported hot/stuffy air, dry air, and cool/drafty air than males.
4.2.3 Self-Reported Odors
Information on odors noticed by employees at their workstations was
obtained through responses to Questionnaire 2, part II, question 8. The
resultant information was coded as a series of 16 binary responses indicating
presence/absence of various types of odors. Clusters of these variates were
defined, and associated binary variables for the clusters were constructed. If
one or more of the component odors was reported, then the cluster variate
received a value of 1. Otherwise, it received a value of 0. The following six
clusters were indicated by PCA applied to the five-point scale data of
Questionnaire 1:
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Items Description
l,m,n,o other chemicals, pesticides, carpet cleaning, paint
a,b,e body odor, cosmetics, food smells other than fishy
j,k photocopying and printing processes
g,h carpet and drapes
d,f,i fishy smells, musty/damp smells, diesel exhaust
c tobacco smoke
The clusters were then used as the basis for defining the odor-related outcome
variables. However, diesel exhaust was isolated as a separate variable. Thus
the following eight binary odor variables were considered.
Ol = 1 if odors from chemicals, pesticides, carpet cleaning, paint
O2 = 1 if body odor, cosmetics, food smells other than fishy
O3 = 1 if odors from photocopying and printing processes
O4 = 1 if odors from carpet and drapes
OS = 1 if fishy smells, musty/damp smells
O6 = 1 if tobacco smoke odor
O7 = 1 if diesel exhaust
OS = 1 if fishy smells, musty/damp smells, diesel exhaust
If the indicated odors were not reported, then the particular variable was
assigned a zero value.
The percentage of respondents, by building and high- and low-complaint
sectors (from Questionnaire 2), for which each odor cluster variate was assigned
a 1 is shown in Table 4-4. The O2 cluster (body odor, cosmetics, and other food
smells) will be called "cosmetic odors". Only the O2 cluster had an appreciable
prevalence (about 35% across all buildings). The prevalence for the other PCA
clusters was less than 12% and also had several zero cells. Hence, only the
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cosmetic odor (O2) variable was included as an odor outcome variable in the
modeling analyses of Chapter 6. The high-complaint areas of Waterside Mall
complex had only a slightly higher prevalence of cosmetic odors than the low-
complaint areas. The Crystal Mall cosmetic odors response rate was similar to
that for the WC_LOW area. The Fairchild Building employees reported the highest
prevalence rate (about 45%). Again, females reported a prevalence (across all
buildings) of 40% for cosmetic odors (O2), compared to 30% for males. Little
difference in the male and female rates was evident for employees in the WC_LOW
sector and in the Fairchild Building. Large gender differences for the cosmetic
odor cluster were found for WC_HIGH and Crystal Mall employees but in opposite
directions.
4.2.4 Self-Reported Overall Air Quality
The respondents were asked to report their perception of the overall air
quality in the vicinity of their work station (Questionnaire 2, question 9) on
the day of environmental monitoring. They were asked to choose one of four
possible categories: poor, fair, good, or excellent. Based on the frequency of
responses to the question, two binary variables were constructed from the data
for use as outcome variables in the modeling analysis:
Al = 1 if a poor or fair rating; Al = 0 otherwise.
A2 = 1 if a poor rating; A2 = 0 otherwise.
Distributional results of the responses for these variables are given in Table
4-5. The air quality was rated poor (variable A2) by about 11% of the 366
respondents to question 9 (5.0% of the 180 males and 17.2% of the 186 females).
It was rated as fair or poor by about 47% of the males and by about 65% of the
females. The gender difference (i.e., females reporting less satisfaction) in
ratings was present for all of the buildings. The high-complaint sector in the
Waterside Mall had a higher percent of both males and females reporting fair and
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poor air quality than the low sector. Crystal Mall and Fairchild percentages for
fair or poor air quality (0 to 60 %) generally fell between the levels reported
for WC_HIGH and WC_LOW (2 to 75%).
4.2.5 Self-Reported Mood States
The mood-state information was derived from the employees' responses to the
five-point scales in part IV of Questionnaire 2. A "1" corresponded to "not at
all" and a "5" indicated "extremely." Items considered were as follows (letters
indicate the questionnaire items):
a. worn out
b. listless
1. fatigued
o. exhausted
q. sluggish
s. weary
x. bushed
c. lively
d. active
g. energetic
n. cheerful
t. alert
u. full of pep
v. carefree
w. vigorous
e. on edge
f. shaky
h. tense
j. uneasy
k. restless
m. nervous
p. anxious
r. panicky
i. relaxed
Three combined mood-state scales derived from previous work of McNair et al..
(1971) were developed representing fatigue, vigor, and tension, as follows.
Ml = Fatigue = (sum of items a,b,l,o,q,s,x)
M2 = Vigor = (sum of items c,d,g,n,t,u,v,w)
M3 - Tension = ([sum of items e,f,h,j,k,m,p,r] - item i)
In contrast with the binary variables defined for the health-symptom and
comfort-concern clusters, the mood-state variables were treated as continuous
variables. The fatigue scale could potentially range from 7 to 35 (35 = more
fatigue); the vigor scale, from 8 to 40 (40 = more vigorous); and the tension
scale, from 3 to 39 (39 = more tension).
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There were no apparent differences between the overall gender means for the
three mood-state variables (summarized in Table 4-6). Table 4-7 shows the means
of the mood-state scales by gender and workstation location. Again, no apparent
gender differences in means by gender were observed.
4.3 Potential Confounding Variables
Models for relating employee-reported health symptoms, comfort concerns,
etc., to the exposure measurements can be influenced by a host of confounding
factors (e.g., workplace, personal, or medical factors) that might modify the
associations between the health and comfort outcomes and the measured
environmental conditions. This section describes the various types of potential
confounding variables considered for use in the statistical models and indicates
how they were developed. Some summary statistics are also presented and
discussed. For instance, means or proportions are reported separately by
workstation location and by gender. Since the sample drawn was not a random
sample, this precludes the development of inferences to other areas or to
employees not sampled. In addition, inferences cannot be made to other periods
in time outside the period of the environmental monitoring study.
Listed below are the Questionnaire 1 items from which potential confounding
variables were constructed.
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Ql Item Description
I l.a Type of work space
I 4.a Years worked at current workstation
II l.b Use of contact lenses at work
II 2 Use of glasses at work
II 3 & II 6 Smoking status
II 16.a Asthma (diagnosed by physician)
II 21 Employee age
II 22 Gender
V 4 Pay plan and grade
The rationale for including such factors as possible confounding effects is
fairly obvious and is based on results of prior studies. For instance, it may
be hypothesized that older individuals have a higher frequency of certain health
symptoms or that females tend to report a higher rate of health symptoms (e.g.,
Skov and Valbjorn, 1987; Burge et al. . 1987). It might also be hypothesized
(e.g., Skov and Valbjorn, 1987; Wilson and Hedge, 1987) that employees in lower
pay grades may experience more health problems due to several factors (e.g.,
poorer medical care). Persons wearing glasses or contact lenses may be more
subject to eye irritation, headaches, and fatigue. With regard to type of
workspace (e.g., open area or enclosed office), it might be hypothesized that
those with less privacy may more frequently incur stress-related symptoms such
as headaches (e.g., Skov and Valbjorn, 1987; Wilson and Hedge. 1987).
In addition, items from part IV of Questionnaire 1 were used to develop the
seven psychosocial scales described below. Each scale was constructed so that
higher values mean "more" and lower values mean "less" of the stated
characteristic (e.g., a high score on "job satisfaction" indicates a high degree
of satisfaction, a high score for "work load" indicates a perception of heavy
work load).
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Job Satisfaction. This measure indicates overall job satisfaction
and lack of job stress, with higher values implying more
satisfaction. Job satisfaction was measured by responses to items
la, Ib, Ic, and Id in Part IV of the Questionnaire 1 (see Appendix
A). Item la has a four-point scale, and the remaining three items
have three-point scales. In each case, lower values correspond to
more satisfaction. An overall measure of job satisfaction is
attained by a reverse scoring of each item followed by averaging.
Job Satisfaction = P4 = [(5-RlA)+(4-RlB)+(4-RlC)+(4-RlD)]/4
= (17-RlA-RlB-RlC-RlD)/4
Role Conflict. Respondents' perceptions of role conflict were sought
via items 4a, 4b, and 4c, each of which consisted of a four-point
scale indicating the frequency with which role conflicts occurred:
Role Conflict = P5 = (R4A+R4B+R4CJ/3
Job Control. Having little job control, as measured by responses to
items 5a, 5b, 5c, and 5d, has been associated with a host of
psychological and physical health complaints. This five-point scale
assesses control over work load, resources needed to do the job,
policies and procedures at work, and workstation surroundings. The
scale is defined as
Job Control = P6 = (R5A+R5B+R5C+R5D)/4
Work Load. Work load, as measured by items 6a, 6b, 6c, and 6d refers
to the amount of work an individual has to do and the pace at which
the individual must work. Such a measure of work load is one of the
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most commonly assessed indicators of job stress and has been linked
to a variety of health complaints (e.g., Murphy and Hurrell, 1987):
Work Load = P7 = (R6A+R6B+R6C+R6D)/4
Utilization of Abilities. This measure assesses the extent to which
a worker is required to use skills and knowledge in completing his or
her work. Underutilization of abilities is a highly prevalent
stressor thought to produce a variety of health complaints. The
measure is the average of the responses to items 6e, 6f, and 6g:
Utilization of Abilities = P8 = (R6E+R6F+R6G)/3
Role Clarity. Role clarity refers to a lack of certainty regarding
expected role behaviors in the job environment. It is the average of
the responses to items 6h, 6i, 6j, and 6k:
Role Clarity = P9 = (R6H+R6I+R6J+R6K)/4
External Stress. The seventh scale, reflecting external stress, is
based on the yes/no responses to question 1, items a, b, c, d, e, and
f, int part IV of Questionnaire 1 (l=no, 2-yes). This measure
attempts to assess nonwork stresses that may tend to increase symptom
reporting.
External Stress = P10 = R7A+R7B+R7C+R7D+R7E+R7F-6
The psychosocial factors described above, which are partly personal and partly
job-related, have been linked to a wide variety of health symptoms (Caplan et
al., 1975; Murphy and Hurrell, 1987).
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Second questionnaire items that were regarded as the main potential
confounders are listed below:
Q2 Item Description
13 Hours spent at workstation today
14 Gone outside today (yes/no)
16 Hours spent at video display terminal (VDT)
17.c Used chemicals at workstation today (yes/no)
Employees spending long hours at their workstations or spending a large amount
of time at a VDT might be hypothesized to have a higher incidence of eyestrain,
muscle pain, or headaches than those who do not. Those using chemicals,
particularly petroleum-based or chlorinated solvents, may experience central
nervous effects. Persons going outdoors may do so for a number of reasons,
including effects of their workstation environment. Thus, associations with
reported health symptoms may be either positive or negative, depending on the
efficacy of the action.
In addition to the questionnaire information, other possible confounders
included the previously indicated carpet-related variables that identify rooms
that had carpet installed since October 1987 (new carpet) and whether or not glue
was used. Research suggests that vapors from new carpet and adhesive materials
may lead to central nervous system complaints. A small group of employees began
to report severe symptoms shortly after installation of the carpet began in
October 1987. Most of these employees were subsequently assigned to alternate
workspace. Since they were not working in the buildings at the time the
monitoring study was conducted, they are not included in these analyses.
Workplace variables used as potential confounders are presented in Table
4-8. Table 4-9 provides the list of personal/medical confounders. The notation
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used in these tables -- workstation variables Wl through W8 and personal/medical
variables PI through P13 -- is employed throughout the remainder of the report.
Table 4-10 shows the distribution, by gender and workstation location, of
the dichotomous variables used as potential confounding variables. Overall, 81%
of the male respondents worked in enclosed offices (includes full-height
partitioning). Almost all of the others worked in areas separated by mid-height
partitions. Thirty-two percent of females worked in areas with mid-height
partitions, 22% in open areas, and 46% in enclosed offices. Seventy percent of
the males went outside on the day of sampling as compared to 53% of the females.
Six percent of the males and 12% of the females used some form of chemicals at
their workstation on the day of sampling. About a third of the responding
employees worked in areas with new carpet (since 1987); about half of these were
cases in which the carpet was glued down. The distribution of persons by pay
grade showed more males in the higher pay grades. The overall rate of smokers
was general low, and the highest number of heavy smokers was among Crystal Mall
males. Eighty percent of males and 70% of females wear either contact lenses or
glasses at least sometimes at work. Eleven percent of the males had
doctor-diagnosed asthma, as compared to 7% for females.
Table 4-11 shows the summary of the distributions of the continuous
potential confounding variables by gender. Females were slightly younger, on
average, than males. Females showed slightly lower scores for job satisfaction,
role conflict, and job control but slightly higher scores for work load,
utilization of abilities, role clarity, and external stress.
Table 4-12 presents means of continuous potential confounding variables by
gender and workstation location. Examination of the largest and smallest
building averages for each variable reveals that the Crystal Mall males, on the
average, are older, have higher role conflict (tied with WC_HIGH males), lower
utilization of abilities, and lower role clarity than males at the other
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buildings. The females at Crystal Mall had the lowest average hours at
workstation and average job control score, but the maximum utilization of
abilities, role clarity, and external stress scale averages. Fairchild males,
on average, spent the most hours at their workstation and at a video display
terminal, but they had the lowest average role conflict score. Fairchild females
were the youngest and had the lowest score on Job satisfaction. The Waterside
Complex high-complaint area males had the highest role conflict (tied with
Crystal Mall males) and the highest job control score averages. The Waterside
Complex low-complaint area males had the lowest work load score mean. Waterside
Mall females, on average, spent less time at video display terminals, had the
lowest external stress score, and the highest job satisfaction score.
4-20
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TABLE
Volume III: Follow-up Survey at
EPA headquarters
DISTRIBUTION OF QUESTIONNAIRE 2 RESPONDENTS BY BUILDING AND
SECTOR
Building.*
WC
FC
CC
Sector
W Tower
SW Tower
SE Tower
NE Tower
Mall 3
Mall 2**
E Tower
01. 02.
No.
UICs
11
3
4
8
15
22
13
10
14
and El Data
Number of
Respondents
42
16
20
40
64
80
30
58
33
01. 02.
No.
UICs
11
1
1
2
10
15
7
4
5
El E2. and E3 Data
Number of
Respondents
42
9
7
11
51
53
14
18
13
Total
100
383
56
218
WC - Waterside Mall, FC - Falrchild Building, CC - Crystal Mall.
Includes four UICs and 15 respondents associated with "fixed site."
4-21
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-2. PERCENTAGE OF RESPONDING EMPLOYEES REPORTING HEALTH SYMPTOMS THAT
BEGAN AT WORK ON THE DAY OF ENVIRONMENTAL MONITORING, BY GENDER
AND WORKSTATION LOCATION
Symptom Cluster
Sex
CC
FC WC_HIGH
WC_LOW
OVERALL
SCHEME 1:
HI
H2
H3
H4
H5
nonspecific IAQ
mucous membrane
combined HI, H2
flu -like
ergonomic
M
F
M
F
M
F
M
F
M
F
17.6
31.3
41.2
31.3
47.1
50.0
11.8
6.2
11.8
31.3
17.4
40.0
39.1
60.0
43.5
68.6
13.0
17.1
13.0
22.9
33.7
56.0
58.1
65.0
62.8
74.0
23.3
25.0
19.8
24.0
17.5
25.6
36.8
35.9
42.1 ,
51.3
5.3
12.8
12.3
10.3
24.6
44.3
48.2
54.7
53.4
65.6
14.7
19.3
15.2
21.4
SCHEME 2:
H6
H7
H8
H9
H10
Hll
headache, nausea
nasal, cough
chest
eyes
throat
tiredness
M
F
M
F
M
F
M
F
M
F
M
F
5.9
12.5
17.6
25.0
5.9
6.2
29.4
31.3
17.6
12.5
17.6
25.0
4.3
25.7
30.4
40.0
8.7
2.9
26.1
42.9
8.7
14.3
17.4
28.6
18.6
36.0
32.6
54.0
11.6
14.0
51.2
51.0
29.1
37.0
26.7
39.0
7.0
15.4
26.3
33.3
1.8
0.0
17.5
12.8
14.0
15.4
10.5
20.5
12.0
27.6
28.3
44.3
7.3
8.3
35.6
39.6
20.9
26.0
18.8
31.8
(continued)
4-22
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-2. (continued)
Symptom Cluster
H12 chills, fever
H13 ergonomic
H14 nervous system
H15 dizziness,
light -headedness
H16 dry, itchy skin
Sex
M
F
M
F
M
F
M
F
M
F
CC
5.9
6.2
17.6
31.3
11.8
18.8
0.0
0.0
5.9
0.0
FC WC_HIGH
0.0
0.0
17.4
22.9
17.4
25.7
0.0
5.7
17.4
11.4
10.5
17.0
22.1
25.0
26.7
41.0
9.3
8.0
11.6
20.0
WC_LOW OVERALL
5.3
5.1
12.3
10.3
7.0
20.5
0.0
7.7
3.5
10.3
Note: Sample sizes upon which the above percentages are based
below. Two rooms in Waterside Mall Complex were not assigned a
"low" health status code. Because the column labeled "overall"
for these rooms, the
"overall."
Sample Sizes:
sample sizes for
Sex
M
F
CC
17
16
the other
FC WC
23
35
columns
_HIGH
86
100
do not
6.8
10.5
17.3
21.9
17.8
31.8
4.7
6.8
9.4
14.6
are given
"high" or
includes data
add to the
WC_LOW OVERALL
57
39
191
192
4-23
-------
Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-3. PERCENTAGE OF RESPONDING EMPLOYEES REPORTING COMFORT CONCERNS ON
THE DAY OF ENVIRONMENTAL MONITORING, BY GENDER AND WORKSTATION
LOCATION
Comfort
Cl too
C2 too
C3 too
C4 too
Concern
hot, stuffy
dry
humid
cool, drafty
Sex
M
F
M
F
M
F
M
F
CC
29.4
50.0
35.3
62.2
0.0
0.0
23.5
12.5
FC
56.5
80.0
34.8
55.9
4.3
5.9
4.3
20.0
WC_HIGH
48.8
62.0
45.8
54.1
1.2
8.2
39.5
42.4
WC_LOW
35.1
33.3
30.4
28.2
7.1
2.6
31.6
31.6
OVERALL
44.0
57.8
38.7
49.2
3.2
5.8
30.4
33.2
Note: Sample sizes upon which the above percentages are based are given
below. Two rooms in Waterside Mall Complex were not assigned a "high" or
"low" health status code. Because the column labeled "overall" includes data
for these rooms, the sample sizes for the other columns do not add to the
"overall."
Sex
CC
FC
WC HIGH
WC LOW OVERALL
Range of
Sample Sizes:
M
F
17
16
23
34-35
83-86
98-100
56-57
38-39
186-191
189-192
4-24
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-4. PERCENTAGE OF RESPONDING EMPLOYEES REPORTING ODORS ON THE DAY OF
ENVIRONMENTAL MONITORING, BY GENDER AND WORKSTATION LOCATION
Type of Odor
01 chemicals , paint
02 cosmetics, body,
non-fish foods
03 copying, printing
04 carpet, drapes
05 fishy, musty/damp
06 tobacco smoke
07 diesel exhaust
08 combined 05, 07
Sex
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
CC
5.9
0.0
41.2
18.8
5.9
12.5
0.0
0.0
5.9
0.0
0.0
0.0
0.0
0.0
5.9
0.0
FC
0.0
11.4
43.5
45.7
4.3
8.6
0.0
2.9
0.0
2.9
0.0
2.9
0.0
2.9
0.0
5.7
WC_HIGH
8.1
9.0
26.7
44.0
7.0
9.0
4.7
2.0
10.5
7.0
3.5
3.0
3.5
2.0
10.5
9.0
WC_LOW
1.8
0.0
28.1
33.3
3.5
0.0
0.0
0.0
0.0
0.0
5.3
0.0
1.8
0.0
1.8
0.0
OVERALL
5.2
6.8
29.8
40.1
5.2
7.3
2.1
1.6
5.2
4.2
3.1
2.1
2.1
1.6
5.8
5.7
Note: Sample sizes upon which the above percentages are based are given
below. Two rooms in Waterside Mall Complex were not assigned a "high" or
"low" health status code. Because the column labeled "overall" includes data
for these rooms, the sample sizes for the other columns do not add to the
"overall."
Sex
Sample Sizes:
M
F
CC
17
16
FC
WC HIGH WC LOW OVERALL
23
35
86
100
57
39
191
192
4-25
-------
TABLE 4-5.
Volume III: Follow-up Survey at
EPA headquarters
PERCENTAGE OF RESPONDING EMPLOYEES REPORTING AIR QUALITY CONCERNS
ON THE DAY OF ENVIRONMENTAL MONITORING, BY GENDER AND WORKSTATION
LOCATION
Air Quality
Rating
Al poor or fair
A2 poor
Sex
M
F
M
F
% for
CC
35.3
56.3
0.0
6.2
% for % for
FC WC_HIGH
50.0
60.0
9.1
28.6
55.4
74.5
7.2
20.2
% for
WC_LOW
33.3
48.7
2.0
5.1
% for
OVERALL
47.2
64.5
5.0
17.2
Note: Sample sizes upon which the above percentages are based are given
below. Two rooms in Waterside Mall Complex were not assigned a "high" or
"low" health status code. Because the column labeled "overall" includes data
for these rooms, the sample sizes for the other columns do not add to the
"overall.n
Sex
CC
FC
WC HIGH
WC LOW OVERALL
Sample Sizes:
M
F
17
16
22
35
83
94
51
39
180
186
4-26
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-6. SUMMARY OF DISTRIBUTIONS OF MOOD-STATE VARIABLES, BY GENDER
No.
Mood-State Sex Employees Min Max Mean Std. Dev.
Ml fatigue
M2 vigor
M3 tension
M
F
M
F
M
F
184
185
183
185
183
185
7.0
7.0
8.0
8.0
3.0
3.0
33.0
35.0
38.0
40.0
35.0
35.0
11.7
12.3
20.7
19.2
9.1
9.0
5.3
6.1
6.5
6.7
5.6
5.3
TABLE 4-7. MEANS OF MOOD-STATE SCALES, BY GENDER AND WORKSTATION LOCATION
Mood-State Scale
Ml fatigue
M2 vigor
M3 tension
Sex
M
F
M
F
M
F
CC
10.5
11.0
23.6
19.9
8.5
8.2
FC
11.7
11.8
21.2
19.4
10.6
8.3
WC_HIGH
12.6
13.5
19.7
18.3
9.7
10.1
WC_LOW
10.8
10.4
21.0
20.6
7.9
7.6
OVERALL
11.7
12.3
20.7
19.2
9.1
9.0
Note: Sample sizes upon which the above means are based are given below. Two
rooms in Waterside Mall Complex were not assigned a "high" or "low" health
status code. Because the column labeled "overall" includes data for these
rooms, the sample sizes for the other columns do not add to the "overall."
Range of
Sample Sizes:
Sex
CC
FC WC HIGH WC LOW
OVERALL
M
F
17
15
23
34
81
96-97
54-55
37-38
183-184
185
4-27
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-8. DEFINITIONS OF WORKSTATION VARIABLES USED AS INDEPENDENT VARIABLES
FOR MODELING HEALTH SYMPTOMS, COMFORT, ODOR, AND MOOD-STATE
VARIABLES
Variable
Code Source" Description
Wl~ Ql:I4.a Years at current workstation
W2A~~ Ql:Il.a Type of work space: 1 = stacks or mid-height partitioned
cubicle
0 = other
W2B~" Ql:Il.a Type of work space: 0 = enclosed office, floor-to-ceiling
cubicle, stacks or mid-height
partitioned cubicle
1 = other (e.g., open area, loading
dock)
W3 Q2:I3 Hours spent at workstation on day of monitoring
W4 Q2:I4 Went outside on day of monitoring: l=yes, 0=no
W5 Q2:I7c Used chemicals at workstation today: l=yes, 0=no
W6 Q2:I6 Hours spent at VDT
W7 New carpet at workstation (1987 or later): 1 = yes, 0=no
W8 New carpet, glued down: 1 = yes, 0 = no
Source identifies the questionnaire (Ql or Q2), the section of the
questionnaire (Part I,II,111, or IV), and the specific question
(e.g., question 4 part a).
The variable Wl, years at current workstation, was initially
considered, but was dropped because 126 missing values (out of 383
cases) occurred.
W2B was not defined for males because there were only five male
respondents for whom W2B would have been equal to 1. For these
five males, W2A was assigned a value of 1. Thus for males, W2A = 0
for enclosed offices or full-height partitions, and W2A = 1
otherwise.
4-28
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-9. DEFINITIONS OF PERSONAL/MEDICAL VARIABLES USED AS INDEPENDENT
VARIABLES FOR MODELING HEALTH SYMPTOMS, COMFORT CONCERNS, ODORS,
AND MOOD-STATE VARIABLES
Variable
Code Source* Description
PI Q1:II21 Age (years)
P2 Ql:II22 Gender (separate models for males and females)
P3A Q1:V4 Pay grade category: 1 - if medium pay grade (GS9-GS12,
or equivalent)
0 = other
P3B Ql:V4 Pay grade category: 1 - if high pay grade (GS13+, or
equivalent)
0 = other
P4
P5
P6
P7
PS
P9
P10
Q1:IV1
Q1:IV4
Ql : IV5
Ql : IV6
Ql : IV6
Ql : IV6
Q1:IV7
Job satisfaction scale = (17-a-b-c-d) /4
Role conflict scale = (a+b+c)/3
Job control scale = (a+b+c+d)/4
Work load scale = (a+b+c+d)/4
Utilization of abilities scale «(e+f+g)/3
Role clarity scale « (h+i+j+k)/4
External stress scale = (a+b+c+d+e+f-6)
(continued)
4-29
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-9 (Continued)
Variable
Code Source" Description
P11A Q1:II3 Tobacco smoking status: 1= smoker (1-10 cigarettes/day)
& Q1:II6 0=otherwise
PUB Q1:II3 Tobacco smoking status: l=smoker (11+ cigarettes/day)
& Q1:II6 O^otherwise
P12A QlzIIl.b Contacts or glasses worn at work: 1 = yes
& Q1:II2 0 = no
P12B Qlrlll.b Contact lenses worn at work: 1 = yes, 0 = no
P13 Ql:II16.a Asthma, diagnosed by physician: 1 = yes, 0 = no
Source identifies the questionnaire (Ql or Q2), the section of the
questionnaire (Part I, II, III, or IV), and the specific question (e.g., 21).
Note: Letters a through k in the definitions of P4 through P9 refer to the
five-point scale responses to subitems a, b, etc. Letters a through f in the
definition of P10 refer to the yes/no responses to subitems, where 1 indicated
a "no" response and 2 indicated a "yes" response.
4-30
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TABLE 4-10.
Volume III: Follow-up Survey at
EPA headquarters
DISTRIBUTION OF DICHOTOMOUS VARIABLES USED AS POTENTIAL
CONFOUNDING VARIABLES, BY GENDER AND WORKSTATION LOCATION
Potential
Confounding Variate !
W2A (l=stacks or
mid-height
partitions)
W2B (l=open area,
no specific
workplace,
loading dock)
W4 (l=went outside
today )
W5 (l=used chemicals
at workstation)
W7 (l=new carpet)
W8 (l=new carpet
glued down)
P3A (l=medium pay
grade )
P3B (l=high pay
grade )
P11A (l=light
smoker )
PUB (l=heavy
smoker )
P12A (l=wear con-
tacts or
glasses at work)
Sex
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
CC
5.9
14.3
•
28.6
52.9
50.0
0.0
6.3
0.0
0.0
0.0
0.0
0.0
20.0
81.3
46.7
0.0
12.5
23.5
6.3
70.6
75.0
FC
69.6
74.3
m
5.7
52.2
22.9
4.3
17.1
73.9
65.7
73.9
65.7
30.4
42.9
60.9
25.7
8.7
11.8
4.3
14.7
81.8
67.6
WC_HIGH
15.1
24.0
•
20.0
75.3
65.7
8.1
13.0
32.6
42.0
8.1
12.0
26.2
37.0
66.7
35.0
4.7
8.2
5.9
3.1
83.3
64.0
WC_LOW
12.3
20.5
•
35.9
73.7
48.7
7.0
10.3
14.0
25.6
0.0
0.0
21.4
22.9
73.2
31.4
5.3
7.7
5.3
5.1
77.2
82.1
OVERALL
19.4
31.6
*
21.6
70.5
52.9
6.3
12.5
27.7
39.1
12.6
18.2
22.5
34.2
70.1
33.2
4.7
9.6
6.8
5.9
79.3
69.6
(continued)
4-31
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-10. (CONTINUED)
Potential
Confounding Variate Sex
CC
FC
WC HIGH
WC LOW OVERALL
P12B
P13
(l=wear contacts
at work)
( l=have asthma)
M
F
M
F
11
18
23
12
.8
.8
.5
.5
18.
26.
0.
0.
2
5
0
0
22
26
9
8
.6
.0
.3
.2
14
23
16
7
.0
.1
.4
.9
17.6
25.1
11.1
6.9
Note: Sample sizes upon which the above percentages are based are indicated
below. Two rooms in Waterside Mall Complex were not assigned a "high" or
"low" health status code. Because the column labeled "overall" includes data
for these rooms, the sample sizes for the other columns do not add to the
"overall."
Sex
CC
FC
WC HIGH
WC LOW OVERALL
Range of
Sample Sizes:
M 16-17
F 14-16
23
34-35
84-86
97-100
53-57
35-39
187-191
187-192
4-32
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-11. SUMMARY OF DISTRIBUTIONS OF CONTINUOUS POTENTIAL CONFOUNDING
VARIABLES, BY GENDER
Variable Sex
W3
W6
PI
PA
P5
P6
P7
P8
P9
P10
hours at
workstation
hours at VDT
age (years)
job satisfaction
scale
role conflict
scale
job control scale
work load scale
utilization of
abilities scale
role clarity
clarity
external stress
scale
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
No.
Employees Min
191
192
191
192
188
185
184
179
186
184
187
184
186
185
187
181
186
185
187
185
0.0
0.0
0.0
0.0
17.0
17.0
1.25
1.25
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.00
0.00
Max
9.5
9.0
7.0
7.0
78.0
67.0
3.25
3.25
4.00
4.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
Mean
4.4
4.2
1.2
1.3
41.9
37.9
2.64
2.63
1.74
1.70
3.19
3.08
3.61
3.68
3.26
3.49
3.60
3.77
1.65
1.93
Std. Dev.
1.9
1.8
1.4
1.8
10.4
10.3
0.53
0.54
0.70
0.76
0.83
0.96
0.90
0.98
1.01
1.02
0.90
0.90
1.13
1.27
4-33
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Volume III: Follow-up Survey at
EPA headquarters
TABLE 4-12. MEANS VALUES FOR CONTINUOUS POTENTIAL CONFOUNDING VARIABLES, BY
GENDER AND WORKSTATION LOCATION
Variable Sex
W3
W6
PI
P4
P5
P6
P7
P8
P9
P10
hours at
workstation
hours at VDT
age (years)
job satisfaction
scale
role conflict
scale
job control scale
work load scale
utilization of
abilities scale
role clarity
scale
external stress
scale
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
CC
4.4
3.5
1.3
1.7
46.2
42.7
2.58
2.72
1.81
1.62
3.08
2.66
3.73
3.73
2.98
3.85
3.53
3.95
1.81
2.31
FC
4.8
4.2
2.0
1.8
38.5
36.2
2.63
2.57
1.56
1.72
2.86
2.88
3.57
3.78
3.23
3.35
3.63
3.71
1.83
1.94
WC_HIGH
4.5
4.5
1.1
1.3
40.3
36.7
2.65
2.61
1.81
1.69
3.26
3.16
3.73
3.69
3.21
3.42
3.54
3.73
1.65
1.96
WC_LOW
4.0
3.7
1.0
0.6
45.2
40.6
2.70
2.72
1.74
1.74
3.20
3.20
3.46
3.59
3.39
3.61
3.68
3.88
1.54
1.69
OVERALL
4.4
4.2
1.2
1.3
41.9
37.9
2.64
2.63
1.74
1.70
3.19
3.08
3.61
3.68
3.26
3.49
3.60
3.77
1.65
1.93
Note: Sample sizes upon which the above means are based are given below. Two
rooms in Waterside Mall Complex were not assigned a "high" or "low" health
status code. Because the column labeled "overall" includes data for these
rooms, the sample sizes for the other columns do not add to the "overall."
Sex CC
FC
WC HIGH WC LOW OVERALL
Range of
Sample Sizes:
M
F
16-17
15-16
21-23
32-35
83-86
92-100
54-57
35-39
184-191
179-192
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5. ENVIRONMENTAL MONITORING RESULTS FOR RESPONDENTS TO THE FOLLOW-UP SURVEY
As reported in Chapter 4, there were three major categories of
environmental measurements: File El (temporal data), File E2 (primarily VOC
data), and File E3 (microbiological data). This chapter describes the
contents of these files and presents summary statistics that characterize the
distributions of the various measurements.
5.1 Temporal Data
The temporal data consisted of measurements of temperature, relative
humidity, CO concentration, CO2 concentration, and RSP concentration. The CO
data were not used because only 55 of 514 values exceeded the limit of
detection. "instantaneous" measurements of these parameters were made three
times (morning, noon, and afternoon) on the day sampling was scheduled at each
primary and secondary site. Data from the special sites were not used for the
analyses described in this report.
For each parameter, averages of the three temporal measurements were
first constructed. These daily averages or transformations of the averages
were then used to produce four exposure variables (T1-T4) in the initial set:
average temperature (Tl), relative humidity (T2), natural logarithm of the
average CO2 concentration (T3), and natural logarithm of the average RSP
concentration (T4). An analogous set of variables based on averages of only
the morning and noon measurements was also considered. The morning and noon
measurements tended to be very highly correlated (temperature, 0.98; relative
humidity, 0.96; CO2 concentration, 0.98; RSP concentration, 0.97.) with the
daily averages and were therefore dropped from further consideration.
In addition, two other variables were considered: T5=(temperature-
70°)2, and T6=temperature change (maximum temperature - minimum temperature).
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T6 was retained as a candidate exposure variate; T5, however, was dropped from
further consideration because of its high correlation (0.94) with average
temperature (Tl). A PCA performed on the temporal variates (Tl, T2, T3, T4,
T6) indicated a moderate association between CO2 and temperature (correlation
= 0.54), whereas the other measurements were essentially independent factors.
The rationale for including these variables as candidate variables is
based on their potential associations with the outcome measures described in
Chapter 4. In particular, the following types of associations might be
anticipated:
Temperature; In addition to the obvious associations that might
exist between temperature and the comfort measures, associations
with the health symptoms may also be hypothesized. For instance,
high temperatures may lead to fatigue and sleepiness, and cold
temperatures may lead to muscle pain.
Relative Humidity; Dry air may lead to mucous membrane (eye,
nose, throat) problems. Moist air may support the growth of molds
and fungi, leading to respiratory symptoms (wheezing, flu-like
illnesses).
Carbon Dioxide; Elevated levels of carbon dioxide resulting from
inadequate ventilation may lead to headaches and sleepiness.
Reapirable Particles; This is a measure of the "dustiness" of the
monitored site. Elevated levels may affect the respiratory
system, resulting in cough, dry throat, sneezing, or runny nose.
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Temperature Change: Large daily variations in temperature may
lead to difficulties in adjusting body temperature and may result
in fever, chills, etc.
Temporal data were available for 100 UICs, and these data were associated with
the 383 respondents providing the Q12 data. The variates are labeled T1-T4
and T6, as shown in Table 5-1.
Tables 5-2 and 5-3 provide summaries of the temporal data. Table 5-2
characterizes the overall distributions observed across all of the primary and
secondary monitoring sites. The mean, standard deviation, minimum, and
maximum are shown for each variable. The C02 and RSP are in natural log
units. The geometric mean, in original units, is also shown for these two
variables, along with their geometric standard deviation. The daily average
temperatures across all sites and times ranged from 68 to 79*F. The largest
temperature change, among the morning, midday, and afternoon measurements at a
given monitoring location, was 8°F. The average humidity was uniformly low,
the maximum relative humidity being 38%.
Table 5-3 presents the means of the temporal variables by gender and
workstation location. These means are weighted by the number of individuals
at each location responding to the first and second questionnaires. Fairchild
females worked in areas that had the highest average temperature (77.VF), and
Waterside Complex high-complaint males worked in areas that had the lowest
average temperature (72.9*F). The lowest average humidity (22.9%) was found
for the work areas of the Crystal Mall females and the highest (25.7%) for
those of the Waterside Complex low-complaint area females. The lowest average
C02 level was also for work areas of Crystal Mall females, and the highest was
for work areas of Fairchild females. The lowest average In(RSP) was 2.17,
corresponding to a geometric mean of 8.8 |ig/m3. This was found for both
Waterside Complex high-complaint areas of females and Waterside Complex
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low-complaint areas of males. The highest average In(RSP) was 2.48 observed
for the work areas of Pairchild females (geometric mean = 11.9 pg/m3). The
smallest average temperature change (0.7°F) was found for the work areas of
Fairchild males, and the largest was found for the high-complaint areas of the
Waterside Complex females (2.2°F).
5.2 Volatile Organic Compound Data3
Concentrations of various VOCs were measured at both the primary and
fixed monitoring sites. Many petroleum-based and/or chlorinated organic
solvents have been associated with "sick building syndrome" (Molhave 1984;
Otto et_al., 1990). In particular, headaches, central nervous system
complaints (difficulty concentrating, loss of memory), and unpleasant odor
have been associated with the presence of organic chemicals. At each primary
site, a single integrated air measurement was made covering approximately a 9h
time frame). Many individually measured VOC concentrations fell below
detection limits for all or almost all sample sites. Nine VOCs, however, had
a sufficient number of measurable concentrations to warrant further
consideration: 1,1,1-trichloroethane, benzene, trichloroethylene, toluene,
tetrachloroethylene, ethylbenzene, o- and p_-xylene (combined), methylene
chloride, and n-octane. In addition, total VOCs (in ppmC or ppm carbon) and
RSP concentrations were measured at the same subset of sites.4 For these nine
VOCs, "not detected" values were set equal to 0.5 times the limit of detection
(LOD), "trace" values were set equal to 0.5 (limit of quantitation+LOD), and
"not calculated" values were treated as missing values. For integrated RSP
3Some models will arbitrarily exclude these variables because data were
available for only a subset of the respondents.
4In contrast to the instantaneous temporal measurements, this RSP
measurement was an integrated measurement of approximately nine hours duration.
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concentrations, all missing values and all values less than 10 A»g/m3 were set
equal to 5
A PCA was applied to the data set consisting of the nine VOC
concentration variables to determine if a reduced set of variables would be
meaningful. The PCA results suggested that the nine specific VOC
concentration variates could be reduced to two major components: (1) total of
concentrations for 1,1,1-trichloroethane and tetrachloroethylene (VI) and (2)
total of remaining seven VOC concentrations (V2). Methylene chloride was
treated separately because of its chemical and physical properties and its
weak association with the other six VOCs in V2. Five VOC-related variables
were used for modeling. VI comprises two solvents, while V2 consists
principally of aromatic compounds.
Vl=ln[ total of concentrations (/^g/m3) for 1,1,1-trichloroethane
and tetrachloroethylene]
V2=ln[ total of concentrations (pg/m3) for benzene, toluene,
trichloroethylene, ethylbenzene, o- and £-xylene, and
n-octane]
V3=ln[methylene chloride concentration (/jg/m3) ]
V4=ln[ total VOCs (in ppmC) ]
V5=ln[ integrated RSP concentration
Factor V2 consisted of six organic compounds. Only toluene and n-octane had
missing values. Toluene had five missing values for the Waterside Complex and
the mean value substituted was 10.48. n-Octane had one missing value for the
Waterside Complex, and the mean value substituted was 0.60. Imputed
concentrations (equal to the overall mean values for Waterside Mall Complex)
were substituted for these compounds V2 was constructed. This allowed the
variable to be analyzed by using the best estimate of the actual value.
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Tables 5-4 and 5-5 provide summaries of the distributions of these five
variables. Table 5-4 summarizes the overall distributions across monitoring
sites, while Table 5-5 gives means by gender and workstation location. All of
these variables are reported in natural log units, with geometric means in
original concentration units. Aromatics were the most prevalent class of
compoxmds. The highest concentrations of V2, V3, V4, and V5 (aromatics,
methylene chloride, total VOCs in ppmC, and RSP, respectively) were found at
the Fairchild building. The concentrations at Crystal Mall were generally
lowest for all the variables except V2.
Most of the targeted VOCs have been measured by EPA in 10 other
buildings (Wallace et al.. 1987). Of these, three were new buildings that
exhibited elevated levels of certain chemicals such as the xylenes and decane.
The seven older buildings -- which included two office buildings, two homes
for the elderly, a school, a hospital, and a nursing home -- are more directly
comparable to the three EPA Headquarters buildings. The range of average
concentration values noted in these seven buildings spans the range found in
the EPA buildings for every compound measured except tetrachloroethylene, as
shown below.
Compound
Range of Mean One-
Day Concentrations
for 3 EPA Buildings
(from Report II)
Range of Mean Three-
Day Concentrations
for 7 Other Buildings
(Wallace et al., 1987)
1 , 1 , 1-Trichloroethane
Tetrachloroethylene
Benzene
Trichloroethylene
Ethylbenzene
Xylenes
E-Dichlorobenzene
Styrene
n-Decane
n-Dodecane
3 to 9 |ig/m3
2 to 7
5 to 8
1 to 3
1 to 5
6 to 21
ND to 6
ND to 2
ND to 6
ND
3 to 41 |ig/m3
1 to 6
3 to 11
ND to 11
1 to 10
4 to 36
ND to 7
1 to 2
1 to 27
ND to 6
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Sample sizes for the Waterside Mall, Crystal Mall, and Fairchild were 51, 5,
and 5, respectively. Sample sizes for the seven other buildings ranged from
18 to 30. Toluene, B-octane, and methylene chloride were not measured in the
prior studies.
Respirable particles were measured in 38 commercial buildings in the
Pacific Northwest (Turk et al.. 1987). The mean RSP value observed in
no-smoking areas of those buildings was 19 §ig/m3, which is quite comparable to
the means observed for the three EPA buildings (16 to 24 itg/m3) .
5.3 Microbiological Data5
At the primary and fixed monitoring sites, the presence and
concentration of various bioaerosols were measured (variables V6 through V14
in Table 5-6). These organisms have been associated with specific building-
related illnesses in other studies; such illnesses include hypersensitivity
pneumonitis and allergic rhinitis. Some of these organisms also produce
materials which can cause inflammation independent of sensitization. For
example, gram-negative bacteria can produce an endotoxin, a
lipopolysaccharide, which has recently been associated with lung inflammation
in lifeguards at an indoor swimming pool (Milton et al.. 1990).
At each primary site, a single air sample was obtained. Air samples
were sent to a laboratory, where they were cultured, quantitated, and further
identified. This is therefore an assay for viable organisms. While this is
the current standard assay for microbiologicals in the environment, it does
not quantitate nonviable organisms which may also cause health effects. The
results were adjusted for the volume of air sampled and are expressed as
logarithms of colony-forming units per cubic meter (Tables 5-7 and 5-8).
5Some models will arbitrarily exclude these variables because data were
available for only a subset of the respondents.
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The variability in the concentration of microbiologicals at a single
site was determined by repeat sampling on each of five days at one location.
The concentration of total fungi ranged from 8 to 35 CFU/m3, the concentration
of human source bacteria ranged from 35 to 100 CFU/m3, and the concentration
of thermophilic bacteria ranged from 1 to 140 CFU/m3. This was judged to be a
low degree of variability for the fungi and human source bacteria and a
moderate degree of variability for the thermophilic bacteria.
The results were compared to previous study data and guidelines
published by the American College of Government and Industrial Hygienists
(ACGIH), "Guidelines for the Assessment of Bioaerosols in the Indoor
Environment," (1989). The ACGIH Guidelines state that for fungi:
"Indoor levels must be interpreted with response to control
environments, such as the outdoor air or interiors with no
complaints or symptoms. In general, indoor levels should be lower
than those outdoors and taxa should be similar indoors and out.
In general, mechanically ventilated interiors, even those with
minimal filtration, should have indoor fungus counts that are less
than half of outdoor levels measured over the 24 hours previous to
indoor sample collection. All interpretations of health risk due
to saprophytic fungus spores should be made with the understanding
that the outdoor aerosol routinely exceeds 1000 cfu/m3 and may
average near 10,000 cfu/m3 in the summer months...levels of any
saprophytic fungus less than 100 cfu/n3 are not of concern."
In this study, the outdoor concentrations of fungi were 10-1000 times
lower than indicated by ACGIH guidelines, and ranged from 1 to 113 CFU/m3.
The weather was extvemely cold during the week of sampling and may have
lowered the levels of outdoor samples. No technical factors were identified
which would have artificially lowered the bioaerosol concentrations. The
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fungal concentrations in the indoor samples were low, with most values ranging
from 1 to 45 CFU/m3. A fungi concentration measured in one area was 883
CFU/m3 (predominantly penicillium). Management was notified of this result.
This finding was reviewed and determined not to have the potential for causing
illness among the general work force. Repeat measurements by a management
contractor have shown lower levels consistent with these measurements
previously made in other areas. Three sites had fungal concentration of
105-120 CFU/m3. According to the ACGIH guidelines, these three values are not
of concern, as they are several times less than the 500 CFU/m3 concentration
which the ACGIH implies occurs routinely. This interpretation does not
exclude the possibility that employees may have reacted to specific fungi.
Allergic reactions can occur in a small percentage of the population in
response to very low concentrations of an antigen.
The ACGIH guidelines for the interpretation of environmental bacterial
concentrations propose four key questions:
1. Are environmental bacteria being selectively amplified in the
building? They indicate that in the normal situation, human
source bacteria (e.g., gram-positives such as micrococcus and
staphyoloccus) should predominate.
2. What is the source of amplification?
3. Are human source organisms accumulating to inappropriate levels?
The guidelines suggested by the ACGIH are that 4500 cfu/m3 is the
upper limit of normal for indoor bacterial aerosol in subartic
homes.
4. Is there a significant health risk associated with exposure to
these organisms? The ACGIH guidelines acknowledge that this is
difficult to assess for any bioaerosol, including bacteria.
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In the EPA study, staphylococci and mlcrococci were the dominant
bacteria, which by item 1 above implies that environmental bacteria are not
being selectively amplified in the building. By the third criteria, human
source organism concentrations measured in EPA Headquarters (5-240 CFU/m3)
were very low compared to the guidelines-suggested 4500 CFU/m3. The ACGIH
guidelines do not have a separate section for the interpretation of data on
thermophilic actinomycetes. They state that "actinomycetes are unusual in
nonfann, indoor environments, and their presence indicates that contamination
is present." The fixed site sampling indicated the largest degree of
variability with the thermophiles (1 to 140 CFU/m3). Outdoor samples ranged
from 1 to 70 CFU/m3, and indoor samples ranged from 1 to 90 CFU/m3. The
health effects which may occur in association with exposure to thermophilic
actinomycetes include hypersensitivity pneumonitis. The presence of low
concentrations does not exclude the possibility that a small percentage of
individuals may be sensitized and are reacting to these low concentrations.
However, the risk of sensitization is thought to rise with increasing
exposure. The low concentrations of thermophiles is consistent with the air
sampling data showing low humidity, since these organisms can thrive in warm,
damp environments. These data suggest that the range of concentrations of
thermophilic actinomycetes in the indoor environment at the EPA Headquarters
buildings is similar to the range of concentrations found outdoors. With
current knowledge, no significant health risks to the general work force would
be expected at the levels measured at the EPA buildings.
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TABLE 5-1. TEMPORAL VARIABLES
VARIABLE
Tl
T2
T3
T4
T6
DESCRIPTION
temperature (°F)
relative humidity (%)
ln[CO2 concentration] In(ppm)
ln[RSP concentration] ln(^g/m3)
temperature change [max(AM,noon,PM temperature)-
min(AM,noon,PM temperature)] (°F)
NOTE: T5=(T1-70)**2 was originally considered but was dropped because of its
high correlation with Tl. T1-T4 are averages over AM, noon, and PM readings;
averages over AM and noon were also considered but were highly correlated with
T1-T4.
TABLE 5-2. SUMMARY OF OVERALL DISTRIBUTIONS OF TEMPORALLY MEASURED VARIABLES
No. Std. Geom. Geom.
Variable UICs Min Max Mean Dev. Mean Std. Dev.
Tl (temp. °F) 100 67.5 79.2 74.1 2.3
T2 (humidity %) 100 18.0 38.0 24.4 4.4
T3 (ln[C02]) 100 5.95 6.75 6.33 0.18 561.2 1.2
T4 (ln[RSP)) 97 0.00 3.58 2.21 0.82 9.1 2.3
T6 (temp. 100 0.0 8.0 1.6 1.4
change °F)
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MEANS OF TEMPORALLY MEASURED VARIABLES, BY GENDER AND WORKSTATION
LOCATION
Variable
Tl
T2
T3
T4
T6
(temp. °F)
(humidity %)
(ln[C02])
(ln[RSP]>
( temp .
change °F)
Sex
M
F
M
F
M
F
M
F
M
F
CC
75.2
74.9
23.6
22.9
6.25
6.21
2.33
2.25
1.5
1.8
FC
77.0
77. 4
24.7
25.3
6.63
6.64
2.37
2.48
0.7
1.0
WC_HIGH
72.9
73.7
24.6
24.3
6.27
6.30
2.31
2.17
1.9
2.2
WC_LOW
73.7
73.2
25.4
25.7
6.31
6.34
2.17
2.31
1.7
1.6
OVERALL
73.8
74.3
25.1
24.7
6.32
6.36
2.26
2.26
1.6
1.8
Note: Sample sizes upon which the above means are based are given below. Two
rooms in Waterside Mall Complex were not assigned a "high" or "low" health
status code. Because the column labeled "overall" includes data for these
rooms, the sample sizes for the other columns do not add to the "overall."
Sex
Range of
Sample sizes:
CC
FC
WC HIGH WC LOW
OVERALL
M
F
17
16
23
35
83-86
98-100
56-57
34-39
187-191
185-192
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TABLE 5-4. SUMMARY OF OVERALL DISTRIBUTIONS OF VARIABLES IN VOC DATA FILE
Variable
No.
UICs
Min
Max
Mean
Std.
Dev.
Geom.
Mean
Georn.
Std. Dev.
VI 56 1.36 3.68 2.40 0.74 11.0 2.1
V2 56 2.49 4.38 3,11 0.45 22.4 1.6
V3 56 -1.83 2.07 0.68 0.81 2.0 2.2
V4 56 -1.10 1.95 -0.16 0.57 0.9 1.8
V5 56 1.61 4.00 2.29 0.75 9.9 2.1
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TABLE 5-5. MEANS OF VARIABLES IN VOC DATA FILE, BY GENDER AND WORKSTATION
LOCATION
Variable Sex CC FC WC HIGH WC LOW OVERALL
VI M
F
V2 M
F
V3 M
F
V4 M
F
V5 M
F
1.79
1.71
3.35
3.09
-0.22
-0.14
-0.73
-0.57
2.33
2.02
2.29
2.30
4.36
4.33
2.02
1.80
0.75
0.61
2.38
2.12
2.16
2.45
3.02
2.98
0.95
0.65
-0.25
0.00
2.15
2.38
2.28
2.56
2.97
2.93
0.50
0.36
-0.28
-0.35
2.28
2.20
2.19
2.40
3.09
3.14
0.75
0.67
-0.22
-0.03
2.25
2.29
Note: Sample sizes upon which the above means are based are given below. Two
rooms in Waterside Mall Complex were not assigned a "high" or "low" health
status code. Because the column labeled "overall" includes data for these
rooms, the sample sizes for the other columns do not add to the "overall."
Sex CC FC WC HIGH WC LOW OVERALL
Sample sizes:
M
F
5
8
4
14
46
70
37
24
100
118
Definitions of Variables:
Vl=ln[l,l,l-trichloroethane + tetrachloroethylene concn
V2=ln[benzene + trichloroethylene + toluene + ethylbenzene
+ o- and p-xylene + n-octane concn.
V3=ln[methylene chloride concn. (fjg/n?) ]
V4=ln[total VOCs (in ppmC)]
V5=ln[RSP concentration (pg/m3) ]
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TABLE 5-6. VOLATILE ORGANIC AND MICROBIOLOGICAL VARIABLES
VARIABLE DESCRIPTION
VI In[total of concentration for 1,1,1-trichloroethane and
tetrachloroethylene]
V2 ln[total of concn. for benzene, trichloroethylene,
toluene, ethylbenzene, o- and p_-xylene, n-octane]
V3 ln[methylene chloride concn.]
V4 In[total VOCs (in ppmC)]
V5 ln[RSP concentration]
V6 log[total fungi]
V7 log[total human source bacteria]
V8 log[total thermophiles]
V9 log[fungi #1 count]
V10 log[total of fungi #9,10,11 counts]
Vll log[total of fungi #5,6 counts]
V12 log[bacteria #7 count ]
V13 log[total of bacteria #2,4 counts]
V14 log[bacteria #1 count]
Index to fungi:
1= Cladosporium 7=Stemphyllium
2=Torulopsis/Rhodotorula 8=Rhizopus
3=Sporobolomyces 9=Stachybotrys
4=Mucor 10=Paecilmyces
5=Penicillium ll=Verticillium
6=Aspergillus 12=Phoma
13=not identified
Index to human source bacteria:
l=Staphylococcus 7=Micrococcus
2=Bacillus 8=Acinetobacter
3=Serratia 9=Aeromonas
4=Pseudomonas 10=Proteus
5=Micropolyspora ll=Klebsiella
6=Streptococcus 12=Alcaligenes
13=not identified
Index to thermophiles:
l=Micropolyspora (Mps) 2=not identified
NOTE: Units for microbiological measurements are log (base 10) of colony-
forming units per cubic meter of air.
NOTE: Zero values for microbiological measurements were replaced by 0.01
CFU/m3 prior to summation and log transformation.
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TABLE 5-7. SUMMARY OF OVERALL DISTRIBUTIONS OF VARIABLES IN MICROBIOLOGICAL
DATA FILE
Variable
V6
V7
V8
V9
V10
Vll
V12
V13
V14
No.
UICs
56
56
56
56
56
56
56
56
56
Min
-0.89
0.71
-1.70
-2.00
-1.52
-1.70
-2.00
-1.70
-2.00
Max
2.95
2.27
2.15
1.68
1.53
2.95
1.82
1.49
1.89
Mean
0.98
1.62
0.84
-0.40
-1.32
-0.45
-0.51
-0.95
0.94
Std. Dev.
0.62
0.30
0.95
1.43
0.66
1.36
1.52
1.17
0.90
Definitions of Variables:
V6 = log[total fungi]
V7 = log[total human source bacteria]
V8 = log[total thermophiles]
V9 = log[fungi #1 count (cladisporium)]
V10 = log[total of fungi #9,10,11 counts (stachybotrys, paecilmyces,
verticillium))
Vll = log[total of fungi #5,6 counts (penicillium, aspergillus)]
V12 = log[bacteria #7 count (micrococcus)]
V13 = log[total of bacteria #2,4 counts (bacillus, pseudomonas)]
V14 = log[bacteria #1 count (staphylococcus)]
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TABLE 5-8. MEANS OF VARIABLES IN MICROBIOLOGICAL DATA FILE, BY GENDER AND
WORKSTATION LOCATION
Variable
V6
V7
V8
V9
V10
Vll
V12
V13
V14
Sex
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
M
F
CC
0.92
1.13
1.03
1.41
0.94
0.85
0.12
-0.20
-1.52
-1.52
-1.16
0.00
0.67
-0.20
-1.70
-1.70
-0.30
0.95
FC
1.09
0.82
1.88
1.61
1.53
1.05
0.78
0.43
-1.52
-1.52
0.21
0.33
1.42
0.99
-1.70
-1.70
1.18
1.09
WC_HIGH
0.99
0.69
1.53
1.64
0.80
0.73
-0.52
-0.73
-1.23
-1.24
-0.90
-0.53
-0.57
-0.55
-0.97
-1.08
0.98
1.11
WCJLOW
1.19
1.21
1.68
1.73
0.60
0.83
-0.51
-0.09
-1.52
-1.52
-0.73
-0.32
-1.38
-1.11
-1.04
-0.57
0.21
0.71
OVERALL
1.03
0.84
1.58
1.64
0.81
0.81
-0.47
-0.43
-1.39
-1.35
-0.87
-0.37
-0.85
-0.48
-1.12
-1.10
0.66
1.02
Note: Sample sizes upon which the above means are based are given below. Two
rooms in Waterside Mall Complex were not assigned a "high" or "low"
health status code. Because the column labeled "overall" includes data
for these rooms, the sample sizes for the other columns do not add to
the "overall."
Sex CC FC WC HIGH WC LOW OVERALL
Sample sizes:
M
F
5
8
4
14
46
70
37
24
100
118
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6. RESULTS RELATING SURVEY DATA TO ENVIRONMENTAL MONITORING DATA
6.1 Analytical Objectives
A prime objective of the EPA Indoor Air Quality Study was to establish
whether the employee-reported health, comfort, odor, mood state, and air quality
measures are related to the environmental monitoring results. The following
notation is used to generically describe the dependent (outcome) variables:
H = indicators for clusters of employee-reported health symptoms
(H1-H16 defined in Section 4.2.1)
C = indicators for clusters of employee-reported comfort concerns
(C1-C4 defined in Section 4.2.2)
O = indicators for clusters of employee-reported odors noticed
(O1-O8 defined in Section 4.2.3)
A = indicators of employee-reported perception of overall air quality
(Al and A2 defined in Section 4.2.4)
M = employee-reported mood-state scales
(M1-M3 defined in Section 4.2.5)
The independent variables can be similarly defined:
T = temporal measures (see Table 5-1)
V = VOC concentrations, integrated RSP, and microbiological measurements
(see Table 5-6)
W = workstation data (see Table 4-8)
P = personal/medical data (see Table 4-9)
In each case, the W and P variables are confounders, which for the most part are
associated with individuals. In contrast, the T and V variables are associated
with monitoring locations.
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Based upon the objectives indicated, a number of generic models can be
postulated (Table 6-1). This type of generic representation is a convenient way
of representing the various hypotheses of interest, that is, the specific
analytical objectives. For example, the first model can be interpreted as "Is
there an association between (one or more of) the temporally measured variates
and a given health symptom, after controlling for workstation and personal
characteristics?" However, an association between a variate X and an outcome Y
does not necessarily imply a cause and effect relationship.
6.2 Analytical Approach
6.2.1 Basic Model Forms and Estimation Procedures
To determine if the various environmental measures are associated with the
previously specified outcome variables, statistical models must be developed.
Two major types of models were considered:
1) ordinary multiple regression models that relate continuous outcome
variables to the independent variables, and
2) logistic multiple regression models that relate binary outcome
variables to the independent variables.
6.2.1.1 Regression Models
In the first type of model, the mood states serve as the dependent
variables. In this case, the model used to characterize the relationship ta
kes the form
y = fl0 + /3,x, + fl2x2 + ... + fl,xr + c
where Y denotes the specific outcome variable (e.g., Ml, M2, or M3); XI, X2,
etc., denote the various independent variables (e.g., the T, V, W, and P
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variates previously defined) in the given model, the £s denote parameters to be
estimated, and « denotes an error term. In other words, after adjustment for the
other independent variables, the expected value of Y is assumed to be linearly
related to each X variate included in the model specification. For such models,
the variability in the error term is generally assumed to be homogeneous. As a
result, ordinary least squares (OLS) is typically employed as the method for
estimating the unknown model parameters (i.e., the £s). Tests of hypotheses
concerning the £s assume also that the parameter estimates are approximately
normally distributed. Such tests are therefore only approximate. Typically, the
tests concern whether a particular £ parameter is or is not zero (i.e., whether
the corresponding X variate is or is not related to the outcome measure). Since
parsimonious models are usually desirable, a revised model that excludes the
extraneous X terms (i.e., those terms having £s not significantly different from
zero) would then typically be used (and reestimated).
The estimates of the £ coefficients represent the estimated change that
occurs in the outcome measure because of a change of one unit in the independent
variable. For those X variates that take on only 0 and 1 values, the associated
& estimate represents the incremental change in going from the 0 category to the
1 category. Estimated standard errors for the estimated £s can be used to
provide approximate confidence intervals for the £s. For instance, a 99%
confidence interval for £{ is given by
est(£{) ± 2.576[standard error of est(£i)].
Such an interval is said to cover, with approximately 99% confidence, the £{
parameter value.
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6.2.1.2 Logistic Regression Models
The second type of model offers a way of relating a binary outcome variate
to a given set of independent variables. Let Y be a variable taking on values
of 0 and 1 only (e.g., one of the H, C, O, or A variables previously defined).
Then the logistic model assumes that p, the (true) probability that Y takes on
a value of 1, can be modeled as
p s Pr[Y=l) =
+ exp(-(J50 + J3.X, + B2X2 + ... + RJ.,)]},
or, equivalently, that the expected value of the (natural) logarithm of the odds
ratio, ln[p/(l - p)), can be represented as
R
J52X2
J3,X,.
Maximum likelihood estimation is usually invoked to estimate the /is in the
model. Hypothesis tests regarding the Rs can be used to address questions such
as "Are different levels of the X variate associated with different proportions,
p?" Since such tests rely on the assumption that the estimated parameters are
asymptotically normally distributed, they should be regarded as approximate.
Predictions of incremental changes in odds ratios can be obtained by
exponentiating the estimated Us. If the R is associated with a continuous X
variate such as age, then exp[fi] is interpreted as the factor by which the odds
ratio is estimated to change when a change of one unit in X occurs. If X is a
binary variable, then exp[/3] is the relative odds ratio for category 1 versus
category 0. To represent the effect of tertiary variables, two binary variables
(e.g., P3A and P3B) are employed in the model. The interpretation in this case
is illustrated below (using P3 (pay grade category]).
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P3=l ==> P3A=0 and P3B=0
P3=2 ==> P3A=1 and P3B=0
P3=3 ==> P3A=0 and P3B=1
The coefficient on P3A is the incremental difference between the first and second
category, and the coefficient on P3B is the incremental difference between the
first and third category. That is, the first category is the baseline category,
and the reported odds ratios are relative to that group. In a manner similar to
the OLS regressions, estimated standard errors for the estimated J3s can be used
to provide approximate confidence intervals for the fls. Exponentiation of the
end points of the 99% confidence interval — that is, exp{est(fij) ±
2.576[standard error of est(ft;)]} — provides an interval that covers, with
approximately 99% confidence, the true relative odds ratio (in the case of a
dichotomous X variable) or the per-unit increment in the odds ratio (in the case
of a continuous X variate).
6.2.2 Choice of Dependent Variables
The initial candidate set of 33 outcome variables, as described in Chapter
4, consisted of 16 health symptom measures, four comfort concern measures, three
mood-state measures, eight odor measures, and two air quality ratings. After the
study objectives and the descriptive results were reviewed, several of the
variables were dropped — namely, humid air (C3) and all of the odor variates
except cosmetic odors (O2). These were eliminated because of the low prevalence
of positive responses. The small sample size is not sufficient for adequate
modeling, since there are so few individuals in any one of the categories. The
same problem potentially exists for some of the other variables (e.g., flu-like
symptoms (H4), headache/nausea (H6), chest symptoms (H8), chills/fever (H12),
dizziness/light-headedness (HIS), dry/itchy skin (H16), and poor air quality
(A2)); attempts were nevertheless made to model these variates.
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6.2.3 Modeling Strategy
6.2.3.1 Strategy for Health Symptoms Outcomes
Based upon the results shown in Chapter 4 which indicated large gender
differences for some of the outcome measures and different male and female
distributions for other variables (e.g., type of workstation), a decision was
made to develop separate models for males and females. This is equivalent to an
overall model in which gender is included and in which gender is allowed to
interact with each of the other independent variables appearing in the model.
The decision to use separate gender models was supported by the results of the
linear modeling exercise, in that gender interactions were often apparent (i.e.,
only rarely were similar significant effects evidenced for both males and
females).
A basic modeling strategy was developed for each of the outcome variables.
Figure 6-1 depicts the strategy for the health symptom outcome measures.
The first step was to use stepwise linear regression to determine which of
the confounding variates were pertinent. The confounding variables were
workstation characteristics (Table 4-8) and personal/medical characteristics
(Table 4-9). The paired variates associated with workstation, pay grade, and
smoking status weze treated simultaneously in the stepwise procedure, so that
both members of the pair either entered or failed to enter the model. The
temporal variables (Tl, T2, T3, T4, T6) were included in the model and were not
allowed to be dropped at this stage, because testing hypotheses concerning these
variables was a primary objective. For each health symptom measure (e.g.,
nonspecific IAQ [HI]), the stepwise procedure was used to arrive at a model for
males and a model for females. Results of applying the stepwise procedure are
summarized in Section 6.3.1
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The second step involved estimation of a logistic regression model that
contained as independent variables the five temporal variables, as well as those
workstation (W) and personal (P) variables that were identified by the stepwise
regression procedure as statistically significant at the 0.10 level of
significance in either the male or the female model. This model is designated
as Model A in Figure 6-1. The purpose of this model is to test for the effects
of the temporal variables on the reported health symptoms.
The next step involved building a more parsimonious model, upon which
subsequent models could be based. This model (Model B in Figure 6-1) contained
the subset of the temporal, workstation, and personal variables in Model A that
were found to be statistically significant in either the male or female model.
This model was also fit via logistic regression methods.
Model C was then developed. Model C added four variables [hot/stuffy air
(Cl), dry air (C2), cool/drafty air (C4), and cosmetic odors (02)] to Model B.
This step examined the association between employee-reported comfort and odor
variables and the health symptom outcome measures. The comfort and odor
variables were not included as independent variables in Models A or B because
those models were designed to test for effects of the objective measurements on
health. Comfort and odor perceptions are subjective variables that depend on
temperature and other measurable parameters. However, it is of interest to
explore whether health effects could be predicted from a knowledge of comfort
complaints; Model C was therefore used to test this hypothesis. However, since
the rooms at which monitoring was performed were selected partly on the basis of
matching comfort and odor complaints, the applicability of Model C may be
limited.
In parallel with Model C, the strategy called for a fourth type of model
-- Model D -- to be estimated. This model augmented the VOC and microbiological
variables (VI-VIA) onto the terms in Model B. Its purpose was to test for the
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effects of these measures on the health outcomes. As indicated in section 4.1.1
and Table 4-1, a significant reduction in sample size occurred for Model D
estimation as contrasted with the other types of models. The VOC and
microbiological data needed for Model D were obtained for only 56 UICs as
compared to 100 UICs for the other models. Because of this reduced sample size
and the larger number of independent variables, many of which were
intercorrelated and exhibited highly skewed distributions, a number of problems
were encountered in the estimation of the parameters for model D. A revised
model was used, subsequently referred to as Model D' , which excluded the
microbiological variables V9 through V14. This tended to reduce the estimation
difficulties.
In terms of testing for associations, the strategy described above
obviously places the highest priority on testing of the temporal measures. This
was regarded as appropriate for two reasons. One was the aforementioned problem
of including the employee-reported comfort and odor variables. The second was
the large reduction in sample size when the V variables were included. Without
this problem, it would have been logical to have developed a single model
involving W, P, T, and V variables from the outset.
6.2.3.2 Strategies for Other Outcomes
Modeling strategies similar to that described above were employed for
testing associations with the other types of outcome variables. In particular,
the strategy for perceived air quality variables (Al and A2) was identical to
that shown for the health variates. The cosmetic odors and the mood-state
variates were also treated the same, except that Model C was omitted. Ordinary
regression, rather than logistic regression, was employed for the mood-state
variables since they were considered to be continuous. The comfort variables
(Cl, C2, and C4) were modeled only up through the Model B step. As a candidate
independent variable in the models for comfort, the 02 variate was added and
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treated like one of the temporal variates (i.e., it was forced into the stepwise
regressions and the Model A logistic regressions).
6.3 Summary of Modeling Results
6.3.1 Stepwise Regression Results: Selection of Relevant Confounders
As indicated in Section 6.2.3, the initial step of modeling for each
dependent variable consisted of performing a stepwise regression to decide which
of the potential confounders (i.e., workstation and personal variables) should
be retained in the model. The temporally measured variates (T1-T4 and T6) were
forced into the stepwise regressions. Actually, four stepwise regressions per
outcome variable were conducted, because separate regressions were performed for
males and females and because two different criteria were employed for entry and
retention of the workstation (W) and personal (F) variables. First, the stepwise
procedure (using SAS)6 was executed by using a 0.10 significance level for
initial entry of a variable into the model and for retention of such a variable
in the model (after inclusion of other variables). Then the procedure was
invoked again, but with a 0.05 level for entry/retention in the model. Those
workstation and personal variables passing the second criterion are identified
with an "M" (males) or "F" (females) in Table 6-2. Those passing the first
(i.e., statistically significant at the 0.10 level) but not the second criterion
are identified with an "mn or "f." Variables associated with tertiary factors
-- namely, type of workstation (W2A and W2B), pay grade (P3A and P3B). and
smoking status (PllA and PUB) -- were treated simultaneously (i.e. , both members
of the pair were either included in a model or excluded from it).
It should be noted that all of the workstation and personal variables
defined in Tables 4-8 and 4-9 were allowed as candidate explanatory variates in
6SAS is the registered trademark of SAS Institute, Inc., Cary. NC.
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the stepwise regressions. However, for some of the outcome measures, P12A, P12B,
and/or P13 (glasses or contacts at work, contacts at work, and asthma,
respectively) had been previously considered and rejected a priori as potential
confounders. (In particular, P12A and P12B were not considered viable predictors
for outcomes H4, H5, H7, H8, H12, H13, H14, HIS, H16, Cl, C2, C4, or 02;
similarly, P13 was not considered a viable predictor for H5, H6, H9, H10, H13,
H14, H15, or H16.) As noted in Table 6-2, these variables were not retained in
the subsequent models (Models A, B, etc.), even though they were sometimes found
by the stepwise procedure to be statistically significant (these cases are
highlighted by asterisks in Table 6-2).
After the stepwise regression results were reviewed, the decision was made
to use 0.10 as the significance level criterion for retaining a workstation (V)
or personal/medical (P) variate in the next step of the modeling strategy. That
is, with the exceptions noted in the prior paragraph, the candidate confounders
for Model A (see Figure 6-1) consisted of those variates identified with either
a small or capital "m" or "f" in Table 6-2. This 0.10 criterion, in contrast to
a more stringent criterion such as 0.05 or 0.01, was adopted because of the
recognition that significance levels emanating from this stepwise approach must
be regarded as approximations -- because of the lack of strict adherence to
underlying assumptions. For instance, most of the outcome variates are
dichotomous-valued, but the stepwise procedure, which is founded on classical OLS
methodology, treats the outcome measures as continuous variables having a
homogeneous error variance structure. Note that use of the 0.10 criterion
permits nonsignificant independent variables to be declared as significant about
10% of the time (false positives). For example, if we exclude the first five
dependent variables because of their redundancy with H6 through H16, then there
are 20 dependent variables. Multiplying this times the 20 independent variables
and 2 sexes results in 800 hypothesis tests concerning the terms in the models.
By chance, then, we would expect about 80 of these tests to indicate
significance, even if none of the terms were pertinent predictors. Among the
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last 20 dependent variables, there are actually 111 terns that were found to be
significant at the 0.10 level or the 0.05 level. Many of the terms indicated
for inclusion in Model A are probably unnecessary (i.e., false positives).
The results in the table also support the notion of building separate
models for males and females. Only rarely was significance achieved for both
genders. Even in those cases where an effect was identified for both, the
direction of the effect was sometimes opposite. Even though separate models were
fit for males and females, we elected to use a common set of terms for both
genders (i.e., the union of those terms found significant at the 0.10 level) in
order to facilitate comparisons among the models (e.g., an estimated odds ratio
>
for a given effect would thus be available for both sexes).
6.3.2 Hypothesis Testing Results
A summary of the hypothesis testing results is given in Appendix D.
Detailed results showing the parameter estimates and associated statistics for
each of the models fitted are presented in Appendices E, F, G, and H -- for
Models A, B, C, and D', respectively. This subsection abstracts information from
these appendices and furnishes a concise presentation of the major results.
Detailed discussions of the results shown in this subsection are presented in the
remaining portions of this chapter.
Table 6-3 summarizes the major hypothesis testing results that address the
objectives listed in Table 6-1. The table indicates, for each dependent
variable, the results for Model A (tests for effects of temporally measured
variates [T1-T4 and T6]), Model C (tests for comfort and odor effects [02, Cl,
C2, and C4J), and Model D' (tests for variables derived from the VOC data file
[V1-V5], and for microbiologicals [V6-V8]). Tabular entries M or m indicate
significance of an effect at the 0.01 or 0.05 significance level, respectively,
for males. Entries F or f are defined similarly for the female models. An
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attached negative sign indicates that the estimated coefficient for the specific
model term was negative (i.e. , a negative association between the independent and
dependent variable).
The rightmost part of Table 6-3 provides information regarding the adequacy
of the logistic regression models. The significance of the likelihood ratio
statistic (LRS) is indicated. A plus sign (or an "N") indicates that the model
tends to overfit the data (i.e. , too many parameters); these cases occur when the
dependent variable exhibits a low prevalence rate (say, less than 12%). Adequate
modeling of such a variable requires a larger sample size than that available in
this study, and interpretation of the modeling results, if attempted at all,
should therefore be made with caution. A minus sign in this part of the table
indicates that the model does not explain as much of the variability as might be
expected and that other predictors might be found that would account for more of
the variation. With this caveat, the presence of a minus sign should not
adversely affect the interpretation of the modeling results.
The results of Table 6-3 indicate that very few significant effects of the
temporal, VOC, and microbiological measured variates (T and V variables) on the
health, comfort, odor, air quality, or mood-state measures were observed. In
fact, at the 0.01 level of significance, only three effects for males and four
effects for females were detected.7 Among the temporal, VOC, and
microbiological variables, only two significant effects common to both males and
females were found: (1) a (negative) temperature (Tl) effect for cool/drafty air
(C4) (0.01 level), indicating that employees reported the air to be too cool and
drafty when measured temperatures were low (relative to other monitoring
locations); and (2) a (negative) total fungi (V6) effect for throat symptoms
7For hypothesis testing, the use of a 0.01 rather than a 0.05 significance
level is recommended, because of the large number of tests being performed (i.e.,
there will be fewer false positives).
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(H10) (0.05 level), indicating that employees reported a higher prevalence of
throat symptoms when total fungi levels were low.
The Model C results show a number of strong associations between the
outcome measures (health and air quality) and the comfort and odor measures. At
least for the comfort measures, the strength of these associations may be partly
due to the manner in which the environmental monitoring sites were selected. As
described in Section 3.1, the initial design called for including sites with high
prevalences of both health and thermal comfort complaints, as reported in the
first questionnaire, and to include sites with low prevalences of both health and
thermal comfort. Had this design been explicitly carried out, and if respondents
to the second questionnaire maintained the same pattern of complaints as in the
first questionnaire, then the design itself would have induced an apparent
association between thermal comfort and health measures, even if no such
associations existed for the overall employee population. Actually, this design
was only partly implemented; it was not used at all for the Crystal Mall and
Fairchild buildings; and at Waterside Mall complex, the health complaint index
was given priority over the comfort index. Hence, even at Waterside Mall, some
low-discomfort/high-health-complaint areas and some high-discomfort/low-
health-complaint areas were included. Nevertheless, at Waterside, the
high-discoofort/high-health-complaint areas and the low-discomfort/low-
health-complaint areas were overrepresented.
In addition to the major hypotheses of interest, the models furnished
information on which confounders were most relevant for each outcome variable.
This information is presented in summary form in Table 6-4. Although this
information is given only for Model B, which was derived from Model A, the
results for the confounders in the other model types were generally similar to
those shown in this table, as can be seen in Appendix D.
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The results summarized above are described more fully in the subsections
that follow. In that discussion, reference to both 0.01 and 0.05 significance
levels is made. For the reasons previously stated, more credence should, we
believe, be given to effects significant at the 0.01 level.
6.4 Discussion of Modeling Results: Health Symptoms
The employee-reported health symptom cluster outcome variables were
evaluated in Model A for the temporal measures (temperature, humidity, etc.).
Model C evaluated the effects of odor and comfort variables on the health symptom
clusters.
6.4.1 Discussion of Models A and C
6.A.1.1 Nonspecific Indoor Air Quality Symptoms (HI)
This group of symptoms included headache, unusual fatigue, and sleepiness.
No significant effects for the temporal variates were found. Males showed a
significant decrease in symptoms with age (p<0.01). Men who wore glasses or
contact lenses showed a higher prevalence of symptoms (p<0.01). For females, no
independent variables were significant at the 0.01 level, although females with
asthma showed an increase in symptoms (p<0.05).
When the comfort and odor indices were added as independent variables
(Model C), the three variables above retained their significant status, providing
some indication of the stability of the results. For females, reports of hot and
stuffy air and reports of increased odor of cosmetics, etc., were significantly
(p<0.01) associated with these general indoor air quality symptoms. At the 0.05
level of significance, both men and women reported that cold or drafty air .was
also associated with increased prevalence of headache and fatigue. At this same
level of significance, females reporting dry air had more symptoms.
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6.4.1.2 Mucous Membrane Symptoms (H2)
This group of symptoms included eye, nose, and throat symptoms. Again,
none of the five temporal variables achieved the 0.01 level of significance, nor
did any of the- personal and workstation factors, for either men or women. At the
0.05 level for men, hours spent at a VDT screen and an external stress index both
were associated with increased symptoms. At this level for women (p<0.05), high
pay and low job satisfaction were both associated with increased symptom
frequencies. The first result appears to be at odds with intuition and with the
results of previous building studies. It should be noted that the models for
both men and women have extremely low significance levels for the likelihood
ratio statistic, which indicates that the models explain very little of the
observed variation.
When odor and comfort variables were added (Model C), complaints of dry air
were highly significantly (p<0.01) associated with increased mucosal membrane
complaints among men. Also among men, the external stress index continued to be
significant at the 0.05 level, but the variable measuring time spent at a VDT
dropped below the 0.05 criterion for significance. For women, hot and stuffy air
was associated with mucosal membrane complaints at p<0.01, while dry air was
significant at p<0.05. Odors of cosmetics and body odor were associated (p<0.05)
with increased symptoms among women. The pay grade and Job satisfaction
variables continued to be significant at p<0.05.
6.4.1.3 Combined General IAQ and Mucous Membrane Symptoms (H3)
This variable is simply the union of the first two health variables. In
model A, younger males reported more symptoms (p<0.05). Time spent at a VDT was
associated with more symptoms in males (p<0.05). Females in open work areas
reported fewer symptoms (p<0.05). Those indicating role conflicts reported more
symptoms.
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In Model C, males complaining of dry air reported more symptoms (p<0.01).
Time spent at a VDT continued to be significant at p<0.05 for males, but age was
no longer significant. Females reporting body and cosmetic odors reported more
symptoms, as did those complaining of hot and stuffy air (p<0.01). Females at
open work areas reported fewer symptoms (p<0.05), and those reporting lower job
satisfaction reported higher health complaints (p<0.05). Finally, women
reporting dry air also reported higher symptoms (p<0.05).
6.4.1.4 Flu-Like Symptoms (H4)
This group of symptoms included fever, cough, aching muscles or Joints,
wheezing, shortness of breath, and chest tightness. Model A for males had an
LRS significance exceeding 0.99, indicating a poor overall fit of the model; this
was primarily due to the low prevalence of the symptom (14.7% of the males). The
model for females showed no effects of the five temporal variables nor of any of
the personal or work-space variables at the chosen level of 0.01 significance.
At the 0.05 level, an increased daily change in temperature and a measure of role
ambiguity were both associated with increased symptom frequency.
Model C for males continued to have an unacceptable LRS significance level
(>0.99). Model C for women indicated that areas with higher levels of RSP were
associated with higher frequencies of wheezing, cough, and other symptoms
associated with dusty areas. This RSP variable had shown only marginal
significance (p<0.10) in Model A. Also at the 0.05 level, females' complaints
that the air was too cold and drafty were associated with increased flu-like
symptoms.
6.4.1.5 Back, Neck, and Shoulder Pain (H5 and H13)
This group of symptoms included back pain, neck and shoulder pain, and
pain/nuir.bness in hands or wrists (H5). H13 included all of these plus muscle and
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joint pain. These symptoms are characteristics not normally associated with air
quality and therefore would not be expected to show associations with
temperature, humidity, etc. In fact, no associations with any of these variables
were noted at the 0.01 level of significance. For females, temperature and C02
levels showed effects for H5 at the 0.05 level, but with opposite signs
(increased symptom frequencies were associated with increasing temperature and
decreasing C02). Because these two variables were collinear, it is likely that
the effects are spurious. For males, new carpet was associated with increased
symptoms at the 0.05 level, but for females, new carpet was associated with
decreased symptoms (H5 at the 0.10 level, H13 at the 0.05 level). Females
working in open areas were less likely to report pain than those in enclosed
offices, again at the 0.05 level of significance. Males feeling less control
over their jobs reported higher frequencies of these symptoms (p<0.05 for H5;
p<0.10 for H13). Males reporting higher workloads reported higher symptom
frequencies (p<0.10 for H5; p<0.05 for H13).
In Model C for males, the significance level associated with the LRS was
0.9898, indicative of model overfitting (too many parameters for too few
observations). With that caveat, increased symptom frequency was associated at
a high level of significance (p<0.01) with perceptions that the air was too dry.
At a lower level of significance (p<0.05), more complaints of pain were received
from areas with new carpet. For women, no variables appeared at the 0.01 level
of significance. At the 0.05 level, four variables showed associations with pain
symptoms: Women in closed offices were more likely to report symptoms than women
in open areas; women in areas with new carpet reported fewer pain symptoms than
those in areas without new carpet; women in areas that were perceived to be cold
and drafty reported more symptoms of muscle pain; and women reporting higher odor
levels also reported higher symptom frequencies.
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6.4.1.6 Headache and Nausea (H6)
These symptoms showed no associations with the temporal variables at the
0.01 level of significance for either males or females. Younger males and those
with high workloads were more likely to report symptoms (p<0.05). Increased
workload was also associated with these symptoms among females, together with
increased time spent at the workstation (p<0.05).
When the comfort variables were added (Model C), the model for men became
overspecified (LRS = 0.9999) because of the low prevalence of the symptoms (12%
of the males). The model for women showed a strong association (p<0.01) of
headache and nausea with complaints of hot and stuffy air and reports of odor.
Among the 91 females not reporting hot and stuffy air, for instance, only eight
(9%) reported the H6 symptoms; among the 111 who did report hot and stuffy air,
45 (or 41%) of the females reported headache or nausea. At a lower level of
significance, the increased workload and increased time at the workstation
continued to be associated with headache and nausea among women. Areas for which
females reported dry air (variable C2) were also associated with these symptoms
(p<0.05).
6.4.1.7 Nasal Symptoms and Cough (H7)
Although CO2 showed a strong association with these symptoms among males,
the collinearity of CO2 and temperature (which showed an effect in the opposite
direction, with p<0.05) makes it impossible to conclude that a true association
has been observed. Time spent at a VDT (variable W6) and a measure of external
stress (variable P10) were both associated with increased symptom frequency
(p<0.01) among males. Pay grade also appeared to be associated with symptom
prevalence, with males in intermediate levels (GS9 through GS12, or equivalent)
exhibiting lower reported symptom frequencies than those below GS9 (p<0.05). The
model for females explains only a small amount of variability (LRS = 0.01), and
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only two variables are indicated as significant at the 0.05 level of significance
in Model A: (1) a negative association of reported symptom prevalence with P8,
the use of abilities scale, and (2) a negative association of symptom prevalence
with PUB, heavy smokers versus nonsmokers.
When the comfort and odor variables were considered, both the W6 and P10
variables continued to be significantly associated with males' reported symptom
frequencies (p<0.01). Among the comfort and odor variables, only the dry air
variable was directly associated (p<0.05) with symptoms for males. Among women,
areas perceived as hot and stuffy were again associated with an increased symptom
frequency (p<0.01): a 56% prevalence among those reporting hot or stuffy air,
as compared to 28% among those who did not. Interestingly, areas perceived as
cold and drafty were also associated with symptoms, although at a lower level of
significance (a 59% rate of symptom reporting among females who complained of
cold or drafty air, as compared to a 37% rate among the others). As was found
in Model A, women who were more satisfied with the utilization of their abilities
were less likely (p<0.05) to report symptoms.
6.4.1.8 Chest Tightness, Shortness of Breath (H8)
Because of the rarity of these symptoms (14 of 183 males, or 7.7%; 16 of
190 females, or 8.4%), meaningful models for both model types A and C could not
be developed.
6.4.1.9 Eye Irritation (H9)
For this cluster of four symptoms, none of the temporal variables achieved
a 0.01 level of significance. For males, the external stress index was
associated with increased symptom frequency at the 0.01 level. Females in open
areas were less likely than those in enclosed offices to report eye irritation
(p<0.01). At a lower level of significance (p<0.05), women with contact lenses
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reported more symptoms and women in areas with new glued-down carpet reported
fewer symptoms.
When comfort variables were considered (Model C), both males and females
reporting hot, stuffy air also reported more eye symptoms at the 0.01 level of
significance. For males, other variables appearing at this level of significance
were the external stress index and problems with dry air. Females wearing
contact lenses at work also reported significantly (p<0.01) higher symptom
frequencies. Females in open areas reported significantly (p<0.01) lower symptom
frequencies than women in enclosed offices.
On the basis of a significance level of 0.05, males reporting drafty or
cold conditions had higher symptom frequencies. For females at this level of
significance, time spent at the workstation was associated with increased eye
irritation, as was working in areas with perceived dry air (p<0.05). Women
reporting lower job satisfaction reported higher levels of eye irritation.
6.4.1.10 Throat Symptoms (H10)
These symptoms included sore throat, dry throat, and hoarseness. No
temporal or other variables achieved the 0.01 level of significance for this set
of symptoms. For males, a measure of role conflict was associated with increased
symptom frequencies (p<0.05).
However, when the comfort variables were added (Model C), a very strong
association (p<0.01) was noted between complaints of throat symptoms and
complaints of dry air reported by men. Among women, the effect of dry air was
only marginal (p<0.10). Among men, the measure of role conflict was strongly
(p<0.01) associated with increased symptom frequency, while the perception of
odors was negatively associated (p<0.05) with throat symptoms.
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6.4.1.11 Fatigue and Sleepiness (Hll)
None of the Model A variables were associated with these symptoms at the
0.01 level of significance. Time spent at the workstation was associated with
increased syrr.ftorn frequency for men (p<0.05). Temperature change during the day
was associated (p<0.05) with increased symptom frequency among women. Younger
women reported more fatigue and sleepiness symptoms than older women (p<0.05).
When comfort variables were added (Model C), cold and drafty air was highly
significantly (p<0.01) associated with fatigue among men, whereas hot and stuffy
air was associated with fatigue among women (p<0.01). Time spent at the
workstation continued to be significantly associated (p<0.05) with fatigue and
sleepiness among men, although it again did not appear significant among women.
Younger women, as in Model A, reported significantly more fatigue and sleepiness
symptoms than older women. Women with asthma and women who reported cold and
drafty air in their workplace were also more likely to report fatigue.
6.A.1.12 Chills and Fever (H12)
Models of types A and C could not be developed for these symptoms for
either men or women. This was due primarily to the low symptom frequencies
reported -- namely, 13 of 183 males (7.1%). and 20 of 190 females (10.5%).
6.4.1.13 Central Nervous System Symptoms (H14)
Increased levels of respirable particles were associated with increased
frequency of feeling depressed or nervous and difficulty remembering among males
(p<0.05). Since RSP levels were extremely low and would not be expected to
affect the central uervous system, this association may be spurious. Although
for females no variable achieved the 0.01 level of significance, the use of
chemicals (including VOCs) at the workstation was associated with high symptom
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frequency (p<0.05). This finding provides some support to the hypothesis that
increased levels of VOCs at low absolute concentrations found in buildings can
have deleterious effects on concentration, memory, and mood.
A perception of increased workload on the part of females and the role
conflict index for males also showed associations with increased symptom
frequency at the 0.05 level.
Upon the addition of the comfort and odor variables, a highly significant
(p<0.01) relationship was noted for females between increased reports of odors
(including cosmetics) and increased frequency of central nervous system symptoms.
This is possibly an indication of a lower odor threshold accompanying increased
sensitivity to chemicals. Women who reported using chemicals at their workstation
were also more likely to report central nervous system symptoms (p<0.05). Less
powerful relationships were noted between central nervous system symptoms and
complaints about air quality (either too hot and stuffy or too cold and drafty).
Women who perceived high workloads were also more likely to report these
symptoms.
Model C for males resulted in no new relationships, although the strong
relationship with RSP concentrations again appeared at the 0.01 level of
significance.
6.4.1.14 Dizziness (HIS) and Dry/Itchy Skin (H16)
Because of low symptom frequencies, neither Model A nor Model C (for men
or women) was acceptable for either of these symptoms. Only 8 of 183 males
(4.4%) and 13 of 190 females (6.8%) reported dizziness. Seventeen of 183 males
(9.3%) reported dry skin, as compared to 28 of 190 females (14.7%).
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6.4.2 Discussion of Model D'
In Model D', the subjective comfort and odor indices of Model C were
replaced by the objective measures of environmental variables; the significant
temporal measures of Model A and the relevant workplace and personal/medical
confounders (i.e., those appearing in Models B and C) were also retained. The
newly included variables consisted of four variables dealing with volatile
organic chemicals (chlorinated solvents, aroma tics, methylene chloride, and total
VOCs), an integrated measure of RSP, and three variables dealing with
microbiological aerosols (total fungi, total bacteria, total thennophiles).
Explicit definitions are given in Table 5-6. Since these measurements were made
at a smaller number of sites than the temporal measurements, the data set has
about half the observations and therefore the statistical tests have less power
to detect associations.
6.4.2.1 Headache, Fatigue, and Sleepiness (HI)
Younger men and men who wear glasses or contact lenses were significantly
(p<0.01) more likely to report headache and fatigue. Increased levels of
chlorinated solvents (variable VI) and decreased levels of human source bacteria
were also associated (p<0.05) with these symptoms in men. In women, no
significant associations were noted.
6.4.2.2. Mucous Membrane Symptoms (H2)
Total thermophilic bacteria levels were significantly (p<0.01) associated
with decreased frequency of mucous membrane symptoms in women. The likely reason
for this is discussed under the section on eye irritation below. Women who
reported higher job satisfaction were less likely to report these symptoms
(p<0.05). No other variables achieved significance at the 0.05 level for either
men or women.
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6.4.2.3 General Symptoms (H3 = HI and H2 combined)
No variables were significantly associated with these symptoms in men.
Women in open areas reported significantly (p<0.01) fewer symptoms than those in
enclosed offices. Total thermophiles were again associated with fewer symptoms
among women, as discussed below in the section on eye irritation. Increased
levels of aromatic VOCs (variable V2) were associated (p<0.05) with increased
symptom frequency among women. Women in the lowest pay grades reported
significantly (p<0.05) higher symptom frequencies than those in the medium
(GS9-12) pay grades.
6.4.2.4 Flu-Like Symptoms (H4)
Among men, those who felt their job utilized their abilities well were less
likely (p<0.05) to report such symptoms. Among women, no variables achieved the
0.05 level of significance.
6.4.2.5 Back, Neck, and Shoulder Pain (H5 and H13)
Among men, no variables achieved the 0.05 level of significance. Among
women, those in open areas reported significantly (p<0.01) fewer symptoms than
those in enclosed offices. Both chlorinated solvents and total VOCs were
associated (p<0.05) with increased symptom frequency.
6.4.2.6 Headache and Nausea (H6)
The model for men was overfit (significance of the likelihood ratio =
0.999). Women spending more time at their workstations reported a higher
frequency of headache and nausea (p<0.05).
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6.4.2.7 Nasal Irritation, Cough (H7)
Men in areas with higher C02 and lower total VOCs were more likely to
report these symptoms (p<0.05). Men reporting more external stress also reported
high symptom frequency (p<0.05). Women spending more time at their workstation
and those who felt their abilities were under-utilized reported higher symptom
frequencies (p<0.05). Women in areas with higher levels of fungi reported fewer
symptoms (p<0.05).
6.A.2.8 Wheezing, Shortness of Breath (H8)
Because of low symptom frequencies, both models for men and women were
overfit (significance level of LRS exceeded 0.99).
6.4.2.9 Eye Irritation (H9)
For males, no variables achieved the 0.01 level of significance; time spent
at the workstation was associated with eye irritation at the p<0.05 level. For
females, time spent at the workstation was highly significantly associated with
eye irritation complaints. Women in enclosed offices were also much more likely
(p<0.01) to report eye irritation than women in open areas. At a lower level of
significance (p<0.05), women in areas with more thermophiles reported less eye
irritation. Since thermophiles thrive under moist warm conditions, these results
are consistent with eye irritation occurring more often in areas with dry air.
6.4.2.10 Throat Symptoms (H10)
Both men and women in areas with higher total fungi levels reported fewer
throat symptoms (p<0.05).
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6.4.2.11 Unusual Fatigue, Sleepiness (Hll)
Men spending more time at their workstation reported higher levels of
fatigue and sleepiness (p<0.05). For women, a significant relationship (p<0.05)
was noted between increased relative humidity and increased fatigue.
6.4.2.12 Chills and Fever (H12)
Because of low symptom frequencies, both models for men and women were
overfit (LRS > 0.99).
6.4.2.13 Central Nervous System Symptoms (H14)
For males, no variables achieved the 0.05 level of significance. For
females, those who reported using chemicals at their workstation, and those in
areas with higher levels of methylene chloride (a common solvent used in many
consumer products) reported higher prevalence of depression, nervousness,•and
memory loss. The association of these symptoms with volatile organic compounds
has been made in other studies, and it was suggested by Models A and C above.
It has also been noted that females appear to show higher sensitivity to
chemicals and greater effects on the central nervous system than males. Thus both
the positive findings for females and the negative findings for males are
consistent with expectations.
6.4.2.14 Dizziness (HIS)
Models were overfit for this symptom because of low symptom frequencies for
both men and women.
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6.4.2.15 Dry, Itchy Skin (H16)
The model for men was overfit because of low symptom frequency. The model
for women failed to include any variables significant at p<0.05.
6.5 Discussion of Modeling Results: Thermal Comfort
The employee-reported thermal comfort cluster outcome variables used
in the logistic multiple regression models were Cl (too little air movement, too
hot, too stuffy), C2 (too dry), and C4 (too much air movement, too cold).
Outcome variable C3 (too humid) was not used because of a low percent of
respondents reporting the effect (about 3% for males and 6% for females). The
thermal comfort outcomes were evaluated only in Model A. In this model, the
effects of 02 (body odor, cosmetics, and other food smells) on the outcome
variables for males and females were tested, in addition to the effects of the
temporally measured variables (temperature, etc.) and the workstation and
personal characteristics.
For Cl (hot and stuffy air) and C2 (dry air), the significance level for
the LRS for both the male and female models was less than 0.01, indicating that
those models accounted for only a limited amount of the variability in these two
thermal comfort outcomes. Lower pay grades and age and higher temperatures were
associated at the 5% significance level with an increase in males reporting the
hot and stuffy air. The 02 odor cluster was found to be associated with hot and
stuffy air for males at the 0.10 level. No independent variables were found to
be significant below the 5% level for the female, hot-and-stuffy-air model,
although the absencs of an open workstation, a lower age, and higher carbon
dioxide levels were found to be associated with hot and stuffy air for females
at the 0.10 level.
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Dry air (C2) was not found to be associated with any of the independent
temporal, workstation, or personal variables at the 0.01 level. At the 5%
significance level, male asthmatics were less likely to report dry air, while
females in higher pay grades were more likely to report dry air. The meaning of
these associations is not clear. A positive association for 02 for the females'
dry-air model was found (10% level).
Males and females in office spaces with lower temperatures (p<0.01) and
males in offices where maximum daily temperature differences are greatest (5%
level) are likely to report the CA thermal comfort cluster (too much air
movement, too cold). For males, higher perceptions of role clarity (1% level)
and greater role conflict (5% level) were also associated with complaints of cool
and drafty air.
6.6 Discussion of Modeling Results: Odors
The one odor outcome variable, 02 (body odor, cosmetics, and food smells
other than fishy smells), was evaluated in Models A and D' for males and females.
6.6.1 Model A
For this model, higher external stress and heavy smoking (greater than 10
cigarettes a day) were found to be related to males' reporting of the odor
cluster at the 0.01 significance level. At less extreme levels of significance,
there was also evidence of an increased awareness of the odors for males with
asthma and for males in areas with higher carbon dioxide concentrations and
higher percent relative humidities.
For females, open workstations and hours working at a VDT were associated
with 02 in Model A at the 1% level. Women with higher perceived levels^of job
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control and less clarity in their work roles tended to notice the 02 odors more
(p<0.05).
6.6.2 Model D'
Inclusion of the VOC and microbiological variables (Model D') had little
or no impact on the importance of the open workstation, hours at workstation, job
control, utilization of abilities, and carbon dioxide variables observed in Model
A. Half-height partitions were associated with lower odor reporting for males
(1%) in Model D', while heavy smoking for males no longer appeared statistically
significant. External stress for males and hours at a VDT for males were not
significant in Model D'. Among the VOC and microbiological variables, V2
(aromatics, trichloroethylene, octane) was found to be significantly related
(p<0.01) to the odor cluster for males, while total volatiles (variable V4) was
significant at the 5% level for females. These chemicals are used heavily in
cosmetics and many other consumer products; however, the concentrations measured
are hundreds of times below the known odor thresholds of these chemicals. It is
possible that an accompanying highly odorous chemical (such as acetone or butyl
acetate) was responsible for the odors. Thermophiles were weakly associated with
02 for females (10% level). These results should be viewed with caution, since
the model results appear unstable (e.g., extremely large odds-ratios).
6.7 Discussion of Modeling Results: Air Quality Acceptability
Both measures of employee-reported air quality acceptability. Al (poor or
fair) and A2 (poor) , were evaluated for associations with the temporally measured
parameters (Model A), the comfort parameters (Model C), and the volatile and
microbiological variables (Model D'). In each case, the models controlled for
potential confounders (workstation and personal characteristics).
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6.7.1 Poor or Fair Air Quality Rating (Al)
The significance levels for the likelihood ratio statistics for the male
Models A and D' were less than 0.01 for outcome variable Al. The temporal
variables were not associated with this air quality acceptability measure for the
models evaluated, except for a weak negative association with temperature for the
male Models A and C. For males, there was a strong association between type of
workstation and reports of poor or fair air quality (Al). Males not occupying
enclosed work areas more frequently reported concern about the air quality. Lack
of role clarity was a consistently significant factor across all models for women
(p<0.05) for the Al outcome.
The most striking associations were those observed between the thermal
comfort clusters and Al (poor or fair air quality) in Model C. Subjective
respondent judgments of Cl (too little air, too hot, stuffy) were positively
associated with overall poor or fair air quality judgements for males and females
(p<0.01). Male and female reports of dry air (C2) were associated with Al (1%
level for women and at the 5% level for males). Too much air or too cold (C4)
were also positively associated with Al for females (5% level). Overall
acceptability of air quality by the responding employees appeared to be closely
associated with the acceptability of the thermal environment.
VOC and microbiological variables (Model D') were not strongly assobiated
with Al. Total human sources of bacteria showed a weak association (10% level)
with Al for women. Lower levels of integrated RSP were associated with Al for
women.
6.7.2 Poor Air Quality Rating (A2)
All the models for males for perceived poor air had LRS significance levels
approaching 1.0, indicating that the reported prevalence was too low to be
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modeled effectively. These models cannot be Interpreted. In addition, the
number of female respondents reporting poor air quality (A2) was only 32, making
the results of these models also difficult to interpret.
6.8 Discussion of Modeling Results: Mood-State Scales
6.8.1 Fatigue (Ml)
Model A had no highly significant effects due to any of the temporal
variables. There was a significant positive effect (p<0.01) due to contact lens
for males. Females with a higher workload had higher fatigue scores (p<0.01),
and those whose abilities were used less had higher fatigue scores (p<0.05).
These relationships tended to hold true for Model D' also. There were no
significant effects due to VOCs. The comfort and odor variables (Model C) were
not tested for the mood-state scores.
6.8.2 Vigor (M2)
Among the temporally measured variates, only two associations appeared
significant (p<0.05) in Model A: a positive relationship for temperature and
vigor among males, a negative relationship between In(RSP) and vigor for females.
There was also a positive association with vigor among males who used chemicals
at their workstation (p<0.01) and among males who had a higher role clarity score
(p<0.05). Male contact lens wearers had a negative association with vigor.
There was a positive association between age (p<0.01) and vigor for females.
In model D', solvents (VI) showed a negative association (p<0.05) with
vigor for males. Females had a weak positive association with thermophiles
(p<0.10). Older women had higher vigor scale scores (p<0.01) than younger women.
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6.8.3 Tension (M3)
There was a negative association between percent relative humidity (p<0.05)
and the tension score for males. Females had no significant associations between
temporal variables and their tension scores. Males with higher role conflict
scores (p<0.01) also had higher tension scores, while those that used chemicals
at work (p<0.05) had lower tension scores. There was a positive relationship
between doctor-diagnosed asthma (p<0.05) and higher workload (p<0.01) for
females' tension scores, but a negative association between job control (p<0.05)
and females' tension scores.
The only variable that was significant at the 0.05 level for the D' model
was the job control variable for females, which was negatively associated with
tension scores.
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FIGURE 6-1. MODELING STRATEGY FOR HEALTH SYMPTOM OUTCOMES
STEP 1
STEP 2
STEP 3
STEP 4
Use stepwise regression,
forcing in T, to select
relevant W and P variates
for male and female models
Model A. Test for temporal
effects (model contains all
T variables plus union of
relevant W and P variables
from male and female models)
Model B. Estimate final
temporal model (model contains
significant effects from Model A)
STEP 5
Model C. Test for comfort
and odor effects (Cl, C2, C4,
and O2 added to Model B)
Model D". Test for VOC and |
microbiological effects
(V Variables added to Model B)
Exposure Measures:
T= temporal measurements={Tl-T4,T6}
V=VOC and microbiological measurements={Vl-V14}
Potential Confounders:
W=workstation-related responses*{W2A,W2B,W3-W8}
P=personal traits={Pl,P3A,P3B,P4-P10,PllA,PllB,P12A,P12B,Pl3}
Some of the microbiologicals were dropped because of overfitting.
The resultant model was called D', which is the model discussed in
this report.
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TABLE 6-1. LISTING OF MAJOR ANALYTIC OBJECTIVES
Generic Model Purpose of Model
H = f(T,W,P) Test for temporal-variate effects (T) on self-reported health
symptom outcomes (H); adjust for workstation (W) and
personal/medical (P) variates
H = f(T,V,W,P) Test for VOC and microbiological effects (V) on self- reported
health symptom outcomes; adjust for W, P, and T variates
H = f(T,C,O,W,P) Test for self-reported comfort (C) and odor (O) effects on
self-reported health symptom outcomes; adjust for W, P, and T
variates
C = f(T,O,W,P) Test for temporal-variate effects and self- reported odor
effects on self-reported comfort measures; adjust for W and P
variates
M = f(T,W,P) Test for temporal-variate effects on self- reported mood-state
scales (M); adjust for W and P variates
M = f(T,V,W,P) Test for VOC and microbiological effects on self- reported
mood-state scales; adjust for W, P, and T variates
O = f(T,W,P) Test for temporal-variate effects on self- reported odor
measures; adjust for W and P variates
O = f(T,V,W,P) Test for VOC and microbiological effects on self- reported
odor measures; adjust for W, P, and T variates
A = f(T,W,P) Test for temporal-variate effects on self- reported air
quality ratings (A); adjust for W and P variates
A = f (T,V,W,P) Test for VOC and microbiological effects on self- reported air
quality ratings; adjust for W, P, and T variates
A = f(T,C,O,W,P) Test for self-reported comfort and odor effects on
self-reported air quality ratings; adjust for W, P, and T
variates
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TABLE 6-2. SUMMARY OF STEPUISE REGRESSION RESULTS
U)
01 01
> u
\-> 1-1
3 «0
cr
O.-O
o w
(*•
§
i-«
o
>
Dependent
Variable
Ml nan- spec. IAQ
H2 MUCOUS «e«t>rane
HI comb. HI, H2
H4 flu- like
HS ergonanic
H6 headache, nausea
H7 nasal, cough
H8 chest
H9 eyes
HID throat
Hit tiredness
N12 chills, fever
H13 ergonoaric
H14 nervous cyst CM
HIS dizziness, etc.
H16 dry/ itchy skin
C1 too hot, stuffy
C2 too dry
C* too cool, drafty
02 cosmetics.
A1 poor or fair air
A2 poor air
N1 fatigue
N2 vigor
N3 tension
Office Work Went Used VDT Mew With
Type Hrs Out Chem Hrt Rugt Clue
U2A.B U3 U4 W5 U6 W7 W8
N
•f
F N
HI •
F Nf
F
f N f
M • •
F Nf f
N
F
F NF
F
F •
F
F mf f
• •
N N
• F
NF
f N
Pay
Age Grade
PI P3A.B
H
f
N N
• •
•
f
HF
•
Nf
f
" :
F
Psychosocial Scales
P4 P5 P6 P7 PB P9 P10
f F N
F F f
N F
M •
NF
f N
M N •
F N
NF
f f
N • f
N F
F F •
N N NF NF
• • •
F NF N
,
m f f
N
N F F
Smoking
P11A.B
f
•
"
(Glass/ Con-
C.lens tacts
P12A P12B
N
•
N f
•
•
•
»\,
^.
Nf
N
N
AsthM
P13
F
".
N
N
f
M*
•
•
•
•
F
«
en
•
SO
KEY: ^statistically significant at 0.05 level, for Mies. SFsaw. but at 0.10 level.
Fatalistically significant at 0.05 level, for females, f'same, but at 0.10 level.
Statistically significant but not regarded as a candidate confounding variable.
-------
TABLE 6-3. SUMMARY OF HYPOTHESIS TESTING RESULTS
a
M
>» M
0) 01
«r
(X-O
9 eg
i U
> .c
o
r-l <
l-l d.
O Ul
(*«
0)
Dependent
Variable
Hi non-spec. IAQ
H2 MUCOUS nenbrane
HJ coni). HI. H2
H4 flu-like
H5 ergonomfc
H6 headache, nausea
H7 nasal, cough
uo rh»cr
H9 eyes
H10 throat
H11 tiredness
H12 chills, fever
H13 ergonomic
HU nervous system
H16 dry/itchy skin
C1 too hot, stuffy
C2 too dry
C4 too cool, drafty
02 cosmetics, etc.
A1 poor or fair air
A2 poor air
Ml fatigue
M2 vigor
M3 tension
MODEL A TESTS
temp XftH CO, RSP T
T1 T2 T3 T4 T6
f
f -f
-• N
s
f
-• •
f
m
a f
•
-MF •
.
', -
MODEL C TESTS
odor and cowfort
02 C1 C2 C4
F F f mf
f F Mf
F F Mf
• f
f M f
F F f
F • f
f
MF Mf •
-• M
F Mf
F
f • f
F f f
f
M
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxxxxxxx
MF mF f
f M
xxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxx
MODEL 0' TEST!
VOC Measures RSP
VI V2 V3 V4 V5
•
f
f f
-•
f f
f
,
xxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxx
1 M -f
-F
-•
biologies
V6 V7 VB
-•
-F
-F
-f
-f
-•f
xxxxxxxxxxxx
xxxxxxxxxxxx
xxxxxxxxxxxx
-f
SIGNIFICANCE OF
LRS, FOR MODEL:
A C D«
N F N F M F
* *
# » •»•
* «• * » N *
* * * * N
- - XX XX
- - XX XX
X X X X
X X
«•«••*•
N/A
N/A
N/A
KEY: M=statistically significant at 0.01 level, for males. •Fsame, but at 0.05 level.
^statistically significant at 0.01 level, for females. f=same, but at O.OS level.
Negative sign indicates a negative association between the independent and dependent variable.
Significance of LRS: - is <0.01 (underfit); » Is >0.99 (overfit); N * model not estimable.
XXX's means the variables were not in the model.
SO
-------
TABLE 6-4. SUMMARY OF MODEL B RESULTS FOR POTENTIAL CONFOUNDERS
01
>> H
4)
Dependent
Variable
Ml non-spec. IAQ
H2 nucous Beobrane
Hi conb. HI, H2
H4 flu-like
N5 ergononic
H6 headache, nausea
H7 nasal, cough
H8 chest
H9 eyes
N10 throat
HIT tiredness
M12 chills, fever
H15 ergononic
HK nervous system
HIS dizziness, etc.
H16 dry/itchy skin
C1 too hot, stuffy
C2 too dry
C4 too cool, draft y
02 cosmetics, etc.
A1 poor/fair air
A2 poor air
Ml fatigue
M2 vigor
to tension
Independent Variables
Office Work Went Used VDT New With
Type Hrs Out Chew Hrs Rugs Clue
U2A W2B UJ W4 W5 W6 W7 W8
m
i -f m
in
i -f m
f
"
-N i
i -F m
m
f
i -F -f
f
i f f
•-f i
IB M
•
M
Pay
Age Grade
PI P3A P3B
-M
i f
-m
-• -m -*
-m
-m i
-• -M
-f
-f
-• i -«
i f
F
. ...
Psychosocial Scales
P4 P5 P6 P7 P8 P9 P10
-f "
•f
mf
-f M
-M
-f M
•
• f
f -f
• N -m»F -F
.
f -«-f M
-F
-F f
F -f
•
M -f F
Sacking
P11A P11B
i M
Glass/ Con-
C.Lera tacts
P12A P12B
N
f
H 1
" i
H
-"
AsthM
P13
f
H
M
f
r^
«*>
>
vO
KEV: M=statistically significant at 0.01 level, for males, apsame, but at 0.05 level.
Fatalistically significant at 0.01 level, for females. f*sane, but at 0.05 level.
i'term included, though not significant, because of inclusion of other independent variable in the pair.
Parameter was considered infinite by estimation procedure; hence no hypothesis test was performed.
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Volume III: Follow-up Survey at
EPA headquarters
7. CONCLUSIONS AND RECOMMENDATIONS
7.1 Conclusions
The third objective of the Indoor Air Quality and Work Environment Study,
which is the subject of this report, was to determine if an association between
self-reported responses (health symptoms, comfort concerns, odors noticed,
perceived indoor air quality, and mood states) and objective environmental
measurements (temperature, humidity, C02, RSP, selected VOCs, and
microbiologicals) could be determined. This objective was addressed by
estimating linear or logistic regression models that allowed the effects of
interest to be tested. The major findings are summarized below. Tests were
conducted at both the 0.01 and 0.05 level of statistical significance. A 0.01
level of statistical significance was used as a basis for judging the
significance of the various associations to reduce the number of false positives.
Logistic regression was used to test for significant associations between
the temporal variables (temperature, relative humidity, carbon dioxide
concentration, integrated RSP concentration, and temperature change) and the
employee-reported health, comfort, odor, perceived air quality, and mood-state
variables (described in Chapter 6). This analysis is referred to as Model A.
In areas that had increased C02 levels, males reported a significantly higher
prevalence of nasal/cough symptoms. However, in this same model, temperature
showed a negative association (at the 0.05 level) with the nasal/cough symptom
prevalence. Because the C02 and temperature variates are highly correlated with
one another, it is unclear as to what extent either of these associations should
be considered real. Both males and females more often reported too cool and/or
too drafty conditions in areas that had lower temperatures measured. The
sparseness of significant relationships among the outcome measures and the
temporal measurements may be due to the limited degree of variability in the
latter.
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Volume III: Follow-up Survey at
EPA headquarters
Model D' tested whether levels of chemicals (VOCs), aerosols (RSP). or
microbiologicals could be associated with the health symptoms, mood states,
odors, and general perceptions of air quality reported by the employees. Because
of the small number of sites at which the measurements were made, this model has
a reduced nuirh<~r of observations (about half as many as in Models A, B, and C)
and correspondingly reduced power to detect associations. In fact, no strong
(p<0.01) associations of VOC or RSP levels occurred simultaneously with any of
the outcomes for both men and women.
For men, only one strong relationship with VOCs was observed. Men in areas
with higher levels of aromatic compounds (e.g., toluene and xylene) were
significantly more likely to complain of cosmetic and other odors. These
chemicals are in fact used heavily in cosmetics and many other consumer products.
However, the concentrations measured in the environmental samples collected are
hundreds of times below the known odor thresholds for these chemicals. It may
be possible, however, that an accompanying highly odorous chemical (such as
acetone or butyl acetate) was responsible for the odor.
For women, a strong relationship with RSP was observed. Indoor air quality
was more often perceived as fair or poor by women in areas with lower levels of
RSP. This result appears spurious, since the reverse would be expected and the
observed levels of RSP were extremely low. A strong negative association between
thermophile levels and prevalence of mucous membrane symptoms was also observed
for women. Thermophile level may be an indirect measure of humidity
(thermophiles tend to thrive in moist air), and this relationship may be indicate
an association between dry air and mucous membrane irritation. However, the lack
of a detectable effect of the measured relative humidity argues against this
interpretation.
We conclude that because of the relatively small number of sites where VOCs,
integrated RSP, and bioaerosols were measured, the development of models that
7-2
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Volume III: Follow-up Survey at
EPA headquarters
allowed testing of relationships between these measures and the various outcome
measures was hampered (i.e., there was limited power to detect such effects).
This was compounded by the fact that the observed levels of the VOCs, integrated
RSP, and mlcrobiologicals were uniformly low across the monitoring sites as
compared to the results from the 10 public-access building study (Wallace et al.,
1987) and other published guidelines (ASHRAE 62-1989).
The statistical analyses conducted in this study did not establish
consistent relationships between measured environmental parameters and
employee-reported health and thermal comfort employees. Employees were selected
from areas having high- and low-complaint rates of health and comfort complaints
in a ratio of 2:1, as determined from an extensive questionnaire administered a
few weeks earlier. This inability to find relationships does not preclude the
possibility that such relationships might, in general, exist. It should be
remembered, for instance, that measurements at a given office were made on only
one day. and on that day the indoor air quality may have been atypical for a
number of reasons. For example, comments suggesting an unusually high airflow
during the monitoring week were heard from some employees and a snow storm
occurred during the week of the study.
In general, this study demonstrated a stronger association between
employees' reported health symptoms and their perceived thermal comfort measures
(including cosmetic/body odors) than between the reported health symptoms and the
environmental measurements. However, the problems with the small number of
environmental measurements and their limited variability may have had an impact
on this finding. Specifically, in Model C, females who reported cosmetic/body
odors and hot/stuffy air tended to report health symptoms previously associated
with poor indoor air quality. Males' reporting of these same types of symptoms
were more generally associated with complaints of dry air. There are several
possible explanations for these interesting findings. First may be the
possibility that the observed associations are partly due to the site selection
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Volume III: Follow-up Survey at
EPA headquarters
procedure. Rooms were ranked on the basis for both health and thermal comfort
indices, and rooms having high values for both indices and rooms having low
values of both indices were overrepresented in the monitoring sample. Second is
the possibility that human "sensors" of thermal comfort, with a great capacity
for memory, are better "instruments" than mechanical/chemical sensors placed in
fixed locations for short periods of time. A third explanation is that common
psychological factors similarly influence perception of thermal comfort and the
reporting of health symptom occurrences. According to this explanation, some
people will report concerns whether the issue is air quality or health. A fourth
possible explanation is that individuals have differential susceptibility.
People's perception of thermal comfort may be affected by the health symptoms
that they are experiencing while at work (e.g., people who develop a headache in
a room may be more likely to describe that room as being uncomfortable). The
perception of the environment reflects the risk of that environment to the
individual. It is not clear which of these various explanations is most
plausible.
7.2 Recommendations
Based on the results of the tests conducted here and the results from both
Volumes I and Volume II, the following recommendations are made. Since
measurements were made only in the winter while the humidity was low, mechanisms
for humidifying the indoor air during the winter heating season should be
considered. However, this recommendation should be carefully studied prior to
implementation. Humidification of the supply air to any office building can
increase the potential for increased airborne microbiological agents, which might
increase the risk of injury to employees.
Because the effects of cosmetics, body, and non-fish food odors on health
symptoms were significant, the employees should be informed of these findings and
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Volume III: Follow-up Survey at
EPA headquarters
encouraged to be sensitive to the concerns of their fellow employees regarding
the use of scented cosmetics, etc.
Providing employees a way to have more control over their work areas may
improve their perception of indoor comfort and air quality. For example, lack
of privacy, meeting areas, furniture arrangement, wall decoration, and other
basic design factors influence a worker's sense of autonomy and productivity.
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Volume III: Follow-up Survey at
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8. REFERENCES
American Society of Heating, Refrigerating, and Air-Conditioning Engineers
(ASHRAE), Inc., "Ventilation for Acceptable Indoor Air Quality," ASHRAE
Standard 62-1989, Atlanta, GA, 1989.
Ashford, N.A., and Miller, C.S., "Chemical Sensitivity," A Report to the
New Jersey State Department of Health. December 1989.
Berglund, L.G., Gonzalez, R.R., and Gagge, A.P., "Predicting Human
Performance Decrement from Thermal Discomfort and ET," in Walkinshaw (ed)
Indoor Air '90: Proceedings of the 5th International Conference on Indoor
Air Quality and Climate. Toronto, Canada, Vol. 1, p. 215-220, 1990.
Burge, P.S., Robertson, A.S., and Hedge, A., "Validation of Self-
Administered Questionnaire in the Diagnosis of Sick Building Syndrome," in
Walkinshaw (ed) Indoor Air '90: Proceedings of the 5th Jnternational
Conference on Indoor Air Quality and Climate. Toronto, Canada, Vol. 1,
p.575-80, 1990.
Burge, S., Hedge, A., Wilson, S., Harris-Bass, J., Robertson, A.S., "Sick
Building Syndrome: A Study of 4373 Office Workers," Annals of
Occupational Hygiene. Vol. 31(4A), p. 493-504, 1987.
Caplan, R.D., Cobb S., French, J.R.P., Harrison, R.V., Pinneau, S.R., Job
Demands and Worker Health. HEW publication no. (NIOSH)75-105, 1975.
Chamberlain, J., memorandum, with attachment, to William Hirzy, August 17,
1988.
Fanger, P.O., "Introduction of the olf and the decipol Units to Quantify
Air Pollution Perceived by Humans Indoors and Outdoors," Energy and
Buildings. Vol. 12, p. 1-6, 1988
Hedge, L.A., "Questionnaire Design Guidelines for Investigations of 'Sick'
Buildings," in Walkinshaw (ed) Indoor Air '90: Proceedings of the 5th
International Conference on Indoor Air Quality and Climate, Toronto,
Canada, Vol. 1, p. 605-10, 1990.
Hedge, A., flurge, P.S., Robertson, A.S., Wilson, S., Harris-Bass, J.,
"Work-Related Illness in Offices: A Proposed Model of the 'Sick Building
Syndrome'," Environment International. Vol. 15, p. 143-158, 1989.
McNair, D.M., Lorr, M. , and Droppleman, L.F., Profile of Mood States.
Educational and Industrial Testing Service, San Diego, CA, 1971.
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Volume III: Follow-up Survey at
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Mendell, M.J., and Smith, A.H., "Consistent Pattern of Elevated Symptoms
in Air-conditioned Office Buildings: A Reanalysis of Epidemiologic
Studies," AJPH. Vol. 80 (10), p. 1193-1199, 1990.
Milton, D.K., Rose, C.S., Martyny, J.W., and Kreiss, K. , "Airborne
Endotoxins Associated with Hypersensitivity Pneumonitis Outbreak Among
Lifeguards," American Review of Respiratory Disease. Vol. 141, p. 315,
1990.
Molhave, L. , "Volatile Organic Compounds as Indoor Air Pollutants," Indoor
Air and Human Health, Proceedings of the Seventh Life Sciences Symposium.
Knoxville, TN, p. 403-414, 1984.
Murphy, L.R. , andHurrell, J.J., Jr., "Stress Measurement and Management,"
in A.W. Riley and S.J. Zaccaro (eds) Occupational Stress and
Organizational Development. Prager, New York, NY, 1987.
Otto, D., Molhave, L., Rose, G., Hudnell, H.K., and House, D.,
"Neurobehavioral and Sensory Irritant Effects of Controlled Exposure to a
Complex Mixture of Volatile Organic Compounds," Neurotoxicology &
Teratology. Vol. 12, Nov-Dec, 1990.
Skov, P., and Valbjorn, 0., "The 'Sick' Building Syndrome in the Office
Environment: The Danish Town Hall Study," Indoor Air '87; Proceedings of
the 4th International Conference on Air Quality and Climate (Seifert et
al.. eds.), Institute of Air, Water, and Soil Hygiene, Berlin, Germany,
1987.
Skov, P., Valbjorn, 0. and Gintelberg, F., Radhusundersogelsen fTown Hall
Study] (in Danish). Arbejtsmilhofondet, Copenhagen, Denmark, 1989.
Turk, B., Brown, J., Geisling-Sobotka, K., Froehlich, D., Grimsrud, D.,
Harrison, J., Koonce, J., Prill, R. and Rezvan, K., Indoor Air Quality and
Ventilation Measurements in 38 Pacific Northwest Commercial Buildings.
Volume 1: Measurement Results and Interpretation. Lawrence Berkeley
Laboratory. University of California, Berkeley, CA, LBL-22315 1/2, 1987.
U. S. Environmental Protection Agency, Atmospheric Research and Exposure
Assessment Laboratory, Indoor Air Quality and Work Environment Study: EPA
Headquarters Buildings -- Volume I: Employee Survey, U.S. Environmental
Protection Agency. Research Triangle Park, NC, 1989a
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Volume III: Follow-up Survey at
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U. S. Environmental Protection Agency, Atmospheric Research and Exposure
Assessment Laboratory, Indoor Air Quality and Work Environment Study: EPA
Headquarters Buildings -- Supplement to Volume I: Additional Employee
Adverse Health Effects Information. U. S. Environmental Protection Agency,
Research Triangle Park, NC, 1989b.
U. S. Environmental Protection Agency, Atmospheric Research and Exposure
Assessment Laboratory, Indoor Air Quality and Work Environment Study: EPA
Headquarters Buildings -- Volume II: Results of Indoor Air Environmental
Monitoring Study. U. S. Environmental Protection Agency, Research Triangle
Park, NC, 1990.
Wallace, L. , The Total Exposure Assessment Methodology (TEAM) Study:
Summary and Analysis: Volume I. U. S. Environmental Protection Agency,
Washington, DC EPA/600/6-87/002a, 1987.
Wallace, L. , Jungers, R. , Sheldon, L. , and Pellizzari, E. , "Volatile
Organic Chemicals in 10 Public-Access Buildings," in Indoor Air '87.
Proceedings of the 4th International Conference on Indoor Air Quality and
Climate. Berlin, Germany, p. 188-192, 1987.
Wilson, S., and Hedge, A., The Office Environmental Survey: A Study of
Building Sickness. Building Use Studies, Ltd., London, England, 1987.
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Volume III: Follow-up Survey at
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APPENDIX A
INDOOR AIR QUALITY AND WORK ENVIRONMENT SURVEY
EPA HEADQUARTERS
-------
INDOOR AIR QUALITY AND
WORK ENVIRONMENT SURVEY
EPA HEADQUARTERS
We are Investigating the air quality and work environment In this building. We
need Information about your work environment and how It affects you. This
Information Is not available anywhere else. Therefore, we must rely on your
answers to this survey, along with monitoring of environmental conditions In
this building, to clearly analyze the situation. We need your participation,
regardless of how satisfied you are with the air quality or your work environment
Attach Label Hert
DO NOT PUT YOUR NAME ON YOUR QUESTIONNAIRE OR THE RETURN
ENVELOPE PROVIDED. PLEASE PUT YOUR COMPLETED QUESTIONNAIRE IN
THE RETURN ENVELOPE. SEAL IT AND TAKE IT TO ONE OF THE RETURN
BOXES NEAR THE ELEVATORS AND BUILDING EXITS.
-------
PLEASE READ BEFORE
COMPLETING QUESTIONNAIRE
Many questions In the questionnaire concern either last week or last year. By
•LAST YEAR" we mean the 12-month period ending today. If you have worked
In the building for less than one year, answer the "LAST YEAR" questions only
for the part of the year that you worked In this building.
Please report your ACTUAL EXPERIENCES LAST WEEK even If last week was
unusual for you. By 'LAST WEEK" we mean any or all days worked from last
Monday through Friday.
CONFIDENTIALITY
To protect your privacy, the Identification for your questionnaire Is the bar-code
label on the cover. Trie bar-code cannot be read by EPA computers or staff.
Additionally, the survey forms will be gathered by staff from Westat, Inc., an
Independent survey research firm, and processed away from EPA. Your name
and other Information necessary for the survey and analysis that might Identify
you, such as your room and telephone number, will not be disclosed to
Individuals, unions, or management of EPA. Reports of the survey will not give
your name, nor will data be presented In such a way that you, or anyone else,
could be identified.
STUDY SPONSORS AND ORGANIZATION
The study has been developed and Is being conducted by the National Institute for Occupational
Safety and Health (NIOSH), the John B. Pierce Foundation Laboratory at Yale University, and
Westat, Inc. It Is being managed by EPA and NIOSH, and Is being supported by funds from EPA.
-------
PART I. DESCRIPTION OF YOUR WORKSTATION
This section asks you to describe your workstation.
Your answers to thes« questions will help us to
construct a picture of your work surroundings.
By WORKSTATION we mean your desk, office, cubicle,
or place that Is your primary work area. This descrip-
tion Is obvious for many people, but more difficult for
those whose jobs require them to move about tht
building. If you do move about the building, your
workstation Is the specific location where you spend
more time than any other single location. If your
workstation has been relocated, use the location
where you are now.
1. There are many different types of workstations.
Please check the categories that best describe
the space In which your current workstation Is
located.
a. Type of space (Check one)
1. Q EncJosed office wfih door
2. O Cublde with floor to cefflng book-
cases or partitions and no door
3. Q Cubicle surrounded by mid-height
bookcases or partMons
4. D Open office area
5. Q Stacks (e.g., books or periodicals)
6. Q Loading dock, laboratory, copy
center, or print shops
7. Q Work aD around the buBdlng
& D Other (specify)
b.
Type of space sharing (Check one)
1. Q Single occupant
2. Q Shared wtth one other person
3. O Shared with two or more other
persons
4. D Other (describe) _
How many years of service do you have with
EPA? (Enter number of months fries* than one
year.)
.years
months
3. a.
How many years have you been working
In this building? (Enter number of months
If less than one year J
yean
months
b.
During a typical week, how many hours do
you spend In this building?
hours per week
How many years have you worked at your
current workstation? (Enter number of months
If less than one year.)
yean
months
b.
During an average workday, how many hours
do you spend at your workstation?
hours per day
5. How many days did you work In this building last
week?
days last week
-------
6. What time do you usually:
a. Arrive at work :
b. Leave work :
c. Varies (describe)
7. Which of the following Hems are
within 15 feet of your workstatior
yes' for each Kern.)
b. Wood or composition desk . .
c. Mela! bookshelves or
d Wood or composition
bookshelves or bookcases . .
t Other metal furniture
g. Other wood or composition
h. Fabric-covered partitions ...
L Portable humidifier
L Laser printer
k. Photocopy machine
t Uve plants
8. Is there carpeting on most or all o
your workstation?
1. D No
2. D Yes
AM PM K<
D D *
D D
a
b.
c.
presently located
i? (Check 'no' or d
No Yes «•
1 2
D D
D D
D D
D D NOTE:
D D
D D
D D 10. w<
D D £
D D
D D
D D a,
D D b.
c.
d.
f the floor at
«. During • typical day LAST WEEK, how much time
did you spend working with each of the following
Hems? (If you worked with an hem at alt, but less
than 1 hour, enter 1 hour per day.)
Computer or word processor
with screen/keyboard
Photocopy machine
Photographic developing
and processing
Hours
per day
Printing processing (press,
binding materials, etc.)
Other chemicals such as
glues, adhesfves, cleansers,
white out, rubber cement,
pesticides, etc.
If you have worked In this building for less
than a year, answer the following questions
for the part of the year that you worked In
this bunding.
10. Were any of the following Items regularly used
at your workstation during the LAST YEAR:
(Check "no' or yes* for each Kern.)
No
1
Yes
2
Portablefan ............... Q
Portable air filter, or cleaner,
or negative-Ion generator ....
Portable heater
Desk lamp
-------
11. During the LAST YEAR (and »lnce you've been In
your current workstation) have any of the following
change* taken place within 15 feet of your current
workstation? (Check 'no' or yes' for each Item.)
12. At any time during the LAST YEAR, have you
noticed evidence of new or continuing water
leaks from the celling, floors, walls, or pipes
near your workstation?
•
b.
c
d
•.
1
New carpeting
New drapes or curtains ....
New furniture
New equipment, such
as a computer
Walls painted
Rearranoed walls
No
1
a
n
n
U_J
n
L_l
n
Yes
2
n
l_l
a
n
t—l
n
1. D NO
2. D Yes
-------
PART II. INFORMATION ABOUT YOUR HEALTH AND WELL-BEING
This section asks questions about the status of
your hearth and well-being. Your answers to these
questions will help us construct a profile of the
heafth status of the employees In this building.
Please answer ail the questions even If you dont
associate these heaHh conditions with your work.
1, a. Do you wear contact tenses?
1. D Never - » \GotoQ.2 ]
2. D Sometimes
3. D Often
4. D Always
b. Do you wear contact tenses at work?
1. D Never
2. D Sometimes — *• \GotoQ.2 \
3. D Often - * \Goto0.2 \
4. D Always • - * (GofoQ.2
c. If never worn at work, why?
During work, how often do you wear eyeglasses
(NOT including contacts) for close-up work?
1.
2. O Sometimes
3. D Often
4. D Always
3.
Which of the following best describes your
history of smoking tobacco products such as
cigarettes, cigar* or pipes?
1. Q Never smoked —* I GotoQ.7|
2. Q Former smoker—»{ Go/oQ.7J
3. Q Current smoker
4. Do you smoke tobacco products at your
workstation?
1. D Never
2. D Sometimes
3. Q Often
5. Do you smoke tobacco products elsewhere at
work?
1. D Never
2. D Sometimes
3. Often
6. In a typical 24 hour day, how many CIGARETTES
do you usually smoke?
2.
3.
4.
5.
None
1to5
6to10
11to20
21to30
6. D 3t or more
-------
7. Please answer the three questions
to the right about each symptom
listed below, even If you bedevt
(he symptom Is not related to ths
building.
(For each symptom, answer the first
question. If the response Is 'never,'
go down to the next symptom.)
:
c. runny noM
d. stuffy nose/sinus congestion
g. wheedng or whistling In chest
h. shortness of breath
"•<> :v.'-T '«• ^-- .-ri<".>.v;-:v-•:•:••->:^ .->v..v., W*^-X-*-'»VJAV.;..:;.; A
dry. Hchlng, or tearing feyesf £^
k. sore/strained eyes
L blurry/double vision
Please Indicate how often
during the LAST YEAR
you have experienced this
symptom while working
In this building.
N«v*r Ra/tty 6m»» Ofttn Afwty*
WKVZ •fW"fa-,-g. < 4S» WiiyFtZ*-1 £**£ ^S>ie?*'
^1 ^|^5^^f3^|g;r4;^^:S^
^^^:i|x7^^^p^y;^.p^^^^;
5^M^^a^ife;^^
12
D D
346
D D D
12
D D
346
D D D
Please Indicate
how many days
LAST WEEK you
experienced this
symptom while
working In this
budding.
(Fill In No. of days)
Does the
symptom usually
change when
not at work?
G«u Suys c*ts
Won* Sam* Bettor
•S»v j x-:5>**^4*<'»;*?v~;
123
D D D
1 2 3
D D D
1
D
2
D
3
D
r^pMi^M^^w';:^
i,, burning eyes ;, A
r jtrf'^V i ^ «. * A «^ \
^x" -2W!'/;^-*'
eore throat -;'»
o. hoarseness
p. dry throat
12346
D D D D D
12346
D D D D D
1 2 3
D D D
1 2 3
D D D
unusual fatkjue or tiredness
„.,. k.,.-vv-"-
sleepiness* or drowsiness
;x-J>y^K.^..^mi^i»a^^^.jtei?i^Ttfyfitft;'xifi
-------
7. (continued)
(For each symptom, answer the first
question. V the response Is "new,'
go down to the next symptom,)
t**\^f fr\ v-; *"•i TV- i™*i x^ifm»
u. aching muscles or Joints
v. problems wtth contact lenses
^"'difficulty remem&rlnp things /, i *
< ''/''diSriessViiflntheaVe^nesix*it;^'''
y. feeling depressed
x. tension or nervousness
cc. pain or stiffness in upper back ..
dd. pain or stiffness In lower back ..
Please Indicate how often
during the LAST YEAR
you have experienced this
symptom while working
In this building.
Rv«|y flnwt OfUn Afwayt
1 2 J 4 8
D D D D D
12348
D D D D D
12946
D D D D D
1 2 9 4 S
D D D D D
12946
D D D D D
12946
D D D D D
Please Indicate
how many days
LAST WEEK you
experienced thle
symptom while
working In this
building.
(Fill In No. of days)
^^^^&
Does the
symptom usually
change when
not at work?
0«t» 8Uy» .__
Wort* tunt B*0*f
i 2 t
D D D
1 2 3
ODD
1 2 9
ODD
121
ODD
1 2 I
D D D
1 2 9
ODD
5tv pain, or numbness
a
Q
-------
NOTE: The next four questions (Questions $-11) refer
Io your symptoms described In Question 7.
If you reported thet you never experienced
•ny of these symptoms, go to Question 12.
How often during the LAST YEAR have sny of
your symptoms reduced your ability to work In
this building?
1. Q Never
2, Q Rarely
3. Q Sometimes
4. Q Often
6. Q Afwayi
9, a. Have any of your symptoms caused you to
stay home from work or leave work early
during the LAST YEAR?
1. Q Never
2. D Rarely
3. Q Sometimes
4, D Often
b. Which symptoms?
to. In which season(s) are you bothered more by tht
symptoms you reported In Question 7? (Check til
thatappfr)
1. Q Winter
2. Q Spring
9. D Summer
4. D FaH
6. Q No relation to seasons
11.
a. Do you associate any of the symptoms you
reported In Question 7 with your work In this
budding?
1.
*
2.
D No
D
\GotoQ.12
b. Have these symptoms:
1. Q Improved over the last year
2. D become worse over the last year
3. O stayed the same
12. During the LAST YEAR, have you had an Illness
In which you had repeated episodes of THREE
OR MORE of the following symptoms at the same
time: wheez/ng, cough, shortness of breath,
fever, chills, aching Joints/muscles?
1.
2.
Q No
D Ye*
13. During the LAST YEAR, nave you had any chest
Illnesses, such as bronchitis or pneumonia,
that have kept you off work. Indoors at home,
or In bed?
1.
2.
D No
D Ye*
14. Has a physician aver told you that you have, or
had, eczema?
1.
2.
D No
D Yf*
16. During the LAST YEAR, have you had any
episodes of wheezing (whistling In the chest)
WITHOUT fever, or chills, or sore throat?
1.
2.
D
D Ye*
-------
16. •• Has a physician ever told you that you have,
or had, asthma?
2. D Yes
b. In what year was tt first diagnosed?
19
c. Have you had an asthma attack during the
LAST YEAR?
1. D NO
2. D Yes
17. Comparing your health since working In this
bunding wtth your health before you began to
work In this building . . .
a. ... do you have Infections (e.g., colefs, flu,
bronchitis, etc.) . . .
1. Q more frequently?
2. D less frequently?
3. []] with the same frequency?
b. ... do your Infections (e.g., colds, flu,
bronchitis, etc.) tend to . . .
1. D last longer?
2. D last a shorter amount of time?
3. Q last about the same amount of time?
18. Do you believe you are or may be allergic to
any of the following? (Check 'no' or yes' for
each Kern.)
No Yes
1 2
c dust D D
d molds D D
i
19. During the LAST YEAR, how often do you believe
you have experienced EYE, NOSE, THROAT, OR
RESPIRATORY IRRITATION at your workstation
from:
i ^^^^^y^^^S^^^^^^?^ ALWAYS ?£
OFTEN
^^M^s^^^/&^^ .SOMETWESilL
RARELY
NEVE
a. Tobacco smoke ...
b. Fumes from a
photocopying
c. Fumes from
printing processing
(press, binding
materials, etc.) ....
d. Fumes from other
chemicals such
as adhesive*,
glues, cleansers*
white out, rubber
e. Fumes from
f. Fumes from
new carpeting
g. Fumes from
new drapes,
curtains, or
h. Fumes from
L Fumes from
cleaning of carpets,
drapes, or other
J. Other (specify) ....
Pi
jjjM
S-^-jgj
'HP
m
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i
pi
i
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11
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-------
20. Do you consider yourself especially sensitive to
any of (he Hems In Questions?
1. D No
2. D Yes
21. How old are you?
years
22. Are you:
1. D Male —
2. Female
Go to Part III on pg. 11 \
Women working In office buildings have occasionally
reported patterns of gynecological or women's health
problems. The following questions have been Included
to help sort out some of these Issues In this building.
As wfth the rest of the questions In this survey, your
responses are entirely voluntary and will be kepi
confidential.
23. During the LAST YEAR have you menstruated
(had a period)?
1
2.
D No-
D Yes
Go to 0.29
24. How. often during the LAST YEAR has your
period been regular? (By regular, we mean
your periods come about once a month, you
can usually predict when they will come plus
or minus 4 days, and each time they last about
the same number of days.)
1. D Never
2. Q Rarely
3. D About half the time
4. D Often
8. D Always
25. a. How many days does your menstrual flow
(period) typically last?
days
b. During the last year, what was the LONGEST
period you had?
days
c. During the last year, what was the SHORTEST
period you had?
days
26. a. How many days does your cycle typically
last? (Count from the first day of one period
to the first day of the next)
days
b. During the last year, what was the LONGEST
cycle you had?
days
c. During the last year, what was the SHORTEST
cycle you had?
days
27. How often during the LAST YEAR has there been
bleeding or spotting between your periods?
1. Q Never
2. D 1-3tlmes
3. D 4-6tlmes
4. D 7-9times
5. Q 10 or more times
-------
28. •.
Some women experience menstrual
symptoms, such as headaches, weight
gain, Irrfcbinty, cramping, breast
tenderness, or back pain. How often
have you experienced any of thesa
menstrual symptoms during the LAST
YEAR?
1.
2.
3.
4.
5.
Never
[ GotoQ.29 \
D
1-3 Umes
4-6tImes
7-9tlme$
10 of more times
b. When you experience these symptoms,
typically how severe are they?
1. Q Mild; could be Ignored at times
2. D Moderate; pain, Moating, or mood
change noticeably present
3. Q Severe; difficult to do most tasks
4. D Extreme; Incapacitating
29. During the LAST YEAR have you been . . .
(Check 'no' or yes" for each Item.)
No
1
a,
b.
c.
Pregnant or nursing?
Taking birth control pros? .
Going through menopause
(change of fife)?
e.
Post-menopausal
'(completed menojause)? ..
Taking estrogen replace-
ment therapy?
Yes
2
D
D
D
D
D
to
30. a. During the LAST YEAR have you been taking
hormones prescribed by a physician?
1.
2,
•{GoroQ.3f
Yes
Specify what klnd(s) and what they weft
prescribed for.
31. a. Has a physician ever told you that you
had... (Check "no" or "yes' for each Item)
Year
No Yes Frst
1 2 Diagnosed
Fibroids? D D
cysts? Q D
Enlarged uterus? Q Q
[If all are'no,'go to Part III]
b. Have there been noticeable changes during
the last year? (Check one box for each Kern.)
Other.
Decreased Increased No Specif)
InSutt kiSz* Chanpt Below
1 234
Fibroids . . .
Cysts
Enlarged
uterus
Specify
D
D
D
D
D
D
D
D
D
D
D
D
-------
PART III. INFORMATION ABOUT YOUR PRESENT WORK ENVIRONMENT
1. At your present workstation, I
HOWOFTEN...
(Please check one box for
last year and one box for
Neve,
. during the LAST YEAR
Bmes Mm
... during the LAST WEEK
Some-
Never Rarely times Often Ah
d. was the temperature
too hot?
t. was the temperature
toocoW?
t did you want
to adjust the
temperature? ,
0666
DDL5]
1 2 3 4 5
D D D D D
00666
a ODD a
J. was the air too
stuffy?
m, was the work
area too dusty?
1 2 3 4 K
D D D D D
k. was It too noisy? .... | Q
I was H too quiet? ...
D
3
D
3
D
4
D
4
D
4
D
D
3
D
3
D
3
D
D
4
D
4
D
4
D
s
D
s
a
s I
a
i
a
11
-------
2. During the LAST YEAR, how often, If at all, have
you noticed any of these types of ODORS at your
present workstation? (Check one box for each hem.)
RARELY
a. Body odor .
b. Cosmetics, such
as perfume or
after-shave
c. Tobacco smoke ...
d. Fishy smells
a. Other food smells..
f. Musty or damp
basement smells ..
g. Odors from
new carpet
h. Odors from new
drapes or curtains
L Odors from dfesaf
or other engine
exhaust
j. Odors from a
photocopying
machine ....
k. Odors from
printing processing
(press, binding
materials,etc.) ....
D
»
D
D
•»
D
M
D
D
D
D
D
^
D
-^
D
-^
D
^
D
D
D
D
D
#%&%•• ALWAYS
OFTEN
[SOMETIMES^
RARELY
2. (continued)
Odors from other
chemicals such as
adhesive*, glues,
cleansers, whHe
out, rubber cement,
pesticides, etc
m. Odors from
pesticides.
n. Odors from clean-
ing of carpets,
drape*, or other
furnishings
o. Odors from
paint
p. Other unpleasant
odors (describe) ...
x;** 7-
$$&!
Ml
IH
D
D
D
D
D
D
D
D
£$i;
1PH
•y*:
3. In which seasons would you most like to adjust
the physical conditions around your workstation?
(Check all that apply)
Nor* Winter Spring Summw Fan
a. Air movement...
12
b. Temperature— ODD
4 5
D D
*
c. Humkflry Q
i
d. Odon D
D D D
-------
4. Please rale the lighting at your workstation.
1. n Much too dim
2. Q AlltUetoodlm
3. D Just right
4. D A little too bright
6. D Much too bright
5. a.
Do you experience a reflection or •glare"
In your field of vision when at your
workstation?
b.
1. n Nevw * I GotoQ.6J
2. n Sometimes
3. D Often
4. D Always
Where does the reflection or glare come
from? (Ch&ck afl that appfy)
1. D Window, sunlight, outside reflection
2. D Overhead fluorescent lights
3. D Video display screen and/or
reflections when looking at screen
4. D Desk lamp
5. D Other (specify)
«. Can you tee out an outside window from your
workstation?
1.
2.
D No
D Yes
7. a. How comfortable Is the chair at your
workstation?
1. Q Reasonably comfortable
2, Q Somewhat uncomfortable
3. D Very uncomfortable
4. D Donl have one specific
{ Go to 0.6 j
b. Is your chair easily adjustable?
I. D No
2, D Yes
3. D Not adjustable
How comfortable Is the current set-up of your
desk or work table (that Is, height and general
arrangement of the table, chair, and equipment
you work with)?
1. Q Reasonably comfortable
2. Q Somewhat uncomfortable
3. D Very uncomfortable
4. Q Donl have one specific desk or
work table
9. a.
During the LAST YEAR, how many times
per week did you go outdoors, weather
permitting, during work hours (for lunch,
break, or other reasons)?
b.
tlme(s) per week —••} H zero, goto Q.10 {
How many of these times did you go
outdoors primarily to get some fresh air?
tlme(s) per week for fresh air
13
-------
NOTE: The next four questions concern the overall
physical environment at your workstation,
that It, the air quality, temperature, light,
noise, odor, etc.
10. During the LAST WEEK, now satisfied were you
wtlh the physical environment at your workstation?
1. Q Very satisfied
2. Q Somewhat satisfied
3. Q Not too satisfied
4. Q Not at an satisfied
11. During the LAST YEAR, how satisfied were you
with the overall physical environment at your
workstation?
1. D Very satisfied
2. Q Somewhat satisfied
3. D Not too satisfied
4. D Not at afl satisfied
14
12. During the LAST YEAR, has the overall physical
environment In the vicinity of your workstation:
1. [] Improved
2. n become worse
3. CD stayed the same
13. During a typical work day, does the overall
physical environment In the vicinity of your
workstation:
1. Q Improve during the day
2. Q] become worse during the day
3. O stay the same
-------
PART IV. CHARACTERISTICS OF YOUR JOB
This section asks you to describe your job In terms
of specific qualities. In order to gain a better under-
standing of your work environment, we would like to
know how you feel about your Job situation. As stated
before, your responses will be kept confidential
1. We would like you to think about the TYPE OF
WORK YOU DO IN YOUR JO& (Check one box
for each statement)
a. All In ad, how satisfied are you with your
Job?
1. D Very satisfied
2. O Somewhat satisfied
3. D Not too satisfied
4. D Not at an satisfied
b. Knowing what you know now, If you had
to decide again whether to take the Job
you now have, what wouW you decide?
Would you...
1. D Decide without hesitation to take the
same Job
2. D Have some second thoughts
3. D Decide definitely not to take the same
Job
c. If you were free right now to go Into any type
of Job you wanted, what would your choice
be? Would you...
1. D Take the same Job
2. D Take a different Job
3. D Notwanttowoffc
d. If a friend of yours told you he/she was
Interested In working In a Job like yours,
what would you tell him/her? Would you...
1. O Strongly recommend I
2. D Have doubts about recommending I
3. D Advise against I
How satisfied are you with your salary?
1. Q Very satisfied
2. Q Somewhat satisfied
3. Q Not too satisfied
4. Q Not at aJI satisfied
How satisfied are you with your opportunity
for advancement at EPA?
1. Q Very satisfied
2. Q Somewhat satisfied
3. Q Not too satisfied
4. D Not at an satisfied
15
-------
Conflicts can occur In any Job. For example,
someone may ask you to do work In • way which
It different from what you think Is best, or you
may find that H Is dii'cult to satisfy everyone.
HOW OFTEN do you face problems In your work
like the ones listed below? (Check one box tor
each statement)
f:f
'^±
VERY OFTEN
FAIRLY OFTEN
•SOMETiMESM
RARLLY OR NEVER
Persons equal hi
rank and authority
over you ask you
to do things which
conflict ........
People In a good
position to see If
you do what they
ask give you things
to do which conflict
wfth one another. ..
People whose
requests should
be met give you
things which
conflict wfth
other work you
have to do.....
D
1
D
1
D
Ife
m
w-
.£»]
%£$:.
o:;-**
2
3
•x| >¥?*'>V:
3
D
3
D
3
D
10
5. The next series of questions asks HOW MUCH
Influence you now have in each of several areas
at work. By Influence we mean the degree to
which you control what Is done by others and
have freedom to determine what you do yourself.
(Check one box for each question)
MUCH
MUCH
LITTLE
b.
c.
d.
How much
Influence do
you have over
the amount of
work you do?..
How much
Influence do
you have over
the availability
of material*
you need to
do your work?
How much do
you Influence
the policies
and procedures
In your work
group?
How much
influence do
you have over
the arrangement
of furniture and
other work equip-
ment at your
workstation? ....
D
-------
The next icrtet of questions asks HOW OFTEN
certain things happen at your Job. (Check one
box for each question)
OCCASIONALLY
a. How often does
your Job requlrt
you to work
very fast?
b.
c.
d.
f.
How often does
your Job require
you to work
very hard? ....
How often doet
your Job lea vt
you with little
time to get
things done?
How often Is
there a greet
deal to be
done?.....i
How often doet
your Job let you
use the skills
and knowledge
you learned In
school?
How often are
you given a
chance to do
the things you
do best?....
VERY OFTEN
FAIRLY OFTEN
OCCASIONALLY H
6. (Continued)
How often can
you use the
skills from
your previous
experience and
training?
h. How often art
you clear on
what your Job
responsibilities
are?
How often can
you predict
what others
wfll expect
of you on the
Job?
How much of
the time art
your work
objectives well
defined?
How often art
you clear about
what others
expect of you
on the Job?
17
-------
7. In order to better understand your responsibilities
outside your normal working day, the next series
of questions deals with other significant aspects
of your life. (Chock 'no' or yes" for each question)
No Yes
1 2
s. Do you have children
athome?
b. Do you have major
responsibility for
chlldcareduties? Q Q
c. Do you have major
responsibility for
housecleanlng duties?
d. Do you have major
responsibility for the
care of an elderly or
disabled person on •
regular basis?
e. Are you taking courses
for credit toward •
degree or a diploma?
Do you have a regular
commitment of five
hours or more per week,
paid or unpaid, outside
of this job? (Includt
volunteer work, charitable
work, second lob, efcj
It
-------
PART V. CONCLUDING QUESTIONS
This section concludes this survey. Your answers
to these questions, like your answers to the previous
questions, will be kept confidential. This Information
Is needed for statistical purposes.
1. What day of the week did you complete this
survey?
1. D Monday
2. Q Tuesday
3. O Wednesday
4. D Thursday
5. D Friday
Which of the following best describes your current
living and financial arrangements?
1,
2.
Uve alone, sole provider of rent/mortgage,
, food, and other IMng expenses.
Uve alone, but receive assistance from
one or more others In paying rent/mortgage.
utiities, food, and other IMng expenses.
Uve wfth one or more other persons, but
sole provider of rent/mortgage, utilities,
food, and other IMng expenses.
Uve with one or more other persons who
help to pay rent/mortgage, utilities, food,
and other IMng expenses.
What Is the highest grade you completed In
school?
1. '.Q 8th grade or less
2. D 9th, 10th, or 11th grade
3. C High school graduate
4. [U 2 years of college or Associate Degree
5. Q Bachelor's or technical degree
6. Q Some graduate work
7. Q Graduate or professional degree
4. a.
What Is your pay plan and grade (e.g.,
GS-5. GM-14, SES-2, WG-2, etc.)?
Which of the following best describes your
Job duties and responsibilities? (If more than
one applies, check the ONE box for Vie Job
duties on which you spend the most time.)
1.
4.
a D
7.
a
Managerial (such as administrator,
manager, eta)
Professional (such as engineer,
scientist, lawyer, etc.)
Technical (such as technician,
programmer, etc.)
Administrative Support (such as
clerical, computer operator, etc.)
Service (such as health services,
food preparation. Janitorial, etc.)
Craftsman (such as mechanic,
repairer, etc.)
Operator or laborer
Olner (specify) _
The following Information Is needed so that your
workstation can be located within this building. This
Is necessary so that we can relate your responses to
the air measurements that will be taken In a few weeks.
As with the rest of the questions In this survey, this
Information win be kept confidential. Please ten us:
6. a. Your room number
b. Your workstation telephone number (your
direct or private number.)
-------
6. Is there anything else you would like to tell us about environmental or health matter* In this building?
If so, please use this space provided for that purpose.
Please put your completed questionnaire In the return envelope provided. Seal H and take H to one of the
return boxes located near the elevators and building exits.
PLEASE READ THE NEXT PAGE
20
-------
»n a few weeks we plan to conduct air measurements In this building.
At that time people whose workstations are close to the air
measurement locations will be asked a few additional questions. You
may be recontacted at that time.
Thank you very much for your time and patience In filling out this
questionnaire.
21
-------
Volume III: Follow-up Survey at
EPA headquarters
APPENDIX B
INDOOR AIR QUALITY AND WORK ENVIRONMENT FOLLOW-UP SURVEY
EPA HEADQUARTERS
-------
mil i u
INDOOR AIR QUALITY AND WORK ENVIRONMENT
FOLLOWUP SURVEY
EPA HEADQUARTERS
Measurements of a variety of environmental conditions are being taken In your work area
throughout the day TODAY. To help determine how these measurements relate to your comfort
and health, please complete the attached questionnaire. Your participation In this part of the
evaluation of this building Is, of course, voluntary.
Your completed questionnaire win be collected by and analyzed by Westat and Yale Investigators
and WILL NOT BE SEEN BY EPA MANAGEMENT OR UNION REPRESENTATIVES.
So that we may combine your responses to this questionnaire with the questionnaire distributed
three weeks ago, we need you to print your name below. As soon as we have matched your
questionnaires, we win remove this cover sheet and save this questionnaire without your name
on IL At that time, we win also remove your name from the final combined data file.
YOUR FULL NAME:
(please print)
FIRST
MIDDLE
LAST
Please complete this questionnaire even H you did not complete the questionnaire distributed
previously.
After you complete this questionnaire, please place R In the attached envelope and seal tt A
study Investigator will collect H from you.
THANK YOU FOR YOUR PARTICIPATION IN THIS SURVEY.
r
-------
INDOOR AIR QUALITY AND
WORK ENVIRONMENT STUDY
Your answer* to the following questions will allow
a better Interpretation of the environmental
measurements taken TODAY In the area around
your workstation.
1. Did you complete and return the yellow*
covered Indoor Air Quality and Work Environ-
ment questionnaire distributed during the
weeks of February 13 and 21,1989?
1.
2.
D NO
D Yes
Have you been In this building at least 4 hours
yet TODAY?
1.
2.
D No
D Ye*
How many hours (to the nearest 1/2 hour) have
you spent at your workstation TODAY? (Enter
0 If you have not been at your workstation today.)
hours this morning (before 12:00 noon)
hours this afternoon (between 12:00
noon and time you complete this
questionnaire)
Since you arrived at work TODAY, have you
gone outside (for lunch, break, or other
reason)?
1.
2.
D No
Q Ye*
7.
How many hour* (to the nearest 1/2 hour)
have you spent TODAY working at a photo-
copy machine?
hours
How many hour* (to the nearest 1/2 hour)
have you spent TODAY working at a video
display terminal?
hour*
During the day TODAY, have you or anyone
else performed any of the following activities
at or near your workstation? (Check "no" or
"yes" for each item.)
a.
b.
c.
Smoked tobacco
Used a humidifier
No
1
D
D
Yes
2
D
D
Used a cleanser, glue,
while out. or other
strong-smelting
chemical
Used a computer or
word processor ...
e. Used a printer,
D D
D D
D D
-------
II. For the following, please check
the response that beat describes your
work environment TODAY...
(Please check one box for this morning
and one box for this afternoon.)
This MORNING
2. Has the TEMPERATURE been:
jyqrm**-* -ff**sf ;***< v-w
:i,
4. Has the NOISE LEVEL been:
s." Has the air been TOO TFF?:
6. Has your work area been
TOO DUSTY?
1. Q too hot
2. Q too cold
3. Q Just right
't'*!t-
1. Q tootoud
2. Q too quiet
3. D Just right
^•^l^^^^if^^^f^-'^'
; £ £?4 •*;.-.' f~J '•': ;''JM(3i':^Si?i &$&:$:'
^w ,\* -"*'•':> -i '',_!•:'•••' ' *'*-•-%-*' "• *••'•-•*•'•'•--•'•• -••^•'---
^^:§fep
1. D N°
2. Q Ye«
1. d too hot
2. Q toocoW
3. just right
1. Q tootoud
2. Q tooquM
3. Q Just right
1. Q No
2. Q Yet
7. •. Would you like to adjust any of the above condition*?
1. Q No—»jGotoQ.8
2. D Yes
b. If yes, which condHlon(s) would you adjust?
-------
9. Have you noticed any of these types of ODORS at
your workstation TODAY? (Check one box for each
Item.)
No Yes
1 2
»«>.. v.« •».>•!• '';:.'.--:. I •<.:£•«.• -V-K'-a
b. Cosmetics, such as
perfume or after-shave ..... Q
d Fishy smefls
D D
f. Musty or damp
basement smells
h. Odors from new
drapes or curtains
D D
D D
J. Odors from a photo-
copying machine Q Q
Odors from other
chemicals such as
adheslves, gfues,
cleansers, white out,
rubber cement.
pesticides, etc.
D D
Other unpleasant
odors (describe)
Odors from cleaning
of carpets, drapes, or
other furnishings
^£ff^^&»lmP^Mli
9. How would you judge the overall air quality In
this building TODAY?
1. Q Excellent
2. Good
3. Q Fair
-------
111. Have you experienced any of the following
symptoms while at work in this building
TODAY? (For each symptom, answer
'no' or yds.* If your response b "no,"
QO down to the next symptom.)
IF YES, when did this symptom begin?
BEFORE
ARRIVING
AT WORK
THIS
MORNING
AT WORK
THIS
AFTERNOON
AT WORK
NO YES
c. runny now
d. stuffy nose/sinus congestion
i.D 2-D
i.D 2.Q
g. wheezing or whistling in chest
h. shortness of breath
'^fifW^lKfi
k. dry, Hchlng, or tearing eytt
I sore/strained eyes
i.D 2.D
s. sleepiness or drowsiness
t chills
w. problem* with contact lensts
x. dmlculty remembering things
at. tension or nervousness
bb. difficulty concentrating
ee. pain or stiffness In lower back
ff. pain or numbness In shoulder/neck ..
gg. pain or numbness in hands or wrtstt .
-------
IV. The quality of Indoor air and other
working conditions may Influence the
way a person feels. For each of the
following, please Indicate how you
have been feeling TODAY. (Check
one box for each Item.)
Not at all A little Moderately Quhe a lot Extremely
. energetic 1- LJ
restless ........................ 1. Q
fatigued ........................ 1. Q
exhausted 1. Q
. vigorous 1> LJ
bushed 1. D
V. What time Is ft now?
PM
Thank you for your time and patience in filling out this questionnaire. Your answers to this questionnaire,
tike the previous questionnaire, will be kept confidential
-------
Volume III: Follow-up Survey at
EPA headquarters
APPENDIX C
TABULATIONS OF RESPONSES TO THE
INDOOR AIR QUALITY AND WORK ENVIRONMENT FOLLOW-UP SURVEY
EPA HEADQUARTERS
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QI1: Completed ques-
tionnaire 1.
No 7 110 1.8 21.7
Yes 374 396 98.2 78.3
381 506
QI2: 4+ hours in
building today.
No 48 61 12.5 11.9
Yes 335 453 87.5 88.1
383 514
QI3A: AM hours at
workstation
0.0 21 29 5.5 5.6
0.5 5 8 1.3 1.6
1.0 24 27 6.2 5.2
1.5 5 10 1.3 1.9
2.0 56 69 14.6 13.4
2.5 20 23 5.2 4.5
3.0 86 116 22.4 22.5
3.5 32 46 8.3 8.9
4.0 85 113 22.1 21.9
4.5 15 22 3.9 4.3
5.0 31 44 8.1 8.5
5.5 2 4 0.5 0.8
6.0 1 2 0.3 0.4
7.0 0 1 0.0 0.2
8.0 1 1 0.3 0.2
384 515
QI3B: PM hours at
workstation.
0.0 118 161 30.7 31.3
0.5 12 23 3.1 4.5
0.7 1 1 0.3 0.2
1.0 86 105 22.4 20.4
1.2 1 1 0.3 0.2
1.5 24 33 6.2 6.4
2.0 79 96 20.6 18.6
2.5 17 24 4.4 4.7
3.0 24 36 6.2 7.0
3.2 0 1 0.0 0.2
3.5 7 7 1.8 1.4
4.0 14 18 3.6 3.5
4.5 0 2 0.0 0.4
5.0 1 5 0.3 1.0
5.5 0 1 0.0 0.2
7.0 0 1 0.0 0.2
384 515
C-l
-------
VARIABLE
QI4: Gone
QI5: Hours
copier.
QI6: Hours
VALUE
outside.
No
Yes
at photo-
0.0
0.1
0.2
0.5
1.0
1.5
2.0
3.5
5.0
6.0
at VOT.
0.0
0.2
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
Q1&Q2
FREQUENCY
147
235
382
280
0
5
79
12
0
5
1
1
1
384
142
1
60
57
11
46
8
17
4
11
5
9
3
5
1
4
384
QI7A: Exposed to smoke.
No 374
Yes 10
384
QI7B: Used humidifier.
No 372
Yes 12
384
Q2
FREQUENCY
203
308
511
377
1
8
97
22
1
6
1
1
1
515
210
1
70
67
16
55
8
25
5
19
6
13
3
11
2
4
515
498
17
515
502
13
515
Q1&Q2
PERCENT
38.5
61.5
72.9
0.0
1.3
20.6
3.1
0.0
1.3
0.3
0.3
0.3
37.0
0.3
15.6
14.8
2.9
12.0
2.1
4.4
1.0
2.9
1.3
2.3
0.8
1.3
0.3
1.0
97.4
2.6
96.9
3.1
Q2
PERCENT
39.7
60.3
73.2
0.2
1.6
18.8
4.3
0.2
1.2
0.2
0.2
0.2
40.8
0.2
13.6
13.0
3.1
10.7
1.6
4.9
1.0
3.7
1.2
2.5
0.6
2.1
0.4
0.8
96.7
3.3
97.5
2.5
C-2
-------
Q1&Q2
VARIABLE VALUE FREQUENCY
QI7C:
QI7D:
HP.
QI7E:
The
Used chemicals.
No 348
Yes 36
384
Used computer/
No 66
Yes 318
384
Used printer.
No 143
Yes 241
384
following questions relate
JII1A: AM air
novement.
Too much 42
Too little 134
Okay 189
365
)II1B: PM air
novement.
Too much 32
Too little 117
Okay 151
(II2A:
III2B:
300
AM temperature.
Too hot 55
Too cold 102
Okay 212
369
PM temperature.
Too hot 50
Too cold 64
Okay 191
Q2
FREQUENCY
461
54
515
101
414
515
203
312
515
to perceived
56
186
242
484
44
159
199
402
78
137
277
492
71
90
244
Q1&Q2 Q2
PERCENT PERCEN'
90.6
9.4
17.2
82.8
37.2
62.8
thermal
11.5
36.7
51.8
10.7
39.0
50.3
14.9
27.6
57.5
16.4
21.0
62.6
89.5
10.5
19.6
80.4
39.4
60.6
comfort.
11.6
38.4
50.0
10.9
39.6
49.5
15.9
27.8
56.3
17.5
22.2
60.2
305
405
C-3
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QII3A: AM humidity.
Too humid 15 18 4.1 3.7
Too dry 155 217 42.3 44.8
Okay 196 249 53.6 51.4
366 484
QII3B: PM humidity.
Too humid 13 15 4.3 3.7
To dry 133 185 44.0 46.2
Okay 156 200 51.7 50.0
302 400
QII4A: AM noise level.
Too loud 105 146 28.6 29.9
Too quiet 9 9 2.5 1.8
Okay 253 334 68.9 68.3
367 489
QII4B: PM noise level.
Too loud 85 115 28.1 28.5
Too quiet 9 11 3.0 2.7
Okay 209 278 69.0 68.8
303 404
QII5A: AM air too
stuffy.
No 225 289 60.6 58.5
Yes 146 205 39.4 41.5
371 494
QII5B: PM air too
stuffy.
No 184 235 59.7 57.2
Yes 124 176 40.3 42.8
308 411
QII6A: AM too dusty.
No 279 362 75.4 73.7
Yes 91 129 24.6 26.3
370 491
QII6B: PM too dusty.
No 232 303 76.1 74.8
Yes 73 102 23.9 25.2
305 405
C-4
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QII7A: Like to adjust
work environment
conditions.
No 103 135 28.7 28.4
Yes 256 341 71.3 71.6
359 476
QII7BJ: Like to adjust
air movement.
Wo 249 347 69.4 72.9
Yes 110 129 30.6 27.1
359 476
QII7B_2: Like to adjust
temperature.
No 243 338 67.7 71.0
Yes 116 138 32.3 29.0
359 476
QII7BT3: Like to adjust
humidity.
No 271 375 75.5 78.8
Yes 88 101 24.5 21.2
359 476
QII7BJ: Like to adjust
noise~level.
No 309 416 86.1 87.4
Yes 50 60 13.9 12.6
359 476
QII7BJ: Like to adjust
air stuffiness.
No 308 413 85.8 86.8
Yes 51 63 14.2 13.2
359 476
QII7BJ: Like to adjust
dustiness.
No 329 442 91.6 92.9
Yes 30 34 8.4 7.1
359 " "~476"~"
C-5
-------
The following questions ask whether certain types of odors were noticed.
Q1&Q2
VARIABLE VALUE FREQUENCY
31 ISA:
QII8B:
QII8C:
QII8D:
QII8E:
5II8F:
3II8G:
JII8H;
Body.
No
Yes
Cosmetics.
No
Yes
Tobacco smoke.
No
Yes
Fishy.
No
Yes
Other foods.
No
Yes
•»
Musty/damp.
No
Yes
New carpet.
No
Yes
v •
New drapes.
No
Yes
371
13
384
310
74
384
374
10
384
377
7
384
289
95
384
370
14
384
377
7
384
383
1
Q2
FREQUENCY
498
17
515
414
101
515
502
13
515
504
11
515
382
133
515
496
19
515
505
10
515
514
1
Q1&Q2
PERCENT
96.6
3.4
80.7
19.3
97.4
2.6
98.2
1.8
75.3
24.7
96.4
3.6
98.2
1.8
99.7
0.3
Q2
PERCENT
96.7
3.3
80.4
19.6
97.5
2,5
97.9
2.1
74.2
25.8
96.3
3.7
98.1
1.9
99.8
0.2
384
515
C-6
-------
QII8I: Diesel/engine
exhaust.
No
Yes
QII8J: Photocopying
machine.
No
Yes
QII8K: Printing
processing.
No
Yes
QII8L: Chemicals.
No
Yes
QII8M: Pesticides.
No
Yes
QII8N: Cleaning.
No
Yes
QII80: Paint.
No
Yes -
-$r
QII8P: Other.
No
Yes
Q1&Q2
FREQUENCY
377
7
384
362
22
384
379
5
384
365 .
19
384
382
2
384
378
6
384
378
6
384
358
26
Q2
FREQUENCY
503
12
515
484
31
515
505
10
515
481
34
515
511
4
515
505
10
515
505
10
515
472
43
Q1&Q2
PERCENT
98.2
1.8
94.3
5.7
98.7
1.3
95.1
4.9
99.5
0.5
98.4
1.6
98.4
1.6
93.2
6.8
Q2
PERCENT
97.7
2.3
94.0
6.0
98.1
1.9
93.4
6.6
99.2
0.8
98.1
1.9
98.1
1.9
*
91.7
8.3
384 515
C-7
-------
/ARIABLE VALUE
}II9: Overall air
quality.
Excel .
Good
Fair
Poor
tealth Symptoms
Q1&Q2
FREQUENCY
14
148
164
41
367
Q2
FREQUENCY
17
184
227
61
489
Q1&Q2
PERCENT
3.8
40.3
44.7
11.2
Q2
PERCENT
3.5
37.6
46.4
12.5
The following questions ask (1) whether a particular health symptom
was experienced on the day of monitoring (no/yes) and (2) when the
symptom began: l=prior to work; 2=in the morning at work; 3=in the
afternoon at work.
JIIIA1: Headache.
No
Yes
JIIIA2: Headache
started.
NA
1
2
3
JIIIB1: Nausea:
No
Yes
IIIIB2: Nausea
tarted.
NA
1
2
3
301
83
384
301
12
46
25
384
367
17
384
367
5
8
4
387
128
515
387
18
77
33
515
486
29
515
486
7
15
7
78.4
21.6
78.4
3.1
12.0
6.5
95.6
4.4
95.6
1.3
2.1
1.0
75.1
24.9
75.1
3.5
15.0
6.4
94.4
5.6
94.4
1.4
2.9
1.4
384
515
C-8
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QIIIC1: Runny nose.
No 279 363 72.7 70.5
Yes 105 152 27.3 29.5
384 515
QIIIC2: Runny nose
started.
NA 279 363 72.7 70.5
1 49 61 12.8 11.8
2 49 79 12.8 15.3
3 7 12 1.8 2.3
384 515
QIIID1: Stuffy nose.
No 213 285 55.5 55.3
Yes 171 230 44.5 44.7
384 515
QIII02: Stuffy nose
started.
NA 213 285 55.5 55.3
1 81 105 21.1 20.4
2 78 110 20.3 21.4
3 12 15 3.1 2.9
384 515
QIIIE1: Sneezing.
No 306 406 79.7 78.8
Yes 78 109 20.3 21.2
384 515
QIIIE2: Sneezing
started.
NA 306 406 79.7 78.8
I 25 34 6.5 6.6
2 45 65 11.7 12.6
3 8 10 2.1 1.9
384 515
C-9
-------
VARIABLE VALUE
Q1&Q2
FREQUENCY
Q2
FREQUENCY
QII1H2: Shortness of
breath started.
NA
1
2
3
358
8
14
4
384
480
11
17
7
515
Q1&Q2
PERCENT
'QIIIF1: Cough.
No 324 432 84.4
Yes 60 83 15.6
384 515"""
QIIIF2: Cough started.
NA 324 432 84.4
1 30 40 7.8
2 26 37 6.8
3 4 6 1.0
384 515
QIIIG1: Wheezing.
No 366 491 95.3
Yes 18 24- 4.7
384 515~~
QIIIG2: Wheezing
started.
NA 366 491 95.3
1 9* 12 2.3
28 9 2.1
3 1 3 0.3
384 515
QIIIH1: Shortness of
breath.
No 358 v- 480 93.2
Yes 26 35 6.8.
384 515.
93.2
2.1
3.6
1.0
Q2
PERCENT
83.9
16.1
83.9
7.8
7.2
1.2
95.3
4.7
95.3
2.3
1.7
0.6
93.2
6.8
93.2
2.1
3.3
1.4
C-10
-------
Q1&Q2
VARIABLE VALUE FREQUENCY
QIIII1: Chest tightness.
No 357
Yes 27
384
QIIII2: Chest tightness
started.
NA 357
1 11
2 14
3 2
384
QIIIJ1: Burning lungs.
No 375
Yes 9
384
QIIIJ2: Burning lungs
started.
NA 375
1 4
2 5
3 0
384
QIIIK1: Dry eyes.
No 263
Yes 121
384
QIIIK2: Dry eyes
started.
NA 263
1 19
2 82
3 20
Q2 .
FREQUENCY
481
34
515
-i
481
14
17
3
515
504
11
515
504
4
5
2
515
353
162
515
353
27
112
23
Q1&Q2
PERCENT
93.0
7.0
93.0
2.9
3.6
0.5
97.7
2.3
97.7
1.0
1.3
0.0
68.5
31.5
68.5
4.9
21.4
5.2
Q2
PERCENT
93.4
6.6
93.4
2.7
3.3
0.6
97.9
2.1
97.9
0.8
1.0
0.4
68.5
31.5
68.5
5.2
21.7
4.5
384
515
C-ll
-------
VARIABLE VALUE
Q1&Q2
FREQUENCY
Q2
FREQUENCY
QIIIN2: Burning eyes
started.
NA
1
2
3
306
11
48
19
384
416
15
59
25
515
Q1&Q2
PERCENT
QIIIL1: Sore eyes.
No 279 376 72.7
Yes 105 139 27.3
384 515
QIIIL2:Sore eyes
started.
NA 279 376 72.7
1 16 17 4.2
2 59 81 15.4
3 30 41 7.8
384 515
QIIIM1: Blurry vision.
No 359 477 93.5
Yes 25 38 6.5
384 515
QIIIM2: Blurry vision
started.
NA 359 477 93.5
17 9 1.8
2 14 24 3.6
34 5 1.0
384 515
QIIIN1: Burning eyes.
No 306 416 79.7
Yes 78 99 20.3
384 515
79.7
2.9
12.5
4.9
Q2
PERCENT
73.0
27.0
73.0
3.3
15.7
8.0
92.6
7.4
92.6
1.7
4.7
1.0
80.8
19.2
80.8
2.9
11.5
4.9
C-12
-------
Q1&Q2 Q2 Q1SQ2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QII101: Sore throat.
No 345 456 89.8 88.5
Yes 39 59 10.2 11.5
384 515
QIII02: Sore throat
started.
NA 345 456 89.8 88.5
1 19 28 4.9 5.4
2 11 20 2.9 3.9
3 9 11 2.3 2.1
384 515
QIIIP1: Hoarseness.
No 353 469 91.9 91.1
Yes 31 46 8.1 8.9
384 515
QIIIP2: Hoarseness
started.
NA 353 469 91.9 91.1
1 14 18 3.6 3.5
2 14 25 3.6 4.9
33 3 0.8 0.6
384 515
QIIIQ1: Dry throat.
No 278 368 72.4 71.5
Yes 106 147 27.6 28.5
384 515
QIIIQ2: Dry throat
started.
NA 278 368 72.4 71.5
1 26 33 6.8 6.4
2 57 82 14.8 15.9
3 23 32 6.0 6.2
384 515
C-13
-------
Q1&Q2
VARIABLE VALUE FREQUENCY
QIIIR1:
QIIIR2:
QIIIS1:
QIIIS2:
started
QIIIT1:
QIIIT2:
QIIIU1:
QIIIU2:
Fatigue.
No
Yes
Fatigue started
NA
1
2
3
Sleepiness.
No
Yes
Sleepiness
•
NA
1
2
3
Chills.
No
Yes
Chills strated.
NA
1
2
3
Fever.
No
Yes
Fever started.
NA
1
2
3
314
70
384
•
314
13
34
23
384
287
97
384
287
14
44
39
384
.344
40
384
344
8
26
6
384
381
3
384
381
2
0
1
384
Q2
FREQUENCY
422
93
515
422
19
46
28
515
384
131
515
384
22
59
50
515
453
62
515
453
12
41
9
515
509
6
515
509
2
3
1
515
C-14
Q1&Q2
PERCENT
81.8
18.2
81.8
3.4
8.9
6.0
74.7
25.3
74.7
3.6
11.5
10.2
89.6
10.4
89.6
2.1
6.8
1.6
99.2
0.8
99.2
0.5
0.0
0.3
Q2
PERCENT
81.9
18.1
81.9
3.7
8.9
5.4
74.6
25.4
74.6
4.3
11.5
9.7
88.0
12.0
88.0
2.3
8.0
1.7
98.8
1.2
98.8
0.4
0.6
0.2
-------
VARIABLE VALUE
Q1IIV1: Aching
muscles.
No
Yes
QII1V2: Aching
muscles started.
NA
1
2
3
QIIIW1*! Problem with
contact lenses.
No
Yes
QIIIW2*: Problem with
contact lenses started
NA
1
2
3
QIIIX1: Difficulty
remembering.
No
Yes
QIIIX2: Difficulty
remembering started.
NA
1
2
3
384 515
* Defined for wearers of contact lenses.
Q1&Q2
FREQUENCY
338
46
384
338
25
15
6
384
57
27
84
d.
57
2
21
4
84
365
19
384
365
3
10
6
Q2
FREQUENCY
455
60
515
455
31
21
8
515
57
27
84
57
2
21
4
84
485
30
515
485
6
14
10
Q1&Q2
PERCENT
88.0
12.0
88.0
6.5
3.9
1.6
67.9
32.1
67.9
2.4
25.0
4.8
95.1
4.9
95.1
0.8
2.6
1.6
Q2
PERCENT
88.3
11.7
88.3
6.0
4.1
1.6
67.9
32.1
67.9
2.4
25.0
4.8
94.2
5.8
94.2
1.2
2.7
1.9
C-15
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QIIIY1: Dizziness.
No 357 476 93.0 92.4
Yes 27 39 7.0 7.6
384 515
QIIIY2: Dizziness
started.
NA 357 476 93.0 92.4
15 6 1.3 1.2
2 16 24 4.2 4.7
36 9 1.6 1.7
384 515
QIIIZ1: Depressed.
No 349 461 90.9 89.5
Yes 35 54 9.1 10.5
384 515
QIIIZ2: Depression
started.
NA 349 461 90.9 89.5
1 8 13 2.1 2.5
2 20 29 5.2 5.6
3 7 12 1.8 2.3
384 515
QIIIAA1: Tension.
No 323 436 84.1 84.7
Yes 61 79 15.9 15.3
384 515
QIIIAA2: Tension
started.
NA 323 436 84.1 84.7
1 11 18 2.9 3.5
2 42 50 10.9 9.7
3 8 11 2.1 2.1
384 515
C-16
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
QIIIBB1: Difficulty
concentrating.
No 319 434 83.1 84.3
Yes 65 81 16.9 15.7
384 515
QIIIBB2: Difficulty
concentrating started.
NA 319 434 83.1 84.3
16 8 1.6 1.6
2 48 59 12.5 11.5
3 11 14 2.9 2.7
384 515
QII1CC1: Dry skin.
No 298 400 77.6 77.7
Yes 86 115 22.4 22.3
384 515
QIIICC2: Dry skin
started.
NA 298 400 77.6 77.7
1 40 58 10.4 11.3
2 35 44 9.1 8.5
3 11 13 2.9 2.5
384 515
QIIIDD1: Pain upper •
back. r
No 341 460 88.8 89.3
Yes 43 55 11.2 10.7
384 515
QIIIDD2: Pain upper »
back started.
NA 341 460 88.8 89.3
1 9 13 2.3 2.5
2 20 23 5.2 4.5
3 14 19 3.6 3.7
384 515
C-17
-------
VARIABLE VALUE
Q1&Q2
FREQUENCY
Q2
FREQUENCY
Q1&Q2
PERCENT
QIIIGG2: Pain hands or
wrists started.
NA 364
1 12
2 7
3 1
384
484
15
13
3
515
94.8
3.1
1.8
0.3
Q2
PERCENT
QIIIEE1: Pain lower
back.
No 327 438 85.2 85.0
Yes 57 77 14.8 15.0
384 Sis'"
QIIIEE2: Pain lower
back started.
NA 327 438 85.2 85.0
1 19 25 4.9 4.9
2 29 37 7.6 7.2
3 9 15 2.3 2.9
384 515
QIIIFF1: Pain shoulder/
neck.
No 333 449 86.7 87.2
Yes 51 66 13.3 12.8
384 515
QIIIFF2: Pain shoulder/
neck started.
NA 333 449 86.7 87.2
1 17 20 4.4 3.9
2 23 31 6.0 6.0
3 11 15 2.9 2.9
384 515
QIIIGG1: Pain hands or
wrists.
No 364 484 94.8 94.0
Yes 20 31 5.2 6.0
384 515
94.0
2.9
2.5
0.6
C-18
-------
The following questions ask for ratings of feelings:
2=a little; 3=moderately; 4=quite a lot; 5=extremely.
VARIABLE VALUE
Q1&Q2
FREQUENCY
Q2
FREQUENCY
Q1&Q2
PERCENT
l=not at all;
Q2
PERCENT
QIVA: Worn out.
1
2
3
4
5
156
128
56
24
8
372
215
171
73
28
9
496
41.9
34.4
15.1
6.5
2.2
43.3
34.5
14.7
5.6
1.8
QIVB: Listless.
1
2
3
4
5
237
73
35
9
1
355
322
96
45
11
1
475
66.8
20.6
9.9
2.5
0.3
67.8
20.2
9.5
2.3
0.2
QIVC: Lively.
1
2
3
4
5
75
79
164
47
3
368
94
105
221
63
5
488
20.4
21.5
44.6
12.8
0.8
19.3
21.5
45.3
12.9
1.0
QIVD: Active.
1
2
3
4
5
61
66
170
64
10
371
76
87
232
81
14
490
16.4
17.8
45.8
17.3
2.7
15.5
17.8
47.3
16.5
2.9
QIVE: On edge.
1
2
3
4
5
240
77
36
11
7
327
97
47
13
9
64.7
20.8
9.7
3.0
1.9
66.3
19.7
9.5
2.6
1.8
371 493
C-19
-------
VARIABLE VALUE
QIVF: Shaky
1
2
3
4
5
QIVG: Energetic.
2
3
4
5
QIVH: Tense.
QIVI: Relaxed.
Q1VJ: Uneasy.
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
QIVK: Restless
1
2
3
4
5
Q1&Q2
FREQUENCY
322
34
10
0
3
369
83
75
163
43
6
370
204
100
40
19
5
368
85
83
155
35
12
370
252
77
25
7
6
367
242
70
41
11
4
368
Q2
FREQUENCY
425
43
17
1
4
490
111
97
221
56
7
492
277
130
52
24
7
490
109
113
206
51
13
492
338
100
34
8
7
487
317
98
51
16
5
487
Q1&Q2
PERCENT
87.3
9.2
2.7
0.0
0.8
22
20,
44,
11,
1.6
55.4
27.2
10.9
5.2
1.4
23.0
22.4
41.9
9.5
3.2
68.7
21.0
6.8
1.9
1.6
65.8
19.0
11.1
3.0
1.1
Q2
PERCENT
86.7
8.8
3.5
0.2
0.8
22.6
19.7
44.9
11.4
1.4
56.5
26.5
10.6
4.9
1.4
22.2
23.0
41.9
10.4
2.6
69.4
20.5
7.0
1.6
1.4
65.1
20.1
10.5
3.3
1.0
C-20
-------
Q1&Q2
VARIABLE VALUE FREQUENCY
QIVL: Fatigues.
1
2
3
4
5
QIVM: Nervous
1
2
3
4
5
QIVN: Cheerful.
1
2
3
4
5
QIVO: Exhausted.
1
2
3
4
5
QIVP: Anxious
1
2
3
4
5
QIVQ: Sluggish.
1
2
3
4
5
175
122
38
25
8
368
269
72
20
3
5
369
61
67
172
54
16
370
211
106
29
15
8
369
227
99
24
14
4
368
200
112
41
9
8
370
Q2
FREQUENCY
232
170
44
31
9
486
361
87
32
4
5
489
82
91
226
73
17
489
286
133
41
21
9
490
311
122
34
16
6
489
271
144
50
16
10
491
Q1&Q2
PERCENT
47.6
33.2
10.3
6.8
2.2
72.9
19.5
5.4
0.8
1.4
16
18
46
14.6
4.3
57.2
28.7
7.9
4.1
2.2
61.7
26.9
6.5
3.8
1.1
54.1
30.3
11.1
2.4
2.2
Q2
PERCENT
47.7
35.0
9.1
6.4
1.9
73.8
17.8
6.5
0.8
1.0
16.8
18.6
46.2
14.9
3.5
58.4
27.1
8.4
4.3
1.8
63.6
24.9
7.0
3.3
1.2
55.2
29.3
10.2
3.3
2.0
C-21
-------
VARIABLE VALUE
QIVR: Panicky.
1
2
3
4
5
QIVS: Weary,
1
2
3
4
5
QIVT: Alert,
1
2
3
4
5
QIVU: Full of pep.
1
2
3
4
5
QIVV: Carefree.
1
2
3
4
5
QIVW: Vigorous.
1
2
3
4
5
Q1&Q2
FREQUENCY
337
17
8
2
3
367
208
113
27
13
7
368
59
51
169
71
17
367
102
82
149
31
6
370
163
75
101
17
10
366
110
76
148
26
9
369
Q2
FREQUENCY
439
26
16
2
4
487
284
142
37
19
7
489
75
68
223
97
23
486
133
110
197
43
8
491
205
108
136
21
13
483
148
102
193
35
12
490
Q1&Q2
PERCENT
91.8
4.6
2.2
0.5
0.8
56.5
30.7
7.3
3.5
1.9
16.1
13.9
46.0
19.3
4.6
27.6
22.2
40.3
8.4
1.6
44.5
20.5
27.6
4.6
2.7
29.8
20.6
40.1
7.0
2.4
Q2
PERCENT
90.1
5.3
3.3
0.4
0.8
58.1
29.0
7.6
3.9
1.4
15.4
14.0
45.9
20.0
4.7
27.1
22.4
40.1
8.8
1.6
42
22
28
4.3
2.7
30.2
20.8
39.4
7.1
2.4
C-22
-------
VARIABLE
Q1&Q2
VALUE FREQUENCY
QIVX: Bushed.
QV: Time
FATIGUE
SCALE
1
2
3
4
5
of day (pm).
1
2
3
4
5
9
10
11
12
14
7 low
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
35 high
183
127
33
14
11
368
84
136
60
9
1
1
12
33
36
3
375
98
33
27
23
29
36
18
24
16
7
7
7
5
8
7
1
2
2
3
2
2
4
1
2
1
1
2
2
370
02
FREQUENCY
256
160
40
19
14
489
115
167
79
13
1
1
14
43
58
3
494
135
45
36
33
38
44
26
27
22
12
8
9
5
9
10
2
4
3
3
2
3
5
1
2
1
2
2
2
491
Q1&Q2
PERCENT
49.7
34.5
9.0
3.8
3.0
22.4
36.3
16.0
2.4
0.3
0.3
3.2
8.8
9.6
0.8
26.5
8.9
7.3
6.2
7.8
9.7
4.9
6.5
4.3
1.9
1.9
1.9
1.4
2.2
1.9
0.3
0.5
0.5
0.8
0.5
0.5
1.1
0.3
0.5
0.3
0.3
0.5
0.5
Q2
PERCENT
52.4
32.7
8.2
3.9
2.9
23.3
33.8
16.0
2.6
0.2
0.2
2.8
8.7
11.7
0.6
27.5
9.2
7.3
6.7
7.7
9.0
5.3
5.5
4.5
2.4
1.6
1.8
1.0
1.8
2.0
0.4
0.8
0.6
0.6
0.4
0.6
1.0
0.2
0.4
0.2
0.4
0.4
0.4
C-23
-------
Q1&Q2 Q2 Q1&Q2 Q2
VARIABLE VALUE FREQUENCY FREQUENCY PERCENT PERCENT
VIGOR 8 low 20 25 5.4 5.1
SCALE 9 9 10 2.4 2.0
10 13 17 3.5 3.5
11 14 19 3.8 3.9
12 11 15 3.0 3.1
13 4 6 1.1 1.2
14 12 14 3.3 2.9
15 10 13 2.7 2.7
16 17 24 4.6 4.9
17 21 24 5.7 4.9
18 11 17 3.0 3.5
19 16 25 4.3 5.1
20 18 22 4.9 4.5
21 29 38 7.9 7.8
22 26 35 7.0 7.2
23 19 28 5.1 5.7
24 35 52 9.5 10.6
25 14 20 3.8 4.1
26 19 23 5.1 4.7
27 12 13 3.3 2.7
28 10 10 2.7 2.0
29 4 7 1.1 1.4
30 6 8 1.6 1.6
31 7 8 1.9 1.6
32 4 6 1.1 1.2
33 3 3 0.8 0.6
35 0 1 0.0 0.2
36 1 1 0.3 0.2
38 2 2 0.5 0.4
40 high 2 3 0.5 0.6
369 489
C-24
-------
VARIABLE
TENSION
SCALE
U.UI
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
24
25
26
29
31
33
35
Q1&Q2
E FREQUENCY
low 6
23
76
39
54
32
21
20
19
9
12
9
6
4
6
6
7
2
5
3
2
1
1
2
1
1
high 2
Q2
FREQUENCY
8
28
103
58
68
44
31
23
23
11
16
11
8
4
9
8
11
4
6
3
2
2
1
2
1
2
2
Q1&Q2
PERCENT
Q2
PERCENT
,6
.6
1.6
6.2
20,
10.
14.6
8.7
5.7
5.4
5.1
2.4
3.3
2.4
1.6
1.1
1.6
1.6
1.9
0.5
1.4
0.8
0.5
0.3
0.3
0.5
0.3
0.3
0.5
1.6
5.7
21.1
11.9
13.9
9.0
6.3
4.7
4.7
2.2
3.3
2.2
1.6
0.8
1.8
1.6
2.2
0.8
1.2
0.6
0.4
0.4
0.2
0.4
0.2
0.4
0.4
369
489
-------
Volume III: Follow-up Survey at
EPA headquarters
APPENDIX D
SUMMARY OF MODELING RESULTS
The following pages provide a summary of the hypothesis tests conducted in
conjunction with the estimation of models relating health, comfort, odor, air
quality ratings, and mood states to various environmental measurements and to
workplace and personal/medical variables. Results are given for models A, B, C,
and D' as defined and described in section 6.2.3. The results presented here
were abstracted from the detailed modeling results given in Appendices E, F, G,
and H.
There is a separate page for each dependent variable, which is indicated
at the top of the page, along with the key for interpreting the results.
Independent variables are listed at the left, and the statistical significance
of such variables is indicated for each of the four models. Variables included
in a model are indicated by the presence of a slash (/). (Note that all
temporally measured variables appear in Model A, that all comfort and odor
variables appear in Model C, and that all VOC and microbiological variables
appear in Model D'.) Plus or minus signs preceding the slash indicate that the
term was statistically significant for the male-specific model; plus or minus
signs following the slash apply similarly for the female-specific model. Plus
signs indicate a positive association between the independent and dependent
variables, while minus signs indicate a negative association. The number of plus
or minus signs signifies the level of statistical significance, with one sign
meaning 0.10, two signs (i.e., ++ or --) meaning 0.05, and three signs meaning
0.01.
With the exception of the mood-state variables (Ml, M2, and M3, for which
ordinary rather than logistic regressions were performed), the significance of
the likelihood ratio statistic (denoted LRSS) is shown at the bottom of each page
for each model (first for males, then for females). Also given are the sample
sizes (n) used in the model estimation (males/females). For the mood-state
variables, adjusted R2 statistics are reported.
-------
DEPENDENT VARIABLE (M/F) : HI NONSPECIF
HI: NONSPECIFIC IAQ (a,r,s): Headach
sleepin
Key: +++/ = p<.01; ++/— = . 010. 1C
variable not included in model
1 A 1 B 1 C 1 D1
•
•
/
•
•
•
•
•
— __/
•
•
•
•
•
•
•
•
•
•
•MY
9
/«•+
/
/
/
/
/+
=======
=======
=======
=======
=======
=======
•
•
•
•
•
•
•
•
___/
•
•
•
•
•
•
•
•
•
•
•
+++/
•
/++
•
•
•
•
/*
=======
=======
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===s=s===
sssscss
=======
29/a I.OS/a
180/1741164/181
•
•
•
•
•
•
•
___ /
•
•
•
•
•
•
•
•
•
•
•
+++/
/++
•
•
•
*
/
•
•
•
•
•
•
•
•
___/
•
•
•
•
•
•
•
•
•
•
•
+++/
•
/
•
•
•
•
++/
/+++ 1 =====»
/+++ 1 =====
•M-/++ =====
=======
=======
=======
asscxsa
++/
/
/
/
+/
/
__y
/
.31/. 18I.64/.02
180/1771 97/111
-------
DEPENDENT VARIABLE (M/F) : H2 MUCOUS ME
H2: MUCOUS MEMBRANE (c, d, k,n,q): Run
gestion; dry itching tearing eyes
Key: +++/ = p(.01; -MY — = .01(p(.
Code Independent Variable Name
W2A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go cvUide today (l=yes)
W5 Used cherns at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
WB New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
PS Role Conflict (higher - more)
PS Job Control (higher = more)
P7 Workload (higher = more)
PB Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher * more)
P11A Moderate smoking «10cigs/d)
PUB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 logCC023 (ppm)
T4 logCRSPD (ug/wA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmet ics/0ther(l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI InCl, 1, l-tri-»perc3 (ug/mA3)
V2 InCArornatics+TCE+octane] (ug/mA3)
V3 lnCMeC123 (ur)/mA3)
V4 InCtotal VOCsJ (ug/mA3)
V5 InCRSPJ (ug/mA3)
V6 log [total fungi] (cfu/mA3)
V7 logCHSBD (cfu/mA3)
V8 logCthermophilesD (cfu/mA3)
MBRANE
ny nose; stuffy nose/sinus con-
; burning eyes; dry throat
05; +/- = .05(p(. 10; / = p>.10;
variable not included in model
1 A 1 B 1 C 1 D'
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n=
(M/F)
(M/F)
(a = (.01)
a/a I a/a I.03/.04I a/. 051
173/1671177/1731173/1691 95/1051
-------
DEPENDENT VflRIflBLE (M/F): H3 MUCOUS MEMBRflNE flND NON-SPECIFIC IflQ
H3: MUCOUS MEMBRflNE flND NON-SPECIFIC'iflQ (a, r, s,c, d, k,n, q): Hfl;
fatigue/tired; sleepiness; runny nose; stuffy nose; tearing eyes;
burning eyes; dry throat
Key: +++/ = p(.01j ++/— = .01)p(.05; +/- = .05)p(. 10 ; / = p).10
. = var. not used; ======= = var. not included in model; I = Infinity
Code Independent Variable Name I fl I B I C I D1
W2fl Workstation-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 flny new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI flge (yrs)
P3fl Pay Grade (GS9-12)
P3B Pay Grade (6513-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
PS Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
Pllfl Moderate smoking «10cigs/d)
PUB Heavy smoking (>10 cigs/d)
PISfl Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 X Relative Humidity (X)
T3 logCC02D (ppm)
T4 logCRSPD (ug/«A3)
T6 Temp Diff (Ipn - ami) (oF. )
02 fit wkst: BO/Cosrnetics/Ot herd -yes)
Cl Too little air/hot, stuffy d-yes)
C2 Too dry d=yes)
C4 Too much air/too cold d=yes)
111 1 ML r 1 4 4 4- Wh« J.MMMM1 /ltM/»«f2\
vi inilf l| 1— tn+percj tug/m 3)
V2 InCflromatics+TCE+octane] (ug/m^S)
V3 lnCMeC123 (ug/mA3) ,
V4 InCtotal VOCsJ (ug/mA3)
V5 ln[RSP3 (ug/mA3)
V6 log [total fungi] (cfu/mA3)
V7 logCHSB] (cfu/w^S)
V8 logCtherrnophiles] (cfu/mA3)
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a/.091 a/.091.037.811 a/.741
172/1601177/1671173/1631 95/1031
_..._•.___«•.-*.•_«—~-«..._.«____..«<_.._•__ I
-------
DEPENDENT VARIABLE (M/F); H4 FLU-LIKE
HA: FLU-LIKE (f,g, h, i, u, v): Fever; aching muscles/jointsj cough;
wheezing/whistling in chest; shortness or breath; chest tightness
Key: +++/ = p(.01; ++/-- = .01.10
. = var not used;n=var not estimable;======= = var not in model;
-I = -(infinity)
Code Independent Variable Name I A I B I C I D'
USA Uorkst at ion-half height (l=yes)
USB Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used cherns at work today (l=yes)
U6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
US New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
PS Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking «10cigs/d)
PUB Heavy smoking O10 cigs/d)
PISA Glasses or contact lens (1-yes)
PI SB Contact lens only (l=yes)
P13 MD diagnosed asthma (1-yes)
Tl Temperature (oF. )
T2 X Relative Humidity (X)
T3 log [COS] (pprn)
T4 logCRSP] (ug/BA3)
T6 Temp Diff (Ipm - am!) (oF. )
OS At wkst: B0/Cosmetics/Other( 1-yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry U=yes)
C4 Too much air/too cold (l=yes)
VI Intl, I,l-tri+perc3 (ug/i»A3)
VS InCArornatics+TCE+octaneD (ug/mA3)
V3 InCMeClS] (ug/mA3)
V4 InCtotal VOCsJ (ug/mA3)
V5 InCRSP] (ug/mA3)
V6 logCtotal fungi] (cfu/mA3)
V7 logCHSB] (cfu/mA3)
V8 logCthermophiles] (cfu/«A3)
LRSS (M/F; b = >.99j NE=Not Est)
n= (M/F)
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b/.75l b/.78l b/. 97 1.99V. 60
176/1641176/1641172/1611 92/99
-------
DEPENDENT VARIABLE (M/F): H5 ERGONOMIC
H5: ERGONOMIC (dd, ee, f f, gg) : pain/sti
pain/stiffness in lower back; pai
pain/numbness in hands/wrists
Key: +++/ = p<.01j ++/-- = .01(p<.
Code Independent Variable Name
W2A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
PS Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher - more)
P9 Role Clarity (higher * more)
P10 External Stress (higher = more)
P11A Moderate smoking ((10cigs/d)
PI IB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
PI 3 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 X Relative Humidity (X)
T3 logCC02J (ppm)
TA logCRSP] (ug/mA3)
T& Tetnp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
CA Too much air/too cold (l=yes)
VI Intl,l,l-tri+perc3 (ug/mA3)
V2 ln[Arornatics+TCE+octane] (ug/mA3)
V3 lnCMeC123 (ug/mA3)
VA InCtotal VOCsD (ug/mA3)
V5 InCRSPD (ug/mA3)
V6 log [total fungi 3 (cfu/mA3)
V7 logCHSBD (cfu/mA3)
V8 logCthermophiles] (cfu/mA3)
ffness in upper back;
n/numbness in shoulders/neck;
05; +/- = .05.10
variable not included in model
1 A 1 B 1 C 1 D'
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LRSS (M/F>
n= (M/F)
92/. 891.94/.561.99/.871.48/.421
182/17A1186/1811183/1771 98/1121
-------
DEPENDENT VARIABLE (M/F): H6 HEADACHE AND NAUSEA
H6: HEADACHE, NAUSEA (a,b)
Key: +++/ = p<.01; ++/-- = .01.10
. = variable not used; ======= = variable not needed for model}
I = Infinity
Code Independent Variable Name I ft I B I C I D'
W2A Workstation-halfheight (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go ^.vside today (l=yes)
W5 UseJ c.-ams at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue .99)
b/.04l b/.09l b/.75l
178/1701 182/1771 179/1731
b/. 181
96/1101
-------
DEPENDENT VARIABLE (M/F): H7 NASAL; COUGH
H7: NflSRL; COUGH (c,d,e,f): runny nose; stuffy nose/sinus congestion
sneezing; cough
Key: +++/ = p(.01; ++/— = .01.10
. = var not used; -I = -(infinity); ======= = Var not in model
Code Independent Variable Name I A I B I C I D'
W2A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
WB New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher = more)
P3 Role Clarity (higher = more)
P10 External Stress (higher = more)
PI 1ft Moderate smoking «10cigs/d)
PI IB Heavy smoking Old cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 logtC02D (ppm)
T4 logCRSP] (ug/mA3>
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosrnet ics/Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI lnCl,l,l-tri+percJ (ug/_A3)
V2 InCAromatics+TCE+octane] (ug/mA3)
V3 lntMeC12D (ug/mA3)
V4 In Ctotal VOCs] (ug/mA3)
V5 InCRSP] (ug/mA3)
V6 logCtotal fungi] (cfu/mA3)
V7 logCHSBD (cfu/mA3)
V8 logfthermophiles] (cfu/m^S)
LRSS (M/F) (a * (.01)
n= (M/F)
+++/
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17/a 1.14/a I.21/.021. 04/.06I
178/1671182/1721178/1691 98/1041
______________________ _ _ _ i
-------
DEPENDENT VARIABLE (M/F): H8 CHEST
HB: CHEST (g,h,i): wheezing/whistling in chest; shortness of breath;
chest tightness
Key: +++/ = p<.01; ++/— = .01.10;
. = variable not used; ======= = variable not included in model;
-I = -(infinity)
Code Independent Variable Name I A I B I C I D'
W2A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
PA Job Satisfaction (higher = more)
PS Role Conflict (higher = more)
PS Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher - more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking «10cigs/d)
PUB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl. Temperature (oF. )
T2 * Relative Humidity (*)
T3 logCC02D (ppm)
T4 logCRSP] (ug/mA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wksti B0/Cosmetics/Other(l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI lnCl,l,l-tri+perc] (ug/mA3)
V2 InCAromatics+TCE+octane] (ug/mA3)
V3 lnCMeC123 (ug/mA3)
V4 InCtotal VOCs] (ug/mA3)
V5 InCRSP] (ug/mA3)
V6 logCtotal fungi] (cfu/mA3)
V7 logCHSB] (cfu/mA3)
V8 logtthermophiles] (cfu/mA3)
LRSS (M/F) (b = >.99)
n= (M/F)
-I/
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b/b 1 b/b 1 b/b 1 b/b
176/1691182/1751178/1721 97/1081
-------
DEPENDENT VARIABLE (M/F): H3 EYES
H9: EYES (k, l,m,n): Dry itching or tearing eyes; sore/strained eyes;
blurry/double vision; burning eyes
Key: +++/— = p<.01; ++/-- = .01.10;
. = variable not used; ======= = variable not included in model;
-I = -(infinity)
Code Independent Variable Name I A I B I C I D1
W2A Workstation-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
PSA Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher = more)
P9 Role Clarity (higher = wore)
P10 External Stress (higher = more)
P11A Moderate smoking «10cigs/d)
PUB Heavy smoking (>10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 log[C02J (ppm)
T4 logCRSP] (ug/wA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 fit wkst: B0/Cosmetics/Other(l=yes)
Cl Too little air/hot, stuffy
-------
DEPENDENT VORIflBLE (M/F): WO THROflT
H10: THROflT (o,p, q): Sore throat; dry throatj hoarseness
Key: +++/ = p(.01; ++/-- = . 01(p(. <
Code Independent Variable Name
W£A Workstation-half height U=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used cherns at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
Wfl New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
Pllfl Moderate smoking ((10cigs/d)
PI IB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2.X Relative Humidity (X)
T3 logtC.023 (ppm)
T4 logCRSP] (ug/mA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Other (l=yes)
Cl Too little air/hot, stuffy U=yes)
C2 Too dry (1-yes)
C4 Too much air/too cold (l=yes)
VI lnCl,l,l-tri+perc3 (ug/mA3)
V2 IntAromatics+TCE+octane] (ug/mA3)
V3 lnCMeC12J (ug/mA3)
V4 InCtotal VOCs3 (ug/mA3)
V5 InCRSP] (ug/mA3)
V6 logttotal fungi] (cfu/nA3)
V7 logCHSBD (cfu/mA3)
V8 logCthermophilesJ (cfu/nA3)
LRSS (M/F)
n= (M/F)
D5; +/- = .05.10;
variable r»ot included in model
1 A 1 B 1 C 1 D'
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. 53/. 09 1 . 90/. 55 1 . 70/. 31 1 . 56/. 20 1
188/177 1186/184 I18£/ 1801 99/1121
-I
-------
DEPENDENT VARIABLE (M/F): Hll TIREDNESS
Hll: TIREDNESS (r,s): Unusual fatigue/tiredness; sleepiness/drowsi-
ness
Key: +++/ = p<.01; -MY— = .01.10;
. = variable not used; ======= = variable not included in model
Code Independent Variable Name I ft I B I C I D'
W2A Workstation-halfheight (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
US Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
PA Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking «10cigs/d)
PI IB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 logtC02D (ppm)
T4 logCRSPD (ug/MA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: B0/Cosrnetics/Other(l=yes)
Cl Too little air/hot, stuffy (l«yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI lnCl,l,l-tri+perc3 (ug/mA3)
V2 InCArornatics+TCE+octane] (ug/mA3)
V3 lnCMeC123 (ug/mA3)
V4 InCtotal VOCs] (ug/mA3)
V5 InCRSP] (ug/mA3)
V6 log [total fungi] (cfu/MA3)
V7 log CHSB] (cfu/MA3)
V8 log[thermophiles] (cfu/mA3)
•MY
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•
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/+ (=======
/+++ | =======
/ =======
+++/++ =======
======= =======
======= =======
======= =======
'*;
LRSS (M/F)
n= (M/F)
51 /. 03 1 . S7/. 06 1 . 76/. 19 1 . 80/. 09 1
183/1751187/1821183/1781 99/1121
-------
DEPENDENT VARIABLE
T3 log[C023 (ppm)
T4 logCRSPJ (ug/mA3)
T6 Temp Oiff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Ot her (1-yes)
Cl Too little air/hot, stuffy
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI lnCl,l,l-tr:>perc3 .99jNE « Not Est. )
n- (M/F)
0
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•
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b/b
178/171
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n/
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n/
n/
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b/b 1 b/b 1 NE/b
184/1791180/1751 97/108
-------
DEPENDENT VARIABLES (M/F) : H13 ERGONOM
H13: ERGONOMIC (y, dd, ee, f f, gg) : fichi
ness in upper back; pain/st if fne
in hands/wrists
Key: +++/ = p(.01; ++/-- = . 01
T3 logCC02] (ppm)
T4 logCRSP] (ug/m^S)
T6 Temp Diff (Ipm - ami) (oF. )
Oc Ht wkst : yu/uosmet ics/utner \ i— yes/
Cl Too little air/hot, stuffy (l=yes)
C£ Too dry (l=yes)
C4 Too much air/too cold (l=yes)
t!1 1 •*. f 1 1 1 4» M « ^naviMl /nn/M^?\
VI intij ij 1— tri+percj lug/m £i
V2 InCflrornatics+TCE+octaneJ (ug/rn"3)
V3 lr,CMeC123 (ug/m^3)
V4 InCtotal VOCs3 (ug/m^3)
V5 InCRSP] (ug/MA3)
V6 logttotal fungi D (cfu/mA3)
V7 logCHSB] (cfu/rn^3)
V8 logttherrnophiles] (cfu/mA3)
LRSS (M/F)
n= (M/F)
1C
ng muscular joints; pain/st if f-
ss in lower back; pain/numbness
05; +/- = .05(p<. 10; / - p).10
ariable not included in model
1 ft 1 P 1 C 1 D'
/
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•
•
•
•
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•
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/
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. 78/. 60 1 . 78/. 49 1 . 92/. 85 1 . 45/. 42
182/1701186/1811183/1771 98/1121
-------
DEPENDENT VARIABLE (K/F) : HI A COGNATIVE
H1A: COGNATIVE (x, z,aa, bb) : difficull
depressed; tension/nervousness; c
Key: +++/ = p(.01; ++/-- = ,01(p<.(
. = variable not used; —
Code Independent Variable Name
W2A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
WA Go outside today (l=yes)
W5 Used chems at Nork today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
WB New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (659-12)
P3B Pay Grade (GS13-15)
PA Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
PS Job Control (higher = more)
P7 Workload (higher = more)
PS Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking ((10cigs/d)
PUB Heavy smoking ()10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 log CC02 3 (ppm)
TA logCRSP] (ug/mA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
CA Too much air/too cold (l=yes)
VI InCl, 1, 1-tri+perc] (ug/mA3)
V2 InCAromatics+TCE+octane]
-------
DEPENDENT VARIABLE (M/F): HIS DIZZINESS
HIS: DIZZ1NESS/LIGHTHEADEDNESS
Key: ++*/ » p<.01| +*/— = ,01(p(.05j +/- - ,05(p(.
. * variable not used; ======= = variable not included in model
n = variable not estimable
Code Independent Variable Name I A I B I C I D'
W2A Workstation-halfheight (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
U4 Go outside today (l=yes)
W5 Used chens at work today (l=yes)
WG Hours at VDT (hrs)
W7 Any new carpet (l=yes)
WB New Carpet w/glue (l=yes)
PI Age (yrs)
PSA Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher «= more)
P5 Role Conflict (higher * more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
PS Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking ((10cigs/d)
PI IB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (*)
T3 logCC023 (ppm)
T4 logCRSP] (ug/mA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: B0/Cosmetics/Other(l=yes)
Cl Too little air /hot, stuffy (l=yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI InCl, 1,1-tri+perc] (ug/mA3)
V2 IntAromatics+TCE+octaneD (ug/mA3)
V3 lnCMeC123 (ug/mA3)
V4 In [total VOCs3 (ug/mA3)
VS InCRSPl (ug/mA3)
V& logCtotal fungi] (cfu/mA3)
V7 logCHSBJ (cfu/mA3)
V8 logtthermophiles] (cfu/mA3)
LRSS (M/F;b « >.99|NE = Not Est. )
n= (M/F)
*
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SBBBBSB
-
9
•
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sssssss
8888888
S8888S8
8B88888
8B88BB8
b/b 1 b/b
160/1671161/167
j"
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++/
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ssssssrs
csesees
sseaaas
n/n
n/n
n/n
n/n
0
n/n
g
n/n
n/n
=======
======
======
n/n
n/n
n/n
n/n
n/n
n/n
b/b 1 NE/NE
178/1641 94/101
-------
DEPENDENT VARIABLE (M/F) : H16 DRY/ITCHY SKIN
H16: DRY/ITCHY SKIN (cc)
Key: +++/ = p(.01; ++/-- = .01.10;
• • • • •
variable not included in model;
n = variable not estimable
Code Independent Variable Name 1 A 1 B 1 C 1 D'
W2A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
WA Go outside today (l=yes)
W5 Used cherns at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
PSA Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
PA Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
PB Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking «10cigs/d)
PI IB Heavy smoking O 10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
PI 3 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 logCC02D (ppm)
TA log[RSP3 (ug/mA3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmet ics /Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
CA Too much air/too cold (l=yes)
VI lnCl,l,l-tri+perc3 (ug/mA3)
V2 InCArornatics+TCE+octaneD (ug/mA3)
V3 lnCMeC123
V5 InCRSPJ .99{NE = Not Est. )
n= (M/F)
,
•
•
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•
•
•
•
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b/b 1 b/b 1 b/b 1 NE/.9&I
180/1751 1BA/181 1181/1771 97/1101
-------
DEPENDENT VARIABLE (M/F): Cl HOT flIR
Cl: HOT flIR: too little air movement; too hot; too stuffy
Key: +++/ = p(.01; ++/— = .01(p(.05; +/- = ,05.10:
. = variable not used; ======= = variable not included in model
Code Independent Variable Name I ft I B I C I D'
W£B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
w»j useu en em 5 av WOPK vooay 1 1— yes/
W6 Hours at VDT (hrs)
W7 finy new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI flge (yrs)
P3fl Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
Hij noie uoriTiict iniynep — inure /
P6 Job Control (higher = more)
P7 Workload (higher - more)
PB Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher - more)
PUP Moderate smoking «10cigs/d)
PUB Heavy smoking O10 cigs/d)
P12ft Glasses or contact lens (1— yes)
Pico Contact lens only u— yes/
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 log[C023 (ppn)
T4 logCRSP3 (ug/mA3)
T6 Temp Diff (I pin - ami) (oF. )
02 fit wkst: B0/Cosmetics/Other(l=yes)
Cl Too little air/hot, stuffy
-------
DEPENDENT VARIABLE (M/F)I
C2: DRY AIR: Too dry
C£ DRY flIR
Key: +++/ = p<.01{ ++/— = .01.10;
. = variable not usedj ======= = variable not included in model
Code Independent Variable Name I A I B I C I D'
W2A Workstation-half height (l=yes)
W2B Wc-rkst at ion-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go oui-ide today (l=yes)
W5 Used ch&ms at work today (l=yes)
W& Hours at VDT (hrs)
W7 Any new carpet (l=yes)
MS New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
P4 Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking «10cigs/d)
PUB Heavy smoking ()10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 X Relative Humidity (X)
T3 logCC023 (pprn)
T4 logCRSPD (ug/nT3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Other (l=yes>
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI InCl, 1, l-tri-rperc3 (ug/mA3)
V2 lnCAromatics+TCE+octane3 (ug/mA3)
V3 lntMeC123 (uj/mA3)
V4 InCtotal VOCs3
-------
DEPENDENT VARIABLE (M/F) : C4 COLD FUR
C4: COLD AIR: Too much air movement 5 too cold
Key: +++/— = p<.01; ++/— = .01.10j
. = variable not used; ======= = variable not included in model
Code Independent Variable Name I A J B I C I D'
W£A Workst at ion-half height (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (1-yes)
W5 Used chems at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W6 New Carpet w/glue (l=yes)
PI Age (yrs)
P3B Pay Grade (6S13-15)
P4 Job Satisfaction (higher — more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
P8 Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
PllA Moderate smoking ((10cigs/d)
PI IB Heavy smoking () 10 cigs/d)
P12A Glasses or contact lens (1— yes)
P12B Contact lens only (l=yes)
PI 3 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 X Relative Humidity (*)
T3 log[C023 (ppm)
TA logCRSPD (ug/mA3)
T6 Ternp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
CA Too much air/too cold (l=yes)
VI InU, 1,1-tri+perc] (ug/mA3)
V£ lnCAromatics+TCE+octane3 (ug/mA3)
V3 lr.CMeC12D (ug/mA3)
VA InCtotal VOCs] (ug/mA3)
V5 InCRSP] (ug/mA3)
V6 log [total fungi] (cfu/mA3)
V7 logCHSB] (cfu/mA3)
V8 logCtherrnophiles3 (cfu/m^3)
LRSS (M/F)
n= (M/F)
•
— /
/___
/
=======
=======
=======
=======
=======
=======
•
•
•
•
•
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=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
. 6A/. 07 1 . 66/. 08 1 =======
181/171 1 185/1 77 1======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
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=======
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-------
DEPENDENT VARIABLE .10;
. = variable not used; ======= = variable not included in model
-I = -(infinity)
Code Independent Variable Name I ft I B I C I D'
W2A Workstation-halfheight
-------
DEPENDENT VARIABLE
-------
DEPENDENT VARIABLE (M/F): A2 POOR AIR 1
A2: POOR AIR QUALITY RATING: Overall
vs poor
Key: +++/ = p<.01j ++/ — = .01(p(.<
-I = -(infinity)
Code Independent Variable Name
W2A Workstation-halfheight (l=yes)
W2B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W5 Used cherns at work today (l=yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
WB New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (659-12)
P3B Pay Grade (GS13-15)
PA Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
PB Abilities are used (higher = more)
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
P11A Moderate smoking ((10cigs/d)
PUB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (l=yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 '/ Relative Humidity (X)
T3 logCCOS] (ppm)
TA logCRSP] (ug/m^3)
T6 Temp Diff (Ipm - ami) (oF. )
02 At wkst: BO/Cosmetics/Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
CA Too much air/too cold (l=yes)
VI lnCl,l,l-tri+perc] (ug/mA3)
V2 InCAromatics+TCE+octane] .99)
n= (M/F)
3UALITY RATING
air quality excel lent/good/fair 1
)5; +/- = .05(p<. 10j / = p).10;
variable not included in model;
1 A 1 B 1 C 1 D»
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b/.92l b/.69l b/.92l b/b
166/1631170/1691166/1661 BG/iOOl
-------
DEPENDENT VARIABLE (M/F):
Ml: FATIGUE
Ml FflTIGUE
Key: +++/— = p<.01; ++/-- = .01.10;
. = variable not used; ======= = variable not included in model
Code Independent Variable Name I A I B I C I D1
U^Q UlrivU^f at i nri— HA 1 f hoi nhf ( 1 —wee)
Wc'B Workstation-open (l=yes)
W3 Hours at Workstation (hrs)
W4 Go outside today (l=yes)
W6 Hours at VDT (hrs)
W7 flny new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
PSA Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
pf joo oavisracvion inigncr ~ more/
P5 Role Conflict (higher = more)
P6 Job Control (higher = more)
P7 Workload (higher = more)
HO Hull ivies are useu iniyner — more/
P9 Role Clarity (higher — more)
P10 External Stress (higher = more)
PI 10 Moderate smoking «10cigs/d)
PUB Heavy smoking O10 cigs/d)
P12A Glasses or contact lens (l=yes)
P12B Contact lens only (1— yes)
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative Humidity (X)
T3 logtCOSD (ppm)
T4 logCRSP] (ug/mA3)
T6 Temp Diff (Ipm - ami) (oF. )
0£ At wkst: BO/Cosmetics/Other (l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
CA Too much air/too cold (1— yes)
114 1 .- T 1 1 1 £> M« J.MV%«MV^1 /lln/MtA*>%
vl JnLl, 1, 1— tri+percj lug/m &i
V2 lnCflrornatics+TCE+octane3 (ug/rn^3)
V3 InCMeClSD (ug/rnA3)
V4 InCtotal VOCs] (ug/m"3)
V5 InCRSPD (ug/m^3)
V6 log [total fungiD (cfu/raA3)
V7 logCHSB] (cfu/rnA3)
V8 logCthermophiles] (cfu/mA3)
Adjusted R-square (M/F)
n= (M/F)
•
•
•
•
•
•
+/
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•
•
•
•
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•
•
•
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•
•
•
•
•
•
•
/+++
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•
•
•
•
•
+-H-/
/
•
•
•
•
•
=======
=======
05/.08I.06/.09I
173/1661178/1721
=======
=======
=======
=======
=======
=======
=======
=======
•
•
•
•
•
•
/
•
•
•
•
•
•
•
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•
•
•
•
•
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/+
•
•
•
•
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=======
/
/
/
/
/
/
/
/
05/.15I
93/1071
-------
DEPENDENT VflRIflBLE (M/F):
M2: VIGOR
M£ VIGOR
Key: +++/— = p<. 01; ++/-- = .01M4«ki/ / 1 — %J«» \
Ytt uo w.u*-ioe too ay ij— yesj
W5 Used c.'ierns at work today (1-yes)
W6 Hours at VDT (hrs)
W7 Any new carpet (l=yes)
W8 New Carpet w/glue (l=yes)
PI Age (yrs)
P3A Pay Grade (GS9-12)
P3B Pay Grade (GS13-15)
PA Job Satisfaction (higher = more)
P5 Role Conflict (higher = more)
Kb JOD uontroi \nigner — mores
r / worKioao iniyner — more/
Ho HDlllfcies are useu Inlyner — morel
P9 Role Clarity (higher = more)
P10 External Stress (higher = more)
PI 1A Moderate smoking (dOcigs/d)
PUB Heavy smoking (>10 cigs/d)
Pl£ft Glasses or contact lens (l=yes)
Hi CD uorivacv lens oniy 11— yes/
P13 MD diagnosed asthma (l=yes)
Tl Temperature (oF. )
T2 % Relative. Humidity (%)
12 logtCOSD (pprn)
T4 logCRSPJ (ug/mA3)
T6 Ternp Diff (Iprn - ami) (oF. )
02 At wkst: BO/Cosrnet ics/Other (l=yes)
Cl Too little air/hot, stuffy U=yes)
C2 Too dry (l=yes)
C4 Too much a;r/too cold (l=yes)
VI lnCl,l,l-tri+perc3 (ug/m"3)
V2 lnCflromatics+TCE+octane3 (ug/m^S)
V3 InCMeCIS] (ug/mA3)
VA InCtotal VOCs3 (ug/m"3)
V5 IntRSPD (ug/mA3)
V6 log Ctotal fungiJ
-------
DEPENDENT VflRIfiBLE (M/F):
K3: TENSION
M3 TENSION
Key: +++/ = p<.01; ++/— = .01
ni? MH t4 » a pivtAe nr4 ae+KwtA f 1 — Ajae \
1-i.j Piu oiagnoseo astnma u— yes;
Tl Temperature (oF. )
T2 % Relative Humidity (*)
T3 logCC023 (pptn)
T4 logCRSP] (ug/mA3)
TC Tnxn n< ff 1 1 n«> at/i 1 ^ trlC \
ID 1 emp uiTT 1 1 pin am 1 1 ior« /
02 At wkst: B0/Cosmetics/Other(l=yes)
Cl Too little air/hot, stuffy (l=yes)
C2 Too dry (l=yes)
C4 Too much air/too cold (l=yes)
VI Intl,l,l-tri+perc3 (ug/m^3)
V2 InCflrornatics+TCE+octaneD (ug/mA3)
V3 lnCMeC12D (ug/mA3)
V4 InCtotal VOCsJ (ug/m^3)
V5 InCRSPD (ug/MA3)
V6 logCtotal fungi 3 (cfu/m^S)
V7 logCHSB] (cfu/m^3)
V8 logCthermophilesD (cfu/mA3)
fid just R-square (M/F)
•,.- fM/Fl
05j +/- = .05.10;
variable not included in model
1 ft 1 B 1 C 1 D'
/
/
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•
— /
•
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•
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/—
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•
•
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•
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/
— /
+/
/
/
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=====SS
•
•
•
•
-/
•
•
•
•
•
•
•
+-MY
/__
/+++
•
•
•
•
•
•
•
/++
•
— /
++/
•
•
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
=======
v= == SS=^
=======
=======
=======
14/.09I.14/. 101=======
7K/ 1 RA 1 1 7«J/ 1 7F, I =======
•
•
•
•
-/
•
•
•
•
•
•
•
+/
/ —
/+
•
•
•
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UN I I I
•
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21/.0&I
-------
Volume III: Follow-up Survey at
EPA headquarters
APPENDIX E
DETAILED MODELING RESULTS FOR MODEL A
Results on the following pages are presented first for males, then for
females. The header for each case identifies the dependent variable (DEPVAR),
the model type, the gender (P2), the significance probability for the likelihood
ratio statistic (labeled LRS), and the sample size (labeled TOTN). For the
logistic regressions (entitled "Maximum Likelihood Estimates"), the following are
provided:
• the estimated coefficients (ESTIMATE)
their estimated standard errors (STDERR)
the chi-squared statistic (CHISQ) for testing whether the
coefficient is zero
the significance probability (PROB) associated with this test
the estimated odds ratio = exp(ESTIMATE)
the approximate lower 99% confidence limit for the true odds
ratio:
exp(ESTIMATE-2.576*STDERR)
the approximate upper 99% confidence limit for the true odds
ratio:
exp(ESTIMATE+2.576*STDERR)
For the mood-state variables, ordinary regressions are performed, and the
resultant information (entitled simply "MALES" or "FEMALES") includes the usual
analysis of variance table and associated statistics such as R: and adjusted R:.
Also included are the parameter (coefficient) estimates, their standard errors,
the value of the t statistic for testing that the coefficients are zero, and the
associated significance probabilities.
-------
KW1 MUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
12
f w
T4
1 ^
16
1 9
W3
"^
PI
P12A
• • fc*»
P13
PARAM
1
2
3
4
5
6
7
8
9
10
vtrtMK-r
ESTIMATE
3.2784
-0.0419
-0.0601
0.0894
0.2178
0.1081
0.1713
-0.0721
1.3727
0.0153
ii rvwc.L-« r<
STDERR
7.0269
0.1161
0.0461
1.4747
0.2882
0.1519
0.1086
0.0208
0.5663
0.5989
c-rv*LC i_nj
CHISQ
0.22
0.13
1.70
0.00
0.57
0.51
2.49
12.00
5.87
0.00
>-V'CO/? IU
PROB
0.6408
0.7179
0.1923
0.9517
0.4498
0.4766
0.1149
0.0005
0.0154
0.9796
ODDS
RATIO
26.5333
0.9590
0.9417
1.0935
1.2433
1.1142
1.1868
0.9304
3.9460
1.0154
LOWER
99%
LIMIT
0.0000
0.7111
0.8362
0.0245
0.5918
0.7534
0.8972
0.8819
0.9175
0.2171
UPPER
99%
LIMIT
1.9?8E9
1.2933
1.0604
48.8243
2.6122
1.6477
1.5700
0.9817
16.9707
4.7496
EFFVAR
INTERCEPT
| 11 1 fct«V»' *
12
• *>
T4
1 ^
16
1 W
PI
P12A
• • fcr^
P13
PARAM
1
2
3
4
5
6
7
8
9
10
— utrvAK=iu
ESTIMATE
-8.0659
0.0656
0.0254
0.2988
0.0751
0.1892
0.1435
-0.0211
0.0624
1.6500
nuutLK« rf.
STDERR
5.8415
0.1035
0.0416
1.2884
0.2454
0.1025
0.0952
0.0160
0.3621
0.6985
=ruw.t LK
CHISQ
1.91
0.40
0.37
0.05
0.09
3.41
2.27
1.74
0.03
5.58
O'v.wic tuin
PROB
0.1673 4
0.5262
0.5422
0.8166
0.7597
0.0649
0.1316
0.1875 (
0.8631 1
0.0182 !
ODDS
RATIO
3.0003
.0678
.0257
.3482
.0780
.2083
.1543
J.9791
1.0644
>.2070
LOWER
99%
LIMIT
0.0000
0.8179
0.9215
0.0488
0.5729
0.9279
0.9033
0.9396
0.4188
0.8613
UPPER
99%
LIMIT
1076.86
1.3941
1.1417
37.2525
2.0284
1.5734
1.4751
1.0203
2.7052
31.4795
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
Tl
T2
T3
i w
T4
16
V6
P3A
P3B
P4
PS
P10
£FFVAR
INTERCEPT
Tl
T2
T3
T4
T6
V6
P3A
P3B
P4
PS
P10
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
.... in.r»«n-n
ESTIMATE
8.3277
-0.1042
-0.0412
•0.0443
0.0138
0.0794
0.257S
-0.9073
-0.1371
-0.0389
0.0406
0.2971
• D£PVAR=H2
ESTIMATE
-5.5746
-0.0658
0.0561
1.5200
0.2160
0.0395
0.1891
0.2674
1.0377
-0.7447
0.4977
-0.2108
c. nuucL«rt i
STDERR
6.6198
0.1042
0.0393
1.3099
0.2413
0.1374
0.1221
0.7083
0.6454
0.3305
0.2492
0.1466
MODEL" A P2
f ^/fitk rl f fc
STDERR
6.0713
0.1098
0.0449
1.3613
0.2489
0.1056
0.1036
0.4337
0.4577
0.3529
0.2554
0.1410
'-u.ww7
PR08
0.2084
0.3174
0.2947
0.9730
0.9544
0.5633
0.0349
0.2005
0.8317
0.9062
0.8706
0.0427
"FEMALE LRS=0 0059
w kt Irlkb Wf»<^ V* Wv^
CH1SQ
0.84
0.36
1.56
1.25
0.75
0.14
3.33
0.38
5.14
4.45
3.80
2.24
PROS
0.3S85
0.5490
0.2117
0.2641
0.3854
0.7084
0.0681
0.537S
0.0234
0.0349
0.0513
0.1349
ODDS
RATIO
4136.89
0.9010
0.9596
0.9567
1.0139
1.0826
1.2937
0.4036
0.8719
0.9618
1.0414
1.3459
TOTN'167 -•-•
ODDS
RATIO
0.0038
0.9363
.0577
.5722
.2411
.0403
.2082
.3066
2.8227
0.4749
1.6449
0.8099
LOWER
99%
LIMIT
0.0002
0.6889
0.8672
0.0328
0.5446
0.7599
0.9446
0.0650
0.1654
0.4105
0.5481
0.9226
LOWER
99%
LIMIT
0.0000
0.7056
0.9422
0.1371
0.6537
0.7925
0.9252
0.4275
0.8682
0.1913
0.8520
0.5633
UPPER
99%
LIMIT
1.05E11
.1785
.0619
2 .9384
.8878
.5424
.7719
2.5057
4.5972
2.2535
1.9789
1.9635
UPPER
99%
LIMIT
23507.4
1.2424
1.1874
152.431
2.3565
1.3655
1.5777
3.9933
9.1773
1.1787
3.1760
1.1646
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Ti
1 •
T2
I fe
TI
1 ^
T6
1 V
W2A
nfcn
V6
nw
PI
w »
P3A
* ^™
P3B
• «r**
PS
P10
PARAM
1
2
3
4
t
6
7
8
9
10
11
12
13
14
ESTIMATE
6.2416
-0.0277
-0.0279
-0.2994
0.1049
0.0477
-0.3895
0.3179
-0.0511
-1.6216
-0.5189
-0.0231
0.2757
0.2397
U rvun.-r\ n
STOERR
7.0917
0.1073
0.0400
1.4184
0.2416
0.1496
0.4995
0.1353
0.0197
0.8886
0.7619
0.3371
0.2588
0.1527
t nf»Li. tr\.
CH1SQ
0.77
0.07
0.49
0.04
0.19
0.10
0.61
5.52
6.70
3.33
0.46
0.00
1.14
2.46
I~V>WCC IU
PR08
0.3788
0.7964
0.4846
0.8328
0.6643
0.7500
0.4354
0.0188
0.0096
0.0680
0.4958
0.9454
0.2867
0.1165
ODDS
RATIO
513.680
0.9727
0.9725
0.7413
1.1106
1.0489
0.6774
1.3742
0.9502
0.1976
0.5952
0.9772
1.3175
1.2709
LOVER
93%
LIMIT
0.0000
0.7378
0.8773
0.0192
0.5960
0.7134
0.1871
0.9698
0.9032
0.0200
0.0836
0.4101
0.6764
0.8576
UPPER
99%
LIMIT
4.41E10
1.2824
1.0780
28.6264
2.0694
1.5420
2.4528
1.9473
0.9996
1.9492
4.2366
2.3286
2.5661
1.8834
EFFVAR
INTERCEPT
jniw*vi»' *
12
*•>
14
i ^
T6
V W
W2A
V2B
Wfc *r
M6
**V
PI
v •
P3A
w ijn
P3B
• vV
PS
P10
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
14
IS
--- utrvAK8n:
ESTIMATE
-6.4731
0.0352
0.0322
0.9676
0.1763
0.1523
-0.3140
-1.6689
0.0712
-0.0317
-0.5817
0.0966
-0.7056
0.7153
-0.2360
i nuytu»« rt-
STDERR
7.4052
0.1301
0.0492
1.5323
0.2831
0.1285
0.4782
0.7175
0.1206
0.0213
0.6185
0.6996
0.4093
0.3284
0.1636
-ruv*i.t un
CHISQ
0.76
0.07
0.43
0.40
0.39
1.40
0.43
5.41
0.35
2.22
0.88
0.02
2.97
4.74
2.08
IJ-U.UO9U I
PR08
0.3820
0.7864
0.5122
0.5278
0.5334
0.2361
0.5114
0.0200
0.5551
0.1365
0.3470
0.8902
0.0848
0.0294
0.1492
vin-jov --•
ODDS
RATIO
0.0015
1.0358
1.0327
2.6316
1.1928
1.1645
0.7305
0.1885
1.0738
0.9688
0.5589
1.1014
0.4938
2.0448
0.7898
LOWER
99%
LIMIT
0.0000
0.7409
0.9098
0.0508
0.5752
0.8363
0.2131
0.0297
0.7870
0.9171
0.1136
0.1817
0.1721
0.8775
0.5182
UPPER
99%
LIMIT
*
297359
1.4482
1.1723
136.293
2.4733
1.6214
2.5039
1.1965
1.4650
1.0234
2.7499
6.6777
1.4173
4.7648
1.2037
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFYAR
INTERCEPT
Ti
T2
T3
T4
T6
to
V6
PI
P3A
P3B
P8
P9
P13
EFFVAR
INTERCEPT
n
T2
n
T4
T6
K3
K6
PI
P3A
P3B
P8
N
P13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
2.2140
-0.2628
-0.0421
3.0012
0.6479
0.1251
0.3399
-0.1958
-0.0547
-2.1314
-1.3455
-0.6266
-0.1255
2.1491
. . . DEPVAR=H4
ESTIMATE
-12.8374
-0.0383
-0.0380
1.9322
1.1787
0.2724
0.1223
-0.0473
0.0246
•1.0482
0.0276
0.2086
-0.6304
-0.2154
i rwwn.-rt r
STOERR
9.5678
0.1617
0.0624
1.9727
0.4666
0.2056
0.1602
0.1919
0.0274
1.0207
0.8641
0.2957
0.3113
0.7059
c-rvM.t LRJ
CHISQ
0.05
2.64
0.46
2.31
1.93
0.37
4.50
1.04
4.00
4.36
2.42
4.49
0.16
9.27
MODE1»A P2= FEMALE LRS
rivt/Lk n r fc • ti yikt. wnv
STDERR
8.1793
0.1459
0.0565
1.8478
0.6547
0.1208
0.1352
0.1283
0.0245
0.5898
0.5375
0.2545
0.2751
0.8817
CHISQ
2.46
0.07
0.45
1.09
3.24
5.09
0.82
0.14
1.00
3.16
0.00
0.67
S.25
0.06
-V,7JOf
PROB
0.8170
0.1042
0.4995
0.1282
0.1650
0.5431
0.0339
0.3075
0.0456
0.0368
0.1194
0.0341
0.6867
0.0023
*0 7525
V • f Vfc«*
PROB
0.1165
0.7932
0.5016
0.2957
0.0718
0.0241
0.3657
0.7127
0.3169
0.0755
0.9591
0.4125
0.0219
0.8070
ODDS
RATIO
9.1523
0.7689
0.9588
20.1097
1.9115
1.1333
1.4048
0.8222
0.9468
0.1187
0.2604
0.5344
0.8821
8.5771
TOTN=164 •
0005
RATIO
0.0000
0.9624
0.9627
6.9047
3.2501
1.3131
1.1301
0.9538
1.0249
0.3506
1.0280
1.2320
0.5324
0.8062
LOWER
99%
LIMIT
0.0000
0.5070
0.8164
0.1249
0.5746
0.6673
0.9298
0.5015
0.8822
0.0086
0.0281
0.2495
0.3956
1.3919
LOWER
99%
LIMIT
0.0000
0.6609
0.8323
0.0591
0.6018
0.9620
0.7977
0.6854
0.9622
0.0767
0.2574
0.6395
0.2621
0.0832
UPPER
99%
LIMIT
4.63E11
1.1662
1.1260
3238.53
6. 3 $89
1.9246
2.1225
1.3479
1.0160
1.6453
2.4119
1.1447
1.9668
52.8522
UPPER
99%
LIMIT
3761.14
1.4015
1.1135
806.040
17.5527
.7924
.6009
.3274
.0917
.6018
.1050
2.3731
1.0814
7.8135
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
Tl
T2
T3
T4
T6
W2A
W7
P6
P7
EFfVAR
INTERCEPT
Tl
T2
T3
T4
T6
V2A
V2B
V7
P6
P7
PARAM
1
2
3
4
5
6
7
8
9
10
PARAM
1
2
3
4
5
6
7
8
9
10
11
---- vLrvnn-r
ESTIMATE
5.5419
-0.1919
-0.0083
1.1944
-0.3943
0.1507
-0.6614
1.0978
-0.5716
0.4680
--- DEPYAR-H5
ESTIMATE
-3.3616
0.3266
0.0608
-3.5734
0.1562
0.1962
0.5719
-1.9397
-0.8523
-0.2482
-0.2081
rwi/Li.-n i
STDERR
8.4798
0.1353
0.0503
1.7638
0.3133
0.1695
0.6589
0.5067
0.2868
0.2621
3DFL=A P2
»/UL.I» A r£
STDERR
7.6342
0.1375
0.0564
1.7048
0.3586
0.1269
0.4758
0.8081
0.4669
0.2218
0.2198
'CTlKLt LH
CHISQ
0.43
2.01
0.03
0.46
1.58
0.79
1.01
4.69
3.97
3.19
a-v.j«tc
PROB
0.5134
0.1562
0.8693
0.4983
0.2082
0.3740
0.3155
0.0303
0.0463
0.0742
sFFMAI F 1 ft
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
K3
PI
P7
P12A
PARAM
1
2
3
4
5
6
7
8
9
10
.... w.rvAK=r
ESTIMATE
-2.7769
0.1105
•0.0400
-1.6207
0.4211
0.3442
0.2070
•0.0678
0.7136
1.4325
IP rwviL'n n
STOERR
9.2873
0.1640
0.0627
2.1254
0.4885
0.1943
0.1455
0.0304
0.3296
0.8443
CHI SO
0.09
0.45
0.41
0.58
0.74
3.14
2.02
4.97
4.69
2.88
>=W.>?>» IU
PROS
0.7649
0.5006
0.5241
0.4457
0.3886
0.0765
0.1549
0.0257
0.0304
0.0898
in-j/o -"-
ODDS
RATIO
0.0622
1.1168
0.9608
0.1978
1.5236
1.4109
1.2300
0.9344
2.0413
4.1892
LOWER
99%
LIMIT
0.0000
0.7320
0.8175
0.0008
0.4329
0.8553
0.8455
0.8641
0.8733
0.4760
UPPER
99%
LIMIT
1.528E9
1.7040
1.1292
47.1970
5.3627
2.3273
1.7893
1.0106
4.7714
36.8707
EFFYAR
INTERCEPT
Tl
T2
T3
T4
T6
V3
PI
P7
P12A
PARAM
1
2
3
4
S
6
7
8
9
10
ESTIMATE
•1.9596
0.0097
0.0052
-0.2895
-0.0205
0.1047
0.2272
-0.0173
0.4011
0.0137
rwi/cu-A re
STDERR
6.3224
0.1146
0.0490
1.4318
0.2620
0.1046
0.1076
0.0192
0.2007
0.3939
TUV«.C un
CHISQ
0.10
0.01
0.01
0.04
0.01
1.00
4.46
0.81
3.99
0.00
lo'u.vtcy H
PROS
0.7566
0.9327
0.9148
0.8398
0.9376
0.3167
0.0347
0.3682
0.0457
0.9722
ooos
RATIO
OJ409
1.0097
1.0053
0.7486
0.9797
1.1104
1.2551
0.9828
1.4935
1.0138
LOWER
99%
LIMIT
0.0000
0.7516
0.8860
0.0187
0.4989
0.8481
0.9513
0.9354
0.8906
0.3675
UPPER
99%
LIMIT
1667611
2
.3565
.1405
.9287
.9240
.4538
.6560
.0327
2.5045
2.7965
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFYAR
INTERCEPT
11
T2
13
14
• ~
T6
V3
V6
P V
uft
f*W
P3A
• »»*
P3B
• «r •*
P8
P10
P11A
PUB
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
ESTIMATE
-3.5591
-0.3092
-0.0704
4.1893
-0.3442
0.1983
0.1243
0.4018
-0.6575
•1.6994
-0.8936
0.0628
0.4980
1.2645
-0.4360
i/ r*/wtw-/\ r
STDERR
8.1537
0.1206
0.0475
1.5514
0.2822
0.1419
0.1078
0.1399
0.7014
0.8341
0.7285
0.1912
0.1690
0.9310
0.7772
t-i-vM.c i.i\;
CH1SQ
0.19
6.57
2.20
7.29
1.49
1.95
1.33
8.25
0.88
4.15
1.50
0.11
8.68
1.84
0.31
>-V.lPf» IM
PROS
0.6625
0.0103
0.1381
0.0069
0.2225
0.1621
0.2490
0.0041
0.3486
0.0416
0.2199
0.7426
0.0032
0.1744
0.5748 .
in=i/o ----
ODDS
RATIO
0.0285
0.7340
0.9320
65.9766
0.7088
1.2193
1.1324
1.4945
0.5181
0.1828
0.4092
1.0648
1.6454
3.5413
0.6466
LOWER
99%
LIMIT
0.0000
0.5380
0.8247
1.2128
0.3426
0.8460
0.8578
1.0423
0.0851
0.0213
0.0626
0.6507
1.0647
0.3218
0.0873
UPPER
99%
LIMIT
3.769E7
.0015
.0533
3589.28
.4663
.7574
.4948
2.1429
3.1560
1.5671
2.6725
1.7425
2.5430
38.9686
4.7878
EFFVAR
INTERCEPT
11
T2
13
T4
T6
V3
V6
W8
P3A
P3B
ft
P10
P11A
PUB
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
--- VLrvAK^iu
ESTIMATE
-10.2629
0.0120
0.0201
1.2862
0.4236
0.1468
0.1861
0.1501
-0.8762
-0.3672
0.7163
-0.3519
-0.1632
0.2719
-1.8054
' rwwtu-« rt
STDERR
6.6882
0.1147
0.0453
1.4056
0.2624
0.1203
0.1032
0.1036
0.4919
0.4398
0.4488
0.1733
0.1384
0.5976
0.8915
-r trv»tt ur
CH1SQ
2.35
0.01
0.20
0.84
2.61
1.49
3.25
2.10
3.17
0.70
2.55
4.12
1.39
0.21
4.10
U-U.W3J 1
PROS
0.1249
0.9166
0.6571
0.3602
0.1064
0.2222
0.0713
0.1474
0.0749
0.4039
0.1105
0.0423
0.2382
0.6491
0.0429
ODDS
RATIO
0.0000
1.0121
1.0203
3.6190
1.5275
1.1581
1.2045
1.1620
0.4164
0.6927
2.0468
0.7034
0.8494
1.3125
0.1644
LOWER
99%
LIMIT
0.0000
0.7532
0.9079
0.0968
0.7770
0.8495
0.9234
0.8898
0.1173
0.2231
0.6442
0.4501
0.5947
0.281S
0.0165
UPPER
99%
LIMIT
1059.87
1.3600
1.1466
135.237
3.0028
1.5788
1.5714
1.5174
1.4784
2.1506
6.5039
1.0991
1.2133
6.1185
1.6341
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
i •»
T6
W2A
^fc««
W7
•••
P3A
f *r»*
P3B
• «r**
P8
• V
P9
P10
• m W
P13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
— - ui.rv«K=r
ESTIMATE
4.9445
-0.3345
-0.0553
3.6423
0.3502
-0.3683
-11.0621
0.3302
1.4464
-3.6639
-3.0188
-0.8815
-0.8652
0.6105
2.7201
w rwutLcA n
STOERR
18.9583
0.3073
0.1044
3.4482
0.6927
0.4315
,
0.2607
1.0219
2.1116
1.8570
0.5300
0.5571
0.4057
1.0565
CHISQ
0.07
1.18
0.28
1.12
0.26
0.73
,
1.60
2.00
3.01
2.64
2.77
2.41
2.26
6.63
> - i . vvvv i i
PROB
0.7942
0.2764
0.5962
0.2908
0.6132
0.3933
.
0.2054
0.1570
0.0827
0.1040
0.0963
0.1204
0.1324
0.0100
OOOS
RATIO
140.401
0.7157
0.9462
38.1795
1.4194
0.6919
.
1.3912
4.2478
0.0256
0.0489
0.4142
0.4210
1.8414
15.1818
LOWER
99%
LIMIT
0.0000
0.3243
0.7231
0.0053
0.2383
0.2277
,
0.7108
0.3054
0.0001
0.0004
0.1057
0.1002
0.6475
0.9986
UPPER
99%
LIMIT
2.27E23
1.5795
1.2382
275093
8.4S36
2.1027
.
2.7231
59.0754
5.9037
5.8406
1.6222
1.7681
5.2361
230.821
EFFYAR
INTERCEPT
Tl
v *
T2 -
T4
T6
V2A
V2B
H7
P3A
P38
P8
P9
P10
P13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
ESTIMATE
-6.7593
0.1530
-0.0236
-1.5315
0.4808
0.4005
0.3100
-0.2644
0.0971
0.6252
0.5312
1.6497
0.2803
•0.3998
0.0222
-0.2758
) rvu
c
f« r<-
STOERR
11
0
0
2
0
0
0
1
0
0
1
1
0
0
0
1
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
3278
2105
0916
5460
7145
1663
6969
3466
1824
6545
0745
1007
3747
3875
2700
2482
•rtrvM-t in
CHISQ
0.36
0.53
0.07
0.36
0.45
5.80
0.20
0.04
0.28
0.91
0.24
2.25
0.56
1.06
0.01
0.05
J-* .
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
WW 1 VIM
PR08
0005
RATIO
5507 0.0012
4674 1
1.1653
7970 0.9767
5475 0.2162
5010
0160
6564
8444
5944
3394
6210
.6174
.4926
.3634
.7677
.1020
.8686
.7010
1339 5.2054
4545 1
1.3235
3021 0.6705
9344 1
1.0224
0.8251 0.7590
LOWER
99%
LIMIT
0.0000
0.6776
0.7714
0.0003
0.2567
0.9725
0.2265
0.0239
0.6888
0.3462
0.1068
0.3055
0.5041
0.2471
0.5100
0.0305
UPPER
99%
LIMIT
5.462E9
2.0042
1.2366
152.474
10.1894
2.2908
8.2089
24.6417
1.7629
10.0864
27.0886
88.6861
3.4748
1.8192
2.0498
18.9076
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFYAR
INTERCEPT
Tl
T2
T3
T4
T6
W2A
W3
W8
PI
P4
P10
P12A
P12B
EFFVAR
IMTERCEPT
Tl
T2
T3
T4
T6
W2A
V2B
W3
W8
PI
P4
P10
P12A
P12B
PARAM
1
2
3
•
\
*
6
7
8
9
10
11
12
13
14
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
ESTIMATE
14.3268
•0.0756
-0.0153
•1.4006
•0.1546
-0.0002
•0.2835
0.1720
0.4980
•0.0183
•0.4406
0.4374
0.7701
0.2511
• •- DEPVAR-H9
ESTIMATE
•8.9925
-0.0151
0.0018
2.0415
-0.1638
0.0161
•0.3369
-2.3011
0.2073
-1.1047
-0.0328
-0.7893
0.0797
-0.3088
0.9954
17 nvuiL.-A I
STDERR
7.6938
0.1087
0.0423
1.4560
0.2523
0.1439
0.5191
0.1008
0.6162
0.0188
0.3336
0.1577
0.4960
0.4838
MODEL* A P2
i ^/vbb n f fc
STDERR
6.9886
0.1188
0.0464
1.4476
0.2784
0.1159
0.4288
6.6421
0.1096
0.5213
0.0200
0.3676
0.1447
0.4501
0.4626
rc-rvM.e una
CHISQ
3.47
0.48
0.13
0.93
0.38
0.00
0.30
2.91
0.65
0.95
1.74
7.70
2.41
0.27
= FEMALE LRS
• ^i"*wfc wr\<^
CHISQ
1.66
0.02
0.00
1.99
0.35
0.02
0.62
12.84
3.58
4.49
2.69
4.61
0.30
0.47
4.63
-U>VW1
PROS
0.0626
0.4867
0.7172
0.3361
0.5400
0.9991
0.5850
0.0879
0.4190
0.3296
0.1866
0.0055
0.1205
0.6037
~0 0168
V • V * \^f
PROS
0.1982
0.8988
0.9689
0.1585
0.5563
0.8897
0.4320
0.0003
0.0585
0.0341
0.1007
0.0318
0.5819
0.4927
0.0314
iuin-i/9
ODDS
RATIO
1667440
0.9272
0.9848
0.2464
0.8568
0.9998
0.7531
1.1877
1.6454
0.9819
0.6437
1.5487
2.1600
1.2854
TOTN-163 ---•
ODDS
RATIO
0.0001
0.9850
1.0018
7.7022
0.8489
1.0162
0.7140
0.1001
1.2304
0.3313
0.9677
0.4542
1.0830
0.7343
2.7058
LOWER
99%
LIMIT
0.0041
0.7007
0.8831
0.0058
0.4473
0.6901
0.1978
0.9161
0.3364
0.9354
0.2725
1.0317
0.6019
0.3697
LOWER
99%
LIMIT
0.0000
0.7253
0.8889
0.1850
0.4144
0.7539
0.2366
0.0192
0.9277
0.0865
0.9191
0.1762
0.7460
0.2303
0.8218
UPPER
99%
LIMIT
6.75E14
1.2268
1.0982
10.4862
1.6410
1.4485
2.8682
1.5398
8.0473
1.0306
1.5201
2.3248
7.7508
4.4699
UPPER
99%
LIMIT
8185.61
1.3377
1.1290
320.704
1.7391
1.3698
2.1548
0.5236
1.6317
1.2689
1.0189
1.1707
1.5722
2.3412
8.9090
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
PS
PARAM
1
2
3
4
5
6
7
ESTIMATE
-0.7522
•0.1551
-0.0536
1.7557
-0.2191
0.2112
0.6581
v rwtu-n r<
5TDERR
7.4341
0.1208
0.0457
1.5426
0.2778
0.1369
0.2598
t-rv»ut LNJ
CHI SQ
0.01
1.65
1.37
1.30
0.62
2.38
6.42
t~v.;><07 iv
PROB
0.9194
0.1989
0.2411
0.2551
0.4302
0.1231
0.0113
ODDS
RATIO
0.4713
0.8563
0.9478
5.7875
0.8032
1.2352
1.9311
LOWER
99%
LIMIT
0.0000
0.6273
0.8425
0.1088
0.3927
0.8681
0.9889
UPPER
99%
LIMIT
9.776E7
1.1689
1.0662
307.796
1.6430
1.7574
3.7710
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
P5
PARAM
1
2
3
4
5
6
7
ESTIMATE
•1.9057
•0.0595
0.0209
0.5331
0.2140
0.0823
0.4147
nvi»ti.-n re
STDERR
6.0693
0.1065
0.0455
1.3659
0.2822
0.1021
0.2217
-rtrvM-t. LII
CHISQ
0.10
0.31
0.21
0.15
0.57
0.65
3.50
1J-V.V7£Q IWin
PROB
0.7535 (
0.5764 (
0.6456
0.6963
0.4483
0.4202
0.0615
ODDS
RATIO
).1487
).9422
.0211
.7042
.2386
.0858
.5139
LOWER
99%
UNIT
0.0000
0.7162
0.9082
0.0505
0.5987
0.8347
0.8552
UPPER
99%
LIMIT
916958
1.2397
1.1481
57.4927
2.5624
1.4124
2.6800
-------
MAXIMUM LIKELIHOOD ESTIMATES
EffyAR
INTERCEPT
11
T2
T3
T4
16
W3
PI
P13
PARAM
1
2
3
4
5
6
7
8
9
ESTIMATE
2.4780
•0.1306
-0.0455
1.1006
0.0773
-0.0046
0.2258
-0.0319
0.0825
i rwvLu-A ri
STDERR
7.0228
0.1197
0.0475
1.4962
0.3044
0.1594
0.1102
0.0198
0.6185
t-rviLC LKJ
CH1SQ
0.12
1.19
0.92
0.54
0.06
0.00
4.20
2.59
0.02
t=V.31HJ IU
PROB
0.7242
0.2752
0.3378
0.4620
0.7995
0.9769
0.0405
0.1077
0.8939
iin-ioj ••-•
ODDS
RATIO
11.9174
0.8776
0.9555
3.0060
1.0804
0.9954
1.2533
0.9686
1.0860
LOWER
99%
LIMIT
0.0000
0.6447
0.8455
0.0637
0.4932
0.6602
0.9436
0.9204
0.2207
UPPER
99%
LIMIT
8.S68E8
1.1945
1.0799
141.856
2.3666
1.5008
1.6648
1.0193
5.3428
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
W3
PI
P13
PARAM
1
2
3
4
5
6
7
8
9
•
ESTIMATE
-8.5183
0.0646
0.0880
0.1026
0.1745
0.2104
0.1272
-0.0360
1.2157
nvutc-n re
STDERR
6.1964
0.1110
0.0450
1.4119
0.2627
0.1029
0.1001
0.0178
0.6260
•I Lrmit UK
CHISQ
1.89
0.34
3.82
0.01
0.44
4.18
1.61
4.10
3.77
u»-v.vjvj ivin
PROB
0.1692 (
0.5605
0.0506
0.9421
0.5064
0.0410
0.2038
0.0430 (
0.0521 3
ODDS
RATIO
).0002
.0667
.0920
.1080
.1907
.2342
.1356
1.9646
1.3727
LOWER
99%
LIMIT
0.0000
0.8014
0.9725
0.0292
0.6052
0.9468
0.8775
0.9214
0.6724
UPPER
99%
LIMIT
1708.94
1.4198
1.2262
42.0834
2.3425
1.6088
1.4697
1.0099
16.9163
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
W5
P4
P9
PARAM
1
2
3
4
5
6
7
8
9
--• utr»ftK=n,
ESTIMATE
21.8703
-0.6112
-0.0138
2.5206
0.2636
0.4578
2.0921
0.9362
-0.0177
ic rvutL-« r<
STDERR
17.0156
0.2533
0.0898
3.1114
0.9357
0.2203
1.0917
0.8195
0.4540
c-rvM-t cnj
CHISQ
1.65
5.82
0.02
0.66
0.08
4.32
3.67
1.31
0.00
I-I.VWV IVI
PR08
0.1987
0.0158
0.8780
0.4179
0.7781
0.0377
0.0553
0.2533
0.9688
ODDS
RATIO
3.149E9
0.5427
0.9863
12.4361
1.3016
1.5806
8.1019
2.5503
0.9825
LOWER
99%
LIMIT
0.0000
0.2826
0.7826
0.0041
0.1169
0.8961
0.4867
0.3089
0.3051
UPPER
99%
LIMIT
3.42E28
1.0422
1.2430
37630.4
14.4973
2.7879
134.871
21.0570
3.1639
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
W5
P4
P9
PARAM
1
2
3
4
5
6
7
8
9
ESTIMATE
20.9881
-0.1764
0.0625
-1.8745
0.4086
0.0788
1.8864
-0.9805
0.3647
i nwLi.-n rc-
STDERR
10.1822
0.1563
0.0678
2.1865
0.4663
0.1671
0.7739
0.5522
0.3086
-r UVM.C in
CHISQ
4.25
1.27
0.85
0.73
0.77
0.22
5.94
3.15
1.40
.J-V.3779 1
PROB
0.0393
0.2590
0.3569
0.3913
0.3809
0.6373
0.0148
0.0758
0.2372
ODDS
RATIO
1.303E9
0.8383
1.0645
0.1534
1.5047
1.0820
6.5956
0.3751
1.4401
LOWER
99%
LIMIT
0.0053
0.5604
0.8939
0.0005
0.4527
0.7035
0.8984
0.0904
0.6503
UPPER
99%
LIMIT
3.21E20
1.2539
1.2676
42.8594
5.0018
1.6640
48.4226
1.5558
3.1888
-------
MAXIMUM LIKELIHOOD ESTIMATES
EffVAR
INTERCEPT
n
• #
T2
• fc
TA
• ^
T6
1 w
W2A
w*»"
W7
n*
07
r/
P8
PARAM
1
2
3
4
5
6
7
8
9
10
11
ESTIMATE
4.4607
-0.1659
-0.0128
1.0921
-0.3314
0.2514
-0.2992
1.0378
-0.5100
0.5427
-0.2319
j rvutu-it ri
STDERR
7.9833
0.1305
0.0494
1.7057
0.3078
0.1566
0.6050
0.4910
0.2898
0.2606
0.2300
t-rw*ut i.i\j
CH1SQ
0.31
1.62
0.07
0.41
1.16
2.58
0.24
4.47
3.10
4.34
1.02
l-W./OJV IV
PROB
0.5763
0.2036
0.7960
0.5220
0.2816
0.1085
0.6209
0.0346
0.0784
0.0373
0.3133
ODDS
RATIO
86.5481
0.8471
0.9873
2.9805
0.7179
1.2858
0.7414
2.8230
0.6005
1.7206
0.7930
LOWER
99%
LIMIT
0.0000
0.6053
0.8693
0.0368
0.3249
0.8590
0.1560
0.7969
0.2846
0.8793
0.4385
UPPER
99%
LIMIT
7.39E10
1.1856
1.1213
241.286
1.5864
1.9248
3.5229
10.0003
1.2668
3.3669
1.4342
EffVAR
INTERCEPT
Tf.
IV
V2A
mU\
V2B
**£••?
V7
••*
P6
r v
P8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
ESTIMATE
-3.7146
0.3001
0.0660
-3.2074
0.1994
0.2018
0.4800
-1.9142
-1.0096
-0.1446
-0.1703
-0.1586
nuucfn re.-
STDERR
7.6849
0.1346
0.0565
1.6716
0.3593
0.1258
0.4772
0.8012
0.4883
0.2361
0.2326
0.2293
TtrVH-C Uf
CH1SQ
0.23
4.97
1.36
3.68
0.31
2.58
1.01
5.71
4.28
0.38
0.54
0.48
o-u.uu.x> i
PROB
0.6288
0.0258
0.2433
0.0550
0.5789
0.1085
0.3145
0.0169
0.0387
0.5403
0.4642
0.4891
ODDS
RATIO
0.0244
1.3500
1.0682
0.0405
1.2207
1.2236
1.6161
0.1475
0.3644
0.8654
0.8434
0.8533
LOWER
99%
LIMIT
0.0000
0.9544
0.9235
0.0005
0.4838
0.8849
0.4727
0.0187
0.1036
0.4710
0.4633
0.4727
UPPER
99%
LIMIT
9642610
1.9095
1.2356
3.0001
3.0801
1.6919
5.5249
.1615
.2818
.5898
.5355
.5405
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
W5
PS
P7
PARAM
1
2
3
4
5
6
7
8
9
--• ULr»«n-ni
ESTIMATE
•7.7034
-0.1445
-0.0852
2.0965
1.5976
-0.2291
0.2616
0.6750
0.2221
•« rv>un.»« n
STDERR
7.9709
0.1373
0.0519
1.7016
0.6273
0.1809
0.8970
0.2954
0.2645
C-n«LC LKJ
CHI SQ
0.93
1.11
2.69
1.52
6.49
1.60
0.09
5.22
0.71
» = V.7J9£ IV
PROB
0.3338
0.2927
0.1010
0.2179
0.0109
0.2053
0.7706
0.0223
0.4011
ODDS
RATIO
0.0005
0.8655
0.9183
8.1376
4.9412
0.7952
1.2990
1.9640
1.2487
LOWER
99%
LIMIT
0.0000
0.6076
0.8034
0.1016
0.9818
0.4990
0.1289
0.9176
0.6318
UPPER
99%
LIMIT
373114
1.2327
1.0497
651.855
24.8666
1.2673
13.0955
4.2036
2.4681
EFFYAR
INTERCEPT
Tl
T2
T3
T4
T6
V5
PS
P7
PARAM
1
2
3
4
5
6
7
8
9
ESTIMATE
1.8709
0.0143
-0.0125
-0.8420
-0.0297
0.0195
1.1082
0.0429
0.4807
i HUULI.-/I re.'
STDERR
5.9908
0.1071
0.0459
1.3384
0.2478
0.1011
0.5316
0.2347
0.2085
-rCJIMLC LI"
CHISQ
0.10
0.02
0.07
0.40
0.01
0.04
4.34
0.03
5.31
Id'V.VCS^ 11
PROB
0.7548
0.8940
0.7858
0.5293
0.9045
0.8473
0.0371
0.8548
0.0212
ODDS
RATIO
6.4941
1.0144
0.9876
0.4308
0.9707
1.0197
3.0289
1.0438
1.6172
LOWER
99%
LIMIT
0.0000
0.7698
0.8774
0.0137
0.5127
0.7859
0.7701
0.5702
0.9452
UPPER
99%
LIMIT
3.271E7
1.3367
1.1115
13.5409
1.8379
1.3230
11.9127
1.9107
2.7671
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
I V
V3
V6
^ w
PI
P7
PS
P10
PARAM
1
2
3
4
•
6
7
8
9
10
11
12
--- ucr¥«r\-ni
ESTIMATE
•4.8115
-0.2587
0.0585
2.0606
2.2166
-0.0307
0.0729
-0.6501
-0.0031
0.0352
0.1369
0.2519
i? rwutu-r* r<
STOERR
17.1491
0.2311
0.0817
3.1261
1.0880
0.3461
0.2177
0.4621
0.0413
0.5047
0.4531
0.3043
tTlTM.1. Lf\J
CHISQ
0.08
1.25
0.51
0.43
4.15
0.01
0.11
1.98
0.01
0.00
0.09
0.69
l-J.Wl/W IV
PROS
0.7790
0.2630
0.4743
0.5098
0.0416
0.9293
0.7376
0.1595
0.9410
0.9444
0.7625
0.4077
ODDS
RATIO
0.0081
0.7721
1.0602
7.8507
9.1761
0.9698
1.0756
0.5220
0.9970
1.0358
1.1467
1.2865
LOWER
99%
LIMIT
0.0000
0.4257
0.8590
0.0025
0.5565
0.3976
0.6139
0.1587
0.8963
0.2823
0.3569
0.5874
UPPER
99%
LIMIT
1.2SE17
1.4002
1.3086
24672.2
151.304
2.3652
1.8846
1.7165
1.1089
3.8012
3.6843
2.8173
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
V3
W6
PI
P7
m 9
P8
P10
FARAM
1
2
3
4
5
6
7
8
9
10
11
12
-- ULrvftK=ru;
ESTIMATE
-12.0640
0.2374
0.1900
-2.0303
-0.0127
0.3084
0.3653
-0.1147
-0.1032
0.9435
-0.8042
0.0645
j rwuct.-« rf.'
STOERR
12.6734
0.2765
0.1084
3.5805
0.4195
0.1826
0.1912
0.1843
0.0499
0.4090
0.3584
0.2523
-rui/tic. ur
CK1SQ
0.91
0.74
3.07
0.32
0.00
2.85
3.65
0.39
4.27
5.32
5.04
0.07
U-l.VWW 1
PROS
0.3411
0.3906
0.0796
0.5707
0.9758
0.0914
0.0560
0.5336
0.0387
0.0210
0.0248
0.7982
ODDS
RATIO
0.0000
1.2679
1.2092
0.1313
0.9874
1.3612
1.4409
0.8916
0.9019
2.5690
0.4474
1.0666
LOWER
99%
LIMIT
0.0000
0.6220
0.9146
0.0000
0.3351
0.8505
0.8805
0.5546
0.7932
0.8958
0.1777
0.5569
UPPER
99%
LIMIT
8.688E8
2.5848
1.5988
1330.18
2.9094
2.1788
2.3580
1.4334
1.0257
7.3675
1.1264
2.0430
-------
MAXIMUM LIKELIHOOD ESTIMATES
CFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
PS
P6
P?
P9
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
PS
P6
P7
P9
PARAM
1
2
3
4
5
6
7
8
9
10
PARAH
1
2
3
4
5
6
7
8
9
10
-•- WtrTHK'CU
ESTIMATE
-49.8985
0.1316
-0.1067
6.0691
0.0657
0.1258
1.2212
1.4799
-0.9545
•0.7433
- DEPVAR=H16
ESTIMATE
-18.2387
0.0451
0.0852
1.8295
•0.2935
0.2522
•0.6008
0.0038
0.9581
-0.9298
U rui/Li~A I
STDERR
16.2463
0.2098
0.0823
2.6108
0.3924
0.2605
0.4971
0.4925
0.3844
0.3953
MODEL *A P2
1 ^^t/kk O V fc
STDERR
8.9577
0.1566
0.0671
1.9876
0.3491
0.1281
0.3724
0.2796
0.3301
0.3178
'c-nnLf. i.nj-
CH1SQ
9.43
0.39
1.68
5.40
0.03
0.23
6.04
9.03
6.17
3.54
"FEMALE LRS
I *•* Irtkh kr««?
CH1SQ
4.15
0.08
1.61
0.85
0.71
3.88
2.60
0.00
8.42
8.56
• J . VWV
PROB
0.0021
0.5305
0.1950
0.0201
0.8671
0.6291
0.0140
0.0027
0.0130
0.0601
"0.9974
V • * + i ^
PROB
0.0417
0.7734
0.2039
0.3573
0.4006
0.0489
0.1067
0.9892
0.0037
0.0034
ODDS
RATIO
0.0000
1.1407
0.8988
432.291
1.0679
1.1341
3.3913
4.3925
0.3850
0.4755
TOTN»175 • ••••
ODDS
RATIO
0.0000
1.0461
1.0889
6.2308
0.7456
1.2869
0.5484
1.0038
2.6067
0.3946
LOWER
99%
LIMIT
0.0000
0.6644
0.7271
0.5188
0.3886
0.5797
0.9424
1.2352
0.1430
0.1718
LOWER
99%
LIMIT
0.0000
0.6989
0.9161
0.0372
0.3034
0.9252
0.2101
0.4885
1.1138
0.1740
UPPER
99%
LIMIT
0.0003
1.9582
1.1110
360238
2.9345
2.2185
12.2036
15.6204
1.0364
1.3165
UPPER
99%
LIMIT
126.007
.5660
.2944
1 42.69
.8327
.7899
.4312
2.0627
6.1009
0.8948
-------
MAXIMUM LIKELIHOOD ESTIMATES
IfFYAR
INTERCEPT
litii.r\vki »
1?
it.
i*
19
TJ
1^
T&
Iw
W9A
Hen
t\
r I
D^l
rjn
DID
rJo
P11A
PHB
PARAH
1
2
3
4
s
6
7
g
9
10
A v
H
• •
12
13
UtrVftK**,
ESTIMATE
-5.8352
0.2568
0.0128
•1.5301
•0.1608
0.0141
0.6292
0.3845
•0.0500
•1.7021
-1.8986
•1.2932
•0.8750
i rwun.=n r<
S1DERR
7.2386
0.1080
0.0389
1.4121
0.2450
0.1437
0.3782
0.4581
0.0198
0.8543
0.7477
0.9638
0.6980
s=rwit IKS
CH1SQ
0.65
5.65
0.11
1.17
0.43
0.01
2.77
0.70
6.40
3.97
6.45
1.80
1.57
>-U.W33 IU
PROB
0.4202
0.0174
0.7417
0.2786
0.5117
0.9221
0.0961
0.4013
0.0114
0.0463
0.0111
0.1797
0.2100
ODDS
RATIO
0.0029
1.2928
1.0129
0.2165
0.8515
1.0142
1.8761
1.4689
0.9512
0.1823
0.1498
0.2744
0.4169
LOWER
99%
LIMIT
o.oooo :
0.9788 :
0.9163 !
0.0057 I
0.4530 1
0.7004 1
0.7082 <
0.4513 <
0.9039 1
0.0202 1
0.0218 1
0.0229 3
0.0690 2
UPPER
99%
LIMIT
366383
I. 7075
1.1196
S.2274
1.6005
1.4685
1.9701
1.7806
1.0010
1.6464
1.0279
I.28S6
!.5!70
EFFVAR
INTERCEPT
t9
1C
V2A
V2B
P3A
P3B
P1U
PUB
PARAM
1
2
m
3
*F
4
5
ft
V
7
f
8
9
10
• V
11
12
13
14
— DIPYAR-U
ESTIMATE
•11.2543
•0.0102
-0.0406
2.3495
-0.2949
0.0209
0.4217
•0.3861
•0.9535
•0.0324
0.3047
0.8036
0.2446
-0.0676
HUDll-A P2'
STOERR
6.3004
0.1131
0.0442
1.3972
0.2518
0.1128
0.3547
0.4154
0.5788
0.0188
0.4902
0.5550
0.5801
0.7266
•rtwit LK
CHI5Q
3.19
0.01
0.84
2.83
1.37
0.03
1.41
0.86
2.71
2.97
0.39
2.10
0.18
o.ot
}«U.W£W 1
PROB
0.0741
0.9282
0.3589
0.0927
0.2414
0.8529
0.2345
0.3526
0.0995
0.0848
0.5342
0.1477
0.6732
0.9259
Uld'JOO •--
ODDS
RATIO
0.0000
0.9899
0.9602
10.4803
0.7446
1.0211
1.5246
0.6797
0.3854
0.9681
1.3562
2.2336
1.2771
0.9346
LOWER
99%
LIMIT
0.0000
0.7397
0.8569
0.2866
0.3892
0.7636
0.6114
0.2331
0.0868
0.9224
0.3836
0.5347
0.2866
0.1438
UPPER
99%
LIMIT
1
3
(4.826
.3246
.0760
3.250
.4244
.3654
.8015
.9817
.7117
.0162
.7944
9.3304
5.6913
6.0746
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
T2
T4
• ~
T6
• V
P3B
t v»
P13
PARAM
1
2
3
4
5
6
7
8
9
10
uirv«K=i
ESTIMATE
10.5165
•0.0995
•0.0399
•0.1067
-0.3764
•0.0029
0.5005
•1.2595
•1.1078
•1.3636
e. rvucL=rt r«
STOERR
6.6004
0.1003
0.0383
1.2919
0.2359
0.1264
0.3732
0.6914
0.6335
0.6235
fruat LKJ
CH1SQ
2.54
0.98
1.09
0.01
2.55
0.00
1.80
3.32
3.06
4.78
>=U.UUI3 IU
PROB
0.1111
0.3214
0.2974
0.9342
0.1106
0.9820
0.1799
0.0685
0.0804
0.0287
ODOS
RATIO
36919.7
0.9053
0.9609
0.8988
0.6863
0.9972
1.6495
0.2838
0.3303
0.2557
LOWER UPPER
99% 99%
LIMIT LIMIT
0.0015 8.94E11
0.6992
0.8706
0.0322 2
0.3738
0.7200
0.6307
0.0478
0.0646
0.0513
.1722
.0605
.0590
.2602
.3809
.3140
.6846
.6889
.2745
EFFVAR
INTERCEPT
Tl
T2
T4
T6
02
v»
P3A
P3B
P13
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
•6.2626
0.0228
•0.0300
0.7444
•0.1426
0.0586
0.5709
0.7137
0.8542
0.5410
rwuLL"« rt>
5TDERR
5.6666
0.1015
0.0407
1.2613
0.2441
0.1000
0.3314
0.3980
0.4116
0.6189
•rLJVtLL UK
CH1SQ
1.22
0.05
0.54
0.35
0.34
0.34
2.97
3.22
4.31
0.76
dsU.W*l H
PROB
0.2691
0.8219
0.4606
0.5551
0.5590
0.5578
0.0850
0.0730
0.0380
0.3821
0005
RATIO
0.0019
1.0231
0.9704
2.1052
0.8671
1.0604
1.7699
2.0415
2.3495
1.7177
LOWER
99%
LIMIT
0.0000
0.7877
0.8739
0.0817
0.4624
0.8195
0.7537
0.7323
0.8138
0.3488
UPPER
99%
LIMIT
4165.38
5
.3288
.0777
.2449
.6261
.3719
.1561
5.6914
6.7834
8.4595
-------
MAXIMUM LIKELIHOOD ESTIMATES
IFFYAR
INTERCEPT
Tl
T2
T3
T4
16
02
P5
PB
M
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
29.8672
-0.5258
-0.0303
0.8955
-0.2066
0.3559
0.2930
0.6450
-0.4740
0.8686
,•» rvucL-n rt
STDERR
8.2546
0.1281
0.0466
1.5930
0.3150
0.1702
0.4680
0.3196
0.2573
0.3133
:-rvM.c unj
CHISQ
13.08
16.84
0.42
0.32
0.43
4.37
0.39
4.07
3.39
7.69
I-V.UJU7 Hj
PROB
0.0003
0.0000
0.5158
0.5740
0.5121
0.0365
0.5312
0.0436
0.0654
0.0056
ODDS
RATIO
9.26E12
0.5911
0.9702
2.4486
0.8133
1.4275
1.3404
1.9060
0.6225
2.3836
LOWER
99%
LIMIT
5395.66
0.4249
0.8604
0.0404
0.3613
0.9208
0.4015
0.8367
0.3208
1.0635
UPPER
99%
LIMIT
1.59E22
0.8222
1.0939
148.275
1.8310
2.2130
4.4753
4.3418
1.2078
5.3423
EFFYAR
INTERCEPT
Tl
T2
T3
T4
T6
02
P5
P8
P9
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
25.2627
-0.4721
-0.0228
1.5689
0.0679
0.0842
0.3752
-0.1141
0.1527
-0.2858
i rvucu-A re1
STDERR
6.9712
0.1232
0.0456
1.3922
0.2992
0.1115
0.3726
0.2543
0.2022
0.2362
T tnnut *-n
CHISQ
13.13
14.69
0.25
1.27
0.05
0.57
1.01
0.20
0.57
1.46
.J-V.V/CV 1
PROB
0.0003
0.0001
0.6168
0.2598
0.8203
0.4503
0.3139
0.6538
0.4501
0.2263
ODDS
RATIO
9.36E10
0.6237
0.9775
4.8014
1.0703
1.0878
1.4553
0.8922
1.1650
0.7514
LOWER
99%
LIMIT
1487.55
0.4541
0.8691
0.1330
0.4952
0.8163
0.5573
0.4634
0.6920
0.4089
UPPER
99%
LIMIT
5.89E18
0.8566
1.0993
173.332
2.3132
1.4498
3.8001
1.7177
1.9612
1.3808
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
W2A
V3
W6
P6
P10
P11A
PUB
P13
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
V2A
V2B
W3
W6
P6
P9
P10
P11A
PUB
P13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
16
.... vt.rinn=v
ESTIMATE
-27.7241
0.0944
0.0941
2.7354
0.1804
0.0404
-0.2491
0.1388
0.0173
-0.1566
-0.4768
0.5274
-0.2678
1.9467
1.1548
DFPVAR=02
ESTIMATE
-6.4727
0.1083
0.0105
-0.7852
0.5314
-0.0227
0.7503
1.6464
0.1970
0.2849
0.5211
•0.5418
0.0643
-0.4009
0.7186
0.0350
C nUL»CL = « 1
STDERR
8.8035
0.1196
0.0467
1.5432
0.2864
0.1773
0.5582
0.1073
0.1422
0.2776
0.2426
0.1733
0.9860
0.7429
0.5736
MODEL * A p9
riv/i/uw n r t
STOERR
6.7394
0.1140
0.0460
1.4143
0.3212
0.1158
0.4254
0.5103
0.1129
0.1104
0.2260
0.2270
0.1436
0.6315
0.7522
0.6856
CHJSQ
9.92
0.62
4.06
3.14
0.40
0.05
0.20
1.67
0.01
0.32
3.86
9.26
0.07
6.87
4.05
sFFMALF. LRS
1 Ul trlLb kf*«?
CHISQ
0.92
0.90
0.05
0.31
2.74
0.04
3.11
10.41
3.04
6.65
5.32
5.70
0.20
0.40
0.91
0.00
PROB
0.0016
0.4296
0.0438
0.0763
0.5288
0.8196
0.6555
0.1958
0.9031
0.5726
0.0494
0.0023
0.7860
0.0088
0.0441
=0 0186
V • V * W
PROB
0.3368
0.3422
0.8190
0.5788
0.0981
0.8444
0.0778
0.0013
0.0811
0.0099
0.0211
0.0170
0.6546
0.5255
0.3394
0.9593
ODDS
RATIO
0.0000
.0990
.0987
1 .4159
.1977
.0412
.7795
.1489
.0175
0.8550
0.6208
1.6945
0.7651
7.0055
3.1734
TOTN«170 ---•
ODDS
RATIO
0.0015
1.1144
1.0106
0.4560
1.7013
0.9776
2.1176
5.1883
1.2177
1.3296
1.6839
0.5817
1.0664
0.6697
2.0516
1.0356
LOWER
99%
LIMIT
0.0000
0.8076
0.9741
0.2894
0.5727
0.6595
0.1851
0.8714
0.7054
0.4182
0.3323
1.0843
0.0603
1.0335
0.7241
LOWER
99%
LIMIT
0.0000
0.8308
0.8976
0.0119
0.7438
0.7254
0.7078
1.3936
0.9104
1.0005
0.9406
0.3241
0.7367
0.1316
0.2955
0.1771
UPPER
99%
LIMIT
0.0064
1.4955
1.2391
821.131
2.5047
1.6440
3.2832
1.5147
1.4676
1.7480
1.1597
2.6480
9.7001
47.4848
13.9070
UPPER
99%
LIMIT
53529.8
1.4948
1.1377
17.4273
3.8916
1.3173
6.3353
19.3160
1.6288
1.7670
3.0140
1.0439
1.5437
3.4070
14.2430
6.0563
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T2
T3
T4
T6
V2A
W3
P9
EFFVAR
INTERCEPT
11
T2
T3
T4
T6
W2A
V2B
W
P9
PARAM
1
2
3
4
b
»
7
8
9
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
8.6286
-0.1870
-0.0475
1.0900
0.0638
-0.1031
1.0222
-0.1659
-0.0220
... DEPVAR'Al
ESTIMATE
2.8785
-0.0039
-0.0264
0.2838
0.0891
0.1382
-0.8073
-0.5481
-0.1240
-0.6773
hi rVUtL'A 1
STDERR
6.6605
0.1015
0.0382
1.2852
0.2372
0.1254
0.4580
0.0939
0.1763
MOOEL»A P2
1 IS/Vh»lft O • fc
STOERR
6.3151
0.1046
0.0456
1.3757
0.2487
0.1138
0.4139
0.4610
0.1054
0.2173
r**rwi.t LKi!
CHISQ
1.68
3.39
1.55
0.72
0.07
0.68
4.98
3.12
0.02
"FEMALE IRS
r i»rmvv LIU
CHISQ
0.21
0.00
0.34
0.04
0.13
1.47
3.80
1.41
1.38
9.72
"U.VUUV
PROB
0.1952
0.0654
0.2130
0.3964
0.7880
0.4111
0.0256
0.0773
0.9005
•0 0167
V »vi V*
PROB
0.6485
0.9706
0.5620
0.8366
0.7200
0.2246
0.0511
0.2344
0.2395
0.0018
IUINM/Z
ooos
RATIO
5589.25
0.8294
0.9536
2.9743
1.0659
0.9020
2.7793
0.8471
0.9782
OD05
RATIO
17.7876
0.9962
0.9739
1.3282
1.0932
1.1482
0.4461
0.5780
0.8834
0.5080
LOWER
99%
LIMIT
0.0002
0.6386
0.8642
0.1085
0.5786
0.6530
0.8542
0.6651
0.6212
LOWER
99%
LIMIT
0.0000
0.7609
0.8660
0.0384
0.5761
0.8565
0.1536
0.1763
0.6733
0.2902
UPPER
99%
LIMIT
1.56E11
1.0773
1.0522
81.5059
1.9637
1.2460
9.0432
1.0789
1.5406
UPPER
99%
LIMIT
2.066E8
1.3042
1 .0953
45.9523
2.0746
1.5393
1.29SS
1.8954
1.1589
0.8891
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
n
V •
T2
T3
I V
14
• ~
T6
V5
W8
P4
• ~
P8
P12B
EFFVAR
INTERCEPT
u
T2
T3
T4
T6
V5
W8
P4
F8
P12B
PARAM
1
2
3
4
5
6
7
8
9
10
11
PARAM
1
2
3
4
5
6
7
8
9
10
11
---. Dirt**-*
ESTIMATE
4.9S21
•0.2213
•0.1402
1.4689
•0.4313
•0.1848
2.7735
2.0831
0.7841
0.0232
2.3590
DFPVAR-A2
ESTIMATE
•25.4776
0.0829
0.0284
2.9031
•0.1264
0.0865
•1.3847
•0.7737
-1.4793
0.6659
0.8873
c nwutu"« re-
STDERR
16.5700
0.2881
0.1366
3.2384
0.5676
0.3222
1.1595
1.002?
1.1376
0.5065
0.9027
•n«Ll. LR3'
CH1SQ
0.09
0.59
1.05
0.21
0.58
0.33
5.72
4.32
0.48
0.00
6.83
MODELBA P2= FEMALE LRS
* ^^t/Wfc r\ W •» * Wl tT*WW lte>*V
5TDERR
8.8337
0.135?
0.0622
1.8329
0.3182
0.1369
1.1160
0.7043
0.4865
0.2859
0.488?
CH1SQ
8.32
0.3?
0.21
2.51
0.16
0.40
1.54
1.21
9.25
5.43
3.30
• 1 . VWW
PROS
0.7650
0.4426
0.3047
0.6501
0.4473
0.5662
0.0168
0.0378
0.4906
0.9635
0.0090
-0.9184
V * 7 *WT
PROS
0.0039
0.5413
0.6486
0.1132
0.6912
0.5276
0.214?
0.2720
0.0024
0.0198
0.0694
ODDS
RATIO
141.472
0.8015
0.8692
4.3445
0.6497
0.8313
16.0146
8.0293
2.1904
1.0235
10.5804
TOTN-163
ODDS
RATIO
0.0000
1.0864
1.0288
18.2306
0.8813
1.0904
0.2504
0.4613
0.2278
1.9462
2.4286
LOWER
99%
LIMIT
0.0000
0.3816
0.6114
0.0010
0.1506
0.3625
0.8079
0.6066
0.1169
0.2776
1.0342
LOWER
99%
LIMIT
0.0000
0.7659
0.8765
0.1623
0.3883
0.7663
0.0141
0.0752
0.0651
0.9319
0.6896
UPPER
99%
LIMIT
4.88E20
1.6835
1.2358
18233.5
2.8034
1.9063
317.466
106.278
41.0404
3.7733
108.240
UPPER
99%
LIMIT
0.0657
1.5410
1.2076
2048.06
2.0003
1.5514
4.4376
2.8308
0.7977
4.0649
8.5522
-------
MALES
Model: MODEL A
Dependent Variable: Ml
Source
Model
Error
C Total
Analysis of Variance
OF
Sum of
Squares
12 601.55525
160 4425.56613
172 5027.12139
Mean
Square
50.12960
27.65979
F Value
1.812
Root MSE
Dep Mean
C.V.
5.25926
11.71676
44.88661
R-square
AdJ R-sq
0.1197
0.0536
Parameter Estimates
Variable D
INTERCEP
Tl
T2
T3
T4
T6
H7
K8
PS
P7
P8
P12B
P13
Parameter
F Estimate
8.875207
-0.150721
-0.034069
1.705283
0.526313
-0.098697
2.199752
-1.213383
0.779417
0.287778
-0.088367
3.047490
-0.559198
Standard
Error
17.40262757
0.25447752
0.10160697
3.27735236
0.58937034
0.32994617
1.20158289
1.70163015
0.64881029
0.55692295
0.45267595
1.11349247
1.29603108
T for HO:
Parameter^)
0.510
-0.592
-0.335
0.520
0.893
-0.299
1.831
-0.713
1.201
0.517
-0.195
2.737
-0.431
Prob>f
0.0501
Prob > |T|
0.6108
0.5545
0.7378
0.6036
0.3732
0.7652
0.0690
0.4768
0.2314
0.6061
0.8455
0.0069
0.6667
-------
FEMALES
Model: MODEL A
Dependent Variable: Nl
Analysis of Variance
rce
!l
>r
>ta1
Root MSE
Dep Mean
C.V.
Sun of Mean
OF Squares Square
12 947.98787 78.99899
153 5367.51213 35.08178
165 6315.50000
5.92299 R-square
12.50000 Adj R-sq
47.38390
F Value
2.252
0.1501
0.0834
Prob>F
0.0119
Parameter Estimates
Variable 0
INTERCEP
Tl
T2
T3
T4
T6
W7
W8
PS
P7
P8
P12B
P13
Parameter
F Estimate
•27.947019
0.325707
0.095683
1.335964
0.791488
0.216152
•0.951298
•2.494468
•0.077111
1.980703
-1.037155
0.481411
3.072302
Standard
Error
17.62067897
0.29560938
0.13087942
3.74386843
0.69817592
0.30626034
1.37467425
1.67732217
0.70480613
0.60095932
0.50986737
1.04614271
1.76880472
T for HO:
Parameter^)
•1.586
1.102
0.731
0.357
1.134
0.706
-0.692
•1.487
-0.109
3.296
•2.034
0.460
1.737
Prob > |T|
0.1148
0.2723
0.4658
0.7217
0.2587
0.4814
0.4900
0.1390
0.9130
0.0012
0.0437
0.6460
0.0844
-------
HALES
Model: MODEL A
Dependent Variable: M2
Analysis of Variance
Model
E
C
*ce
il
»r
>tal
Root KSE
Dep Mean
C.V.
Sun of Mean
OF Squares Square
9 1042.13760 115.79307
162 5879.55426 36.29354
171 6921.69186
6.02441 R-square
20.58721 Adj R-sq
29.26289
F Value
3.190
0.1506
0.1034
Prob>F
0.0014
Parameter Estimates
Parameter
Variable OF Estimate
INTERCEP
Tl
T2 -v
T3
T4
T6
W5
PI
P9
3.999469
0.697905
0.015579
-6.019448
•0.624396
0.098093
5.419547
0.004125
1.141631
P12B 1 -3.205318
Standard
Error
18.40610710
0.28525880
0.11147015
3.64741618
0.66896232
0.38203161
2.00022055
0.04615919
0.51724335
1.28078477 •
T for HO:
Parameter^
0.217
2.447
0.140
•1.650
-0.933
0.257
2.709
0.089
2.207
-2.503
Prob > |T|
0.8283
0.0155
0.8890
.1008
.3520
,7977
.0075
0.9289
0.0287
0.0133
0.
0.
0.
0,
-------
FEMALES
Model: MODELJl
Dependent Variable: M2
Source
Model
Error
C Total
Analysis of Variance
OF
9
157
166
Sun of
Squares
989.64586
6209.65953
7199.30539
Mean
Square
109.96065
39.55197
f Value
2.780
Prob>F
0.0048
Root NSC
Dep Mean
C.V.
6.28904
19.16766
32.81065
R-square
Adj R-sq
0.1375
0.0680
Parameter Estimates
Variable D
1NTERCEP
Tl
T2
T3
T4
T6
H5
PI
P9
P128
Parameter
F Estimate
11.702455
0.374465
-0.096955
-3.892735
-1.430794
-0.247428
•1.010421
0.176101
1.050383
-0.068799
Standard
Error
17.91686413
0.32958604
0.12715978
3.97677659
0.72103218
0.30453616
1.61017378
0.05026258
0.54546703
1.15719264
T for HO:
Parameter-0
0.653
1.136
-0.762
-0.979
-1.984
-0.812
-0.628
3.504
1.926
-0.059
Prob > |T|
0.5146
0.2576
0.4469
.3292
.0490
.4178
0.5312
0.0006
0.0560
0.9527
0.
0,
0.
-------
Volume III: Follow-up Survey at
EPA headquarter*
APPENDIX F
DETAILED MODELING RESULTS FOR MODEL B
(INTERIM MODEL)
(Notation used in thii appendix is identical to that in Appendix E.
The first page of Appendix E defines the notation.)
-------
EFFVAR
EFFVAR
INTERCEPT
T6
PI
P12A
P13
MAXIMUM LIKELIHOOD ESTIMATES
OEPVAR=H1 MOOEL=B P2=MALE LRS=0.
PARAM ESTIMATE
DEPYAR*H1
PARAH ESTIMATE
1
2
3
4
5
0.2078
0.1788
-0.0226
0.0023
1.5774
STDERR
INTERCEPT
T6
PI
P12A
P13
1
2
3
4
5
0.2061
0.1812
-0.0704
1.5022
0.0338
0.8988
0.1324
0.0203
0.5582
0.5754
STOERR
0.6474
0.0948
0.0156
0.3428
0.6855
TKLC UK;
CHISQ
0.05
1.87
12.07
7.24
0.00
"MAI F 1 G
.Tin 1C LP
CHISQ
0.10
3.56
2.08
0.00
5.29
>-\i.vyyi
PROB
0.8186
0.1712
0.0005
0.0071
0.9532
PROB
0.7483
0.0591
0.1492
0.9946
0.0214
iuinsioi ----
ODDS
RATIO
1.2289
1.1987
0.9320
4.4916
1.0344
ODDS
RATIO
1.2310
1.1958
0.9777
1.0023
4.8423
LOWER
99%
LIMIT
0.1213
0.8523
0.8845
1.0664
0.2349
LOWER
99%
LIMIT
0.2323
0.9367
0.9391
0.4145
0.8282
UPPER
99%
LIMIT
12.4461
1.6858
0.9821
18.9182
4.5541
UPPER
99%
LIMIT
6.5241
1.5265
1.0177
2.4238
28.3109
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W6
P3A
P3B
P4
PS
P10
PARAM
1
2
3
4
5
6
7
ESTIMATE
-0.3205
0.2280
-0.8770
-0.1906
-0.1024
0.0394
0.3083
ic. HUWI.I.-W r<
STOERR
1.2441
0.1163
0.6962
0.6316
0.3161
0.2417
0.139S
i-rvMt ir\j
CH1SQ
0.07
3.84
1.59
0.09
0.10
0.03
4.89
i-V.WIJ IU
PROB
0.7967
0.0500
0.2078
0.7628
0.7460
0.8706
0.0271
ODDS
RATIO
0.7258
1.2561
0.4160
0.8265
0.9027
1.0402
1.3611
LOWER
99%
LIMIT
0.0294
0.9309
0.0692
0.1624
0.3998
0.5581
0.9502
UPPER
99%
LIMIT
17.8911
1.6948
2.5003
4.2055
2.0378
1.9387
1.9497
EFFVAR
INTERCEPT
W6
P3A
P3B
P4
PS
P10
PARAM
1
2
3
4
5
6
7
ESTIMATE
1.2750
0.1717
0.1995
0.8752
-0.7072
0.3684
-0.1564
; rwucL-o re-
STDERR
1.0708
0.0988
0.4101
0.4274
0.3344
0.2442
0.1318
TUVM.C Ll»
CHISQ
1.42
3.02
0.24
4.19
4.47
2.27
1.41
ii»v.woo n
PROB
0.2338
0.0823
0.6266
0.0406
0.0344
0.1315
0.2353
uin-i/j •--•
ODDS
RATIO
3.5787
1.1873
1.2208
2.3994
0.4930
1.4454
0.8552
LOWER
99%
LIMIT
0.2269
0.9205
0.4245
0.7979
0.2083
0.7705
0.6090
UPPER
99%
LIMIT
56.4514
1.5314
3.5111
7.2151
1.1667
2.7114
1.2010
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
K2A
W6
PI
P3A
P3B
P4
P5
PARAM
1
2
3
4
5
6
7
8
---- utr»HK=r
ESTIMATE
2.3643
-0.4600
0.2901
-0.0463
-1.3526
-0.3930
-0.1508
0.2546
u rwuct.=D n
STDERR
1.5989
0.4493
0.1301
0.0184
0.8251
0.7099
0.3218
0.2464
L-TOLC LKJ
CHISQ
2.19
1.05
4.97
6.31
2.69
0.31
0.22
1.07
t-U.WJO IV
PROB
0.1392
0.3059
0.0258
0.0120
0.1012
0.5798
0.6394
0.3016
ODDS
RATIO
10.6366
0.6313
1.3366
0.9548
0.2586
0.6750
0.8600
1.2899
LOWER
99%
LIMIT
0.1730
0.1984
0.9560
0.9106
0.0309
0.1084
0.3754
0.6838
UPPER
99%
LIMIT
653.973
2.0085
1.8687
1.0011
2.1659
4.2026
1.9702
2.4335
EFFYAR
INTERCEPT
W2A
W2B
K6
PI
P3A
P38
P4
P5
PARAM
1
2
3
4
5
6
7
8
9
• -• uc.r»«K=nj
ESTIMATE
3.5772
-0.2332
-1.7079
0.0837
-0.0288
-0.7764
-0.1995
-0.7103
0.5873
rwvcu'o rf.'
STDERR
1.5298
0.4386
0.6836
0.1137
0.0201
0.5900
0.6597
0.3927
0.3082
-rc.rvu.c in
CHJSQ
5.47
0.28
6.24
0.54
2.05
1.73
0.09
3.27
3.63
J-V.UO99 1
PROB
0.0194
0.5950
0.0125
0.4619
0.1520
0.1882
0.7623
0.0705
0.0567
ODDS
RATIO
35.7732
0.7920
0.1812
1.0873
0.9716
0.4601
0.8191
0.4915
1.7991
LOVES UPPER
99% 99%
LIMIT LIMIT
0.6952 1840.82
0.2559 2.4513
0.0312
0.8112
0.9226
0.1006
0.1497
0.1787
.0545
.4573
.0232
.1032
.4812
.3516
0.8133 3.9798
-------
KAXIMUH LIKELIHOOD ESTIMATES
EFFYAR
INTERCEPT
T4
T6
W3
PI
P3A
P3B
P8
P9
P13
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
-0.0367
0.8757
0.1566
0.3196
-0.0532
-2.2693
-1.6513
-0.5281
-0.1263
2.1122
11 rwutu*D ri
STDERR
1.9278
0.4623
0.1762
0.1496
0.0265
1.0134
0.8347
0.2804
0.3055
0.6688
c-nMLt LK;
CH1SQ
0.00
3.59
0.79
4.56
4.02
5.01
3.91
3.55
0.17
9.97
t-v.yyo't lu
PROB
0.9848
0.0582
0.3741
0.0327
0.0449
0.0251
0.0479
0.0597
0.6793
0.0016
m=i/o - —
ODDS
RATIO
0.9640
2.4006
1.1695
1.3766
0.9482
0.1034
0.1918
0.5897
0.8814
8.2664
LOWER
99%
LIMIT
0.0067
0.7297
0.7428
0.9363
0.8856
0.0076
0.0223
0.2864
0.4012
1.4761
UPPER
99%
LIMIT
138.284
7.8978
1.8413
2.0238
1.0152
1.4067
1.6469
1.2144
1.9361
46.2948
EFFYAR
INTERCEPT
T4
16
W3
M
P3A
P3B
P8
P9
P13
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
-4.8041
1.3298
0.2826
0.0970
0.0237
-0.9770
0.0024
0.1824
-0.5539
-0.4178
rvvtu»o re.-
STDERR
2.0485
0.5960
0.1182
0.1242
0.0237
0.5724
0.5276
0.2484
0.2653
0.8571
*rtrvti.c ur
CHISQ
5.50
4.98
5.71
0.61
1.00
2.91
0.00
0.54
4.36
0.24
is-v./oto twin
PROB
0.0190 (
0.0257 :
0.0168
0.4350
0.3172
0.0878
0.9963
0.4629
0.0368 (
0.6260 (
ODDS
RATIO
0.0082
{.7803
.3266
.1019
.0240
.3764
.0024
.2001
K5747
1.6585
LOWER
99%
LIMIT
0.0000
0.8142
0.9784
0.8002
0.9633
0.0862
0.2575
0.6329
0.2902
0.0724
UPPER
99%
LIMIT
1.6045
17.5507
1.7987
1.5173
1.0884
1.6446
3.9021
2.2757
1.1383
5.9900
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
• w
W2A
W7
P6
P7
PARAM
1
2
3
4
5
6
7
ESTIMATE
5.7061
-0.1386
0.4140
-0.6289
0.9253
-0.4870
0.4055
13 nuutLED r<
STOERR
7.5425
0.1176
1.5540
0.6456
0.4696
0.2763
0.2524
C*n*LL LK3
CHISQ
0.57
1.39
0.07
0.95
3.88
3.11
2.58
<*v*yjyo ivi
PROS
0.4493
0.2387
0.7899
0.3300
0.0488
0.0780
0.1082
'in* JOB ----
ODDS
RATIO
300.696
0.8706
1.5129
0.5332
2.5226
0.6145
1.5001
LOWER
99%
LIMIT
0.0000
0.6430
0.0276
0.1011
0.7525
0.3016
0.7830
UPPER
99%
LIMIT
8.2SE10
1.1786
82.8560
2.8128
8.4S70
1.2520
2.8739
EFFVAR
INTERCEPT
Tl
T3
K2A
H28
H7
P6
P7
PARAM
1
2
3
4
5
6
7
8
ESTIMATE
-0.1969
0.1898
-2.0837
0.4194
-1.7366
-0.8660
-0.3792
-0.1152
rvvc.L-D re-
STDERR
6.9224
0.1115
1.3883
0.4384
0.6879
0.4431
0.2111
0.1967
-rcrvM_c. LK
CHISQ
0.00
2.90
2.25
0.92
6.37
3.82
3.23
0.34
3-V.99/7 11
PROB
0.9773
0.0887
0.1334
0.3387
0.0116
0.0506
0.0724
0.5582
ODDS
RATIO
0.8213
1.2090
0.1245
1.5210
0.1761
0.4206
0.6844
0.8912
LOWER UPPER
99% 99%
LIMIT LIMIT
0.0000 4.559E7
0.9072 1.6113
0.0035 4.4485
0.4917
0.0299
0.1343
0.3973
0.5369
.7054
.0361
.3171
.1789
.4792
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
V3
PI
P7
P12A
PARAM
1
2
3
•
5
6
---- utrtMK-n
ESTIMATE
-5.1783
0.4134
0.2130
•0.0612
0.6890
1.4195
10 nuucL-o r<
STDERR
2.0380
0.1733
0.1476
0.0291
0.3249
0.8388
:-rv»Lt. tug
CHISQ
6.46
5.69
2.08
4.41
4.50
2.86
i - 1 . VUMV 1 U
PROB
0.0111
0.0170
0.1488
0.0357
0.0340
0.0906
ODDS
RATIO
0.0056
1.5119
1.2374
0.9406
1.9917
4.1351
LOWER
99%
LIMIT
0.0000
0.9675
0.8460
0.8727
0.8625
0.4765
UPPER
99%
LIMIT
1.0742
2.3627
1.8098
1.0139
4.5995
35.8825
DEPYAR=H6 MODEL-B P2-FEMALE LRS-0.0921 TOTN-177
EFFVAR
PARAM* ESTIMATE
STDERR
INTERCEPT
T6
W3
PI
P7
P12A
1
2
3
4
5
6
•3.1884
0.0986
0.2565
•0.0195
0.4325
0.0415
1.1500
0.1005
0.1049
0.0192
0.1950
0.3816
CHISQ
7.69
0.96
5.98
1.03
4.92
0.01
'U.U2C1 II
PROB
0.0056
0.3263
0.0145
0.3107
0.0266
0.9133
ODDS
RATIO
0.0412
1.1036
1.2924
0.9807
1.5411
1.0424
LOWER
99%
LIMIT
0.0021
0.8519
0.9864
0.9334
0.9326
0.3900
UPPER
99%
LIMIT
0.7977
1.4297
1.6934
1.0304
2.5468
2.7857
-------
MAXIMUM LIKELIHOOD ESTIMATES
EffVAR
INTERCEPT
u
T3
W
K6
K8
P3A
P3B
P8
P10
PliA
PUB
PARAM
1
Z
3
4
5
6
7
8
9
10
11
12
ESTIMATE
-0.3826
-0.2224
2.3442
0.1324
0.35S2
-0.4555
-1.6467
-0.9221
0.0427
0.4913
1.1491
•0.2820
/ nwucu-p r<
STDERR
7.1846
0.1044
1.3394
0.1033
0.1306
0.6612
0.8032
0.7036
0.1818
0.1598
0.9154
0.7472
C-WLt LKJ
CHISQ
0.00
4.53
3.06
1.64
7.39
0.54
4.20
1.72
0.06
9.45
1,58
0.14
>**V»U.7C IV
PROS
0.9575
0.0332
0.0801
0.2000
0.0065
0.4627
0.0404
0.1900
0.8145
0.0021
0.2094
0.7059
ODDS
RATIO
0.6821
0.8006
10.4249
1.1416
1.4265
0.6154
0.1927
0.3977
1.0436
1.6344
3.1554
0.7543
LOWER
99%
LIMIT
0.0000
0.6118
0.3308
0.8749
1,0190
0.112]
0.0243
0.0649
0.6534
1.0829
0.2985
0.1101
UPPER
99%
UNIT
7.44E7
1.0476
328.486
1.4896
1.9970
3.3796
1.5255
2.4360
1.6670
2.4669
33.3538
5.1696
EffYAR
INTERCEPT
Tl
T3
V3
U6
MS
P3A
P3B
P8
P10
P11A
PUB
PARAM
1
2
3
4
5
6
7
-8
9
10
11
12
--- i»trr«n=n/
ESTIMATE
-5.9257
-0.0608
1.7090
0.1711
0.1664
-0.8551
-0.3154
0.6354
-0.3588
-0.0717
0.0802
-1.5159
rwutL'O rt.-
STDERR
6.1996
0.0923
1.2075
0.0976
0.0996
0.4628
0.4236
0.4338
0.1662
0.1297
0.5794
0.8746
TCTVU.C UK
CHISQ
0.91
0.43
2.00
3.08
2.79
3.41
0.55
2.15
4.55
0.31
0.02
3.00
3-U.W17 II
PROS
0.3392
0.5096
0.1570
0.0795
0.0947
0.0647
0.4565
0.1430
0.0329
0.5802
0.8900
0.0831
ODDS
RATIO
0.0027
0.9410
5.5234
1.1866
1.1810
0.4252
0.7295
1.8878
0.6985
0.9308
1.0835
0.2196
LOWER UPPER
99% 99%
LIMIT LIMIT
0.0000 23028.1
0.7419 1.1936
0.2462 123.906
0.9228
0.9138
0.1291
0.2450
0.6175
0.4529
0.6664
0.2436
.5258
.5265
.4008
.1723
.7711
.0773
.3001
.8198
0.0231 2.0898
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
T6
W2A
P3A
P3B
P8
P13
EFFVAR
INTERCEPT
T6
W2A
W2B
P3A
P3B
P8
• v
P13
PARAM
1
2
3
4
5
6
7
PARAM
1
2
3
4
5
6
7
8
ESTIMATE
3.4564
•0.2569
•10.5591
-3.2826
•2.8037
•1.1126
2.0687
DFPVAR=HS
ESTIMATE
•4.5882
0.4417
0.4112
-0.5086
0.7468
1.6540
0.0267
0.0994
9 rwutL-o r
STOERR
1.5095
0.3036
•
1.3473
1.0668
0.3529
0.7460
MODEL =B P2
riwt/LW \f r fc
STOERR
1.5705
0.1448
0.6634
1.3043
1.0319
1.0689
0.2965
1.1331
•*-rvM.t tnj-
CHISQ
5.24
0.72
•
5.94
6.91
9.94
7.69
'FEMALE LRS
1 VJTrlCfc kn^
CHISQ
8.53
9.31
0.38
0.15
0.52
2.39
0.01
0.01
' i . vuy v
PROS
0.0220
0.3974
•
0.0148
0.0086
0.0016
0.0056
•i 0000
A . WW
PROB
0.0035
0.0023
0.5354
0.6966
0.4692
0.1218
0.9282
0.9301
ODDS
RATIO
31.7026
0.7734
•
0.0375
0.0606
0.3287
7.9145
ODDS
RATIO
0.0102
1.5553
1.5086
0.6013
2.1102
5.2278
1.0271
1.1045
LOWER
99%
LIMIT
0.6492
0.3538
•
0.0012
0.0039
0.1324
1.1584
LOWER
99%
LIMIT
0.0002
1.0711
0.2732
0.0209
0.1479
0.3330
0.4785
0.0596
UPPER
99%
LIMIT
1548.24
1.6908
•
1.2069
0.9459
0.8158
54.0763
UPPER
99%
LIMIT
0.5812
2.2S85
8.3321
17.3099
30.1135
82.0629
2.2045
20.4558
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W2A
H3
K8
P4
P10
P12B
PARAM
1
2
3
4
5
6
7
ESTIMATE
-0.6959
-0.5856
0.1997
0.2764
-0.5457
0.3995
0.2420
i» nuutL-p r<
STOERR
0.9636
0.4390
0.0976
0.5314
0.3121
0.1438
0.4210
c-rv*LC LKJ
CHI SQ
0.52
1.43
4.18
0.27
3.06
7.71
0.33
i-V.Vt/U IV
PROB
0.4702
0.2311
0.0408
0.6030
0.0804
0.0055
0.5654
ODDS
RATIO
0.4986
0.5568
1.2210
1.3184
0.5794
1.4911
1.2738
LOWER
99%
LIMIT
0.0417
0.1580
0.9496
0.3354
0.2593
1.0295
0.4306
UPPER
99%
LIMIT
5.9675
1.9622
1.5701
5.1825
1.2947
2.1596
3.7678
EFFVAR
INTERCEPT
W2A
W2B
V3
H8
P4
P10
P12B
PARAM
1
2
3
4
5
6
7
8
ESTIMATE
0.9475
-0.2805
-1.5950
0.1907
-0.8373
-0.7617
0.0948
0.9626
i CVVCL-D rt.
STDERR
1.0718
0.3790
0.5273
0.0977
0.4458
0.3288
0.1327
0.3826
TUWIL LA
CHI SO.
0.78
0.55
9.15
3.81
3.53
5.37
0.51
6.33
.J-V.VJ17 It
PROB
0.3767
0.4592
0.0025
0.0510
0.0603
0.0205
0.4748
0.0119
ODDS
RATIO
2.5793
0.7554
0.2029
1.2101
0.4329
0.4669
1.0994
2.6185
LOVER
99%
LIMIT
0.1631
0.2846
0.0522
0.9408
0.1373
0.2001
0.7811
0.9773
UPPER
99%
LIMIT
40.7908
2.0053
0.7892
1.5564
1.3649
1.0890
1.5475
7.0159
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
P5
EFFVAR
INTERCEPT
P5
PARAM
1
2
PARAM
1
2
ESTIMATE
•2.5221
0.6351
.. DEPVAR=H10
ESTIMATE
•1.6896
0.3878
j rwuc.u*o re
STOERR
0.5080
0.2472
MODEL'S P2»
STOERR
0.4097
0.2091
.-rvac LKJ*
CHISQ
24.65
6.60
FEMALE LRS
CHISQ
17.01
3.44
u.yuji
PROS
0.0000
0.0102
=0 5457
PROB
0.0000
0.0636
IUINS1BD
ODDS
RATIO
0.0803
1.8872
TOTH'184
ODDS
RATIO
0.1846
1.4737
LOWER
99%
LIMIT
0.0217
0.9983
LOWER
99%
LIMIT
0.0642
0.8600
UPPER
99%
LIMIT
0.2972
3.5676
UPPER
99%
LIMIT
0.5304
2.5255
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFYAR
INTERCEPT
T2
T6
W3
PI
P13
PARAH
1
2
3
4
5
6
--- ULr»flK=ni
ESTIMATE
-0.6682
•0.0268
0.0415
0.2363
-0.0310
0.1133
i nuucL^o n
STOERR
1.5261
0.0438
0.1492
0.1090
0.0195
0.6095
:-PlHLt LKJ
CHISQ
0.19
0.38
0.08
4.69
2.52
0.03
>-V.3/J* IU
PR08
0.6615
0.5399
0.7810
0.0303
0.1125
0.8525
in-tor ----•
ODDS
RATIO
0.5126
0.9736
1.0424
1.2666
0.9695
1.1200
LOWER
99%
LIMIT
0.0101
0.8697
0.7098
0.9565
0.9220
0.2330
UPPER
99%
LIMIT
26.1287
1.0898
1.5309
1.6771
1.0194
5.3837
EFFVAR
INTERCEPT
T2
T6
V3
PI
P13
PARAN
1
2
3
4
5
6
ESTIMATE
-2.4098
0.0770
0.2045
0.1511
-0.0381
1.1328
nyuLL-D re.'
STOERR
1.2327
0.0405
0.1013
0.0977
0.0177
0.6108
TUVU.C Lit
CHISQ
3.82
3.62
4.08
2.39
4.65
3.44
J~V.U9OJ II
PROS
0.0506
0.0571
0.0435
0.1218
0.0310
0.0636
ODDS
RATIO
0.0898
1.0800
1.2269
1.1631
0.9626
3.1043
LOVER
99%
LIMIT
0.0038
0.9730
0.9451
0.9043
0.9197
0.6436
UPPER
99%
LIMIT
2.1504
1.1988
1.5927
1.4960
1.0075
14.9726
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
EFFVAR
INTERCEPT
Tl
T6
W5
P4
PARAM ESTIMATE
INTERCEPT
Tl
T6
W5
P4
1
2
3
A
5
31.3471
-0.5201
0.4161
1.8423
0.9442
1
2
3
4
5
PARAM ESTIMATE
20.0407
-0.2777
0.0730
1.4729
-0.8068
rWWLL-O 1
STDERR
•£-rv*LC i.nj-
CHISQ
13.9674 5.04
0.1954 7.08
0.1815 5.26
1.0208 3.26
0.7464 1.60
ODEL«B P2=FEMALE LRS
STDERR CHISQ
8.0002
0.1061
0.1528
0.6950
0.4633
6.28
6.85
0.23
4.49
3.03
• i . wvu
PROB
0.0248
0.0078
0.0219
0.0711
0.2059
=0.9980
PROB
0.0122
0.0088
0.6328
0.0341
0.0816
ODDS
RATIO
4.11E13
0.5945
1.5160
6.3110
2.5708
TOTN»179
ODDS
RATIO
5.053E8
0.7575
1.0757
4.3619
0.4463
LOWER
99%
LIMIT
0.0097
0.3594
0.9499
0.4551
0.3759
LOVER
99%
LIMIT
0.5668
0.5764
0.7257
0.7280
0.1353
UPPER
99%
LIMIT
1.74E29
0.9834
2.4197
87.5212
17.5829
UPPER
99%
LIMIT
4.51E17
0.9956
1.5946
26.1336
1.4721
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
H2A
W7
P6
P7
EFFVAR
INTERCEPT
Tl
T3
K2A
V28
V?
P6
P7
PARAM
1
2
3
4
5
6
7
PARAM
1
2
3
4
5
6
7
8
ESTIMATE
7.4042
•0.1116
-0.1281
-0.2068
0.7622
-0.5014
0.38S6
' DEPVAR=H13
ESTIMATE
-1.4617
0.1678
-1.6231
0.3460
-1.7888
-0.9382
-0.3527
-0.1181
j rrjvt.^-o Ti
STDERR
7.1467
0.1121
1.4940
0.5830
0.4494
0.2634
0.2374
MODEL'S P2«
rlvl/bk V • fc
STDERR
6.8702
0.1090
1.3606
0.4343
0.6851
0.4412
0.2089
0.1949
:-rv\Lt LHJ'
CH1SQ
1.07
0.99
0.01
0.13
2.88
3.62
2.64
FEMALE LRS
1 UTlnLb LIU
CHISQ
0.05
2.37
1.42
0.63
6.82
4.52
2.85
0.37
•V./OJl
PROS
0.3002
0.3195
0.9316
0.7227
0.0899
0.0569
0.1043
=0 4009
V.~7v«F
PROB
0.8315
0.1234
0.2329
0.4256
0.0090
0.0335
0.0914
0.5445
ODDS
RATIO
1642.87
0.8944
0.8798
0.8132
2.1430
0.6057
1.4705
TOTN*181
ODDS
RATIO
0.2318
1.1827
0.1973
1.4134
0.1672
0.3913
0.7028
0.8886
LOWER
99%
LIMIT
0.0000
0.6701
0.0187
0.1811
0.6734
0.3073
0.7978
LOWER
99%
LIMIT
0.0000
0.8932
0.0059
0.4617
0.0286
0.1256
0.4103
0.5379
UPPER
99%
LIMIT
1.63E11
1.1938
41.2827
3.6510
6.8200
1.1938
2.7105
UPPER
99%
LIMIT
1.125E7
1.5661
6.5653
4.3265
0.9763
1.2194
1.2037
1.4681
-------
EFFVAR
EFFVAR
INTERCEPT
T4
K5
P5
P7
1
2
3
4
5
MAXIMUM LIKELIHOOD ESTIMATES
DEPYAR=H14 MODEL=B P2=MALE LRS=0.8935 TOTN=181
PARAM ESTIMATE
INTERCEPT
T4
V5
P5
P7
1
2
3
4
5
-8.1112
1.7198
0.1956
0.6105
0.3264
— DEPVAR=I
STDERR
1.9159
0.5910
0.8661
0.2856
0.2556
CH1SQ
17.92
8.47
.05
0.
4,
57
1.63
PROB
0.0000
0.0036
0.8213
0.0326
0.2017
DEPVAR=H14 MODEL«B P2*FEMALE LRS=0.0260 TOTK
PARAH ESTIMATE
-2.7069
-0.0303
1.0469
0.0608
0.4797
STDERR
0.9511
0.2251
0.5134
0.2297
0.2032
CHI SQ
8,
0,
4,
0.
10
02
16
07
5.57
PROB
0.0044
0.8928
0.0415
0.7913
0.0182
ODDS
RATIO
0.0003
5.5834
1.2160
1.8414
1.3860
U17&
ODDS
RATIO
0.0667
0.9702
2.8488
1.0627
1.6156
LOWER
99%
LIMIT
0.0000
1.2182
0.1306
0.8823
0.7175
LOWER
99%
LIMIT
0.0058
0.5433
0.7591
0.5881
0.9572
UPPER
99%
LIMIT
0.0418
25.5904
11.3212
3.8428
2.6773
UPPER
99%
LIMIT
0.7735
1.7325
10.6912
1.9203
2.7268
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
T4
T6
V3
PI
P7
P8
PARAM
1
2
3
4
5
6
7
8
ESTIMATE
•12.8581
0.0775
2.5501
0.0320
0.1030
-0.0048
0.0494
0.2725
u rvL»(.i.-B r«
STDERR
4.2954
0.0750
1.0237
0.3284
0.2083
0.0376
0.4690
0.4115
t-rvu.c i-nj
CH1SQ
8.96
1.07
6.21
0.01
0.24
0.02
0.01
0.44
>~I.WW IV
PR06
0.0028
0.3018
0.0127
0.9223
0.6210
0.8981
0.9161
0.5079
ODD5
RATIO
0.0000
1.0806
12.8084
1.0325
1.1085
0.9952
1.0506
1.3132
LOWER
99%
LIMIT
0.0000
0.8907
0.9167
0.4431
0.6482
0.9033
0.3139
0.4550
UPPER
99%
LIMIT
0.1664
1.3109
178.958
2.4060
1.8957
1.0964
3.5168
3.7906
EFFVAR
INTERCEPT
12
T4
T6
K3
PI
P?
P8
PARAM
1
2
3
4
5
6
7
8
ESTIMATE
•6.0331
0.1497
•0.1545
0.2969
0.3547
-0.0972
0.9509
-0.8277
i ruwtw-p re-
STDERR
3.5243
0.0869
0.3747
0.1725
0.1851
0.0475
0.4001
0.3502
-rUYU.C. I.K
CHISQ
2.93
2.96
0.17
2.96
3.67
4.18
5.65
5.59
.J-l.WW II
PROB
0.0869
0.0851
0.6801
0.0851
0.0554
0.0408
0.0175
0.0181
ODDS
RATIO
0.0024
1.1615
0.8568
1.3457
1.4258
0.9074
2.5880
0.4371
LOWER
99%
LIMIT
0.0000
0.9285
0.3264
0.8629
0.8850
0.8029
0.9233
0.1773
UPPER
99%
LIMIT
21.0205
1.4529
2.2495
2.0986
2.2968
1.0255
7.2540
1.0773
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T3
T6
PS
P6
P7
P9
PARAM
1
2
3
4
5
6
7
--- i/Lrv/ui-nj
ESTIMATE
-50.0359
7.2247
0.2295
1.1220
1.2911
-0.7959
-0.7001
ID rwutL'D r<
STDERR
14.2274
2.1133
0.2387
0.4627
0.4528
0.3540
0.3751
t-rvM-t CKJ
CHISQ
12.37
11.69
0.92
5.88
8.13
5.06
3.48
l-i.V/WV IV
PROB
0.0004
0.0006
0.3363
0.0153
0.0044
0.0246
0.0620
ODDS
RATIO
0.0000
1372.93
1.2580
3.0710
3.6368
0.4512
0.4965
LOWER
99%
LIMIT
0.0000
5.9348
0.6802
0.9325
1.1328
0.1813
0.1889
UPPER
99%
LIMIT
0.0000
317604
2.3266
10.1139
11.6757
1.1230
1.3049
EFFVAR
INTERCEPT
T6
I V
PS
f «r
P6
* V
P7
f 9
P9
PARAM
1
2
3
4
5
6
7
ESTIMATE
-14.5982
2.0210
0.1450
-0.4674
-0.0383
0.8625
-0.7704
rwutL»o re-
STDERR
7.4572
1.1595
0.1169
0.3449
0.2591
0.3096
0.2900
*rtnm-t LK
CHISQ
3.83
3.04
1.54
1.84
0.02
7.76
7.06
>3~V.79CD l<
PROB
0.0503
0.0813
0.2150
0.1754
0.8826
0.0053
0.0079
ODDS
RATIO
0.0000
7.5459
1.1560
0.6266
0.9624
2.3691
0.4628
LOWER
99%
LIMIT
0.0000
0.3807
0.8554
0.2577
0.4937
1.0671
0.2193
UPPER
99%
LIMIT
100.640
149.586
1.5623
1.5236
1.8760
5.2595
0.9769
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
02
K2A
PI
P3A
P3B
PARAH
1
2
3
4
5
6
7
8
. VLrV/\K~l
ESTIMATE
-5.8273
0.2668
-1.6885
0.5012
0.4386
-0.0501
•1.6228
-1.6827
.1 nyuiL=o n
STDERR
6.3225
0.0946
1.3001
0.3617
0.4480
0.0195
0.8674
0.7474
C=ri8Lt LK3
CHJSQ
0.85
7.95
1.69
1.92
0.96
6.57
3.50
5.07
*=U.W/J IU
PROB
0.3567
0.0048
0.1941
0.1658
0.3275
0.0104
0.0614
0.0244
in=ioi - —
ODDS
RATIO
0.0029
1.3058
0.1848
1.6507
1.5505
0.9511
0.1973
0.1859
LOWER
99%
LIMIT
0.0000
1.0234
0.0065
0.6502
0.4890
0.9045
0.0211
0.0271
UPPER
99%
LIMIT
34872.7
1.6661
5.2622
4.1910
4.9168
1.0001
1.8434
1.2746
EFFVAR
INTERCEPT
Tl
T3
02
K2A
H2B
PI
P3A
P3B
PARAM
1
2
3
4
5
6
7
8
9
--- viriwvi
ESTIMATE
-14.7832
0.1214
1.1292
0.3309
-0.5167
-0.6199
•0.0344
0.2917
0.7735
rvucL'-p Tt.-
STOERR
5.8744
0.0922
1.1908
0.3368
0.3935
0.5428
0.0181
0.4665
0.5205
TLflMLC Lr
CKISQ
6.33
1.73
0.90
0.97
1.72
1.30
3.62
0.39
2.21
[3**V.IAJ£* II
PROB
0.0119
0.1879
0.3430
0.3258
0.1892
0.2534
0.0569
0.5318
0.1373
ODDS
RATIO
0.0000
1.1291
3.0932
1.3922
0.5965
0.5380
0.9662
1.3387
2.1673
LOWER
99%
LIMIT
0.0000
0.8904
0.1439
0.5847
0.2165
0.1329
0.9222
0.4025
0.5670
UPPER
99%
LIMIT
1.4180
1.4318
66.4667
3.3151
1.6437
2.1779
1.0123
4.4522
8.2839
-------
EFFYAR
EFFVAR
MAXIMUM LIKELIHOOD ESTIMATES
DEPYAR-C2 MODEL-B P2*MALE LRS-0.1881 TOTN'180
PARAN ESTIMATE
INTERCEPT
02
P3A
P3B
P13
1
2
3
t
5
0.5778
0.3146
•1.1081
•1.0658
-1.1096
— - DEPYAR*
5TDERR
0.5924
0.3452
0.6719
0.6127
0.6014
CH15Q
0.95
0.83
2.72
3.03
3.40
PROB
0.3294
0.3621
0.0991
0.0820
0.0650
DEPVAR=C2 MODEL=B P2*FEMALE LRS-0.8235 TOTN-180
PARAN ESTIMATE
STDERR
CH1SQ
PROS
INTERCEPT
02
P3A
P3B
P13
1
2
3
4
5
-0.7353
0.5492
0.6773
0.8409
0.3986
0.3355
0.3209
0.3802
0.3960
0.6032
4.80
2.93
3.17
4.51
0.44
0.0284
0.0870
0.0748
0.0337
0.5087
ODDS
RATIO
1.7821
1.369?
0.3302
0.3445
0.3297
1*180
ODDS
RATIO
0.4794
1.7319
1.9686
2.3185
1.4897
LOWER
99%
LIMIT
0.3874
O.S629
0.0585
0.0711
0.0700
LOVER
99%
LIMIT
0.2020
0.7577
0.7393
0.8359
0.3150
UPPER
99%
LIMIT
8.1974
3.3329
1.8640
1.6695
1.5521
UPPER
99%
LIMIT
1.1376
3.9584
5.2420
6.4301
7.0459
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T6
P5
P8
P9
PARAM
1
2
3
4
5
6
— -- ULr»«K=U
ESTIMATE
29.2332
-0.4527
0.3304
0.5482
-0.4198
0.8221
H rvutL=o r«
STOERR
6.6845
0.0903
0.1558
0.3006
0.2444
0.3011
t*rv\Li LKJ
CHI SQ
19.13
25.13
4.50
3.32
2.95
7.45
"U.OOJO IV
PROB
0.0000
0.0000
0.0339
0.0683
0.0859
0.0063
ODDS
RATIO
4.96E12
0.6359
1.3915
1.7301
0.6572
2.2753
LOWER
99%
LIMIT
165038
0.5039
0.9315
0.7976
0.3502
1.0476
UPPER
99%
LIMIT
1.49E20
0.8024
2.0787
3.7530
1.2334
4.9418
EFFVAR
INTERCEPT
Tl
T6
P5
P8
P9
PARAM
1
2
3
4
5
6
--- utr»«n=ui
ESTIMATE
26.6549
-0.3585
0.1225
-0.1385
0.1239
-0.3175
rvutL*o rt.1
STDERR
5.8880
0.0792
0.1061
0.2451
0.1972
0.2267
•ruvu.t UH
CHISQ
20.49
20.47
1.33
0.32
0.39
1.96
3-V.V//O 1
PROB
0.0000
0.0000
0.2481
0.5720
0.5298
0.1615
ODDS
RATIO
3.77EU
0.6987
1.1303
0.8707
1.1319
0.7280
LOWER
99%
LIMIT
97480.9
0.5698
0.8600
0.4631
0.6811
0.4060
UPPER
99%
LIMIT
1.46E18
0.8569
1.4856
1.6370
1.8812
1.3054
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
T3
T4
U2A
V3
V6
P6
P9
P10
P11A
PUB
P13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
---- utrf/wvi
ESTIMATE
-24.5641
0.0802
3.4363
0.1067
-0.2497
0.1370
0.0310
-0.1659
-0.4797
0.5332
-0.2688
1.9140
1.1117
if. PHJUCI.-O r<
STDERR
7.3460
0.0425
1.1404
0.2658
0.5530
0.1067
0.1396
0.2758
0.2418
0.1727
0.9760
0.7320
0.5740
&-rv«.t. I.HJ
CHISQ
11.18
3.55
9.08
0.16
0.20
1.65
0.05
0.36
3.94
9.54
0.08
6.84
3.75
>-V.JICO IU
PROB
0.0008
0.0594
0.0026
0.6882
0.6517
0.1994
0.8243
0.5474
0.0473
0.0020
0.7830
0.0089
0.0528
ODDS
RATIO
0.0000
1.0835
31.0718
1.1126
0.7790
1.1468
1.0315
0.8471
0.6190
1.7044
0.7643
6.7802
3.0395
LOWER
99%
LIMIT
0.0000
0.9711
1.6465
0.5610
0.1875
0.8712
0.7199
0.4163
0.3320
1.0923
0.0619
1.0288
0.6929
UPPER
99%
LIMIT
0.0036
1.2089
586.381
2.2065
3.2376
1.5096
1.4779
1.7239
1.1539
2.6593
9.4440
44.6847
13.3341
EFFVAR
INTERCEPT
T2
f •»
Ti
• ~
W2A
Hfcrt
V2B
Wfc V
W6
*W
P6
• V
P9
f y
P10
* 4V
P11A
• • • f*
PUB
• 4 » Ir
P13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
•4.3102
-0.0038
0.2482
0.4125
0.7715
1.6597
0.1918
0.2972
0.4858
-0.5464
0.0701
-0.3366
0.8197
0.1206
: nuutL=B re.-
STDERR
6.1649
0.0409
0.9345
0.2923
0.4209
0.5058
0.1118
0.1094
0.2209
0.2244
0.1407
0.6235
0.7324
0.6799
TUTMLC. L*
CHISQ
0.49
0.01
0.07
1.99
3.36
10.77
2.94
7.38
4.84
5.93
0.25
0.29
1.25
0.03
Id-U.VClQ II
PROB
0.4845
0.9261
0.7906
0.1582
0.0668
0.0010
0.0862
0.0066
0.0278
0.0149
0.6185
0.5892
0.2631
0.8590
ODDS
RATIO
0.0134
0.9962
1.2817
1.5106
2.1630
5.2577
1.2114
1.3461
1.6255
0.5790
1.0726
0.7142
2.2698
1.1284
LOWER
99%
LIMIT
0.0000
0.8966
0.1154
0.7114
0.7314
1.4287
0.9083
1.0155
0.9201
0.3248
0.7465
0.1433
0.3441
0.1958
UPPER
99%
LIMIT
10S935
1.1069
14.2317
3.2074
6.3964
19.3490
1.6157
1.7843
2.8715
1.0322
1.5412
3.5592
14.9747
6.5027
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
W2A
H3
P9
PARAM
1
2
3
4
5
.... UtrVHK-*
ESTIMATE
7.5825
-0.0998
1.0804
-0.1178
-0.0032
LI rwutt=p r<
STDERR
4.9916
0.0663
0.4310
0.0892
0.1731
C'n/VLC LK3
CHISQ
2.31
2.27
6.28
1.75
0.00
CU.UU1C IU
PROS
0.1288
0.1321
0.0122
0.1863
0.9855
>in-i/o • ---
ODOS
RATIO
1963.53
0.9050
2.9459
0.8889
0.9969
LOWER
99%
LIMIT
0.0051
0.7629
0.9706
0.7064
0.6382
UPPER
99%
LIMIT
7.54E8
1.0736
8.9411
1.1185
1.5570
CFFYAR
INTERCEPT
TI
W2A
V2B
P9
PARAM
1
2
3
4
5
6
ESTIMATE
2.8868
0.0132
-0.8301
-0.3542
-0.0915
-0.6367
rnjutu-o rt-
STDERR
4.9389
0.0653
0.3844
0.4411
0.0983
0.2082
TUIMLC I*
CHISQ
0.34
0.04
4.66
0.64
0.87
9.35
J-V.VJOO 1
PROB
0.5589
0.8399
0.0308
0.4220
0.3519
0.0022
ODDS
RATIO
17.9358
1.0133
0.4360
0.7017
0.9126
0.5290
LOWER
99%
LIMIT
0.0001
0.8564
0.1620
0.2253
0.7084
0.3094
UPPER
99%
LIMIT
6012832
1.1989
1.1736
2.1860
1.1755
0.9045
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFYAR
INTERCEPT
VS
K8
P4
P8
WB
EFFVAR
INTERCEPT
• HI fcfA^WI •
V5
w<0
W8
*>v
P8
PI2B
PARAM
I
2
3
4
5
6
PARAM
1
2
3
4
5
6
ESTIMATE
-5.7988
2.6277
2.0667
0.2796
0.1475
2.2631
DEPVAR=A2
ESTIMATE
•0.2710
•1.1883
•0.1224
•1.2680
0.5166
0.6973
. rwci.'-o r
STOERR
2.4622
1.0713
0.8005
0.9796
0.4341
0.8193
MODEL'S P2
rlWl/fcW ff m &
STDERR
1.0980
1.0867
0.5719
0.4356
0.2586
0.4461
t-rvM-t unj-
CHISQ
5.46
6.02
6.67
0.08
0.09
7.63
"FEMALE LRS
f u inkfc c.nv
CHISQ
0.06
1.20
0.05
8.47
3.99
2.44
1 . VWV
PROB
0.0195
0.0142
0.0098
0.7753
0.7606
0.0057
•0.6916
W • V^* w
PROB
0.8051
0.2742
0.8305
0.0036
0.0458
0.1180
ODOS
RATIO
0.0030
13.8419
7.8987
1.3226
1.1589
9.6128
TOTM*169 ---.
ODOS
RATIO
0.7626
0.3047
0.8848
0.2814
1.6763
2.0083
LOWER
99%
LIMIT
0.0000
0.8764
1.0046
0.1060
0.3330
1.1648
LOWER
99%
LIMIT
0.0451
0.0185
0.2028
0.0916
0.8611
0.6364
UPPER
99%
LIMIT
1.8137
218.627
62.1026
16.4949
4.0331
79.3300
UPPER
99%
LIMIT
12.9028
5.0080
3.8606
0.8642
3.2633
6.3373
-------
Model: MODEL 0
Dependent Variable: Ml
Model
E
C
KALES
Analysis of Variance
*ce
>,]
>r
>ta1
Root MSE
Dep Mean
c«v»
Sun of Mean
DP Squares Square
5 461.32201 92.26440
172 4598.77349 26.73706
177 5060.09551
5.17079 R-square
11.65730 Adj R-sq
44.35664
f Value
3.451
0.0912
0.0647
Prob>F
0.0054
Parameter Estimates
Variable 01
INTERCEP
W7
P7
P3
P12B
P13
Parameter
F Estimate
9.430923
2.030909
0.622951
•0.321712
3.006361
-0.478797
Standard
Error
1.85282911
0.87693693
0.46993307
0.41039563
1.03379284
1.25192783
T for HO:
Parameter*0
5.090
2.316
1.326
•0.784
2.908
-0.382
Prob > |T|
0.0001
0.0217
0.1867
0.4342
0.0041
0.7026
-------
Model: MODEL B
Dependent Variable: Ml
Model
Tr
C
FEMALES
Analysis of Variance
*ce
•1
>r
>tal
Root MSE
Oep Mean
C.V.
Sum of Mean
OF Squares Square
5 725.56539 145.11308
166 5759.06252 34.69315
171 6484.62791
5.89009 R-square
12.45349 Adj R-sq
47.29670
F Value
4.183
0.1119
0.0851
Prob>F
0.0013
Parameter Estimates
Variable Dl
INTERCEP
H7
P7
pe
P12B
P13
Parameter
F Estimate
9.154313
•1.448256
1.985433
•1.113692
0.633955
2.715142
Standard
Error
2.23536488
0.95327031
0.49732090
0.47582304
1.01433492
1.70730926
T for HOt
Parameter^
4.095
-1.519
3.992
•2.341
0-625
1.590
Prob > JT|
0.0001
0.1306
0.0001
0.0204
0.5328
0.1137
-------
HALES
Model: MODEL B
Dependent Variable: M2
Source
Model
Error
C Total
Root MSE
Dep Mean
C.V.
Analysis of Variance
DF
Sun of
Squares
6 920.59002
165 6001.10184
171 6921.69186
Mean
Square
153.43167
36.37031
F Value
4.219
Prob>F
0.0006
6.03078
20.58721
29.29382
R-squar*
Adj R-sq
0.1330
0.1015
Parameter Estimates
Variable Dl
INTERCEP 1
Tl 1
74
K5
PI
P9
P12B
Parameter
F Estimate
1 -7.266006
1 0.349554
-0.872779
5.618934
0.004705
1.133826
-3.119279
Standard
Error
15.21342770
0.19767093
0.65027907
1.98938541
0.04619709
0.51770978
1.27385550
T for HO:
Parameter«0
-0.478
1.768
-1.342
£.824
0.102
2.190
-2.449
Prob > |T|
0.6336
0.0788
0.1814
0.0053
0.9190
0.0299
0.0154
-------
FEMALES
Model: MODEL B
Dependent Variable: K2
Source
Model
Error
C Total
Root MSE
Dep Mean
c.v.
Analysis of Variance
OF
Sun of
Squares
6 879.34632
160 6319.95907
166 7199.30539
Mean
Square
146.55772
39.49974
F Value
3.710
Prot»F
0.0018
6.28488
19.16766
32.78898
R-square
Adj R-sq
0.1221
0.0892
Parameter Estimates
Variable Dl
INTERCEP
Tl
T4
W5 -.
PI
P9
P12B
Parameter '•
F Estimate
0.100440
0.171500
•1.583314
-0.920322
0.166323
1.005985
v -0.105366
Standard
Error
16.32324488
0.21171089
0.66748473
1.59799074
0.04956138
0.53633951
1.14255806
T for HO:
Parameter^)
0.006
0.810
-2.372
-0.576
3.356
1.876
-0.092
Prob > |T|
0.9951
0.4191
0.0189
0.5655
0.0010
0.0625
0.9266
-------
KALES
Model: MODEIJ
Dependent Variable: M3
Source
Model
Error
C Total
Root USE
Mean
Depf
C.V.
Analysis of Variance
OF
7
171
178
Sun of
Squares
997.39933
4677.13699
5674.53631
Mean
Square
142.48562
27.35168
F Value
5.209
Prob>F
0.0001
5.22988
9.11732
57.36208
R-square
Adj R-sq
0.1758
0.1420
Parameter Estimates
Variable 0
INTERCEP
T2
T3
W5
P5
P6
P7
P13
Parameter
F Estimate
-20.494956
-0.189134
4.643948
-3.093422
2.424422
-0.224039
0.472796
-0.577664
Standard
Error
13.46827217
0.08808575
2.14270466
1.59876902
0.61177252
0.50731152
0.49399589
1.25527022
T for HO:
ParametersO
-1.522
-2.147
2.167
-1.935
3.963
-0.442
0.957
-0.460
Prob > |T|
0.1299
0.0332
.0316
.0547
.0001
.6593
0.3399
0.6460
0.
0.
0.
0.
-------
FEMALES
Model: MODELJ
Dependent Variable: M3
Source
Model
Error
C Total
Root MSE
Dep Mean
C.V.
Analysis of Variance
OF
7
167
174
Sum of
Squares
691.59692
4238.43736
4930.03429
Mean
Square
98.79956
25.37986
F Value
3.893
Prob>F
0.0006
5.03784
9.07429
55.51779
R-square
AdJ R-sq
0.1403
0.1042
Parameter Estimates
Variable D
INTERCEP
T2
T3
V5
PS
P6
P7
P13
Parameter
F Estimate
18.008372
-0.130891
•1.319268
1.070589
-0.067300
-0.944714
1.460581
2.940433
Standard
Error
13.12954879
0.09286740
2.04704672
1.27980013
0.58132821
0.40942133
0.44614300
1.48165159
T for HO:
Parameter*0
1.372
-1.409
-0.644
0.837
-0.116
-2.307
3.274
1.985
Prob > JTJ
0.
0.
0.
0.
.1720
.1606
.5202
.4041
0.9080
0.0223
0.0013
0.0488
-------
Volume III: Follow-up Survey at
EPA headquarters
APPENDIX G
DETAILED MODELING RESULTS FOR MODEL C
(TESTING FOR EFFECTS OF SELF-REPORTED
THERMAL COMFORT AND ODOR VARIABLES)
(Notation used in this appendix is identical to that in Appendix E.
The first page of Appendix E defines the notation.)
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
PI
P12A
P13
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
ESTIMATE
-0.9013
0.1314
•0.0655
1.5269
0.1875
0.5966
0.6098
0.8990
0.2813
11 nvi>ti.-v n
STDERR
1.0206
0.1418
0.0210
0.5808
0.6230
0.4059
0.3889
0.4266
0.4131
c-n«Lt LKJ
CH1SQ
0.78
0.86
9.71
6.91
0.09
2.16
2.46
4.44
0.46
i-V.JJI/ IV
PROB
0.3772
0.3541
0.0018
0.0086
0.7634
0.1416
0.1169
0.0351
0.4959
in-jov ----
ODDS
RATIO
0.4060
1.1404
0.9366
4.6039
1.2062
1.8159
1.8401
2.4571
1.3249
LOWER
99%
LIMIT
0.0293
0.7915
0.8873
1.0312
0.2424
0.6383
0.6757
0.8188
0.4571
UPPER
99%
LIMIT
5.6281
1.6433
0.9887
20.5537
6.0035
5.1665
5.0109
7.3737
3.8399
EFFVAR
INTERCEPT
T6
PI
P12A
P13
Cl
C2
C4
02
PARAH
1
2
3
4
5
6
7
8
9
ESTIMATE
-1.7684
0.1294
-0.0184
-0.1944
1.7639
1.4056
0.7841
0.9760
1.0068
, nvi/tu-v rt1
5TDERR
0.8332
0.1065
0.0181
0.3940
0.7479
0.3968
0.3733
0.3884
0.3680
-I i_nni_t in
CHISQ
4.50
1.48
1.04
0.24
5.56
12.55
4.41
6.31
7.49
.J-V.J/O^ 11
PROB
0.0336
0.2243
0.3078
0.6217
0.0183
0.0004
0.0357
0.0120
0.0062
ODDS
RATIO
0.1706
1.1381
0.9818
0.8233
5.8352
4.0780
2.1904
2.6538
2.7368
LOVER
99%
LIMIT
0.0199
0.8651
0.9370
0.2984
0.8499
1.4673
0.8373
0.9758
1.0606
UPPER
99%
LIMIT
1.4593
1.4974
1.0286
2.2717
40.0645
11.3334
5.7300
7.2176
7.0623
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W6
P3A
P3B
P4
PS
P10
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
---- utrvHK=n
ESTIMATE
-2.5969
0.2501
-0.4922
0.4550
0.0950
0.0634
0.3580
0.5869
1.6167
0.6034
•0.3188
t. nui»CL-v r<
STOERR
1.4547
0.1311
0.7714
0.7139
0.3505
0.2782
0.1571
0.3747
0.3707
0.3884
0.4123
:-rwut LKJ
CHI SQ
3.19
3.64
0.41
0.41
0.07
0.05
5.19
2.45
19.02
2.41
0.60
=U.VJUJ IU
PROB
0.0742
O.OS64
0.5235
0.5239
0.7865
0.8197
0.0227
0.1173
0.0000
0.1202
0.4395
in-i/j • —
ODDS
RATIO
0.0745
1.2842
0.6113
.5762
.0997
.0655
.4305
.7984
5.0364
1.8283
0.7270
LOWER
99%
LIMIT
0.0018
0.9161
0.0838
0.2506
0.44S8
0.5204
0.9544
0.6850
1.9382
0.6723
0.2514
UPPER
99%
LIMIT
3.1595
1.8000
4.4590
9.9146
2.7125
2.1816
2.1440
4.7215
13.0871
4.9725
2.1028
EfFVAR
INTERCEPT
K6
P3A
P3B
P4
PS
P10
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
--• utr»/ut«n£
ESTIMATE
0.2908
0.1326
0.3036
1.1067
•0.8614
0.2789
•0.2181
1.1337
0.7555
0.5963
0.7880
rvutL-v re*
STDERR
1.1769
0.1047
0.4644
0.5012
0.3678
0.2667
0.1458
0.3773
0.3722
0.3928
0.3879
TU-VM_t L"
CHISQ
0.06
1.60
0.43
4.87
5.48
1.09
2.24
9.03
4.12
2.30
4.13
3-U.UJ9C 1!
PROB
0.8048
0.2054
0.5132
0.0273
0.0192
0.2958
0.1349
0.0027
0.0424
0.1290
0.0422
ODDS
RATIO
1.3375
1.1418
1.3547
3.0244
0.4226
1.3217
0.8040
3.1071
2.1287
1.8154
2.1990
LOWER
99%
LIMIT
0.0645
0.8719
0.4096
0.8316
0.1638
0.6649
0.5523
1.1756
0.8160
0.6600
0.8096
UPPER
99%
LIMIT
27.7294
1.4953
4.4812
10.9989
1.0899
2.6272
1.1706
8.2122
5.5527
4.9936
5.9729
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W2A
W6
^ w
PI
• •
P3A
f ^fr%
P3B
• vf*r
PS
• w
C2
Vfc
C4
V~
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
ESTIMATE
-0.0928
-0.7080
0.3432
-0.0379
-0.9249
0.1819
0.0158
0.2718
0.5833
1.4648
0.7347
0.1359
j nuutL-v. n
STOERR
1 .8230
0.5145
0.1489
0.0200
0.9102
0.7959
0.3613
0.2812
0.3778
0.3784
0.3949
0.3969
t-IT/^Lt LHJ
CHISQ
0.00
1.89
5.31
3.60
1.03
0.05
0.00
0.93
2.38
14.98
3.46
0.12
-W.UJWW IU
PROB
0.9S94
0.1688
0.0212
0.0577
0.3096
0.8192
0.9651
0.3337
0.1226
0.0001
0.0628
0.7320
ODDS
RATIO
0.9114
0.4926
1.4095
0.9628
0.3966
.1995
.0159
.3123
.7919
.3267
2.0849
1.1456
LOWER
99%
LIMIT
0.0083
0.1309
0.9604
0.9145
0.0380
0.1544
0.4006
0.6360
0.6771
1.6324
0.7538
0.4121
UPPER
99%
LIMIT
99.8080
1.8540
2.0684
1.0137
4.1362
9.3198
2.5767
2.7079
4.7423
11.4680
5.7659
3.1846
EFFVAR
INTERCEPT
U2A
ncn
V9R
PCD
V6
"V
P3A
r*«
P3B
rjp
PS
• »
C4
v~
02
PARAM
1
2
3
V
4
5
6
7
8
9
10
11
12
13
— DLP¥AK=HJ
ESTIMATE
2.0534
-0.1026
•1.7596
-0.0468
-0.0230
•0.7830
0.0176
-0.9673
0.4767
1.9699
0.9868
0.8400
1.4460
i nuutu't re.-
STDERR
1.7845
0.5361
0.8518
0.1289
0.0226
0.7107
0.7998
0.4802
0.3320
0.4682
0.4540
0.5093
0.5063
= rLTlHLC LK
CHISQ
1.32
0.04
4.27
0.13
1.03
1.21
0.00
4.06
2.06
17.70
4.72
2.72
8.16
id=U.Ol£J I'
PROB
0.2499
0.8483
0.0389
0.7166
0.3091
0.2706
0.9825
0.0440
0.1511
0.0000
0.0297
0.0991
0.0043
ODOS
RATIO
7.7944
0.9025
0.1721
0.9543
0.9773
0.4570
1.0178
0.3801
1.6108
7.1700
2.6826
2.3164
4.2461
LOWER
99%
LIMIT
0.0786
0.2268
0.0192
0.6847
0.9220
0.0733
0.1297
0.1103
0.6849
2.1465
0.8330
0.6238
1.1523
UPPER
99%
LIMIT
772.995
3.5909
1.5444
1.3301
1.0358
2.8513
7.9876
1.3096
3.7884
23.9504
8.6391
8.6017
15.6462
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T4
T6
W3
PI
P3A
P3B
P8
P9
P13
Cl
C2
C4
02
EFFVAR
INTERCEPT
T4
T6
¥3
Pi
P3A
P3B
P8
P9
P13
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
.... utrvAK-n
ESTIMATE
-3.0471
1.1173
0.2190
0.3111
-0.0515
•1.6110
•1.2440
-0.6055
0.0074
2.9633
0.6177
1.5516
0.0055
0.7640
f)FPVAR=H4
ESTIMATE
-9.2781
2.5554
0.2595
0.0559
0.0290
•0.8841
-0.1355
0.2249
•0.5613
-0.1047
1.0182
-0.0482
1.0313
0.1692
s nuucL'i, r<
STOERR
2.1666
0.5409
0.1858
0.1688
0.0296
1.0661
0.9086
0.3361
0.3644
0.8422
0.6024
0.6135
0.6240
0.5886
{.-nnLi. LKO-
CHISQ
1.98
4.27
1.39
3.40
3.02
2.28
1.87
3.25
0.00
12.38
1.05
6.40
0.00
1.68
MODEL«C P2=FEMALE LRS
fn/WLk v • fc V l»l ••»•».•• l»r\*/
STOERR
2.7209
0.8275
0.1357
0.1370
0.0279
0.6263
0.6160
0.2991
0.2934
0.8782
0.5776
0.5516
0.4979
0.4958
CHISQ
11.63
9.54
3.66
0.17
1.07
1.99
0.05
0.57
3.66
0.01
3.11
0.01
4.29
0.12
• I . VWV
PROS
0.1596
0.0389
0.2387
0.0653
0.0824
0.1307
0.1709
0.0716
0.9837
0.0004
0.3051
0.0114
0.9929
0.1943
=0 9686
V * J W V
PROB
0.0006
0.0020
0.0559
0.6834
0.2998
0.1580
0.8259
0.4520
0.0557
0.9051
0.0779
0.9303
0.0383
0.7330
iuin-i/4
ODDS
RATIO
0.0475
3.0566
1.2448
1.3649
0.9498
0.1997
0.2882
0.5458
1.0075
19.3618
1.8547
4.7190
1.0056
2.1468
TOTN*161
OOOS
RATIO
0.0001
12.8764
1.2963
1.0575
1.0294
0.4131
0.8733
1.2522
0.5705
0.9006
2.7682
0.9529
2.8047
1.1844
LOWER
99%
LIMIT
0.0002
0.7588
0.7713
0.8836
0.8801
0.0128
0.0277
0.2296
0.3941
2.2118
0.3929
0.9716
0.2015
0.4713
LOWER
99%
LIMIT
0.0000
1.5277
0.9139
0.7430
0.9580
0.0823
0.1787
0.5795
0.2679
0.0938
0.6252
0.2301
0.7778
0.3302
UPPER
99%
LIMIT
12.6046
12.3)30
2.0090
2.1084
1.0251
3.1120
2.9938
1.2973
2.5758
169.492
8.7538
22.9192
5.0177
9.7790
UPPER
99%
LIMIT
0.1034
108.531
1.8387
1.5050
1.1061
2.0735
4.2687
2.7058
1.2147
8.6499
12.2570
3.9461
10.1137
4.2477
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
W2A
W7
P6
P7
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
.... uirvAK-r
ESTIMATE
2.0195
-0.0620
-0.0899
-1.2595
1.1532
-0.5262
0.4834
0.6488
1.2525
0.4296
-0.1132
19 nvutL»v n
STDERR
9.1699
0.1325
1.7533
0.7590
0.5130
0.2971
0.2665
0.4945
0.4729
0.5449
0.5210
c*rvu.t LKJ
CHISQ
0.05
0.22
0.00
2.75
5.05
3.14
3.29
1.72
7.02
0.62
0.05
t-M.yoyo lu
PROB
0.8257
0.6402
0.9591
0.0971
0.0246
0.0766
0.0697
0.1895
0.0081
0.4305
0.8280
in=ioj •--•
ODDS
RATIO
7.5346
0.9399
0.9140
0.2838
3.1683
0.5908
1.6216
1.9132
3.4991
1.5366
0.8930
LOWER
99%
LIMIT
0.0000
0.6681
0.0100
0.0402
0.8451
0.2749
0.8162
0.5352
1.0349
0.3775
0.2333
UPPER
99%
LIMIT
1.37E11
1.3222
83.6466
2.0051
11.8780
1.2701
3.2217
6.8389
11.8306
6.2543
3.4175
EFFVAR
INTERCEPT
Tl
T3
V2A
W2B
W7
P6
P7
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
ESTIMATE
-2.6254
' 0.2411
-2.4845
0.2521
-1.8479
-0.9466
-0.3198
-0.2606
0.6450
0.4645
1.0552
0.9713
i nwutL-v, rf.
STDERR
7.8392
0.1256
1.4977
0.4952
0.7560
0.4820
0.2454
0.2250
0.4829
0.4620
0.4634
0.4500
TLlTKLt UK
CHI SQ
0.11
3.69
2.75
0.26
5.97
3.86
1.70
1.34
1.78
1.01
5.19
4.66
IJ-V.O/CC 1
PROB
0.7377
0.0548
0.0971
0.6106
0.0145
0.0495
0.1924
0.2469
0.1816
0.3146
0.0228
0.0309
ODDS
RATIO
0.0724
1.2726
0.0834
1.2867
0.1576
0.3881
0.7263
0.7706
1.9060
1.5912
2.8725
2.6414
LOWER
99%
LIMIT
0.0000
0.9209
0.0018
0.3593
0.0225
0.1121
0.3860
0.4316
0.5494
0.4840
0.8706
0.8287
UPPER
99%
LIMIT
4.264E7
1.7588
3.9494
.6077
.1047
.3432
.3666
.3758
6.6124
5.2310
9.4775
8.4191
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
H3
PI
P7
P12A
Cl
C2
C4
02
EFFVAR
INTERCEPT
T6
W3
PI
P7
P12A
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
-5.9569
0.4148
0.2035
-0.0540
0.6987
1.3676
0.6007
0.2286
0.3264
0.1375
DEPVAR-H6
ESTIMATE
-5.4583
0.0354
0.2606
-0.0261
0.4740
-0.1214
1.6332
0.9273
0.6297
1.3419
W nWDLL-V, 1
STDERR
2.1612
0.1861
0.1521
0.0295
0.3301
0.8597
0.5491
0.5171
0.5702
0.5483
MODEL'C P2
r*vi/bw v « fc
STDERR
1.4660
0.1139
0.1259
0.0237
0.2387
0.4551
0.5046
0.4521
0.4390
0.4132
'*»»
PROB
0.0058
0.0259
0.1810
0.0673
0.0343
0.1117
0.2740
0.6584
0.5670
0.8020
«0 7504
V . . 3w™
PROB
0.0002
0.7559
0.0385
0.2707
0.0471
0.7897
0.0012
0.0403
0.1515
0.0012
iw»n»i/» ----
ODDS
RATIO
0.0026
1.5141
1.2257
0.9474
2.0111
3.9259
1.8234
1.2568
1.3860
1.1474
TOTN»173 • •-•
iv i n *» v
ODDS
RATIO
0.0043
1.0360
1.2977
0.9742
1.6064
0.8857
S.1202
2.5277
1.8770
3.8263
LOWER
99%
LIMIT
0.0000
0.9374
0.8284
0.8781
0.8593
0.4287
0.4432
0.3317
0.3190
0.2795
LOWER
99%
LIMIT
0.0001
0.7726
0.9383
0.9165
0.8686
0.2742
1.3956
0.7887
0.6058
1.3198
UPPER
99%
LIMIT
0.6773
2.4454
1.8136
1.0222
4.7069
35.9521
7.5021
4.7619
6.0209
4.7111
UPPER
99%
LIMIT
0.1860
1.3893
1.7948
1.0356
2.9710
2.8603
18.7848
8.1004
5.8157
11.0929
-------
MAXIMUM LIKELIHOOD ESTIMATES
CFFVAR
INTERCEPT
Tl
T3
K3
W6
W8
P3A
P38
P8
P10
P11A
PUB
Cl
C2
C4
02
PARAM
1
2
3
s
5
6
7
8
9
10
11
12
13
14
15
16
i/i.rf«n-n
ESTIMATE
-2.1586
-0.2244
2.5095
0.1200
0.3470
-0.6492
-1.4609
-0.5263
0.0470
0.5607
1.5731
-0.0453
0.5364
0.8356
-0.1964
-0.4275
\l nwuLL-V T^
STDERR
8.3146
0.1159
1.4593
0.1088
0.1412
0.7150
0.8457
0.7336
0.1914
0.1750
0.9773
0.7862
0.4200
0.3911
0.4596
0.4642
c-rv*LC t.n;
CHISQ
0.07
3.75
2.96
1.22
6.04
0.82
2.98
0.51
0.06
10.26
2.59
0.00
1.63
4.56
0.18
0.85
t-v.ev/i iu
PROB
0.7952
0.0529
0.0855
0.2702
0.0140
0.3639
0.0841
0.4731
0.8060
0.0014
0.1075
0.9540
0.2015
0.0327
0.6691
0.3571
ODDS
RATIO
0.1155
0.7990
12.2988
1.1275
1.4148
0.5225
0.2320
0.5908
1.0481
1.7519
4.8216
0.9557
1.7098
2.3062
0.8217
0.6521
LOWER
99%
LIMIT
0.0000
0.5928
0.2866
0.8519
0.9834
0.0828
0.0263
0.0893
0.6402
1.1162
0.3889
0.1261
0.5795
0.8421
0.2515
0.1973
UPPER
99%
LIMIT
2.314E8
1.0770
527.768
1.4922
2.0355
3.2958
2.0496
3.9097
1.7161
2.7497
59.7772
7.2424
5.0446
6.3159
2.6846
2.1561
EFFVAR
INTERCEPT
Tl
T3
W
W6
W8
P3A
P38
P8
P10
P11A
PUB
Cl
C2
€4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
ESTIMATE
•9.0422
-0.0192
1.6006
0.1351
0.1425
-1.0879
•0.2694
0.7707
•0.4300
•0.1246
0.1539
-1.6261
1.2859
-0.1872
1.0217
0.3812
nvi/tu-v ft.
STDERR
7.1098
0.1078
1.2903
0.1041
0.1065
0.4956
0.4561
0.4846
0.1868
0.1412
0.6321
0.9349
0.4020
0.3873
0.4227
0.3805
-i t,nni.c, kr
CHISQ
1.62
0.03
1.54
1.68
1.79
4.82
0.35
2.53
5.30
0.78
0.06
3.03
10.23
0.23
5.84
1.00
IJ-V.Vl^J 1
PROB
0.2034
0.8584
0.2148
0.1943
0.1807
0.0282
0.5548
0.1117
0.0213
0.3778
0.8076
0.0820
0.0014
0.6289
0.0156
0.3164
ODDS
RATIO
0.0001
0.9810
4.9560
1.1447
1.1532
0.3369
0.7638
2.1613
0.6505
0.8828
1.1664
0.1967
3.6179
0.8293
2.7779
1.4640
LOWER
99%
LIMIT
0.0000
0.7431
0.1785
0.8754
0.8765
0.0940
0.2359
0.6203
0.4020
0.6136
0.2289
0.0177
1.2845
0.3058
0.9350
0.5494
UPPER
99%
LIMIT
10642.9
1.2950
137.609
1.4967
1.5172
1.2078
2.4732
7.5310
1.0525
1.2701
5.9429
2.1863
10.1905
2.2490
8.2530
3.9015
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
W2A
P3A
P3B
P8
P13
Cl
C2
C4
02
EFfVAR
INTERCEPT
T6
W2A
V2B
P3A
P3B
P8
P13
Cl
CZ
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
.... uLrvnn-n
ESTIMATE
1.2266
-0.2251
•10.6463
•2.5949
•2.0068
•0.9062
2.6456
-0.0275
1.5013
-0.3450
0.3213
DFPVAR=H8
ESTIMATE
-10.5839
0.4653
0.6445
1.2339
2.5330
3.6390
0.3092
0.1310
2.7910
•0.7118
0.8193
1.3924
o nuvcL-i r
STDERR
2.0115
0.3091
•
1.4513
1.1775
0.3816
0.9285
0.8339
0.9235
0.9428
0.8039
MODPL-C P2
r^/k/Li. v * •*
STOERR
3.0113
0.1750
0.8006
1.5386
1.4502
1.5662
0.3906
1.2969
1.2921
0.7963
0.7408
0.7169
•£-rVtLC LKJ'
CH1SQ
0.37
0.53
•
3.20
2.90
5.64
8.12
0.00
2.64
0.13
0.16
=FFMALE LRS
I krmtvfe wf\v
CHISQ
12.35
7.07
0.65
0.64
3.05
5.40
0.63
0.01
4.67
0.80
1.22
3.77
•i .vvvv
PROS
0.5420
0.4666
*
0.0738
0.0883
0.0176
0.0044
0.9737
0.1040
0.7144
0.6895
•1.0000
M • W W
PROB
0.0004
0.0078
0.4208
0.4226
0.0807
0.0202
0.4286
0.9195
0.0308
0.3713
0.2687
0.0521
ODDS
RATIO
3.4096
0.7984
•
0.0747
0.1344
0.4041
14.0919
0.9729
4.4875
0.7082
1.3789
TOTN-172 -- •
ODOS
RATIO
0.0000
1.5925
1.9050
3.4346
12.5912
38.0538
1.3623
1.1400
16.2973
0.4908
2.2689
4.0245
LOWER
99%
LIMIT
0.0192
0.3601
t
0.0018
0.0065
0.1512
1.2889
0.1135
0.4158
0.0624
0.1739
LOVER
99%
LIMIT
0.0000
1.0146
0.2422
0.0652
0.3004
0.6733
0.4981
0.0404
0.5842
0.0631
0.3366
0.6349
UPPER
99%
LIMIT
606.815
1.7703
•
3.1382
2.7911
1.0798
154.071
8.3364
48.4356
8.0338
10.9369
UPPER
99%
LIMIT
0.0592
2.4995
14.9819
180.790
527.799
2150.66
3.7262
32.1951
454.614
3.8170
15.2961
25.5116
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W2A
W3
W8
P4
P10
P12B
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
---- uLr¥«n-r
ESTIMATE
-2.7023
-0.9376
0.1969
0.1963
-0.3735
0.4349
-0.0559
1.2513
1.1137
0.8629
0.6874
13 rvucL-i. r<
STOERR
1.1314
0.5663
0.1071
0.6424
0.3489
0.1626
0.4666
0.4111
0.3689
0.4065
0.4024
C-IVU.C LKJ
CHISQ
5.70
2.74
3.38
0.09
1.15
7.15
0.01
9.27
9.11
4.51
2.92
I'U.CJOU IU
PROS
0.0169
0.0978
0.0659
0.7599
0.2844
0.0075
0.9046
0.0023
0.0025
0.0338
0.0875
ODDS
RATIO
0.0671
0.3916
1.2176
1.2169
0.6883
1.5448
0.9456
3.4949
3.0456
2.3700
1.9885
LOWER
99%
LIMIT
0.0036
0.0910
0.9240
0.2326
0.2802
1.0162
0.2843
1.2120
1.1775
0.8317
0.7053
UPPER
99%
LIMIT
1.2364
1.6840
1.6045
6.3670
1.6909
2.3485
3.1458
10.0774
7.8773
6.7534
5.6068
EFFVAR
INTERCEPT
W2A
W28
V3
W8
P4
P10
P12B
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
--- ucr»/\K-nj
ESTIMATE
-1.4270
-0.3212
-1.7314
0.2906
-1.1333
-0.7393
0.0957
1.3810
1.6634
1.0760
-0.3668
0.6623
r nuutL»w re.-
STDERR
1.3013
0.4475
0.6112
0.1209
0.5155
0.3740
0.1495
0.4552
0.4411
0.4220
0.4232
0.4076
-rcn«Lt LK
CHISQ
1.20
0.52
8.03
5.78
4.83
3.91
0.41
9.20
14.22
6.50
0.75
2.64
u**v.jjuj r
PROB
0.2728
0.4729
0.0046
0.0162
0.0279
0.0481
0.5223
0.0024
0.0002
0.0108
0.3861
0.1042
OOOS
RATIO
0.2400
0.7253
0.1770
1.3372
0.3220
0.4774
1.1004
3.9789
5.2772
2.9329
0.6929
1.9392
LOWER
99%
LIMIT
0.0084
0.2290
0.0367
0.9794
0.0853
0.1822
0.7487
1.2317
1.6941
0.9890
0.2329
0.6786
UPPER
99%
LIMIT
6.8562
2.2969
0.8547
1.8258
1.2149
1.2512
1.6174
12.8532
16.4393
8.6978
2.0614
5.5416
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFYAR
INTERCEPT
PS
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
ESTIMATE
-3.8221
0.7780
0.4727
1.9682
0.2308
-1.2568
v rvuti.-\. ri
STOERR
0.6913
0.3013
0.4428
0.4514
0.4600
0.5374
t-n«Li LKJ
CHISQ
30.56
6.67
1.14
19.01
0.25
5.47
>-U./VJO IV
PROS
0.0000
0.0098
0.2857
0.0000
0.6159
0.0194
m=ioc - —
ODDS
RATIO
0.0219
2.1771
1.6043
7.1578
1.2596
0.2846
LOWER
99V
LIMIT
0.0037
1.0019
0.5128
2.2376
0.3851
0.0713
UPPER
99%
LIMIT
0.1299
4.7311
5.0196
22.8970
4.1196
1.1360
EFFYAR
INTERCEPT
P5
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
ESTIMATE
•2.7550
0.3701
0.6592
0.6927
0.4202
0.2775
' rvvci~w re.1
STDERR
0.5699
0.2217
0.4096
0.3901
0.3696
0.3571
TUVM.C LM
CHISQ
23.37
2.79
2.59
3.15
1.29
0.60
io^v.jiiv luin
PROB
0.0000 (
0.0950 ]
0.1076 ]
0.0758 1
0.2556 1
0.4370 1
= JOW
ODDS
RATIO
).0636
1.4479
1.9332
1.9991
1.5223
1.3198
LOWER
99%
LIMIT
0.0147
0.8179
0.6731
0.7318
0.5875
0.5260
UPPER
99%
LIMIT
0.2761
2.5631
5.5530
5.4608
3.9444
3.3115
-------
MAXIMUM LIKELIHOOD ESTIMATES
EfFVAR
INTERCEPT
12
t •»
T6
W3
PI
w •
P13
r *^
Cl
V*
C2
V*
ri
v~
02
PARAM
1
2
3
4
5
6
7
8
9
10
--• ULr»AK=nj
ESTIMATE
-1.4468
-0.0373
-0.062S
0.2320
-0.0289
0.1439
0.5687
0.5622
1.3677
0.3400
i rwucL'v, n
STOERR
1.6350
0.0464
0.1551
0.1151
0.0203
0.6547
0.4298
0.4136
0.4369
0.4331
:-n«Li inj
CHISQ
0.78
0.65
0.16
4.07
2.02
0.05
1.75
1.85
9.80
0.62
i-v./ojy lu
PROS
0.3762
0.4211
0.6869
0.0437
0.1557
0.8261
0.1857
0.1741
0.0017
0.4324
inaioj ----•
ODDS
RATIO
0.2353
0.9634
0.9394
1.2611
0.9715
1.1548
1.7660
1.7545
3.9263
1.4049
LOWER
99%
LIMIT
0.0035
0.8549
0.6300
0.9375
0.9220
0.2138
0.5836
0.6046
1.2741
0.4604
UPPER
99%
LIMIT
15.8784
1.0857
1.4008
1.6964
1.0237
6.2364
5.3434
5.0918
12.0994
4.2873
EFFYAR
INTERCEPT
12
I *
16
1 V
PI
r J
P13
r Jrf
fi
VI
C2
V-t
C4
v™
02
PARAM
1
2
3
4
5
6
7
8
9
10
-- ut
PVAK'IUI
ESTIMATE
-3
0
0
0
-0
1
1
0
0
0
.6470
.0853
.1493
.1127
.0415
.2963
.0967
.0171
.9396
.7184
rvuc.L"i re.
STOERR
1.4247
0.0433
0.1066
0.1063
0.0196
0.6487
0.4209
0.4005
0.3825
0.3690
•riTVU-C LH
CHISQ
6.55
3.88
1.96
1.12
4.51
3.99
6.79
0.00
6.04
3.79
ij*«
0
0
0
0
0
0
0
0
0
0
. 131O 1
PROB
.0105
.0488
.1611
.2892
.0337
.0457
.0092
.9660
.0140
.0515
ODDS
RATIO
0.0261
1.0890
1.1610
1.1193
0.9593
3.6557
2.9943
1.0172
2.5590
2.0511
0
0
0
0
0
0
1
0
0
0
LOWER
99%
LIMIT
.0007
.9741
.8822
.8512
.9121
UPPER
99%
LIMIT
.0233
.2176
.5279
.4719
.0090
.6875 19.4404
.0125 8.8546
.3626 'i
1.8542
.9553 6.8546
.7928 5.3066
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T6
H5
P4
Cl
v»
C2
C4
02
EFFVAR
INTERCEPT
Tl
W5
" W
P4
C2
%»••
C4
02
PARAM
1
2
3
4
5
6
7
8
9
PARAH
1
2
3
4
5
6
7
8
9
--- ULrv«K=nn
ESTIMATE
18.6693
-0.3798
0.4058
2.2000
1.1739
-0.0640
1.3625
1.4590
0.9040
DFPVAR=H12
ESTIMATE
8.1095
-0.1400
0.0745
1.7475
-0.9445
0.4749
-0.2025
2.7591
0.0832
i ruLitL-x r<
STDERR
15.9247
0.2194
0.2185
1.1268
0.8142
0.8829
0.8383
0.9611
0.8915
:-rv*Lc UKJ-
CHISQ
1.37
3.00
3.45
3.81
2.08
0.01
2.64
2.30
1.03
MODEL*C P2-FEMALE LRS
rivi/tak v • fc • v>i lokb k«i>«f
STDERR
9.6221
0.1245
0.1814
0.8516
0.5453
0.6952
0.6983
0.8550
0.6047
CHISQ
0.71
1.27
0.17
4.21
3.00
0.47
0.08
10.41
0.02
1 .VWV
PROB
0.2411
0.0834
0.0634
0.0509
0.1494
0.9422
0.1041
0.1290
0.3106
*1 0000
A • WW
PROB
0.3993
0.2607
0.6814
0.0402
0.0832
0.4946
0.7718
0.0013
0.8906
ODDS
RATIO
1.282E8
0.6840
1.5005
9.0250
3.2346
0.9380
3.9059
4.3017
2.4695
TOTN«175 ---•
ODDS
RATIO
3325.91
0.8694
1.0773
5.7402
0.3889
1.6079
0.8167
15.7856
1.0868
LOWER
99%
LIMIT
0.0000
0.3887
0.8547
0.4953
0.3971
0.0965
0.4507
0.3618
0.2485
LOWER
99%
LIMIT
0.0000
0.6308
0.6752
0.6400
0.0954
0.2682
0.1352
1.7448
0.2289
UPPER
99%
LIMIT
8.39E25
1.2037
2.6344
164.455
26.3450
9.1189
33.8507
51.1516
24.5449
UPPER
99%
LIMIT
1.93E14
1.1981
1.7191
51.4815
1.5844
9.6382
4.9348
142.819
5.1598
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
W2A
W7
P6
P7
Cl
C2
C4
02
EFFVAR
INTERCEPT
Tl
T3
V2A
W2B
W7
P6
P7
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
ESTIMATE
5.3954
-0.0366
-0.9026
-0.7154
0.9627
-0.5064
0.4560
0.8255
0.9270
0.5758
0.2616
DEPVAR-H13
ESTIMATE
-4.4652
0.2275
-2.0486
0.2068
•1.8876
-1.0327
-0.2788
-0.2640
0.6803
0.5180
1.1409
0.8921
j rvucL^u r
STDERR
8.5450
0.1254
1.6634
0.6652
0.4853
0.2793
0.2519
0.4648
0.4341
0.5085
0.4690
C-ITMLC LKO-
CHI SQ
0.40
0.09
0.29
1.16
3.93
3.29
3.28
3.15
4.56
1.28
0.31
MODEL-C P2=FEMALE LRS
1 IV VI. k W • fc 1 ^1 tr*W W H» *f
STDERR
7.7919
0.1234
1.4703
0.4921
0.7557
0.4813
0.2436
0.2238
0.4797
0.4592
0.4603
0.4465
CH1SQ
0.33
3.40
1.94
0.18
6.24
4.60
1.31
1.39
2.01
1.27
6.14
3.99
-U.7C1/
PROS
0.5278
0.7702
0.5874
0.2822
0.0473
0.0698
0.0703
0.0757
0.0327
0.2575
0.5770
=0.8476
V • VT / V
PROS
0.5666
0.0652
0.1635
0.6743
0.0125
0.0319
0.2525
0.2381
0.1561
0.2593
0.0132
0.0457
iuin-ioj ---•
ODDS
RATIO
220.390
0.9641
0.4055
0.4890
2.6188
0.6027
1.5778
2.2830
2.5269
1.7786
1.2990
IOTN»177 --•
ODDS
RATIO
0.0115
1.2555
0.1289
1.2297
0.1514
0.3560
0.7567
0.7680
1.9745
1.6787
3.1296
2.4402
LOWER
99%
LIMIT
0.0000
0.6979
0.0056
0.0881
0.7502
0.2935
0.8246
0.6895
0.8259
0.4799
0.3881
LOWER
99%
LIM1C
0.0000
0.9136
0.0029
0.3462
0.0216
0.1031
0.4040
0.4315
0.5738
0.5143
0.9562
0.7725
UPPER
99%
LIMIT
8E11
1.3317
29.4389
2.7133
9.1416
1.2375
3.0189
7.5596
7.7310
6.5909
4.3481
UPPER
99%
LIMIT
5996787
1.7253
5.6910
4.3686
1.0609
1.2301
1.4172
1.3668
6.7938
5.4789
10.2434
7.7082
-------
MAXIMUM LIKELIHOOD ESTIMATES
tfFVAR
INTERCEPT
14
f ^
PS
W V
P7
i *
C2
V*
C4
^»^
02
PARAM
1
2
3
4
5
6
7
8
9
— utr¥/tK=iu
ESTIMATE
•8.6261
1.6605
0.0949
O.S603
0.3442
0.2620
0.5053
0.5274
0.4160
«j nuutL-v rt
STOERR
1.9729
0.5990
0.8824
0.2976
0.26S2
0.4780
0.4S85
0.4693
0.4S77
:-n*LC Lnj
CHISQ
19.12
7.68
0.01
3.54
1.68
0.30
1.21
1.26
0.83
"v.»j/^ ii/in*
PROB
0.0000 (
0.0056
0.9144
0.0597
0.1944
0.5836
0.2705
0.2611
0.3634
I/O -•--
ODDS
RATIO
5.0002
5.2619
.0995
.7512
.4109
.2995
.6575
.6945
.5159
LOWER
99*
LIMIT
0.0000
1.1247
0.1132
0.8136
0.7125
0.3793
0.5088
0.5058
0.4662
UPPER
99%
LIMIT
0.0289
24.6191
10.6756
3.7694
2.7937
4.4519
5.4000
5.6764
4.9285
CFFYAR
INTERCEPT
14
v ~
US
••^
PS
• w
C2
w*
C4
w «
02
PARAM
1
2
3
4
5
6
7
8
9
-- viriwni*
ESTIMATE
•4.6309
-0.0253
1.3068
-0.0123
0.5639
1.1003
•0.1689
0.9937
1.4769
rvvcL~v •*
STDERR
1.2335
0.2775
0.6011
0.2578
0.2354
0.4449
0.4137
0.4034
0.3812
TUVM.C. tn
CHISQ
14.09
0.01
4.73
0.00
5.74
6.12
0.17
6.07
15.01
>J-V.«37C II
PROB
0.0002
0.9274
0.0297
0.9621
0.0166
0.0134
0.6830
0.0138
0.0001
ODDS
.RATIO
0.0097
0.9750
3.6943
0.9878
1.7575
3.0051
0.8446
2.7012
4.3793
LOWER
99%
LIMIT
0.0004
0.4770
0.7853
0.5084
0.9584
0.9553
0.2910
0.9556
1.6404
UPPER
99%
LIMIT
0.2338
1.9928
17.3785
1.9190
3.2229
9.4533
2.4517
7.6359
11.6916
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
T4
T6
PI
P7
P8
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
ESTIMATE
-14,7924
0.0670
2.5741
0.1367
0.1098
0.0108
0.0127
0.4938
0.7725
-0.2158
-0.2472
0.9754
a rwwtL-v n
STDERR
5.0201
0.0768
1.0523
0.3591
0.2270
0.0410
0.4496
0.4473
0.8961
0.8093
0.9470
0.7845
t-rv*LC LNJ
CHISQ
8.68
0.76
5.98
0.14
0.23
0.07
0.00
1.22
0.74
0.07
0.07
1.55
1 - 1 . WW 1 V
PROB
0.0032
0.3832
0.0144
0.7035
0.6285
0.7917
0.9774
0.2695
0.3886
0.7897
0.7941
0.2137
ODDS
RATIO
0.0000
1.0693
13.1195
.1465
.1161
.0109
.0128
.6385
2.1652
0.8059
0.7810
2.6522
LOWER
99%
LIMIT
0.0000
0.8774
0.8723
0.4546
0.6219
0.9095
0.3181
0.5177
0.2153
0.1002
0.0681
0.3515
UPPER
99%
LIMIT
0.1556
1.3032
197.320
2.8914
2.0028
1.1235
3.2248
5.1864
21.7770
6.4815
8.9555
20.0108
EFFVAR
INTERCEPT
T2
14
T6
W
PI
P7
P8
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
ESTIMATE
-8.9097
0.2256
0.0154
0.4705
0.4696
•0.1628
0.8351
•0.8152
0.2323
-1.8572
1.8583
2.4120
i rwucL»u re-
STDERR
5.0646
0.1267
0.6311
0.2530
0.2277
0.0693
0.5285
0.5004
0.9090
1.0286
1.0769
1.0326
-ruaHUL UK
CHISQ
3.09
3.17
0.00
3.46
4.25
5.52
2.50
2.65
0.07
3.26
2.98
5.46
13'l.VUW 1
PROB
0.0785
0.0750
0.9805
0.0630
0.0392
0.0188
0.1141
0.1033
0.7983
0.0710
0.0844
0.0195
ODDS
RATIO
0.0001
1.2531
1.0155
1.6008
1.5994
0.8498
2.3050
0.4426
1.2615
0.1561
6.4128
11.1563
LOWER
99%
LIMIT
0.0000
0.9041
0.1998
0.8342
0.8896
0.7108
0.5908
0.1219
0.1213
0.0110
0.4002
0.7804
UPPER
99%
LIMIT
62.5965
1.7367
5.1609
3.0717
2.8753
1.0158
8.9936
1.6061
13.1167
2.2089
102.760
159.489
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T3
T6
PS
P6
P7
P9
Cl
C2
C4
02
EFFVAR
INTERCEPT
T3
T6
PS
P6
P7
P9
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
11
PARAM
1
2
3
4
5
6
7
8
9
10
11
— Utr»AK=nii
ESTIMATE
-56.3194
7.8970
0.2348
0.7829
1.4184
-0.6627
-0.8434
0.4597
2.9568
•0.1613
0.4701
DFPVAR=H16
ESTIMATE
-13.6088
1.7142
0.1166
-0.5324
-0.0424
0.8712
-0.7394
0.6519
0.9581
0.0860
-0.2849
o nuutLBt r,
STDERR
17.0800
2.5573
0.2659
0.6030
0.5247
0.4202
0.4637
0.771S
0.9081
0.9946
0.7080
C-fVU-t LKi"
CHISQ
10.87
9.54
0.78
1.69
7.31
2.49
3.31
0.36
10.60
0.03
0.44
MOD£L«C P2=FEMALE LRS
i ^yt/L. k %* v fr I f mivk kn«?
STDERR
8.1191
1.2609
0.1313
0.3739
0.2904
0.3372
0.3122
0.6088
0.5644
0.5117
0.5051
CHISQ
2.81
1.85
0.79
2.03
0.02
6.68
5.61
1.15
2.88
0.03
0.32
•1 .VWU
PROB
0.0010
0.0020
0.3771
0.1942
0.0069
0.1148
0.0690
0.5512
0.0011
0.8712
0.5067
=0.9980
v • & &\^f
PROB
0.0937
0.1740
0.3744
0.1545
0.8840
0.0098
0.0179
0.2842
0.0896
0.8665
0.5726
ivin-JOJ ----
ODOS
RATIO
0.0000
2689.20
1.2647
2.1878
4.1305
0.5155
0.4302
1.5836
19.2363
0.8510
1.6002
TOTN=177 ---•
ODDS
RATIO
0.0000
5.5522
1.1237
0.5872
0.9585
2.3898
0.4774
1.9192
2.6067
1.0898
0.7521
LOWER
99%
LIMIT
0.0000
3.7039
0.6375
0.4628
1.0691
0.1746
0.1303
0.2170
1.8544
0.0657
0.2583
LOWER
99%
LIMIT
0.0000
0.2157
0.8012
0.2241
0.4536
1.0026
0.2136
0.4000
0.6091
0.2917
0.2047
UPPER
99%
LIMIT
0.0000
1952468
2.5087
10.3422
15.9591
1.5216
1.4206
11.SS46
199.550
11.0319
9.9136
UPPER
99%
LIMIT
1489.21
142.919
1.5759
1.5384
2.0252
5.6964
1.0670
9.2089
11.1562
4.0720
2.7628
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
IKTERCEPT
Tl
V2A
V3
P9
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
.... mrvnn-*
ESTIMATE
17.3192
•0.2496
1.4432
-0.2349
0.0890
2.2841
0.9476
0.3268
0.1098
.1 r»juti."\» r<
STOERR
7.2505
0.0970
0.5534
0.1087
0.2114
0.4305
0.3795
0.4616
0.4083
c-nftLt uno
CHISQ
5.71
6.62
6.80
4.67
0.18
28.15
6.24
0.50
0.07
•^VJ.J^VJ t\l
PROS
0.0169
0.0101
0.0091
0.0307
0.6736
0.0000
0.0125
0.4789
0.7881
000$
RATIO
3.324E7
0.7791
4.2342
0.7906
1.0931
9.8168
2.5795
1.3865
1.1161
LOWER
99%
LIMIT
0.2572
0.6069
1.0178
0.5976
0.6341
3.2386
0.9705
0.4222
0.3899
UPPER
99%
LIMIT
4.3E15
1.0003
17.6151
1.0461
1.8843
29.7571
6.8565
4.5534
3.1950
EFFVAR
IKTERCEPT
W2A
W2B
U3
C2
V*fc
C4
w^
02
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
5.0787
•0.0465
•0.7268
0.3509
•0.0856
-0.6700
1.9385
1.4989
0.9901
0.4858
, nuucu'c re.-
STDERR
6.7922
0.0906
0.4695
0.5467
0.1229
0.2428
0.4508
0.4346
0.5019
0.4335
'ruvtLt LH
CHISQ
0.56
0.26
2.40
0.41
0.49
7.61
18.49
11.89
3.89
1.26
;o-u.ooio i
PROB
0.4546
0.6078
0.1216
0.5210
0.4860
0.0058
0.0000
0.0006
0.0485
0.2624
ODDS
RATIO
160.565
0.9546
0.4835
1.4203
0.9180
0.5117
6.9483
4.4768
2.6915
1.6255
LOWER
99%
LIMIT
0.0000
0.7559
0.1442
0.3474
0.6689
0.2738
2.1755
1.4614
0.7388
0.5321
UPPER
99%
LIMIT
6.373E9
1.2055
1.6203
5.8078
1.2598
0.9564
22.1926
13.7142
9.8060
4.9654
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W5
H8
P4
P8
P12B
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
•13.6244 '
4.1840
2.5298
2.0894
-0.2261
2.4237
1.6420
3.7198
-0.6151
0.5155 1
kSi/(.k-v r<
STOERR
1.4873
.6061
.2007
.2807
.5176
.0772
.3287
.4075
.4146
1.0545
t-nntt ung
CHISQ
9.22
6.79
4.44
2.66
0.19
5.06
1.53
6.98
0.19
0.24
- 1 . vvv/v i v
PROB
0.0024
0.0092
0.0351
0.1028
0.6622
0.0244
0.2165
0.0082
0.6637
0.6249
ODDS
RATIO
0.0000
65.6278
12.5510
8.0801
0.7976
11.2875
5.1655
41.2561
0.5406
1.6745
LOWER
99%
LIMIT
0.0000
1.0478
0.5694
0.2983
0.2103
0.7039
0.1685
1.0986
0.0141
0.1107
UPPER
99%
LIMIT
0.1268
4110.56
276.664
218.871
3.0260
181.013
158.338
1549.24
20.6747
25.3275
EFFVAR
INTERCEPT
W5
H8
P4
P8
P12B
Cl
C2
C4
02
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
-1.4320
-1.4182
-0.1835
-1.3494
0.4993
0.7076
1.2809
0.7361
0.4168
-0.1527
nwuti.~v rf
STOERR
1.2974
1.1235
0.6075
0.4653
0.2836
0.4835
0.5617
0.4944
0.4665
0.4689
-r LrvM-c. LK
CHISQ
1.22
1.59
0.09
8.41
3.10
2.14
5.20
2.22
0.80
0.11
J-V.7JOO 11
PROB
0.2697
0.2068
0.7626
0.0037
0.0783
0.1433
0.0226
0.1365
0.3716
0.7447
ODOS
RATIO
0.2388
0.2421
0.8324
0.2594
1.6476
2.0291
3.5999
2.0878
1.5171
0.8584
LOWER
99%
LIMIT
0.0084
0.0134
0.1740
0.0782
0.7935
0.5840
0.8470
0.5842
0.4562
0.2565
UPPER
99%
LIMIT
6.7538
4.3751
3.9805
0.8600
3.4207
7.0505
15.2998
7.4609
5.0455
2.8725
-------
Volume III: Follow-up Survey at
EPA headquarters
APPENDIX H
DETAILED MODELING RESULTS FOR MODEL D'
(TESTING FOR EFFECTS OF VOCs, INTEGRATED RSP, AND MICROBIOLOGICALS)
(Notation used in this appendix is identical to that in Appendix E.
The first page of Appendix E defines the notation.)
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
PI
P12A
P13
¥1
V2
V3
¥4
¥5
¥6
¥7
¥8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
utrvMK^n
ESTIMATE
0.8198
0.8106
-0.1216
2.5075
-0.9998
1.4924
-0.3513
0.7015
0.4814
0.9561
-0.6208
-2.7645
-0.4073
i nuucu-u rt
STDERR
4.0609
0.3597
0.0388
0.9654
0.9404
0.6306
0.8809
0.4762
0.6443
0.4882
0.5062
1.1342
0.3272
S.-IWLC LKJ
CHISQ
0.04
5.08
9.82
6.75
1.13
5.60
0.16
2.17
0.56
3.84
1.50
5.94
1.55
-U.OJ33 IU
PROB
0.8400
0.0242
0.0017
0.0094
0.2877
0.0180
0.6900
0.1407
0.4549
0.0502
0.2201
0.0148
0.2131
ODDS
RATIO
2.2700
2.2493
0.8855
12.2742
0.3680
4.4478
0.7038
2.0168
1.6183
2.6015
0.5375
0.0630
0.6654
LOWER
99%
LIMIT
0.0001
0.8905
0.8013
1.0208
0.0326
0.8763
0.0728
0.5914
0.3078
0.7397
0.1459
0.0034
0.2865
UPPER
99%
LIMIT
79275.0
5.6813
0.9786
147.580
4.1482
22.5746
6.8066
6.8771
8.5089
9.1495
1.9801
1.1702
1.5458
EFFVAR
INTERCEPT
T6
PI
P12A
P13
¥1
¥2
¥3
¥4
¥5
¥6
¥7
¥8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
ESTIMATE
-0.9652
0.2722
-0.0133
0.0927
1.3066
0.1567
0.3341
0.4565
0.2931
-0.3066
-0.4574
0.1776
-0.3869
nuwLL*u rt-
STDERR
2.6917
0.1909
0.0245
0.5118
0.9671
0.3500
0.5606
0.4709
0.3166
0.3716
0.4166
0.9150
0.2385
-rcrvu.c i_n
CHISQ
0.13
2.03
0.29
0.03
1.83
0.20
0.36
0.94
0.86
0.68
1.21
0.04
2.63
0=U.U13U 11
PROB
0.7199
0.1538
0.5882
0.8563
0.1767
0.6544
0.5512
0.3324
0.3546
0.4093
0.2723
0.8461
0.1048
ODDS
RATIO
0.3809
1.3128
0.9868
1.0971
3.6936
1.1696
1.3967
1.5785
1.3406
0.7359
0.6329
1.1943
0.6792
LOWER
99%
LIMIT
0.0004
0.8029
0.9264
0.2936
0.3059
0.4748
0.3296
0.4693
0.5931
0.2826
0.2164
0.1131
0.3674
UPPER
99%
LIMIT
390.965
2.1468
1.0511
4.1005
44.6052
2.8815
5.9192
5.3097
3.0303
1.9168
1.8511
12.6119
1.2554
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W6
P3A
P3B
P4
PS
P10
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
W6
P3A
P3B
P4
P5
P10
VI
V2
V3
V4
VS
V6
V7
V8
PARAM
1
2
3
4
S
6
7
8
9
10
11
12
13
14
15
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
UL.r»«K-n
ESTIMATE
2.5341
0.0943
•0.4521
0.2609
-0.2928
•0.0536
0.1967
•0.1100
-0.5671
0.5070
0.0255
0.0519
-0.4212
-0.3147
0.4084
nrpVARsM?
ESTIMATE
1.3728
0.2062
-0.3962
1.0778
-1.0717
0.1441
-0.0255
0.3188
0.7263
-0.0164
0.3738
•0.2171
-0.4753
-0.1618
•0.7868
e. nuuc.ucu r
STDERR
3.5739
0.2081
1.1987
1.0604
0.4284
0.4402
0.2140
0.4395
0.7047
0.3114
0.4856
0.3242
0.4921
0.8978
0.2701
t=n«Lt LKi =
CHISQ
0.50
0.21
0.14
0.06
0.47
0.01
0.85
0.06
0.65
2.65
0.00
0.03
0.73
0.12
2.29
MODELED P2-FEMALE LRS
1 ft/ I/ k k V it, 1 l_l V\ d* 1 1\ v
STOERR
3.0684
0.1363
0.5783
0.6342
0.5083
0.3668
0.1855
0.3619
0.6126
0.4020
0.3772
0.3811
0.3605
1.0001
0.2973
CHISQ
0.20
2.29
0.47
2.89
4.45
0.15
0.02
0.78
1.41
0.00
0.98
0.32
1.74
0.03
7.00
=U.UU£1
PROB
0.4783
0.6502
0.7060
0.8056
0.4942
0.9031
0.3579
0.8024
0.4210
0.1035
0.9581
0.8727
0.3920
0.7259
0.1305
=0 0474
V • V • 1 ~
PROB
0.6546
0.1304
0.4933
0.0892
0.0350
0.6944
0.8908
0.3784
0.2358
0.9675
0.3217
0.5689
0.1873
0.8715
0.0081
ium=»3 -----
ODDS
RATIO
12.6051
1.0989
0.6363
1.2981
0.7462
0.9478
1.2174
0.8958
0.5672
1.6603
1.0258
1.0533
0.6563
0.7300
1.5044
TOTN=105
ODDS
RATIO
3.9464
1.2290
0.6729
2.9382
0.3424
1.1550
0.9748
1.3755
2.0674
0.9837
1.4532
0.8048
0.6217
0.8506
0.4553
LOWER
99V
LIMIT
0.0013
0.6429
0.0290
0.0845
0.2475
0.3050
0.7015
0.2888
0.0923
0.7444
0.2936
0.4569
0.1847
0.0723
0.7502
LOWER
99%
LIMIT
0.0015
0.8651
0.1517
0.5736
0.0925
0.4490
0.6045
0.5415
0.4267
0.3493
0.5500
0.3016
0.2456
0.0647
0.2117
UPPER
99%
LIMIT
125551
1.8783
13.9538
19.9353
2.2496
2.9457
2.1127
2.7792
3.4841
3.7031
3.5837
2.4279
2.3314
7.3745
3.0167
UPPER
99%
LIMIT
10689.3
1.7460
2.9847
15.0518
1.2683
2.9712
1.5720
3.4940
10.0177
2.7708
3.8400
2.1482
1.5736
11.1837
0.9793
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W2A
we
PI
P3A
P3B
P4
P5
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
W2A
W2B
W6
PI
P3A
P3B
P4
PS
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
ie
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
ESTIMATE
4.0254
-1.4327
0.0494
•0.0487
-1.0210
-0.4984
-0.1496
0.8509
-0.2267
-0.1311
0.2646
0.3472
0.0238
-0.1875
-0.8332
0.3762
DEPVAR=H3
ESTIMATE
1.7070
0.3294
-2,8979
-0.0302
-0.0435
-2.1492
-0.4783
-1.1398
0.8384
0.4495
2.2115
-0.9086
0.6918
-0.1299
-0.6933
-0.6164
-1.0585
STDERR
4.0463
0.8772
0.2243
0.0330
1.4622
1.2536
0.4642
0.5107
0.4672
0.7918
0.3234
0.5693
0.3430
0.5052
1.0242
0.2849
. rinLt ur\j
CHISQ
0.99
2.67
0.05
2.18
0.49
0.16
0.10
2.78
0.24
0.03
0.67
0.37
0.00
0.14
0.66
1.74
-V/.VV/i 1
PROB
0.3198
0.1024
0.8256
0.1401
0.4850
0.6909
0.7573
0.0957
0.6275
0.8684
0.4133
0.5419
0.9447
0.7106
0.4159
0.1867
MODELED P2-FEMALE LRS=0 7384
1 fW^l* V f mm 1 kJ m^b kr\^ V • f JW™
STDERR
3.9475
0.8141
1.1232
0.1791
0.0381
1.0841
1.1098
0.6534
0.5323
0.4944
0.9252
0.5951
0.4493
0.4658
0.4889
1.3393
0.3643
CHISQ
0.19
0.16
6.66
0.03
1.30
3.93
0.19
3.04
2.48
0.83
5.71
2.33
2.37
0.08
2.01
0.21
8.44
PROB
0.6654
0.6857
0.0099
0.8660
0.2536
0.0474
0.6665
0.0811
0.1153
0.3633
0.0168
0.1268
0.1236
0.7803
0.1562
0.6453
0.0037
ODDS
RATIO
56.0027
0.2387
1.0506
0.9525
0.3602
0.6075
0.8611
2.3418
0.7972
0.8771
1.3029
1.4151
1.0241
0.8290
0.4347
1.4567
TOTN»103 •
ODDS
RATIO
5.5124
1.3901
0.0551
0.9703
0.9574
0.1166
0.6198
0.3199
2.3127
1.5675
9.1294
0.4031
1.9973
0.8782
0.4999
0.5399
0.3470
LOWER
99%
LIMIT
0.0017
0.0249
0.5895
0.8748
0.0083
0.0240
0.2474
0.6283
0.2393
0.1141
0.5664
0.3265
0.4233
0.2256
0.0311
0.6993
LOWER
99%
LIMIT
0.0002
0.1707
0.0031
0.6117
0.8679
0.0071
0.0355
0.0594
0.5870
0.4386
0.8421
0.0870
0.6278
0.2645
0.1419
0.0171
0.1358
UPPER
99%
LIMIT
1883550
2.2864
1.8724
1.0370
15.5744
15.3463
2.9973
8.7274
2.6560
6.7435
2.9972
6.1332
2.4778
3.0462
6.0808
3.0347
UPPER
99%
LIMIT
143740
11.3194
0.9955
1.5390
1.0562
1.9030
10.8108
1.7218
9.1121
5.6017
98.9698
1.8671
6.3547
2.9154
1.7614
17.0072
0.8869
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T4
T6
W3
PI
P3A
P3B
P8
P9
P13
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
T4
T6
W3
PI
P3A
P3B
P8
P9
P13
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
ESTIMATE
-1.5408
0.6260
0.5656
0.1254
-0.0578
-1.8408
-2.0016
-1.0570
-0.7129
1.9195
0.9267
1.2211
0.4370
-1.3889
0.2404
-1.7515
0.2759
0.3781
nfPVAR=H4
ESTIMATE
-8.6147
0.5111
0.1048
0.1839
0.0433
-1.5316
0.8864
0.2811
-0.3673
-12.0596
0.0098
0.7124
-0.1415
0.8989
-0.5199
-0.7206
2.4175
-0.6434
it rwutu^u i
STDERR
7.5541
0.7896
0.4213
0.2722
0.0647
2.1504
1.6158
0.5294
0.4953
1.1244
0.8483
1.3174
0.5934
1.2415
0.6510
1.1105
1.8801
0.4883
MODELED P?
rf\/lSL.I» I/ « ff
STDERR
4.7386
0.7625
0.2778
0.1896
0.0402
0.7924
0.8124
0.3879
0.3848
,
0.5695
0.9428
0.8794
0.5290
0.5639
0.6596
1.9010
0.4494
'£-IT«LC LK;
CH1SQ
0.04
0.63
1.80
0.21
0.80
0.73
1.53
3.99
2.07
2.91
1.19
0.86
0.54
1.25
0.14
2.49
0.02
0.60
>=v.yoot
PROB
0.8384
0.4279
0.1794
0.6449
0.3710
0.3920
0.2154
0.0459
0.1500
0.0878
0.2746
0.3540
0.4615
0.2632
0.7119
0.1147
0.8833
0.4387
*FFMAI F 1 R^=ft fiOPO
I l»rmii.lto LF\
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
W2A
H7
P6
P7
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
Tl
T3
H2A
W2B
W7
P6
P7
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
ESTIMATE
-4.0831
-0.0987
1.4338
-1.7995
0.2005
-0.4887
0.3766
-0.3687
0.6166
0.0100
0.2342
-0.0350
0.2684
-0.2949
0.2841
nFPVAR=He>
ESTIMATE
10.8577
0.2600
-5.5171
0.7678
-3.0925
-1.0483
-0.4793
-0.4944
1.3292
0.9365
-0.5564
0.9586
0.1014
0.0857
0.9363
-0.3349
a nvyti.-i/ r<
STDERR
16.2190
0.2220
2.8997
1.2216
0.8390
0.3929
0.3709
0.6041
0.9172
0.3915
0.6033
0.4621
0.6407
1.2044
0.3655
t-n«i.t LH.J-I
CHISQ
0.06
0.20
0.24
2.17
0.06
1.55
1.03
0.37
0.45
0.00
0.15
0.01
0.18
0.06
0.60
is. tout
PROB
0.8012
0.6565
0.6210
0.1407
0.8112
0.2135
0.3098
0.5416
0.5014
0.9796
0.6979
0.9397
0.6752
0.8066
0.4370
MODELED P2= FEMALE LRS=0 4156
rfwL'L.L. V Ifc 1 1_J vMK. kr\w W»~*JW
STDERR
16.6332
0.1783
3.5117
0.7077
1.0879
0.7239
0.3196
0.3033
0.6120
1.0282
0.5765
0.4847
0.4942
0.4221
1.1474
0.3082
CHISQ
0.43
2.13
2.47
1.18
8.08
2.10
2.25
2.66
4.72
0.83
0.93
3.91
0.04
0.04
0.67
1.18
PROB
0.5139
0.1448
0.1162
0.2780
0.0045
0.1476
0.1336
0.1031
0.0299
0.3624
0.3345
0.0480
0.8375
0.8391
0.4145
0.2773
ODDS
RATIO
0.0169
0.9060
4.1946
0.1654
1.2220
0.6134
1.4573
0.6916
1.8526
1.0101
1.2639
0.9656
1.3079
0.7446
1.3286
TOTN*112
ODDS
RATIO
51932.5
1.2969
0.0040
2.1550
0.0454
0.3505
0.6192
0.6099
3.7780
2.5510
0.5733
2.6080
1.1067
1.0895
2.5505
0.7154
LOWER
99%
LIMIT
0.0000
0.5114
0.0024
0.0071
0.1408
0.2229
0.5605
0.1459
0.1745
0.3684
0.2672
0.2936
0.2511
0.0335
0.5182
LOWER
99%
LIMIT
0.0000
0.8193
0.0000
0.3481
0.0028
0.0543
0.2718
0.2792
0.7809
0.1805
0.1298
0.7483
0.3099
0.3673
0.1327
0.3234
UPPER
99%
LIMIT
2.35E16
1.6051
7357.14
3.8472
10.6097
1.6878
3.7888
3.2787
19.6742
2.7690
5.9793
3.1752
6.8131
16.5707
3.4063
UPPER
99%
LIMIT
2.11E23
2.0530
34.0912
13.3409
0.7482
2.2625
1.4106
1.3323
18.2783
36.0585
2.5311
9.0901
3.9529
3.2318
49.0089
1.5825
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
W3
PI
P7
P12A
VI
V2
V3
V4
vs
V6
V7
V8
EFFVAR
INTERCEPT
T6
W3
PI
P7
P12A
VI
V2
V3
V4
VS
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
-11.5797
0.8820
0.0020
•0.0750
0.7683
9.8689
0.9159
-0.7276
0.6669
1.1562
0.7329
-0.0493
-1.9471
-0.0535
DFPVAR=H6
ESTIMATE
0.2109
0.1552
0.3782
-0.0325
0.1766
-0.1110
0.3990
-0.7757
0.3351
0.3944
-0.6913
-0.1305
0.3468
-0.4221
0 DUUCL'U rt
STOERR
5.8347
0.4413
0.2557
0.0471
0.5083
•
0.7889
1.1899
0.6489
0.7067
0.6986
0.6958
1.4811
0.4972
MODELED P2=
• ffvshfk.i» v r •»
STDERR
3.4844
0.2192
0.1648
0.0297
0.2917
0.5730
0.4130
0.6710
0.5809
0.3698
0.4647
0.4753
1.0523
0.2568
:=H«LC LK;
CHISQ
3.94
3.99
0.00
2.53
2.28
•
1.35
0.37
1.06
2.68
1.10
0.01
1.73
0.01
>=u.»»»u
PROB
0.0472
0.0457
0.9937
0.1118
0.1307
•
0.2456
0.5409
0.3041
0.1018
0.2941
0.9435
0.1886
0.9143
FEMALE LRS^O 1775
1 krmi>h tr\ j^v • * f f w
CHISQ
0.00
0.50
5.26
1.20
0.37
0.04
0.93
1.34
0.33
1.14
2.21
0.08
0.11
2.70
PROB
0.9517
0.4791
0.0218
0.2742
0.5449
0.8464
0.3341
0.2477
0.5640
0.2861
0.1368
0.7836
0.7417
0.1002
iuin=»o
ODDS
RATIO
0.0000
2.4157
1.0020
0.9277
2.1561
•
2.4990
0.4831
1.9482
3.1778
2.0811
0.9519
0.1427
0.9479
TOTN=110 ---•
ODDS
RATIO
1.2348
1.1679
1.4597
0.9680
1.1932
0.8949
1.4903
0.4604
1.3981
1.4835
0.5009
0.8777
1.4145
0.6557
LOWER
99V
LIMIT
0.0000
0.7751
0.5186
0.8217
0.5821
•
0.3275
0.0225
0.3662
0.5147
0.3441
0.1586
0.0031
0.2633
LOWER
99%
LIMIT
0,0002
0.6640
0.9547
0.8967
0.5628
0.2045
0.5143
0.0817
0.3131
0.5722
0.1513
0.2580
0.0941
0.3384
UPPER
99%
LIMIT
31.5157
7.5292
1.9362
1.0474
7.9859
•
19.0698
10.3562
10.3653
19.6222
12.5848
5.7149
6.4767
3.4120
UPPER
99%
LIMIT
9766.51
2.0541
2.2316
1.0450
2.5295
3.9159
4.3184
2.5929
6.2432
3.8459
1.6583
2.9858
21.2748
1.2705
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
n
W3
W6
W8
P3A
P3B
P8
P10
P11A
PUB
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
Tl
n
V3
W6
W8
P3A
P3B
P8
P10
P11A
PUB
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
ESTIMATE
-31.3869
-0.1032
5.7526
0.1803
-0.0173
-7.8992
-1.5527
-0.9960
-0.1855
0.5992
0.2667
0.6318
-0.1469
0.4341
0.1582
-1.9555
-0.2264
-0.2565
0.4117
0.3897
DEPVAR=H7
ESTIMATE
-10.3542
-0.1199
3.0796
0.3615
0.2269
0.9136
-0.5704
0.6520
-0.6581
0.1062
0.5063
-1.1267
0.0853
0.2893
0.3134
-0.1499
-0.5891
-0.9101
0.6931
-0.3767
STDERR
16.1544
0.2018
2.8319
0.1479
0.2268
•
1.5012
1.2558
0.2680
0.2728
1.2456
1.0699
0.5143
0.9416
0.3580
0.8666
0.4226
0.5724
1.2051
0.3649
MODELED P2=
1 IVVkk I/ • fc
STDERR
16.1793
0.1519
3.1830
0.1653
0.1566
1.5710
0.6627
0.6544
0.2900
0.2034
0.8923
1.1692
0.5355
0.9087
0.4316
0.5513
0.4601
0.4179
1.0340
0.2734
-rinuc LHJ
CHISQ
3.77
0.26
4.13
1.49
0.01
•
1.07
0.63
0.48
4.83
0.05
0.35
0.08
0.21
0.20
5.09
0.29
0.20
0.12
1.14
-v.vito i
PROB
0.0520
0.6091
0.0422
0.2228
0.9392
•
0.3010
0.4277
0.4887
0.0280
0.8305
0.5548
0.7751
0.6447
0.6586
0.0240
0.5922
0.6541
0.7326
0.2855
FEMALE LRS=0 0609
i ^i mkb Lv\^ v • w v^
CHISQ
0.41
0.62
0.94
4.78
2.10
0.34
0.74
0.99
5.15
0.27
0.32
0.93
0.03
0.10
0.53
0.07
1.64
4.74
0.45
1.90
PROB
0.5222
0.4299
0.3333
0.0288
0.1473
0.5609
0.3894
0.3190
0.0232
0.6016
0.5704
0.3352
0.8735
0.7502
0.4677
0.7857
0.2004
0.0294
0.5027
0.1683
ODDS
RATIO
0.0000
0.9019
315.009
1.1976
0.9828
•
0.2117
0.3694
0.8307
.8207
.3056
.8810
0.8634
.5436
.1714
0.1415
0.7974
0.7738
1.5094
1.4765
TOTN=104
ODDS
RATIO
0.0000
0.8870
21.7497
1.4355
1.2547
2.4933
0.5653
1.9194
0.5178
.1120
.6591
.3241
.0890
.3355
.3681
0.8608
0.5548
0.4025
1.9999
0.6861
LOWER
99%
LIMIT
0.0000
0.5363
0.2139
0.8182
0.5480
•
0.0044
0.0145
0.4165
0.9016
0.0528
0.1195
0.2295
0.1365
0.4658
0.0152
0.2685
0.1771
0.0677
0.5768
LOWER
99%
LIMIT
0.0000
0.5998
0.0060
0.9377
0.8382
0.0436
0.1025
0.3557
0.2453
0.6585
0.1666
0.0159
0.2741
0.1285
0.4500
0.2080
0.1696
0.1372
0.1394
0.3393
UPPER
99%
LIMIT
27634.9
1.5169
463970
1.7529
1.7629
•
10.1188
9.3834
1.6568
3.6764
32.3096
29.6026
3.2477
17.4556
2.9459
1.3190
2.3684
3.3804
33.6508
3.7798
UPPER
99%
LIMIT
4.02E13
1.3118
79142.7
2.1975
1.8782
142.665
3.1165
10.3577
1.0930
1.8779
16.5248
6.5874
4.3265
13.8753
4.1587
3.5617
1.8151
1.1811
28.6939
1.3876
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T6
W2A
PSA
P3B
P8
P13
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
.... U[_rVMK-l
ESTIMATE
-0.8493
-0.2763
-7.4962
-4.6768
-4.2233
-1.7419
4.3842
-0.5228
2.3767
2.8677
0.0300
0.8168
0.5712
-3.3684
-0.3699
XJ nuucL^u r<
STDERR
16.9553
0.9519
•
3.2032
2.8137
0.8601
1.9160
1.2757
2,7216
1,5763
1.9489
0.9148
1.5695
3.5501
0.6339
c=nm.t LRJ
CHISQ
0.00
0.08
*
2.13
2.25
4.10
5.24
0.17
0.76
3.31
0.00
0.80
0.13
0.90
0.34
>=I.UUUU IV
PROB
0.9601
0.7716
*
0.1443
0.1334
0.0428
0.0221
0.6819
0.382S
0.0689
0.9877
0.3719
0.7159
0.3427
0.5595
M«=S/
ODDS
RATIO
0.4277
0.7586
»
0.0093
0.0147
0.1752
80.1741
0.5929
10.7693
17.5965
1.0305
2.2632
1.7704
0.0344
0.6908
LOWER
99%
LIMIT
0.0000
0.0653
•
0.0000
0.0000
0.0191
0.5761
0.0222
0.0097
0.3034
0.0068
0.2144
0.0311
0.0000
0.1350
UPPER
99%
LIMIT
3.98E18
8.8092
•
35.6819
20.5897
1.6060
11156.9
15.8537
11938.7
1020.70
156.080
23.8868
100.910
322.679
3.5361
EFFVAR
INTERCEPT
T6
W2A
W2B
P3A
P3B
P8
P13
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
ESTIMATE
-2.6100
1.1906
0.6242
-7.7119
1.4682
3.8324
-0.0003
-5.7583
0.4425
•1.4213
2.2491
1.6497
-0.9129
-0.3733
-0.9375
0.2268
> nuuc.i.">i* re-
STOERR
6.0228
0.6080
1.1834
•
1.3868
1.8080
0.4271
•
1.0283
1.5553
1.7329
0.8749
0.9732
1.0575
2.5205
1.1071
-rurvM.t i.r
CHISQ
0.19
3.83
0.28
•
1.12
4.49
0.00
*
0.19
0.84
1.68
3.56
0.88
0.12
0.14
0.04
tj-*.vwv i
PROB
0.6648
0.0502
0.5978
*
0.2897
0.0340
0.9995
*
0.6669
0.3608
0.1943
0.0593
0.3482
0.7241
0.7099
0.8377
ODDS
RATIO
0.0735
3.2891
1.8668
*
4.3414
46.1732
0.9997
•
1.5566
0.2414
9.4792
5.2054
0.4014
0.6885
0.3916
1.2546
LOWER
99%
LIMIT
0.0000
0.6869
0.0885
•
0.1219
0.4382
0.3327
•
0.1101
0.0044
0.1092
0.5466
0.0327
0.0452
0.0006
0.0724
UPPER
99%
LIMIT
402211
15.7495
39.3556
•
154.562
4864.93
3.0040
•
22.0079
13.2653
823.078
49.5728
4.9237
10.4942
258.607
21.7299
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
V2A
W3
H8
P4
P10
P12B
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
ESTIMATE
-2.9635
-2.2612
0.3420
-9.4419
-0.8847
0.2791
0.0678
0.0920
1.1457
0.5674
-0.3341
0.2822
-0.2003
-1.3331
0.3796
\y nuuc.L"u n
STDERR
3.9387
1.1848
0.1633
•
0.4999
0.2469
0.7479
0.4846
0.9037
0.3790
0.6423
0.3594
0.5153
0.9644
0.3135
1-riMLL LKJ
CHISQ
0.57
3.64
4.39
•
3.13
1.28
0.01
0.04
1.61
2.24
0.27
0.62
0.15
1.91
1.47
-U.1JIO IU
PROB
0.4518
0.0563
0.0362
•
0.0767
0.2584
0.9278
0.8494
0.2049
0.1343
0.6029
0.4323
0.6975
0.1669
0.2259
in-»a -----
ODDS
RATIO
0.0516
0.1042
1.4078
*
0.4128
1.3219
1.0702
1.0964
3.1446
1.7637
0.7160
1.3260
0.8185
0.2637
1.4617
LOWER
99%
LIMIT
0.0000
0.0049
0.9244
•
0.1139
0.6998
0.1559
0.3146
0.3066
0.6644
0.1369
0.5254
0.2170
0.0220
0.6518
UPPER
99%
LIMIT
1316.31
2.2053
2.1440
•
1.4964
2.4971
7.3477
3.8203
32.2536
4.6819
3.7452
3.3468
3.0867
3.1620
3.2778
EFFVAR
INTERCEPT
W2A
W2B
W3
W8
P4
P10
P12B
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
--- uc.rvKK-n»
ESTIMATE
-3.1265
0.0858
-2.4333
0.4694
•2.1327
-0.9720
0.3484
0.9259
0.6506
0.8476
0.0234
0.6994
0.0751
-0.2008
-0.4469
-0.5755
ITUUC.I.-U re.-
STDERR
3.2450
0.6454
0.8168
0.1729
1.2665
0.5095
0.1996
0.5877
0.4228
0.6884
0.4503
0.4947
0.4327
0.3927
1.0632
0.2781
TLTTMLC. UH
CHISQ
0.93
0.02
8.88
7.37
2.84
3.64
3.05
2.48
2.37
1.52
0.00
2.00
0.03
0.26
0.18
4.28
J-U.I/3U IV
PROB
0.3353
0.8942
0.0029
0.0066
0.0922
0.0564
0.0810
0.1151
0.1239
0.2182
0.9586
0.1574
0.8622
0.6090
0.6742
0.0385
ODDS
RATIO
0.0439
1.0896
0.0877
1.5990
0.1185
0.3783
1.4168
2.5241
1.9167
2.3340
1.0237
2.0125
1.0780
0.8181
0.6396
0.5624
LOWER
99%
LIMIT
0.0000
0.2066
0.0107
1.0243
0.0045
0.1018
0.8472
0.5554
0.6450
0.3962
0.3209
0.5627
0.3536
0.2975
0.0413
0.2748
UPPER
99%
LIMIT
187.283
5.7451
0.7195
2.4963
3.0950
1.4056
2.3692
11.4709
5.6958
13.7483
3.2654
7.1976
3.2862
2.2497
9.8937
1.1513
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
P5
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
PS
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
PARAM
1
2
3
4
5
6
7
8
9
10
ESTIMATE
4.4631
0.1636
0.5767
-1.3227
0.3427
1.0169
-0.4213
-1.2356
-0.8742
0.1749
f1FPVAR=H10
ESTIMATE
0.0978
0.4924
0.2296
-0.5253
0.0477
0.0652
0.0198
-0.7963
-0.0933
-0.1434
iu rwL>ci."u
STDERR
3.5709
0.4985
0.5585
0.8339
0.3732
0.5479
0.4109
0.6206
1.2143
0.2957
MODEL «D P2
rPvULk I/ ifc
STOERR
2.7360
0.3159
0.3552
0.5913
0.4007
0.3243
0.3993
0.3373
0.9034
0.2525
rf.=rv\Lf. LKJ
CHISQ
1.56
0.11
1.07
2.52
0.84
3.44
1.05
3.96
0.52
0.35
= FEMALE LRS
I u mkk t»r\^
CHISQ
0.00
2.43
0.42
0.79
0.01
0.04
0.00
5.57
0.01
0.32
=U.3D1C
PROB
0.2113
0.7429
0.3018
0.1127
0.3584
0.0635
0.3053
0.0465
0.4716
0.5542
*0 1975
V • 4 7 r ?
PROB
0.9715
0.1190
0.5180
0.3744
0.9053
0.8406
0.9605
0.0183
0.9177
0.5701
iuin=»» ----
ODDS
RATIO
86.7560
1.1777
1.7802
0.2664
1.4087
2.7646
0.6562
0.2907
0.4172
1.1911
TOTN«112 ---•
ODDS
RATIO
.1027
.6362
.2581
0.5914
.0489
.0674
.0200
0.4510
0.9109
0.8664
LOWER
99%
LIMIT
0.0088
0.3261
0.4223
0.0311
0.5387
0.6740
0.2277
0.0588
0.0183
0.5561
LOWER
99%
LIMIT
0.0010
0.7252
0.5039
0.1289
0.3736
0.4629
0.3647
0.1892
0.0889
0.4521
UPPER
99%
LIMIT
857467
4.2535
7.5037
2.2829
3.6842
11.3395
1.8911
1.4377
9.5242
2.5514
UPPER
99%
LIMIT
1268.68
3.6920
3.1412
2.7125
2.9444
2.4611
2.8531
1.0753
9.3358
1.6604
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
T6
H3
PI
P13
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
-6.8254
-0.0333
0.1937
0.4060
-0.0378
-0.1991
0.8349
1.3360
0.2997
-0.7706
0.5434
0.1155
-1.4753
-0.2598
ill riuui.i_-u i
STDERR
5.0444
0.0822
0.4203
0.1930
0.0381
1.0363
0.6507
0.9786
0.5225
0.7708
0.4765
0.5993
1.2034
0.3354
'tsn«i.c un
CHISQ
1.83
0.16
0.21
4.42
0.98
0.04
1.65
1.86
0.33
1.00
1.30
0.04
1.50
0.60
Id'U.OUUO 11
PROB
0.1760
0.6858
0.6448
0.0354
0.3212
0.8476
0.1995
0.1722
0.5663
0.3175
0.2541
0.8471
0.2202
0.4386
jin"-yy ----•
OOOS
RATIO
0.0011
0.9672
1.2137
1.5008
0.9629
0.8195
2.3046
3.8038
1.3495
0.4627
1.7219
1.1224
0.2287
0.7712
LOWER
99V
LIMIT
0.0000
0.7827
0.4111
0.9129
0.8729
0.0568
0.4311
0.3058
0.3512
0.0635
0.5046
0.2397
0.0103
0.3250
UPPER
99%
LIMIT
477.696
1.1954
3.5837
2.4674
1.0622
11.8273
12.3185
47.3172
5.1844
3.3702
5.8759
5.2556
5.0767
1.8298
EFFVAR
INTERCEPT
T2
T6
H3
PI
P13
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
-3.9356
0.1414
0.3785
0.1887
-0.0010
1.8769
0.2464
-0.3130
1.0358
0.2690
-0.3114
-0.4098
-0.4482
-0.5064
m/1/b.u-is rt-
STDERR
3.2073
0.0710
0.2189
0.1428
0.0272
1.0543
0.4413
0.6830
0.5921
0.3428
0.4162
0.4362
1.1396
0.2585
-rtn/M-c in
CHISQ
1.51
3.96
2.99
1.75
0.00
3.17
0.31
0.21
3.06
0.62
0.56
0.88
0.15
3.84
J-U.UOU3 IV
PROB
0.2198
0.0465
0.0838
0.1863
0.9706
0.0750
0.5766
0.6468
0.0802
0.4327
0.4543
0.3475
0.6941
0.0501
ODDS
RATIO
0.0195
1.1519
1.4601
1.2077
0.9990
6.5332
1.2794
0.7312
2.8174
1.3087
0.7324
0.6638
0.6388
0.6027
LOWER
99%
LIMIT
0.0000
0.9594
0.8308
0.8360
0.9314
0.4322
0.4105
0.1259
0.6130
0.5412
0.2507
0.2158
0.0339
0.3097
UPPER
99%
LIMIT
75.6717
1.3831
2.5661
1.7446
1.0715
98.7683
3.9876
4.2478
12.9494
3.1647
2.1398
2.0418
12.0301
1.1729
-------
EFFVAR
MAXIMUM LIKELIHOOD ESTIMATES
DEPVAR=H12 MODEL=D P2=MALE LRS=1.0000 TOTN=97
PARAM ESTIMATE
INTERCEPT
Tl
T6
W5
P4
VI
V2
V3
V4
V5
V6
V7
V8
1
2
3
4
5
6
7
8
9
10
11
12
13
-131.600
1.8222
2.9589
18.5819
4.4450
-19.9640
1.4145
•8.3031
-13.4916
-22.3241
-20.1781
34.6892
7.0664
— DEPVAR=I
STDERR
100.0000
•
•
3^0176
29^2469
CHISQ
1.73
•
•
2.17
o!oo
PROB
0.1884
•
*
o!l407
o!9614
Jf ._._.
ODDS
RATIO
LOWER
99%
LIMIT
UPPER
99%
LIMIT
0.0000
•
•
85!l999
4!ll44
0.0000 5.26E54
0.0359
o!oooo
202468
2.16E33
18.61 0.0000 1.16E15 1171785 1.15E24
EFFVAR
INTERCEPT
Tl
T6
us
P4
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
-- uc.r»/vn=ni4
ESTIMATE
30.2303
-0.3180
0.0426
1.3581
-1.2084
0.2198
-0.1889
0.2562
0.4902
-0.3367
•1.0791
-2.8563
-0.0990
: nuucu-u re-
STDERR
13.3531
0.1910
0.3001
1.0567
0.6749
0.8339
0.9964
0.7958
0.4820
0.7763
0.6361
1.7966
0.4188
-rcJvtLt i_r
CHISQ
5.13
2.77
0.02
1.65
3.21
0.07
0.04
0.10
1.03
0.19
2.88
2.53
0.06
iJ-U.97/1 1
PROB
0.0236
0.0959
0.8871
0.1987
0.0733
0.7921
0.8497
0.7475
0.3092
0.6645
0.0898
0.1119
0.8132
ODDS
RATIO
1.35E13
0.7276
1.0435
3.8888
0.2987
1.2458
0.8279
1.2920
1.6326
0.7141
0.3399
0.0575
0.9057
LOWER
99%
LIMIT
0.0155
0.4449
0.4817
0.2556
0.0525
0.1454
0.0636
0.1663
0.4717
0.0967
0.0660
0.0006
0.3079
UPPER
99%
LIMIT
1.17E28
1.1901
2.2607
59.1549
1.6992
10.6752
10.7814
10.0360
5.6510
5.2754
1.7498
5.8811
2.6640
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
T3
W2A
H7
P6
P7
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
ESTIMATE
-0.5534
-0.0560
0.3576
-1.9143
0.1827
-0.6081
0.3208
-0.3563
1.1120
-0.1752
0.0955
0.0111
0.2004
-0.7644
0.2657
uj nvwti.-i/ i
STDERR
16.0590
0.2220
2.9377
1.2131
0.8427
0.3985
0.3609
0.6120
0.9175
0.3903
0.5995
0.4521
0.6435
1.2172
0.3632
"C-rVALC LFI
CHISQ
0.00
0.06
0.01
2.49
0.05
2.33
0.79
0.34
1.47
0.20
0.03
0.00
0.10
0.39
0.54
.j-w.tjia M
PROB
0.9725
0.8010
0.9031
0.1146
0.8284
0.1270
0.3741
0.5604
0.2255
0.6535
0.8735
0.9804
0.7555
0.5300
0.4644
ODDS
RATIO
0.5750
0.9455
1.4299
0.1474
1.2005
0.5444
1.3782
0.7003
3.0404
0.8393
1.1002
1.0112
1.2219
0.4656
1.3043
LOWER
99%
LIMIT
0.0000
0.5337
0.0007
0.0065
0.1370
0.1950
0.5440
0.1447
0.2861
0.3071
0.2348
0.3155
0.2329
0.0202
0.5118
UPPER
99%
LIMIT
5.32E17
1.6751
2765.88
3.3557
10.5223
1.5196
3.4920
3.3879
32.3133
2.2938
5.1542
3.2404
6.4113
10.7092
3.3244
EFFVAR
INTERCEPT
Tl
T3
W2A
W2B
W7
P6
P7
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
-- utr»«n-nu
ESTIMATE
10.8577
0.2600
-5.5171
0.7678
-3.0925
-1.0483
-0.4793
-0.4944
1.3292
0.9365
-0.5564
0.9586
0.1014
0.0857
0.9363
-0.3349
i rvucL-u re"
STDERR
16.6332
0.1783
3.5117
0.7077
1.0879
0.7239
0.3196
0.3033
0.6120
1.0282
0.5765
0.4847
0.4942
0.4221
1.1474
0.3082
•r crime, i-rt
CHISQ
0.43
2.13
2.47
1.18
8.08
2.10
2.25
2.66
4.72
0.83
0.93
3.91
0.04
0.04
0.67
1.18
3-V.1J9O H
PROB
0.5139
0.1448
0.1162
0.2780
0.0045
0.1476
0.1336
0.1031
0.0299
0.3624
0.3345
0.0480
0.8375
0.8391
0.4145
0.2773
ODDS
RATIO
51932.5
1.2969
0.0040
2.1550
0.0454
0.3505
0.6192
0.6099
3.7780
2.5510
0.5733
2.6080
1.1067
1.0895
2.5505
0.7154
LOWER
99%
LIMIT
0.0000
0.8193
0.0000
0.3481
0.0028
0.0543
0.2718
0.2792
0.7809
0.1805
0.1298
0.7483
0.3099
0.3673
0.1327
0.3234
UPPER
99%
LIMIT
2.11E23
2.0530
34.0912
13.3409
0.7482
2.2625
1.4106
1.3323
18.2783
36.0585
2.5311
9.0901
3.9529
3.2318
49.0089
1.5825
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T4
W5
P5
P7
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
ESTIMATE
-10.9692
2.5455
0.7348
1.2775
-0.1155
0.9385
0.0758
0.1822
-0.0267
0.5864
-0.1484
-2.2683
1.0241
lit rwucu«u i
STDERR
5.6243
1.3799
1.1247
0.7053
0.4756
0.6569
1.1145
0.5630
0.8629
0.5535
0.6829
1.7229
0.9136
'£-Pl«LC LK
CHISQ
3.80
3.40
0.43
3.28
0.06
2.04
0.00
0.10
0.00
1.12
0.05
1.73
1.26
i=U.3OlJ 1
PROB
0.0511
0.0651
0.5135
0.0701
0.8081
0.1531
0.9458
0.7462
0.9753
0.2894
0.8280
0.1880
0.2623
ODDS
RATIO
0.0000
12.7496
2.0851
3.5877
0.8909
2.5561
1.0787
1.1999
0.9737
1.7975
0.8621
0.1035
2.7846
LOWER
99%
LIMIT
0.0000
0.3645
0.1150
0.5831
0.2617
0.4706
0.0611
0.2814
0.1054
0.4320
0.1484
0.0012
0.2647
UPPER
99%
LIMIT
33.7506
445.912
37.7893
22.0729
3.0333
13.8831
19.0440
5.1166
8.9902
7.4799
5.0066
8.7573
29.2984
EFFVAR
INTERCEPT
14
W5
PS
P7
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
-- uc.rvKK=nii
ESTIMATE
-0.1088
-0.2774
1.6491
0.3173
0.3264
0.1065
-0.7980
0.8430
0.4912
-0.7158
-0.3096
1.0576
-0.2389
nuutu-u re.-
STDERR
3.0633
0.3244
0.7575
0.3641
0.3176
0.4012
0.5855
0.4281
0.3605
0.4182
0.4076
1.0728
0.2445
-rcn«i.c un
CHISQ
0.00
0.73
4.74
0.76
1.06
0.07
1.86
3.88
1.86
2.93
0.58
0.97
0.95
>dsu.iuc i'
PROB
0.9717
0.3925
0.0295
0.3835
0.3041
0.7907
0.1729
0.0490
0.1730
0.0869
0.4475
0.3242
0.3286
ODDS
RATIO
0.8969
0.7578
5.2023
1.3734
1.3860
1.1124
0.4502
2.3233
1.6343
0.4888
0.7337
2.8795
0.7875
LOWER
99%
LIMIT
0.0003
0.3286
0.7392
0.5376
0.6116
0.3957
0.0996
0.7712
0.6457
0.1664
0.2568
0.1816
0.4195
UPPER
99%
LIMIT
2397.69
1.7476
36.6136
3.5086
3.1410
3.1267
2.0345
6.9991
4.1365
1.4355
2.0967
45.6559
1.4784
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
T4
T6
H3
PI
P7
P8
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
---- uc.r»nrv-r
ESTIMATE
-2.9467
0.2174
3.4715
-0.7228
-0.0434
-0.1960
-0.4198
0.5946
1.5452
-0.2696
0.6193
-0.6677
1.2623
-4.5177
-5.2026
-0.7265
>u nv/ui.L.~is i
STDERR
15.5878
0.2778
3.3037
1.4812
0.4950
0.1639
0.9147
0.8359
2.7490
2.6512
2.3290
1.6323
1.3761
3.1717
5.8598
0.7988
-c-rvtut I_H
CHISQ
0.04
0.61
1.10
0.24
0.01
1.43
0.21
0.51
0.32
0.01
0.07
0.17
0.84
2.03
0.79
0.83
J-l.VWV 1
PROB
0.8501
0.4339
0.2933
0.6256
0.9301
0.2317
0.6463
0.4769
0.5741
0.9190
0.7903
0.6825
0.3590
0.1543
0.3746
0.3630
ODDS
RATIO
0.0525
1.2428
32.1850
0.4854
0.9575
0.8220
0.6572
1.8123
4.6889
0.7637
1.8576
0.5129
3.5335
0.0109
0.0055
0.4836
LOWER
99%
LIMIT
0.0000
0.6076
0.0065
0.0107
0.2675
0.5389
0.0623
0.2104
0.0039
0.0008
0.0046
0.0077
0.1020
0.0000
0.0000
0.0618
UPPER
99%
LIMIT
1.44E16
2.5422
159824
22.0381
3.4271
1.2538
6.9342
15.6095
5578.21
706.195
749.049
34.3673
122.380
38.5748
19776.4
3.7856
EFFVAR
INTERCEPT
T2
T4
T6
W3
PI
P7
P8
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
-- i»Lr»«n-ni
ESTIMATE
-71.6158
1.1975
0.4062
1.0726
0.0333
-0.1180
1.4229
-1.3960
-3.8939
15.9512
-12.0261
8.0976
5.3057
-4.3422
-7.5497
-0.6683
3 nuULU-u re-
STDERR
153.3000
2.1313
3.6427
•
0.3626
0.0919
0.9392
0.8256
10.5330
34.1274
29.5946
18.1487
8.8983
9.9350
•
10.0499
TtrVMt Ll\
CHISQ
0.22
0.32
0.01
•
0.01
1.65
2.30
2.86
0.14
0.22
0.17
0.20
0.36
0.19
•
0.00
J-l.WVU I1
PROB
0.6404
0.5742
0.9112
•
0.9267
0.1990
0.1298
0.0909
0.7116
0.6402
0.6845
0.6555
0.5510
0.6621
•
0.9470
ODDS
RATIO
0.0000
3.3118
1.5011
•
1.0339
0.8887
4.1491
0.2476
0.0204
8462879
0.0000
3286.57
201.482
0.0130
•
0.5126
LOWER
99%
LIMIT
0.0000
0.0137
0.0001
•
0.4063
0.7014
0.3692
0.0295
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
•
0.0000
UPPER
99%
LIMIT
252E138
802.498
17850.6
•
2.6310
1.1261
46.6316
2.0766
1.24E10
1.28E45
7.69E27
6.61E23
1.82E12
1.694E9
•
8.97E10
-------
MAXIMUM LIKELIHOOD ESTIMATES
IFFVAR
PARAM ESTIMATE
INTERCEPT
T3
T6
P5
P6
P7
P9
VI
V2
V3
V4
V5
V6
V7
V8
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
-484.300
7.7447
22.3725
60.0125
56.4753
-48.0385
-63.1875
-4.0977
99.7000
-52.7175
-40.8373
46.0907
-69.5702
35.0655
21.3663
— DEPVAR=l
iij rnjwuu~u
STDERR
983.2000
48i6706
109.3000
7318410
73.7868
228^5000
82!0523
rc-rmuc. ur
CHISQ
0.24
O.*21
0.30
o!73
o!i9
0*32
tj~ I .VVUV 1
PROS
0.6223
0*64S8
O.S829
CK5153
0.3918
o!&625
o!5743
ODDS
RATIO
0.0000
5.203E9
1.16E26
o!oooo
0.0000
1.99E43
1.04E20
LOWER
99%
LIMIT
0.0000
o!oooo
0.0000
oioooo
0.0000
o!oooo
oioooo
UPPER
99%
LIMIT
*
1.47E64
22E147
5.57E61
1.26E55
855E296
649E109
EFFVAR
INTERCEPT
T3
T6
P5
P6
P7
P9
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
-- uc.r»Kiv-rut
ESTIMATE
-23.9633
3.6845
-0.0649
-0.4607
-0.3032
0.8894
-0.9274
0.4373
-0.6005
-0.1359
0.464S
-0.1451
-0.3767
1.4642
-0.5137
j nuuci."*u rt-
STDERR
20.0552
3.4612
0.2846
0.5556
0.4201
0.5216
0.4811
0.6499
1.1860
0.8476
0.5042
0.6656
0.6743
1.3966
0.3536
-rtrwut ur
CHISQ
1.43
1.13
0.05
0.69
0.52
2.91
3.72
0.45
0.26
0.03
0.85
0.05
0.31
1.10
2.11
13-U.73/C 1
PROB
0.2321
0.2871
0.8196
0.4069
0.4705
0.0882
0.0539
0.5010
0.6126
0.8726
0.3566
0.8275
0.5764
0.2944
0.1463
ODDS
RATIO
0.0000
39,8252
0.9372
0.6308
0.7385
2.4337
0.3956
1.5485
0.5485
0.8729
1.5917
0.8649
0.6861
4.3241
0.5983
LOWER
99%
LIMIT
0.0000
0.0053
0.4502
0.1508
0.2502
0.6349
0.1146
0.2903
0.0258
0.0983
0.4343
0.1557
0.1208
0.1184
0.2406
UPPER
99%
LIMIT
1.07E12
296722
1.9508
2.6393
2.1792
9.3282
1.3660
8.2601
11.6422
7.7486
5.8335
4.8042
3.8973
157.881
1.4876
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
T2
T3
T4
W2A
H3
MS
P6
P9
P10
P11A
PUB
P13
VI
V2
Y3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
T2
T3
T4
W2A
W2B
«
W6
P6
P9
P10
P11A
PUB
P13
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
ESTIMATE
-76.9378
0.0394
10.4252
0.4482
•5.7061
0.2651
0.4238
-0.9645
-1.2681
0.7481
0.7436
1.7007
1.3886
-1.2117
5.5257
-0.7150
-1.4363
-1.0572
1.1972
0.1144
-0.6934
DFPVAR=02
ESTIMATE
-8.4113
0.1289
0.8180
0.6541
0.9076
3.3423
0.3638
0.2306
1.0584
-1.1207
0.0612
-0.7184
0.5843
-10.2070
0.2263
-0.6340
0.7399
0.9623
-0.7471
-0.4564
-0.2452
-0.1729
t i-iwui.i.-u rt
STOERR
27.3994
0.1097
4.4891
0.6279
2.0254
0.2018
0.3711
0.6884
0.6289
0.4487
1.5366
1.9556
1.0506
0.9661
2.0170
0.5194
1.0022
0.7109
0.8942
1.5795
0.4091
:-rvtLC LKO=
CH1SQ
7.88
0.13
5.39
0.51
7.94
1.73
1.30
1.96
4.07
2.78
0.23
0.76
1.75
1.57
7.51
1.89
2.05
2.21
1.79
0.01
2.87
MODELED P2=FEMALE LRS
i rvi/uw v r fc i krvi^b tf\j
STOERR
15.2934
0.1023
2.7272
0.4516
0.8596
1.0334
0.1774
0.1549
0.4283
0.4430
0.2241
0.9857
1.1651
•
0.6018
1.0339
0.4998
0.4541
0.4598
0.4391
1.1887
0.3161
CHISQ
0.30
1.59
0.09
2.10
1.11
10.46
4.21
2.22
6.11
6.40
0.07
0.53
0.25
•
0.14
0.38
2.19
4.49
2.64
1.08
0.04
0.30
U.91JU 1
PROB
0.0050
0.7196
0.0202
0.4754
0.0048
0.1889
0.2535
0.1612
0.0438
0.0955
0.6284
0.3845
0.1863
0.2098
0.0062
0.1687
0.1518
0.1370
0.1806
0.9422
0.0901
=0 1320
V • *WbV
PROB
0.5823
0.2076
0.7642
0.1475
0.2911
0.0012
0.0403
0.1364
0.0135
0.0114
0.7848
0.4661
0.6160
•
0.7069
0.5398
0.1388
0.0341
0.1042
0.2986
0.8366
0.5845
\vin=ye. ------
ODDS
RATIO
0.0000
1.0402
33698.2
1.5655
0.0033
1.3036
1.5278
0.3812
0.2814
2.1130
2.1035
5.4778
4.0092
0.2977
251.062
0.4892
0.2378
0.3474
3.3108
1.1212
0.4999
TOTN=«101
ODDS
RATIO
0.0002
1.1376
2.2660
1.9234
2.4784
28.2841
1.4388
1.2594
2.8818
0.3261
1.0631
0.4875
1.7937
•
1.2540
0.5305
2.0957
2.6177
0.4737
0.6336
0.7825
0.8412
LOWER
99V
LIMIT
0.0000
0.7841
0.3202
0.3106
0.0000
0.7751
0.5873
0.0647
0.0557
0.6651
0.0402
0.0355
0.2677
0.0247
1.3908
0.1284
0.0180
0.0557
0.3308
0.0192
0.1743
LOWER
99%
LIMIT
0.0000
0.8740
0.0020
0.6010
0.2707
1.9744
0.9110
0.8450
0.9561
0.1042
0.5969
0.0385
0.0892
•
0.2661
0.0370
0.5783
0.8126
0.1449
0.2044
0.0366
0.3726
UPPER
99V
LIMIT
0.0017
1.3799
3.546E9
7.8906
0.6134
2.1923
3.9739
2.2453
1.4218
6.7124
110.154
844.146
60.0361
3.5858
45319.5
1.8645
3.1436
2.1686
33.1373
65.5748
1.4340
UPPER
99%
LIMIT
2.86E13
1.4806
2548.52
6.1560
22.6901
405.183
2.2723
1.8769
8.6859
1.0207
1.8936
6.1766
36.0749
•
5.9094
7.6090
7.5942
8.4322
1.5486
1.9635
16.7248
1.8991
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
Tl
W2A
W3
P9
VI
V2
V3
V4
V5
V6
V7
V8
EFFVAR
INTERCEPT
Tl
W2A
W2B
W3
P9
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
-2.6637
0.0154
-0.8386
-0.1525
-0.3715
-0.4679
0.8388
-0.0631
0.1241
-0.0384
0.1604
1.2306
-0.0558
nrpVARsAI
ESTIMATE
-1.4371
0.1044
-1.2485
-1.0264
-0.0386
-0.7176
-0.2834
-0.6577
0.5197
0.5442
-1.1153
-0.2227
1.9998
-0.0208
i nyucu^u r
STDERR
10.8998
0.1497
0.8067
0.1292
0.2690
0.4578
0.7571
0.3122
0.4964
0.3421
0.4672
1.0036
0.2384
MODELED P2
rfwl/Wk. I/ rC»
STDERR
8.8506
0.1257
0.6304
0.6034
0.1428
0.3196
0.4220
0.7056
0.4097
0.4584
0.4295
0.3552
1.0308
0.3048
*-nm.c LK3=
CH1SQ
0.06
0.01
1.08
1.39
1.91
1.04
1.23
0.04
0.06
0.01
0.12
1.50
0.05
=FFMALE 1 RS
i &*rm^(» L*\W
CHISQ
0.03
0.69
3.92
2.89
0.07
5.04
0.45
0.87
1.61
1.41
6.74
0.39
3.76
0.00
U.UU1O
PROB
0.8069
0.9183
0.2985
0.2380
0.1673
0.3067
0.2679
0.8400
0.8026
0.9106
0.7313
0.2201
0.8148
PROB
0.8710
0.4062
0.0476
0.0890
0.7872
0.0248
0.5018
0.3513
0.2047
0.2351
O.OD94
0.5308
0.0524
0.9456
iuifi=yu
ODDS
RATIO
0.0697
1.0155
0.4323
0.8586
0.6897
0.6263
2.3136
0.9388
1.1321
0.9623
1.1740
3.4233
0.9457
TOTN-108
ODDS
RATIO
0.2376
1.1100
0.2869
0.3583
0.9621
0.4879
0.7532
0.5180
1.6815
1.7232
0.3278
0.8004
7.3876
0.9794
LOWER
99%
LIMIT
0.0000
0.6906
0.0541
0.6155
0.3449
0.1926
0.3291
0.4201
0.3152
0.3987
0.3524
0.2580
0.5118
LOWER
99%
LIMIT
0.0000
0.8030
0.0566
0.0757
0.6660
0.2142
0.2540
0.0841
0.5853
0.5291
0.1084
0.3206
0.5192
0.4467
UPPER
99%
LIMIT
1.09EH
1.4934
3.4537
1.1976
1.3791
2.0368
16.2662
2.0983
4.0667
2.3230
3.9114
45.4164
1.7477
UPPER
99%
LIMIT
1.894E9
.5345
.4556
.6955
.3899
.1115
2.2337
3.1897
4.8312
5.6127
0.9911
1.9983
105.124
2.1476
-------
MAXIMUM LIKELIHOOD ESTIMATES
EFFVAR
INTERCEPT
W5
W8
P4
P8
P12B
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
4.6100
4.3720
-15.6605
2.2531
-1.3152
4.1738
-2.6337
-1.5839
0.9868
2.6909
0.9774
-2.0299
-1.7215
-0.0632
\c nuuc.u-u ri
STDERR
18.0903
2.1454
,
2.0951
0.9820
2.3889
3.5707
4.9135
1.4443
3.3337
1.1222
2.4933
5.4205
0.8291
:-n/M.t LHJ
CHISQ
0.06
4.15
,
1.16
1.79
3.05
0.54
0.10
0.47
0.65
0.76
0.66
0.10
0.01
"l.VUUV IU
PROB
0.7989
0.0416
*
0.2822
0.1805
0.0806
0.4608
0.7472
0.4944
0.4196
0.3838
0.4156
0.7508
0.9393
ODDS
RATIO
100.484
79.2019
.
9.5172
0.2684
64.9618
0.0718
0.2052
2.6826
14.7449
2.6575
0.1313
0.1788
0.9388
LOWER
99%
LIMIT
0.0000
0.3152
.
0.0431
0.0214
0.1381
0.0000
0.0000
0.0650
0.0027
0.1476
0.0002
0.0000
0.1109
UPPER
99%
LIMIT
1.74E22
19901.5
9
2100.81
3.3684
30564.9
709.403
64426.2
110.755
79103.4
47.8556
80.8698
207256
7.9451
EFFVAR
INTERCEPT
W5
W8
P4
P8
P12B
VI
V2
V3
V4
V5
V6
V7
V8
PARAM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
ESTIMATE
4.5533
-1.5782
-9.7796
-1.2164
0.8611
1.1863
0.2004
-0.3181
0.8413
1.3422
-1.1268
-1.4016
-1.7192
-0.3710
nuutu-w re-
STDERR
5.1940
1.6609
.
0.7778
0.5403
0.7609
0.6272
1.1033
0.6452
0.9287
0.7852
0.5822
1.4794
0.3651
•r trvM-t MI
CHISQ
0.77
0.90
*
2.45
2.54
2.43
0.10
0.08
1.70
2.09
2.06
5.80
1.35
1.03
J-V.73U7 1
PROB
0.3807
0.3420
.
0.1178
0.1110
0.1190
0.7493
0.7731
0.1923
0.1484
0.1513
0.0161
0.2452
0.3096
ODDS
RATIO
94.9452
0.2063
*
0.2963
2.3658
3.2749
1.2219
0.7275
2.3194
3.8275
0.3241
0.2462
0.1792
0.6900
LOWER
99%
LIMIT
0.0001
0.0029
*
0.0400
0.5882
0.4613
0.2429
0.0424
0.4401
0.3499
0.0429
0.0549
0.0040
0.2694
UPPER
99%
LIMIT
6.141E7
14.8839
*
2,1973
9.5154
23.2517
6.1476
12.4784
12.2232
41.8684
2.4495
1.1031
8.0989
1.7674
-------
MALES
Model: MODEL Dl
Dependent Variable: Ml
Source
Model
Error
C Total
Analysis of Variance
OF
13
79
92
Sum of
Squares
521.03110
2363.89363
2884.92473
Mean
Square
40.07932
29.92270
F Value
1.339
Root MSE
Dep Mean
C.V.
5.47016
11.44086
47.81253
R-square
Adj R-sq
0.1806
0.0458
Parameter Estimates
Variable D
INTERCEP
W7
P7
f>8
P12B
P13
VI
V2
V3
V4
V5
V6
V7
V8
Parameter
F Estimate
10.313542
2.246219
-0.747321
-0.211099
4.326826
-0.103268
0.801810
-0.033745
0.053318
0.739749
0.953317
-0.474724
0.578121
-0.734133
Standard
Error
7.72265190
1 .86580927
0.71643255
0.62094438
1.73986160
1.73837831
1.05394734
1.70007087
0.76748920
1.25852430
0.85629877
1.31116356
2.29275960
0.66930873
T for HO:
Parameter*0
1.335
1.204
-1.043
-0.340
2.487
-0.059
0.761
-0.020
0.069
0.588
1.113
-0.362
0.252
-1 .097
Prob>F
0.2087
Prob > |T|
0.1856
0.2322
.3001
.7348
0.0150
0.9528
.4491
.9842
.9448
0.5583
0.2690
0.7183
0.8016
0.2760
0.
0.
0.
0.
0.
-------
FEMALES
Model: MODEL_D1
Dependent Variable: Ml
Analysis of Variance
Model
t
C
•ce
(1
>r
>ta1
Root HSE
Dep Mean
C.V.
Sun of Mean
OF Squares Square
13 1245.11395 95.77800
93 3589.80193 38.60002
106 4834.91589
6.21289 R-square
12.97196 Adj R-sq
47.89477
F Value
2.481
0.2575
0.1537
Prot»F
0.0060
Parameter Estimates
Variable OF
Parameter
Estimate
Standard
Error
INTERCEP
H7
P7
P8
P12B
P13
VI
V2
V3
V4
V5
V6
V7
V8
7.290475
-0.158693
2.261391
-1.567023
0.064140
4.971034
1.505314
1.429719
0.410835
0.754019
-0.596457
-0.465497
-1.796028
-1.056179
8.32434480
1.65798219
0.71555145
0.69711399
1.48384973
2.75382882
0.94401205
1,58597457
1.08066346
1.03006835
1.02373986
0.97588138
2.50981862
0.68711302
T for HO:
Pararoeter*0
0.876
•0.096
3.160
-2.248
0.043
1.805
1.595
0.901
0.380
0.732
-0.583
-0.477
-0.716
-1.537
Prob > |T|
0.3834
0.9240
0.0021
0.0269
0.9656
0.0743
0.1142
0.3697
0.7047
0.4660
0.5616
0.6345
0.4760
0.1277
-------
MALES
Mode]: MODELJ)l
Dependent Variable: M2
Source
Model
Error
C Total
Analysis of Variance
OF
14
73
87
Sum of
Squares
1069.67218
2776.31646
3845.98864
Mean
Square
76.40516
38.03173
F Value
2.009
Prob>F
0.0286
Root MSE
Dep Mean
C.V.
6.16699
21.23864
29.03664
R-square
AdJ R-sq
0.2781
0.1397
Parameter Estimates
Variable D
1HTERCEP
Tl
T4
W5
PI
P9
P12B
VI
V2
V3
V4
V5
V6
V7 ]
V8 1
Parameter
F Estimate
-20.289128
0.608883
-1.726251
4.028964
0.137834
0.929187
-2.884950
-3.294250
0.002242
0.024471
-0.618600
-1.138699
-1.809992
2.122726
-0.496599
Standard
Error
31.45227438
0.43716552
1.09080158
2.36653240
0.07960607
0.78933618
2.11821609
1.38700413
2.01113450
0.89422832
1.57349053
0.99657106
1.61495519
2.79950079
0.79633489
T for HO:
Parameter»0
-0.645
.393
- .583
.702
.731
.177
- .362
-2.375
0.001
0.027
-0.393
•1.143
•1.121
0.758
-0.624
Prob > JT|
0.5209
0.1679
0.1178
0.0929
0.0876
0.2429
0.1774
0.0202
0.9991
0.9782
0.6954
0.2569
0.2661
0.4507
0.5348
-------
FEMALES
Model: MODEL Dl
Dependent Variable: M2
Analysis of Variance
Model
E
C
•ce
n
>r
>ta1
Root KSE
Dep Mean
C.V.
Sum of Mean
DP Squares Square
14 1337.61427 95.54388
87 3263.87592 37.51582
101 4601.49020
6.12502 R-square
19.50980 Adj R-sq
31.39455
F Value
2.547
0.2907
0.1765
Prob>F
0.0042
Parameter Estimates
Variable D
INTERCEP
Tl
T4
W5
PI
P9
P12B
VI
V2
V3
V4
V5
V6
V7
V8
Parameter
F Estimate
15.543931
-0.032071
-1.343817
-0.820179
0.244765
1.242762
1.860575
1.263904
-2.425527
0.351513
-0.567223
0.739171
0.880146
-2.394214
1.299691
Standard
Error
25.93350572
0.36386808
0.85155843
2.01443764
0.06621643
0.72502113
1.54338429
1.11011012
1.85576750
1.16160590
0.95488534
1.02847732
0.98893318
2.69108813
0.69109043
T for HO:
Parameter^)
0.599
-0.088
-1.578
-0.407
3.696
1.714
1.206
1.139
•1.307
0.303
-0.594
0.719
0.890
-0.890
1.881
Prob > |T|
0.5505
0.9300
0.1182
0.6849
0.0004
0.0901
0.2313
.2580
.1946
.7629
.5540
0.4742
0.3759
0.3761
0.0634
0.
0.
0.
0.
-------
KALES
Model: MODEL_D1
Dependent Variable: M3
Analysis of Variance
Model
E
C
•ce
(1
>r
ital
Root HSE
Oep Mean
C.V.
Sum of Mean
OF Squares Square
15 946.69857 63.11324
78 1873.01419 24.01300
93 2819.71277
4.90031 It-square
8.88298 Adj R-sq
55.16513
F Value
2.628
0.3357
0.2080
Prob>F
0.0030
Parameter Estimates
Variable D
INTERCEP
T2
T3
W5
P5
P6
P7
P13 1
VI 1
V2
V3
V4
V5
V6
V7
V8
Parameter
F Estimate
1 16.287025
-0.234105
•2.197584
-3.270905
1.821833
-0.913257
-0.195602
1 -1.096215
1 2.130235
2.422823
1.334015
1.953129
0.485459
-0.531957
-0.584275
1.158243
Standard
Error
26.56121494
0.12316023
4.81502143
1.74777538
1.06730202
0.69092699
0.67164744
1.55888181
1 .09657767
1.76119267
0.70610566
1.16438544
0.73443112
1.21693225
2.07819047
0.63251675
T for HO:
Parameter*©
0.613
-1.901
-0.456
-1.871
1.707
-1.322
-0.291
-0.703
1.943
1.376
1.889
1.677
0.661
-0.437
-0.281
1.831
Prob > |T|
0.5415
0.0610
0.6494
0.0650
0.0918
0.1901
0.7717
0.4840
0.0557
0.1729
0.0626
0.0975
.5106
.6632
.7793
0.
0.
0.
0.0709
-------
FEMALES
Model: MODEL_D1
Dependent Variable: M3
Source
Model
Error
C Total
Analysis of Variance
OF
Sum of
Squares
15 617.50117
93 2630.64562
108 3248.14679
Mean
Square
41.16674
28.28651
F Value
1.455
Root HSE
Dep Mean
C.V.
5.31851
8.78899
60.51328
R-square
AdJ R-sq
0.1901
0.0595
Parameter Estimates
Variable 01
INTERCEP
T2
T3
W5
P5
P6
P7
P13
VI
V2
V3
V4
V5
V6
V7
V8
Parameter
F Estimate
25.138122
0.051972
•2.300396
0.510478
•0.052145
-1.212113
1.265693
2.173542
1.908019
•1.921348
1.256366
0.802433
-1.414973
-0.547471
0.220751
0.033675
Standard
Error
30.73146475
0.15696598
5.53207714
1.78211507
0.89697741
0.6074582*
0.67075577
2.35872173
1.15866852
1.88479446
0.95251600
0.84644240
0.91826406
0.80518645
2.13812929
0.59452547
T for HO:
Parameter«0
0.818
0.331
-0.416
0.286
-0.058
-1.995
1.887
0.921
1.647
-1.019
1.319
0.948
-1.541
-0.680
0.103
0.057
Prob>F
0.1389
Prob > |T|
0.4155
0.7413
0.6785
0.7752
0.9538
0.0489
0.0623
0.3592
0.1030
0.3107
0.1904
0.3456
0.1267
0.4982
0.9180
0.9550
------- |