United States	Office of Policy,	EPA-230/12-84-002
Environmental Protection	Planning, and Evaluation	December 1984
Agency	Washington, DC 20460
Survey Management
Volume II:
Overseeing the
Technical Progress
of a Survey Contract

For additional copies, please contact:
Statistical Policy Branch,
Office of Standards and Regulations
U.S. Environmental Protection Agency
PM-223, 401 M Street, S.W.,
Washington, D.C. 20460	
December 1984
Project Manager
Principal Writer
Statistical Advisor
Consultant, Committee on
National Statistics,
National Academy of Science
Editor and Proofreader

*when published
fPlette read Instructions on the reverse before completing)
1. report no.
3 wrtmwrc
4. title and subtitle
: Guideline
3 Vol. II Dec. 84*
tor planning and Managing a statistical burvey
ing the Technical Prepress nf a Snrvpy Cnpt-rarf
Cynthia Croce
Environmental Protection Agency
Statistical Policy Branch (PM-223)
401 M Street, SW
Washington, DC 20460
N / A

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EPA 400/00

Volume I focuses or. survey design principles and ways program
officials might productively apply them in planning a contract
Volume II emphasizes the conduct and management of an Agency-
sponsored survey. Each of th six chapters of volume II
corresponds to a major component of a typical work plan for a
statistical survey of human populations.

c. COSAT. Held/Group
statistical surveys, human
populations, questionnaires,
sampling plan, interviewing


19. SECURITY CLASS /Tins Report j
t:n r.LAs

Release Unlimited

20 SECURITY CLASS /This paff)
EPA Form 2220-1 (R.V. 4-77) previous

Overseeing the Technical Progress
of a Survey Contract

1 . Define the Purpose of the Survey		9
2.	Define the Research Objectives 		11
3.	Define the Study Variables 		13
4.	Specify the Analytic Approaches and Methods .	14
5.	Define the Preliminary Tabulations 		15
1.	Traditional Survey Research Methods 		20
2.	Exploratory Research Methods 		22
1.	Special Characteristics of Face-to-Face
Surveys		24
2.	Special Characteristics of Telephone
Surveys		26
3.	Special Characteristics of Mail Surveys ...	29
1.	Characteristics of the Target Population . .	31
2.	Data Requirements		31
3.	Respondent's Obligation to Reply 		32
4.	Target Response Rate		32
5.	Available Time			32
6.	Available Funds		33

1.	Determine the Analysis Requirements 	 40
2.	Draft a List of Topics or Suggested
Questions	 40
3.	Conduct Exploratory Interviews with a
Few Individuals in the Population	 41
4.	Prepare First Draft of the Questionnaire . . 42
5.	Review and Approve First Draft of the
Questionnaire 		43
6.	Prepare Plan for Pretest	 44
7.	Initiate Clearance Request for the Pretest . 46
8.	Pretest on a Sample of the Target
Populat ion		 46
9.	Debrief Interviewers and Assess Pretest
Findings			 48
10.	Revise Questionnaire and Prepare Plan for
the Pilot Test . 		 48
11.	Review Revised Questionnaire and Pilot Test
Plan	 50
12.	Recruit Interviewers and Prepare Training
Materials	 50
13.	Pilot Test Questionnaire and Assess Results . 51
14.	Revise Questionnaire and Collection
Procedures for Main Survey	 51
15.	Review and Approve Procedures for the Main
Survey	 52
16.	Print Questionnaire 	 52
1.	Reviewing Individual Questions 		53
2.	Reviewing the Overall Content and
Organization 		64
3.	Reviewing the Format		69
1.	Lower Costs		78
2.	Reduced Paperwork Demands 		78
3.	More Timely Results		79
4.	More Accurate Results		79

CHAPTER 4 - SAMPLING (Continued)
1.	Sampling Errors		80
2.	Measuring and Expressing Sampling Errors . .	81
3.	Determining Sample Size		82
1.	Probability Sampling Methods . .		86
2.	Non-Probability Sampling Methods 		95
1.	Sampling Frames		98
2.	Sample Selection Procedures ... 		100
3.	Estimation Procedures 		101
4.	Calculation of Sampling Errors 		105
1.	Respondent Rules 		112
2.	Follow-Up Rules	113
3.	Quality Control	114
1.	Preparing Instructions and Training Materials 123
2.	Staffing the Field Operations 		126
3.	Training the Interviewers	129
4.	Coordinating and Controlling the
Fieldwork	130
1.	Locating Respondents 		132
2.	Gaining Responsents' Cooperation	132
3.	Asking Questions			134
4.	Recording and Editing Responses 		135

1.	Develop the Processing Procedures 		140
2.	Select and Train Staff	141
3.	Screen the Questionnaires	143
4.	Review and Edit the Questionnaires	143
5.	Code Open Questions	144
6.	Enter Data	145
7.	Detect and Resolve Errors in the Data File. .	146
8.	Prepare the Outputs	150
No. Title	Page
Components of the Work Plan		3
1	Guide for Choosing a Data Collection Method ....	34
2	The Sponsoring Office's Tasks in the Questionnaire-
Development Process		39
3	Criteria for Reviewing Survey Questionnaires ....	54
4	Absolute and Relative Sampling Errors for Different
Types of Estimates of Families Using Contaminated
Drinking Water Sources 		83
5	Multi-State Design for a National Household Survey .	94
vi i

Statistical surveys are playing an increasingly important role
in Agency decisionmaking. As policymakers demand more quanti-
tative support for Agency decisions, program managers are giving
careful consideration to statistical survey reports and their im-
plications in the framing of regulatory decisions and long-range
environmental policies. Reliable survey data on the duration,
magnitude, and physical distribution of pollutants in the en-
vironment have proven invaluable for determining the precise
degree of pollutant control needed to respond to various statu-
tory mandates and the manner in which the Agency should exercise
such control.
There have been extraordinary advances in survey methodology
in the past two decades, the most striking of them in sampling,
data processing, and statistical analysis. This has made large-
scale collections of demographic and economic facts easier,
faster, less costly, and more reliable. Moreover, the quality
of reporting both survey methods and survey results has consis-
tently improved. These advances have motivated those who spon-
sor surveys to demand increasingly higher standards in ques-
tionnaire design, data collection methodology, sampling, inter-
viewing, data processing, and analysis.
The growing reliance on high-quality statistical work for Agency
planning and policymaking, coupled with the recent advances in
survey methodology, in fact, prompted the development of this
two-volume Survey Management Handbook.
In Volume I of the Handbook, published in November of 1983, we
focused on survey design principles and ways program officials
might productively apply them in planning a contract survey. In
the present volume, our emphasis is on the conduct and manage-
ment of an Agency-sponsored survey. Specifically, we examine --
	The methods, procedures, and quality-assurance
techniques typically used to collect, process,
and analyze survey data; and
	The actions EPA project officials can take to
ensure the technical soundness of all contract
work performed during the course of a survey.

Volume II is organized into six chapters, which correspond to
the major components of a typical work plan for a statistical
survey of human populations. Normally the work plan -- and the
subsequent fieldwork, data processing, and often the analysis --
is done by a large survey research contractor, with the EPA
sponsoring office playing an oversight role throughout the term
of the contract.
The work plan establishes the methods and procedures to be used
in collecting, processing, and analyzing data from or about the
survey population. Usually it consists of --
	An analysis plan
	Specification of the method(s)
of collection
	A draft questionnaire and
specifications for any pretests
	A sampling plan
	Interviewing procedures
	Data processing procedures
A summary of the topics covered in each of the six chapters
is given on the next page.
As in the previous volume, the survey methods and techniques
we discuss are applicable to fairly large-scale surveys. This
is because most of EPA's demographic, economic, and social
investigations as well as field studies deal with large popula-
tions and Issues that the Agency must necessarily view from a
national perspective.
Of course, not every empirical research project EPA undertakes
requires the formal apparatus needed for a large-scale survey.
Sometimes it is more appropriate to study a handful of cases
intensively rather than investigate a representative sample,
to interview a few individuals or groups informally rather than
use the structured interviews prescribed for major statistical
surveys, or to develop in-depth descriptions of a few individ-
uals rather than aim for a set of statistics about a group. In
fact, several approaches may be used to resolve a particular
survey research problem. The researcher's challenge is to iden-
tify those approaches that are most likely to achieve the
specific objectives of the project. The purpose of this Hand-
book is to help you meet this challenge.
Throughout, we discuss theoretical issues in very general terras.
No background knowledge of statistics is presumed. In the event
you wish to delve further into survey theory, a list of excel-
lent sources is given at the end of each chapter. A complete

Chapter 1 examines the steps involved in
ANALYSIS	defining the research objectives of the
survey and choosing the analytic approach
PLAN	most appropriate for achieving these ob-
j ectives.
Chapter 2 describes the principal methods
of collecting survey data and the factors
influencing the choice of methods, and
suggests ways of evaluating the method
proposed for a particular EPA survey.
Chapter 3 examines the sequence of steps
involved in developing a sound survey
questionnaire, presents criteria for re-
viewing draft questionnaires, and recom-
mends ways of monitoring pretests.
Chapter 4 describes the advantages of
sampling, the principal methods of choos-
ing a sample, and the components of a
sampling plan, and recommends ways of
monitoring the sampling activities.
Chapter 5 discusses the administrative
and quality-assurance procedures typi-
cally used to organize, manage, and mon-
itor a survey where interviewing is used
to collect the majority of the data.
Chapter 6 looks at the steps involved in
processing the raw data collected from
the sample to produce tabulations and
analyses that will achieve the research
obj ectives.

list of these sources appears at the end of the Handbook along
with a glossary of terras.
We strongly suggest that you have a survey statistician review
your survey design and analysis plan early in the planning stage
-- certainly before you take steps to procure outside technical
support. You also may find it necessary to get the advice of
experts at various points of the survey in order to effectively
apply the methods and techniques we recommend, especially with
respect to sampling and data analysis. All too frequently, sta-
tisticians are called in after the data are collected, given a
stack of completed questionnaires, and asked to make what they
can of thera. Unfortunately, because of gaps and omissions in
the data, flaws in the survey design, mistakes in the question-
naire, and other problems that could easily have been avoided
if a survey expert had been called In during the planning stage,
there is very little that can be done.
Keep in mind that the Statistical Policy Branch (SPB) of the
Chemicals and Statistical Policy Division within the Office
of Standards and Regulations, which prepared this Handbook,
offers technical assistance to the programs in all facets of
survey management.

In a given research situation, survey designers usually have a
choice of research designs, methods of observation, methods of
measurement, and types of analysis. All must fit together and
be appropriate to the research problem. The choices the re-
searchers make in each case will depend on how much is already
known about the problems they are investigating and the specific
reasons the information is needed.
Whether, as the survey sponsors, you intend to collect descrip-
tive facts about a population or to delve deeper and attempt to
explain certain facts requires a clear understanding of what
you expect the research effort to achieve. Collecting data in
the field is no substitute for well-thought out decisions be-
forehand about what is, and what is not, worth investigating.
Without a clear idea of the objectives of your research, the
survey is likely to result in much wasted time and money and
the accumulation of much unwanted data.
In this chapter, we discuss --
c The general approaches survey statisticians
use to analyze and interpret survey data;
	How to develop an analysis plan that will
clearly define the purpose of your survey,
the research objectives, the type of data
to be collected, and the most appropriate
method of analysis for achieving your re-
search objectives; and
	The major components of the work plan,
around which this volume is organized. In
a contract survey, the work plan describes
the methods and procedures the contractor
plans to use to collect, process, and
analyze the survey data.
In survey research, analysis means categorizing, ordering,
manipulating, and summarizing data to obtain answers to

research questions. The purpose of analysis is to reduce
data to intelligible and interpretable form. The data
first are broken down into constituent parts to obtain an-
swers to research questions and test research hypotheses.
Analyzing data does not provide answers to research ques-
tions. Interpretation is necessary. To interpret is to
explain. Interpretation takes the results of the data
analysis, makes inferences relevant to the relationships
among the data, and draws conclusions about these rela-
tionships. The researcher who makes the interpretation
searches the results for their meaning and implications.
A host of analysis techniques are available for studying
survey data. However, here we will focus on the four main
approaches to analysis, which are --
	Qualitative analysis
and evaluation
	Statistical descriptions
	Statistical inference
	Analytic interpretation
We'll discuss each of these approaches briefly in the order
of their complexity and sophistication.
(1)	Qualitative analysis and evaluation.
In a qualitative analysis, the researcher's goal is to
understand the characteristics of a few individuals,
rather than the characteristics of a population or sub-
groups of that population. A qualitative approach gen-
erally is not indicated for sample surveys, which are
of major interest in this Handbook, but it may be the
most suitable approach in some research situations.
For example, non-quantitative analysis is often the
preferred approach (a) for analyzing the results of
case studies (or field studies) where a relatively
small number of individuals (or specimens) are being
investigated; (b) for evaluating the results of infor-
mal research prior to conducting a full-scale statisti-
cal survey; and (c) for developing hypotheses to test
in a pilot study or a full-scale survey.
(2)	Statistical descriptions.
Statistical descriptions are by far the most common
method of reporting survey data. They often are

referred to as "statistical analysis," but this funda-
mental approach to the analysis of survey data simply
involves working out statistical distributions, con-
structing tables and graphs, and calculating simple
measures such as means, medians, measures of dispersion,
percentages, proportions, etc. It can be used to de-
scribe data collected from a probability sample or an
entire study population.
In other words, statistical descriptions are the tab-
ulations researchers prepare after the data are proc-
essed to aggregate the features of the data file so
the analysts can view the database in some intelligible
and interpretable form. Statistical descriptions often
are done in series, one variable or research question
at a time being cross-classified with others, thus
producing a descriptive summary of the relationships
between the study variables.
(3)	Statistical inference.
In the broadest sense of the word, inference is the
principal approach for analyzing statistical data.
Inference is brought into play whenever data are col-
lected from a probability sample rather than an entire
population. When a probability sample is used, the re-
searchers must estimate the population characteristics
from those of tTie sample as well as estimate sampling
errors. Statistical inference is the linking of the re-
sults derived from data collected from or about a sample
to the population from which the sample was drawn.
(4)	Analytic interpretation.
This last and most complex approach is a form of sta-
tistical inference called analytic interpretation. It
refers to the statistician^ attempts to explain the
relationships between variables using various statis-
tical analysis techniques. For example, researchers
may employ a multivariate regression analysis technique
to better understand the relationships between exposure
to a particular pollutant and the socio-economic char-
acteristics of a study population.
In this section we will show you how to construct an anal-
ysis plan to complement the design specifications you
establish for your survey. The basic criteria for the
survey design and the analysis plan should be developed

simultaneously early in the planning stage. Constructing
a well thought-out analysis plan will help you define the
design criteria so that you can achieve your research ob-
jectives with some desired level of accuracy considering
the resources you have available. These design criteria
and the analysis plan together provide a sound conceptual
framework for whatever work you and the contractor subse-
quently do during the course of the survey.
In Volume 1, we described the sponsoring office's respon-
sibility for defining the following minimum design cri-
teria for the survey along with clear statements of the
purpose and objectives of the research.
	Target population and coverage
	Specific data needs
	Use of probability sampling
	Sampling error (precision)
	Target response rate
The intent of these criteria is to guide the project staff
in developing the statement of work to procure whatever
outside technical support may be necessary and to help the
contractor prepare a technically - and statistically -
sound work plan. They may possibly be modified during the
contract negotiations before being incorporated into the
contract. We will not further elaborate on these minimum
design specifications because they were amply covered in
Chapters 3 and 5 of Volume I.
Constructing the analysis plan is a five-step process. The
plan should be developed by the project office with the
assistance of Agency statisticians, computer programmers,
specialists in the subject area of the research, and sys-
tems analysts, as appropriate.
The end-products of the five steps, discussed below, are
clear definitions of (1) the purpose of the survey, (2)
the objectives of the research (the main areas of investi-
gation) , (3) the data or the variables to be investigated,
(4) the analytic approaches and methods to be used to
achieve the research objectives, and (5) the preliminary
tabulations to be prepared from the completed data file
after the data are processed.
Later, after the Agency and the contractor have studied the
preliminary tabulations, the analysis plan can be refined
-- usually this is done by the contractor -- to include
specifications for additional, perhaps more sophisticated

tabulations and the types of statistical analysis tech-
niques that should be applied to fully reveal the informa-
tional content of the data base.
 Step 1: Define the
Purpose of the Survey
Generally speaking, to define the purpose of a survey
is to give the specific reasons why certain information
is needed. For any EPA-sponsored survey the reasons
must relate to some specific legislative, regulatory,
or judicial mandate that either directs the Agency to
explore a particular environmental problem or to take
certain corrective actions, and EPA cannot faithfully
comply with the mandate unless some new or additional
empirical information is collected and analyzed.
From a practical standpoint stating the purpose of the
survey means defining its operational usefulness for
flanning and policy analysis. The statement of purpose
n your analysis plan, therefore, must clearly show
how the data you plan to collect will result in infor-
mation that will clarify or resolve some specific
environmental problem that some authority has directed
EPA to deal with. In other words, you must specify --
Below is a statement of purpose that appeared in a
recent report on an EPA field study of carbon monoxide
(CO) using hand-held personal exposure monitors to
test levels of CO in a variety of commercial settings.
The survey was conducted by EPA staff in the Office
of Monitoring Systems and Quality Assurance, Office
of Research and Development. The statement clearly
shows how the study results will be applied for plan-
ning and policymaking purposes, the problems the re-
searchers intend to deal with, and their relationship
to a specific EPA mandate.
"The goal of air pollution control programs in the
U.S., as mandated by Federal law and implemented
by the States, is to attain National Ambient Air
Quality Standards (NAAQS). The NAAQS for carbon
monoxide (CO), for example, specify two different
 How the information is
to be used
	The problems to be addressed
	Their relationship to a
specific EPA mandate

concentrations and averaging times, neither of
which is to be exceeded more than once per year:
35 parts per million (PPM) for 1 hour
9 ppm for 8 hours.
"Both standards are intended to protect against the
accumulation of more than 2% carboxyhemoglobin in
the blood....
"Nondispersive infrared (NDIR) monitoring at fixed
stations is the usual way for determining a given
city's compliance with the NAAQS for CO. During
the past decade, a number of studies have revealed
that concentrations observed at fixed air monitor-
ing stations have not been representative of con-
centrations sampled throughout an urban area. Some
field studies have shown, for example, that commut-
ers in traffic and pedestrians on downtown streets
encountered CO levels above the NAAQS on a given
date, while official aTr monitoring stations re-
ported CO values below the NAAQS at the same time.
Furthermore, studies of human activities suggest
that most people spend the greatest proportion of
any given 24-hour period indoors -- in residences,
stores, offices, factories, etc. These settings
are not necessarily identical to sites selected for
fixed air monitoring stations.
"These studies have raised questions about the use-
fulness of data generated by today's monitoring
stations for protection of public health. An unan-
swered question is the degree to which conventional
fixed stations either underestimate or overestimate
the actual exposure of people as they go about
their daily activities. The studies have stimulated
interest in 'exposure monitoring,' which treats
the person as a receptor and measures the pollutant
levels actually contacting the person's body....
"Prior to the late 1970's there was no low cost,
accurate means available for measuring CO concen-
trations to which people ordinarily were exposed
in their daily lives. The advent of microelectron-
ics has brought considerable progress in develop-
ing reliable, compact air quality monitoring in-
struments that can operate on batteries. The most
dramatic of these are the new miniaturized personal
exposure monitors (PEM's) .... The present inves-
tigation is the first large-scale microenviron-
mental field study to make use of the new CO PEM

Since the kinds of problems EPA has been directed to
explore and manage encompass such a wide range of
health and environmental issues, you may find it rela-
tively easy to develop an adequate statement of purpose
for your survey. What normally is far more difficult
is building a set of arguments to justify the expendi-
ture of program funds for your particular project,
given the limited resources available to each program
to address a mind-boggling number of priority issues.
A comprehensive, well-reasoned analysis plan will help
you build just such a set of arguments.
 Step 2: Define the
Research Objectives
Once you have justified the need for the survey from a
planning or policymaking standpoint, you can begin to
think about how to define its usefulness in "scienti-
fic" terms. The desired result should be a clear state-
ment of the research objectives in terms of the --
	Kinds of questions you
want answered
	Hypotheses to be tested
	Information to be collected
Continuing with the previous example, lets look at how
the objectives of the PEM CO study were framed. EPA
staff defined several sets of research questions.
The first set of research questions addressed the CO
concentrations typically found in commercial settings,
e.g. --
"What levels of CO ordinarily are present in typi-
cal commercial settings?"
"Are CO levels in typical commercial settings usu-
ally zero, negligible, or above the NAAQS?"
The second set of questions concerned the variability
of CO concentrations and factors that may be associated
with that variability. Examples from this set of ques-
tions are --
"How do CO concentrations vary over time within and
between different cities for a given commercial

"If CO is a street-level pollutant associated with
vehicular traffic, do workers have greater protec-
tion in offices on the upper floors of a high-rise
Another set of research questions addressed the accu-
racy of the fixed-station monitors operated by air
quality management districts to measure the air pollu-
tion to which the public is actually exposed, e.g. --
"Do CO concentrations measured in commercial set-
tings using PEM's correlate with ambient concen-
trations measured at fixed stations using NDIR
There also was a set of questions concerning the re-
search methodology itself, including the following
"Is the CO PEM an effective tool for sampling air
quality at a variety of urban locations?"
"What are the implications of the present study
for future research on exposures of the popula-
tion to CO?"
Several hypotheses were formed and tested. For ex-
ample, the researchers tested to see if the indoor
concentrations were appreciably less than the outdoor
concentrations when the entrance door to each commer-
cial setting was closed.
The information to be collected was identified as --
"5,000 concentrations of CO at one-minute inter-
vals using PEM's for instantaneous measurement
in a variety of commercial settings in several
California cities over a nine-month period."
Ultimately five principal objectives were framed:
(1)	"To determine the CO concentrations typically
found in commercial settings";
(2)	"To determine the variability of CO concentra-
tions in commercial settings and the time and
spatial factors that may be associated with that
(3)	"To define and classify microenvironments which
are applicable to commercial settings";

(4)	"To determine how accurately fixed station moni-
tors measure the CO settings"; and
(5)	"To develop research methodology for measuring
CO concentrations in field surveys using PEM's."
When you frame your research objectives be sure they
are both specific and answerable. For example, a ques-
tion like Is water contaminated by aldicarb?" is not
answerable. However, the following is a question re-
searchers can undertake to answer: "What proportion
of the U.S. population is consuming water that contains
more than seven parts per million (ppm) of aldicarb?"
This question, in fact, was an attempt to frame the
objectives of an EPA-sponsored field study concerning
the pesticide aldicarb, which was believed to be con-
taminating drinking water in certain communities. Lat-
er, because of time and budget constraints, it was
reframed as follows, "What proportion of the households
in high-risk areas are drinking water with more than
seven ppm of aldicarb, where high-risk is defined to be
either in counties growing crops that are licensed for
aldicarb or in which sales are reported."
It is impossible to overestimate the importance of
framing the research objectives of your survey fully
and precisely. No amount of data manipulation later
can overcome the problems that may result from poorly-
defined objectives. Furthermore, once you have defined
them, do not attempt to broaden their scope with fur-
ther research topics or include other types of informa-
tion unless you are sure of achieving your initial
objectives with the resources you have available.
Step 3: Define the
Study Variabilis
Once the objectives are clearly defined, the next step
is to define the key variables of the study. In other
words, you will have to identify the specific data
items that will be required to meet your stated objec-
tives. A variable is a characteristic of a sample or
of a population that varies in magnitude. In surveys
of human populations, common variables are age, sex,
race, income level, education level, etc.
Returning to our CO PEM example, the basic variable
"the average (mean) of two simultaneously taken
one-minute samples of CO concentrations."

Other variables were developed to test different hy-
potheses such as those used for comparing indoor and
outdoor CO concentrations using different settings of
the personal exposure monitor and door entrances of the
commercial establishments open and closed, e.g.
"mean CO concentration of indoor PEM setting i
with entrance door closed";
"mean CO concentration of outdoor setting i with
entrance door closed";
"mean CO concentration of indoor setting j with
entrance door open"; and
"mean CO concentration of outdoor setting j with
entrance door open."
 Step 4: Specify the Analytic
Approaches and Methods
Following the guidelines we provided in section A, the
next step in developing the analysis plan is to deter-
mine which analytic approach will allow you to achieve
your research objectives most efficiently given the
time and resources you have available. This means de-
termining which analysis methods are most likely to
achieve each of your research objectives. Note that
different observation methods, measurement techniques,
and analysis methods may be needed to fulfill each
one of your research objectives.
For most studies of human populations, a questionnaire
is the basic information gathering tool. If you choose
this "method of observation," you may want to prepare
a list of preliminary questions that will measure the
magnitude of the study variables you identified in the
previous step (see Chapter 3 for details on preparing
a questionnaire). You'll also have to decide what
level of accuracy (or precision) you will require. As
discussed in Chapter 3 of Volume I, the level of accu-
racy you determine should depend on how you plan to
use the results of the survey. And, finally, you'll
have to determine what minimally acceptable rate of
response (target response rate) is necessary to achieve
your research objectives. (See Chapter 3 of Volume I
for more information on establishing the level of pre-
cision and the target response rate for your survey.)
You do not have to determine either the measurement
techniques or any specific analysis techniques that

may be needed to meet your research objectives. It
usually is best to leave that to the contractor.
The method of analysis used in the CO PEM study was to
use the recently-developed miniaturized personal expo-
sure monitors to measure CO in commercial settings in
five California cities and suburbs. Then, a number of
hypotheses were tested by determining whether there
were significant differences between sample results.
In all, 588 commercial facilities were visited, includ-
ing retail stores, office buildings, hotels, restau-
rants, department stores, and adjacent sidewalk and
street intersections. Altogether 5,000 observations
were recorded instantaneously at one-minute intervals
as the investigators walked along sidewalks and into
 Step 5: Define the
Preliminary Tabulations
At a minimum, you should prepare a list of the prelim-
inary tabulations (table shells) describing the form
and content of the tables and graphs you want the con-
tractor to generate when the data file is complete.
There is nothing statistically sophisticated about
tabulations. They are simply mathematical counts of
the number of responses (or specimens) falling into
each of several categories that have previously been
defined to describe one or more relationships between
the variables.
The list of preliminary tabulations should include the
title of each table and graph you want the contractor
to prepare from the completed data file, and define
the horizontal and vertical headings of each. Later,
the contractor will total all the responses, specimens,
or other items falling under each heading. Note that
rarely is it possible to draw up a list of the final
tabulations during the planning stage, especially if
the subject matter is complex. Usually, most of the
tabulations and analyses are not decided on until the
results of the data file are in some intelligible and
interpretable form.
Let's look at four examples of the tabulations created
for the CO PEM study --
(1)	Field Survey Dates, Hours, Locations, and Numbers
of CO Samples;
(2)	Number of Commercial Settings by Type of Setting
and Geographic Location;

(3)	Statistical Summary of Mean CO Concentrations for
Commercial Settings Visited Twice on the Same Date;
(4)	Summary of CO Concentrations Collected Simulta-
neously from Fixed Monitoring Stations and Personal
Exposure Monitors.
A slightly abbreviated version of one of the table
shells EPA created for the CO PEM study -- the second
title listed above -- was the following:
Table 2: Number of Commercial Settings by Type
of Setting and Geographic Location
Un ion
San Francisco
Palo Alto
Ho t e 1 s





For additional information, see "Preparing the Prelimi-
nary Tabulations" in section A of Chapter 6.

What data collection method should be used for a particular
Agency survey? -- There is no general answer and, in many cases,
any one of the major traditional collection methods -- face-to-
face interviews, telephone interviews, or self-administered
mail questionnaires -- may be equally suitable as the primary
Researchers no longer arbitrarily consider face-to-face inter-
views the most effective way of obtaining reliable survey data.
If many open-ended questions and extensive probing must be
used, it is likely that the presence of a skilled interviewer
will motivate the respondents to provide the richest and the
most comprehensive data. However, in many other research situa-
tions, phone interviews or mail surveys may be just as effective
in eliciting the needed data or even more so -- and at a lower
In some cases, the nature and scope of the problems the survey
proposes to address may not be defined well enough to begin
designing an effective questionnaire and systematically collect
data from the target population -- especially when the Agency
is dealing with an emerging problem, a new field of science or
technology, or a population that has never been studied before.
Using exploratory research techniques such as focus groups or
in-depth interviews with a few of the potential respondents may
be a fruitful way of identifying key topics or hypotheses for
subsequent investigation using more traditional statistical
measurement techniques.
In the remainder of this chapter we will look at --
	The main characteristics of the methods most
often used to collect survey data for EPA;
	The factors that must be taken into account
in determining the most appropriate method
for a particular Agency-sponsored survey;
	How to assess the suitability of the pro-
posed collection method(s).

This section examines the five most frequently used meth-
ods of collecting survey data. First we will look at the
three traditional methods used in statistical research and
then at two exploratory research techniques that are appli-
cable when the study objectives are not defined precisely
enough to begin a systematic data gathering effort.
A combination of collection methods may be used for a
major survey. For example, exploratory techniques may be
used early on to clarify key topics. Or, if a mail survey
is chosen as the primary collection method, telephone or
face-to-face interviews may be used later to contact
respondents who do not reply within a certain time-limit.
A combination of mail and telephone interviewing may be
used, whereby respondents are mailed background informa-
tion and a telephone interview is scheduled later. Tele-
phone interviews may also be used as a back-up for face-
to-face interviews after several attempts to contact the
respondent in person have failed.
1. Traditional Survey Research Methods
The three most frequently used methods for collecting
quantitative (statistical) survey data are --
The data collection instrument for all three tradi-
tional collection methods is a "structured" question-
naire. The questions, their sequence, and their word-
ing are fixed in a structured questionnaire. Tf inter-
viewers are used, they may be allowed some leeway in
asking the questions, but generally very little. How
Touch leeway is specified in advance.
 Face-to-Face Interviews
Face-to-face interviewing has been the mainstay of
survey research methodology for more than 30 years.
It has been used for many EPA surveys during the
past ten years. Coupled with a well designed,
well tested questionnaire, the face-to-face inter-
view is a powerful, indispensable research tool.
	Face-to-face interviews
	Telephone interviews
	Self-administered mail

It is adaptable to a wide variety of research
situations and is uniquely suited to in-depth ex-
plorations of issues.
In a face-to-face interview, selected individuals
(the members of the sample) are visited in their
homes or workplaces by trained interviewers and
asked to respond to a fixed set of questions.
The interviewers record the respondents' answers
on a printed questionnaire. The answers are the
"raw data" that are subsequently processed, studied,
and analyzed to solve the problems the survey was
designed to address.
 Telephone Interviews
Telephone interviewing is rapidly becoming the prin-
cipal method of collecting survey data in research
situations where probing or in-depth exploration
of the issues is not required.
There are two kinds of telephone interviewing tech-
niques: (1) traditional and (2) computer-assisted
telephone interviewing (CATI).
Traditional telephone interviews are similar
to face-to-face interviews. The interviewers
pose questions to individual respondents at their
homes or workplaces by telephone and record the
answers directly onto a printed questionnaire.
The interviewers generally work from one central
location under the supervision of an experienced
CATI, on the other hand, is a recent innovation
in survey methodology. A printed questionnaire
is not used. Instead, researchers program a set
of questions onto a computer tape. The inter-
viewer sits in front of a video terminal and
reads the questions to the respondents over the
telephone as they appear on the screen. The
interviewer types the respondent's answers on a
keyboard attached to the terminal, and they are
automatically entered into the computer.
This radically different interview technique not
only speeds up the collection and processing
of respondent information, but also avoids the
human errors normally associated with handling,
checking, and transferring data from a printed
questionnaire into machine-readable form.

CATI also has other advantages. It permits the
use of very complex "skip" patterns. Depending
on the response the interviewer enters, the com-
puter can be programmed to determine which ques-
tion to present on the screen next. It also pro-
vides the interviewer with instant fpedback if
an impossible or out-of-range answer is entered.
 Self-Administered Mail Questionnaires
Like face-to-face interviews, self-administered mail
questionnaires have been used for several decades to
collect survey data. EPA relies heavily on this
traditional survey research method to collect com-
plex technical and scientific information from busi-
ness and industry. In a mail survey, researchers
send printed questionnaires to the respondents at
their homes or businesses. The respondents complete
the forms and return them by mail,
2. Exploratory Research Methods
The Agency sometimes must explore emerging problems
dealing with issues about which little is known. We
may have determined that a statistical survey is the
only way to get the data that will allow us to explore
the central issues of these emerging problems, but
some aspects of the issues are not defined well enough
for us to begin constructing a structured survey ques-
tionnaire. In such cases, "unstructured" survey re-
search methods may prove effective in clarifying the
key issues.
The two most frequently used unstructured interviewing
techniques are 
Let's briefly examine these techniques.
 Individual In-Depth Interviews
This exploratory research technique involves indi-
vidual in-depth discussions with a few individuals
in the populations of interest who are knowledgeable
about, or involved in, the issues the Agency propo-
ses to study. The interviews will be guided by a
	Individual in-depth
ervi ews
	Focus group interviews

topic outline rather than a fixed set of questions
characteristic of a structured questionnaire, which
is used for virtually all statistical surveys.
In in-depth individual interviewing, probability se-
lection methods generally are not used to choose
those who will be interviewed. Instead, a "conven-
ience" sample, representative of different segments
of the target population, will be drawn. Any number
of individuals may be chosen to participate in the
study. The interviewers who are picked to conduct
the in-depth interviews must be carefully chosen.
They should have experience in conducting in-depth
interviews and knowledge of the subject matter.
In-depth individual interviews are particularly val-
uable when researchers are unsure about (a) which
topics are most relevant to the research objectives;
(b) whether members of the target population are
likely to have the kinds of information the Agency
needs; (c) how to phrase certain items on the survey
questionnaire; (d) what type of question format is
likely to be most effective for obtaining specific
information on certain topics (e.g., open or closed
questions); (e) which topics the members of the tar-
get population are likely to consider threatening or
particularly sensitive; and (f) which subgroups in
the target population are most likely to be able to
supply specific data the Agency needs.
 Focus Group Interviews
Focus group interviews are another valuable "un-
structured" research technique featuring informal
discussions with individuals selected from the tar-
get population. The participants are members of
the target population who are called together for
discussions focussed on specific issues or specific
parts of the proposed survey questionnaire. Focus
groups often will unearth aspects of emerging prob-
lems that might not surface in individual in-depth
discussions. They are especially appropriate for
exploring the attitudes, opinions, concerns, and
experiences of selected segments of a population
of interest; of identifying key concepts; of helping
to phrase questions so they will be clear to all
potential respondents; and of evaluating drafts of
survey questionnaires. Focus groups also may be
used early in the development stage of a research
project to help the Agency determine whether a quan-
titative survey is feasible.

As with in-depth individual interviews, probabili-
ty sampling techniques generally are not used to
select the study participants. Instead, several
relatively homogeneous groups of six to twelve
people are selected at random from various subgroups
of the target population. From two to as many as
twelve groups may be formed, each led by a skilled
moderator knowledgeable about the study objectives.
The moderator interacts with the participants and
"focuses" the discussion on a few topics of special
interest to the researchers.
A topic outline is prepared at the beginning of the
study. Usually, fairly general topics are identi-
fied for the first group to discuss, then research-
ers gradually focus the discussions on more specific
subject matter. The groups usually meet for about
two hours. Although the topic outline is used as
a general discussion guide, the participants are
given ample opportunity for spontaneous comment --
provided they do not stray too far from the material
in the outline.
Earlier in this chapter we said that no collection method
is intrinsically better than any other. However, certain
methods are clearly more appropriate in certain research
situations and just as clearly contraindicated in others.
This section highlights some of the principal distinguish-
ing features of each of the three traditional collection
1. Special Characteristics of Face-to-Face Surveys
Face-to-face interviewing is frequently used at EPA
for collecting survey data from the general public.
Moreover, it often is the only viable approach for
collecting highly complex, sensitive, technical in-
formation from business and industry. Because face-
to-face interviewing is still the predominant method
of collecting data for EPA survey work,, much of the
discussion in this Handbook pertains to this method.
In-person interviews have many advantages. They gen-
erally achieve a higher response rate, greater coopera-
tion, and more complete and consistent data, especially
when in-depth exploration of the issues is desirable.
Face-to-face interviews are uniquely suited to probing
-- a technique used to study the respondent's knowledge

of key issues, frames of reference, or, more typically,
to clarify and learn the reasons for their answers.
The disadvantages of face-to-face interviewing are
higher costs and personnel requirements, and the need
for extensive training of field staff and close super-
vision of interviewers throughout the data collection
pe riod.
More specifically --
	Face-to-face interviews are the only viable data
collection method when first-hand observations of
the respondents or the interview site are necessary.
Both telephone interviews and mail surveys are in-
appropriate when eye-witness reports are desirable.
	In some household surveys, particularly when the
general public is being interviewed, respondents
are more cooperative and give less biased replies
if visual aids are used to prompt their answers.
Face-to-face interviews are uniquely suited to the
use of these aids.
For example, interviewers can show respondents a
calendar to refresh their memories about specific
events or time intervals. Or, instead of reading
a long list of possible replies, interviewers can
hand respondents a checklist (or "prompt card") of
suggested answers to elicit an appropriate reply.
When an interviewer verbally gives respondents a
choice of three or four possible answers, they often
have difficulty remembering them all. The net re-
sult is a bias towards the first or the last item
mentioned. In addition, if interviewers must ques-
tion respondents about their income or other topics
that many people consider too sensitive to discuss
with a stranger, prompt cards listing the reply
categories tend to cut down on inaccuracies and
outright refusals to answer the question.
Similarly, in a survey of the general public where
respondents are required to evaluate a product or
other object (a new pollution-control device, for
example), face-to-face interviews may be the only
viable data collection option. If interviewers are
given products for business or industrial respond-
ents to evaluate, however, it may be feasible to
mail the firms a sample of the item (or different
versions of the product) in advance, and schedule a
follow-up telephone or mail interview to get their
reports or opinions.

	In face-to-face surveys, smaller, more geographi-
cally concentrated samples must be used to hold down
costs. Setting up a complex field operation in a
large number of sampling areas to interview only a
few respondents in each area obviously is prohibi-
tively expensive. To hold down costs, researchers
"cluster" respondents in a few selected geographic
areas and set up mobile field units to collect the
data. Field supervisors remain at a more central
location. Clustering does increase the sampling
error of the survey, however.
Widely dispersed samples have little effect on both
telephone and mail surveys, on the other hand, be-
cause they are generally operated from a centrally-
located office.
	Face-to-face surveys are more costly to administer
than either telephone or mail surveys. Coordinating,
hiring, training, and supervising interviewers and
field staff at several locations is complicated.
Moreover, the paperwork is much more involved. In
addition to the questionnaire, it may be necessary
to use as many as 20 different forms and documents
to coordinate and control the fieldwork and the
processing operations, i.e., confidentiality state-
ments, prompt cards, interviewer calling cards,
press releases, interviewer progress reports, inter-
viewer evaluation forms, respondent verification/
evaluation forms, and letters giving respondents
advance notice of the survey.
2. Special Characteristics of Telephone Surveys
Telephone surveys cost about half as much as face-to-
face surveys of comparable size, given the present
development of both technologies. They are also easier
to manage, produce faster results, and, with few modi-
fications, can be used in most research situations
where one-to-one interviewing is indicated.
Some of the advantages of telephone interviews are --
	In Surveys by Telephone, Groves and Kahn report that
the overall cost of a telephone survey is 45 to 65
percent lower than a comparable face-to-face survey
(see the list of recommended sources at the end of
this chapter). Cost savings result from the fact
that about one-quarter as many interviewers are
needed to reach the same size sample, and the cost
of training the interviewers is about one-fifth as

much. Moreover, travel costs for interviewers and
field staff are virtually nil, and repeated call-
backs to the respondents produce no significant
cost increases.
	Monitoring, administration, and quality control are
simpler than in face-to-face surveys because no far-
flung field operation is necessary. Moreover, it
is easier to correct interviewer mistakes quickly.
People on the contractor's staff who review and edit
the completed questionnaires are typically close by,
and can provide feedback to the interviewers about
errors and omissions relatively quickly. Finally,
respondents can easily be recontacted after the
initial interview to correct inaccuracies, incon-
sistencies, and omissions.
	Results can be obtained more quickly from telephone
surveys than from either of the other two major col-
lection methods, The interviewing, monitoring,
training, editing, and coding operations are usu-
ally centralized in one location. If any changes
in the questionnaire or interviewing procedures
have to be made because of problems encountered in
the pretest, the researchers can incorporate them
into the main survey quickly. Even after the
interviewing in the main survey is under way,
it is easy to notify the interviewers immediately
about any needed changes. Follow-up interviews to
check the interviewers also are much easier.
If computer-assisted telephone interviewing is used,
all.the time-consuming manual screening, editing,
coding, and data entry operations required for the
other data collection methods (including traditional
telephone interviewing) are unnecessary.
	With a few modifications, telephone interviews can
be used in almost all research situations where
face-to-face surveys are suitable.
For example, if pictures or products must be shown
to the respondents to motivate or enable them to
answer certain questions, these items can be mailed
to the respondents and an interview scheduled at a
later date. This combined mail/telephone technique
is widely used in marketing surveys.
The "prompt cards" face-to-face interviewers show
respondents to motivate their replies are not pos-
sible in phone surveys, of course. However, the

questionnaire can be modified to obtain the same
information. The raost common procedure is to break
questions with multiple-choice replies into a series
of simpler questions and offer the respondents a set
of Yes/No alternatives until all possible answers
are covered.
	Telephone interviews permit access to respondents
located in areas where face-to-face interviews are
especially difficult -- locked apartment or office
buildings, subdivisions with security guards pre-
venting access, dangerous neighborhoods, etc.
At the same time, telephone interviewing has several
di sadvantages.
0 Response rates for national telephone surveys re-
main at least five percent lower than comparable
face-to-face surveys despite considerable improve-
ments in interviewer training, feedback procedures,
and monitoring techniques during the past few years.
The reason is that respondents generally find tele-
phone interviews more tedious and less rewarding
than face-to-face interviews, hence they tend to
be less cooperative over the phone.
0 Telephone interviews are not the best way of col-
lecting factual data if respondents have to search
their records or consult with others. However, it
is possible to mail respondents background informa-
tion in advance and schedule a follow-up phone in-
terview later to obtain the needed data.
	Of course, interviewers cannot reach people who
have no phones. This means that important subgroups
such as low-income people will be underrepresented
in surveys of the general public if telephone inter-
views are used exclusively as the collection method.
Surprisingly, it is possible to reach people with
unlisted numbers in a phone survey. Researchers
use a sample-select ion technique called "random
digit dialing," which features a computer-assisted
random selection of telephone numbers. If they
simply chose numbers at random from a telephone
book, unlisted numbers would be excluded from the
sample. About 20 percent of all phone numbers are
Random digit selection has two main disadvantages,
however. It is difficult (a) to distinguish between

commercial and residential units in the sampling
frame and (b) to determine whether units that do not
respond are eligible respondents because there is
no one on the other end to ask.
3. Special Characteristics of Mall Surveys
Like face-to-face interviews, self-administered mail
questionnaires have been used effectively for decades
to collect survey data. Mail questionnaires are par-
ticularly appropriate for obtaining detailed technical
and scientific data and are the least costly of the
three collection methods.
More specifically --
	Mail surveys are indispensable for collecting cer-
tain kinds of detailed technical data. They are
especially appropriate if respondents must consult
their records or other people for the necessary
data. Self-administered questionnaires allow re-
spondents great flexibility in preparing replies.
Respondents have time to think about the questions,
gather information from their files, and get advice
from others at their own convenience.
	Mail questionnaires are the least costly of the
three traditional collection methods, largely be-
cause the cost of interviewers is nil or limited to
call-backs to assure an acceptable response rate.
Moreover, broad geographic coverage is possible with
comparatively little effect on the overall cost of
the survey.
	Respondents generally are most honest in mail sur-
veys and tend to give fewer "socially-desirable"
responses. In an interview survey, particularly
in the presence of an interviewer with whom they
have established a good rapport, respondents tend
to give more socially-acceptable, less critical
replies. For example, if respondents are asked if
they like living in their community, they tend to
say they do, even though on the whole they may
dislike it greatly. The same question on a mail
questionnaire will elicit more truthful responses.
Mail surveys have some limitations. For example --
	Mail questionnaires must be very carefully designed
to compensate for the lack of social interaction
that other collection methods provide. Researchers

must depend entirely on the questions and written
instructions to elicit satisfactory responses and
motivate the respondents to cooperate.
The kinds of questions that are suitable for self-
adrainistered questionnaires are relatively limited,
especially for household surveys. Open questions
must be used sparingly. More than a few requests
for lengthy answers may result not only in many
refusals to answer those questions but also may push
respondents to abandon the questionnaire alto-
gether. Generally, if respondents are required to
read any but the simplest language or to write out
answers in their own words rather than circle or
check a printed response, the results tend to be
very poor. Of course, these concerns are less likely
to be a problem if the respondents are representa-
tives of businesses or industries.
	Mail surveys may be inappropriate if the researchers
want respondents to complete the questionnaire with
no involvement from others. When questionnaires
are self-administered, it is impossible to know
the circumstances under which they were completed.
	A substantial follow-up effort is almost always
necessary to achieve a reasonable response rate in
any voluntary mail survey. To increase the response
rate, researchers sometimes give respondents the
option of telephoning their replies rather than
mailing back the completed questionnaire.
	Compared to other data collection methods, self-
administered questionnaires produce a higher num-
ber of inaccurate and incomplete responses, largely
because no interviewer is present to instruct and
motivate the respondents.
A host of interrelated design factors as well as the time
and funds available for the survey affect the contractor's
choice of the primary data collection method for a partic-
ular survey.
In the remainder of this section we will briefly examine
the selection factors that normally determine the choice
of the primary data collection method for a statistical
survey. They are --

	Characteristics of the
target population
	Data requirements
	Obligation to reply
	Target response rate
	Time available
	Funds available
1 . Characteristics of the Target Population
The characteristics of the target population often
are an important consideration in selecting the pri-
mary data collection method.
For example, mail surveys of the general public have
lower response rates than either of the direct inter-
viewing techniques. Most surveys of business popula-
tions, on the other hand, use mail questionnaires as
the primary collection method and follow up incomplete
or incorrect responses with telephone interviews.
Face-to-face interviews are generally the preferred
approach for elderly respondents and those with limited
education. Low-income respondents also do best in
face-to-face interviews.
The location and distribution of the target population
are also factors. Face-to-face interviews are more
cost-effective when the target population is concen-
trated in a small geographic area, such as a particular
city or county. If the target population is widely
dispersed, however, travel and administrative costs may
make a face-to-face survey prohibitively expensive and
time-consuming. Self-administered questionnaires or
telephone interviews are more realistic options. Mail
surveys are least affected by a widely dispersed
2. Data Requirements
The general nature, extent, and complexity of the data
requirements are important determinants in choosing the
primary collection method. Mail questionnaires should
not be used to survey the general public except when
answers to a few short questions are needed. Other-
wise, it is best to use face-to-face or telephone
The data requirements of many organizational surveys
require respondents to consult their records or other

people in order to prepare adequate replies. A self-
administered mail questionnaire may be the only fea-
sible way of getting the necessary data in such cases.
If it is necessary to ask a large number of questions
respondents may consider "threatening" or unusually
sensitive, it is preferable to use face-to-face inter-
views. To minimize the impact of what may be perceived
as potential threats to their operations, business or
industrial respondents, for example, may furnish in-
accurate or incomplete replies. If it is necessary
to collect highly sensitive technical data, the con-
tractor may recommend using trained investigators to
make first-hand observations of records or physical
facilities to ensure that the Agency obtains complete
and valid data,
3.	Respondent's Obligation to Reply
The respondent's "obligation" to provide the informa-
tion the Agency needs often has a critical impact on
the choice of the primary collection method. In some
cases, the Agency can make responses from businesses
and other organizations mandatory. If so, the re-
spondents must provide the required data or face civil
or criminal sanctions. Whenever a mandatory response
is required, a relatively high response rate is ensured,
no matter what collection method is used. Even self-
administered mail questionnaires become a viable op-
tion. They normally yield so few responses in a volun-
tary survey they cannot be used for collecting Agency
data. (In a survey of the general public, of course,
response is always voluntary.)
4.	Target Response Rate
The collection method likely to produce the highest
response rate given the available funds is obviously
preferable. Face-to-face surveys tend to have the
highest response rate, other things being equal. How-
ever, telephone surveys can produce response rates
nearly as high if they are skillfully designed and
carried out. As for mail surveys, unless responses
are mandatory and considerable follow-up work is done
using telephone or in-person interviews, they are
unlikely to achieve the 75 percent minimum response
rate we recommend for all Agency-sponsored surveys.
5.	Available Time
The length of time the Agency can wait to get results
also may be a deciding factor in the selection of the
-3 2-

data collection method. Computer-assisted telephone
interviews have by far the fastest turn-around time.
Conventional telephone surveys also can be done more
quickly than face-to-face surveys. Mail surveys are
generally not appropriate if time is of the essence.
6. Available Funds
The amount of money available for the survey, too, is
almost always a critical factor in choosing the pri-
mary data collection method.
As we indicated earlier, individual face-to-face inter-
views are the most expensive way of collecting survey
data, other things being equal. Personnel costs (for
interviewers, supervisors, trainers, and quality con-
trol staff at different field locations) are approxi-
mately double that of a comparable telephone survey,
where the interviews are usually conducted at one
central location. Mail surveys always are the least
costly option, largely because the cost of interview-
ers is limited to some follow-up calls to increase
the response rate or to correct inconsistencies and
missing or inaccurate replies.
Nevertheless, the least expensive option should not be
selected unless it will produce results of acceptable
quality. Sometimes it is better to use a higher-cost
method and reduce the size of the sample. For example,
a mail survey using face-to-face or telephone inter-
viewers to follow up incomplete or unanswered question-
naires usually produces higher quality results than a
"pure" mail survey, even if a smaller sample is used
to hold down costs.
We have recommended that you leave the selection of the
collection method(s) up to the contractor. However, as
the representative of the sponsoring office, you will
have to approve the contractor's choice. The previous
discussion of the special features of the three tradition-
al data collection methods and the influence of various
survey design factors will help you assess the appro-
priateness of the proposed method.
To further guide your assessment, Exhibit 1 on the next
page indicates the methods most likely to produce satis-
factory results under a variety of circumstances.

	Fast turn-around
	Lowest possible
per unit cost
	Highest possible
response rate
	Fewest possible errors
and biases
	Mai 1
	Face-to-Face or
	Complex technical data
(in a mandatory survey)
	Detailed data (in a
voluntary survey)
	Respondent's opinion
of a product or device
	Highly sensitive infor-
	Face-to-Face or
Mai 1
	Face-to-Face or
	Coverage of all sub-
groups in population
	Coverage of widely dis-
persed sample
	Coverage of high-crime
or remote areas
	Face-to-Face or
	Mai 1
	Telephone or Mail
	Extensive probing
	Third-party observation
of records or facilities
	Respondent diaries
	Respondent consultation
with others or record
	Visual aids (calendars,
scales, etc.)
	Face-to-Face or
	Face-to-Face or
* CATI is especially effective.
** Telephone may be satisfactory if visual aids are mailed to
the respondents in advance.

Although one of the traditional collection methods will
ultimately be selected for testing purposes and for the
main survey, using one of the exploratory research tech-
niques discussed in section A may considerably improve
the survey design. At a relatively low cost either indi-
vidual in-depth interviews or focus group interviews often
can clarify problems that raay be difficult and costly to
correct once the survey proper is under way.
	Mail and Telephone Surveys: The Total PesiRn Method,
D. A. Dillman, John Wiley fit Sons, New York, NY
1978. Chapter 2.
	Survey Research Practices, G. Hoinville, R. Jowell,
and associates, Heinmann Educational Books, London,
England, 1978. Chapter 2, "Unstructured Design
	Surveys by Telephone, R. M. Groves and R. L. Kahn,
Academic Press, Inc., New York, NY, 1976.

A well-designed, thoroughly-tested questionnaire is the most
basic tool in survey research. Developing a valid questionnaire
for an Agency-sponsored survey requires close collaboration by
the sponsors and the contractor throughout the design and test-
ing process.
In this chapter we discus9 --
	The principal steps in the development of a
good survey questionnaire;
	The respective roles of the project officer
and the contractor in designing and testing
	How to review drafts submitted for Agency
approval; and
	How to monitor the activities designed to
pretest the questionnaire.
This section discusses the steps normally involved in
developing a structured questionnaire for a statistical
survey. The development process we will discuss involves
16 steps, the majority of which are performed by the con-
tractor. Agency-sponsored surveys that are largely repe-
titions of earlier studies may shortcut many of the steps,
but for surveys that address new environmental concerns,
a thorough questionnaire-development effort is strongly
Preparing a survey questionnaire appears to be an easy
task, but it is extremely difficult -- even for an exper-
ienced questionnaire designer. In no case should you or
the contractor begin to draft the questionnaire until
the Agency's data requirements have been clearly framed.
The reason is that each question must have an obvious
link with the data requirements. These requirements then
must be transformed into operational concepts and expressed

in a logical series of questions, which, when combined and
analyzed, will be the measures of those concepts.
Usually several drafts of the questionnaire -- for one or
more pretests and for a pilot test replicating the actual
conditions of the main survey -- must be prepared and
reviewed before a final version is ready to be printed for
the raain survey. If several versions of the questionnaire
have to be designed to accommodate the needs of different
types of respondents, more drafts may be necessary.
A summary of the questionnaire-development process we will
discuss is given in Exhibit 2 on the next page. The check-
marks indicate the six steps in which the Agency sponsors
play the primary role. This role is generally limited to
(a) specifying the research topics, (b) reviewing drafts,
and (c) monitoring the overall design and testing process.
More specifically, as the survey sponsors, you are respon-
sible for
= Preparing a preliminary analysis plan establishing
the research and analytical objectives of the survey
(Step 1);
Supplying the subject matter of the questionnaire,
either in the form of a list of topics or a prelimi-
nary set of questions (Step 2);
Overseeing reviews of all draft questionnaires sub-
mitted by the contractor and expediting any internal
or OMB approvals or clearances that may be required
(Steps 5, 7, 11, and 15); and
In addition, you are responsible for monitoring all the
questionnaire-development and testing activities the con-
tractor performs, which are covered in Steps 3, A, 6, 8,
9, 10, 12, 13, 14, and 16.
The discussion of the individual steps, which follows, will
cover the Agency's role in specifying the research topics.
Section B will show you how to review questionnaire drafts,
and section C explains how to monitor field tests of the
We recommend that you use only members of the target popu-
lation in any preliminary investigations you intend to
conduct in preparation for writing the questionnaire, such
as --
= Exploratory studies (individual in-depth interviews or
focus group studies) to clarify difficult issues or
test draft questions you expect to ask (see Step 3) ;






= Informal pretests to check the content, wording, or
format of a proposed questionnaire (see Steps 6-9);
= A pilot test of the near-final version of the question-
naire and the data collection and processing procedures
(see Steps 10, 11, 13, and 14).
Let's look now at the individual steps in the development
of the questionnaire.
	Step 1: Determine the
Analysis Requirements
The first step in constructing a structured question-
naire for an EPA-sponsored survey is to prepare a pre-
liminary analysis plan. Because you, as sponsors of
the project, are likely to have greater expertise in
the subject matter of the research, you -- not the con-
tractor -- should prepare the analysis plan. As dis-
cussed in Chapter 1, the analysis plan should define (a)
the purpose of the survey, (b) the research objectives,
(c) the key variables, (d) the analytic approaches and
methods to be used to achieve the stated objectives,
and (e) a list of preliminary tabulations, which will
allow you and the contractor to decide which types of
analyses will best reveal the full informational content
of the data base. You should include at least a draft
analysis plan in the Request for Proposals (RFP) the
Agency issues for contract support for the survey.
Then, after a contract is awarded, the contractor can
refine the draft and submit it for approval along with
the other components of the work plan.
	Step 2: Draft a List of Topics
or Suggested Questions
We suggest that you prepare a comprehensive list of
research questions and, perhaps, an informal list of
the items you would like to see on the final question-
naire. Keep in mind that all questionnaire items must
be clearly relevant to the informational and analytical
objectives of the research. Questions should not ask
for information that may be "nice to have." If you
decide to draft an informal list of questions, there-
fore, as you write each item, ask yourself, "Why do I
want to know this?" "It would be interesting to know"
is not an acceptable response. Also, don't attempt to
write the questions verbatim or to format the question-
naire. It's best to leave those tasks to the contractor
(see Step 4).

Before preparing your list of research topics and pre-
liminary questions, we suggest you look for questions
or scales that have been used in earlier Agency surveys
to explore various environmental issues. In addition,
you may find questions or scales used in other survey
reports helpful in framing your research questions.
A search of this type may seem time-consuming and tedi-
ous, but it often is time well spent. Even if you find
only a few good items, this may cut down on the time
required to test the questionnaire. Moreover, the
search generally will give you a better perspective on
your analysis needs.
If you do find some usable questions, they are unlikely
to cover all aspects	^	urvey is
evolving issues on which little research previously
has been done. No doubt, you will have many new ques-
tions you expect the contractor to explore.
Keep in mind that any list of topics or questions pre-
pared at this stage should be regarded as prelimi-
nary. Only after exploratory studies and one or more
advance tests of the data collection instrument are
completed can you be reasonably confident of having a
questionnaire that will meet your data and analysis
objectives. Some compromises in the data requirements
may be necessary if it turns out that respondents are
unable or unwilling to answer certain kinds of questions.
 Step 3: Conduct Exploratory Interviews
with a Few Individuals in the Population
Even if you succeed in preparing a reasonably complete
list of topics or preliminary questions, you may find
that there are still gaps in your understanding of the
issues. If so, before the contractor begins to draft
the initial draft of the survey questionnaire, it may
be productive to explore some of the key issues with a
few members of the populations you plan to investigate.
A series of focus group interviews or in-depth inter-
views may prove fruitful in resolving uncertainties
at this early stage of the questionnaire's development.
To date, EPA has not used either of these exploratory
research techniques extensively, but other agencies
have found them highly effective in resolving a range of
conceptual problems that would be prohibitively costly
or impossible to resolve later in the development of
the questionnaire. Individual in-depth interviews or
intended to addressT

focus groups can be used to explore attitudes, opinions,
concerns, and experiences of potential respondents; de-
velop data specifications; test the wording of questions;
or even to evaluate an entire draft of a questionnaire.
These techniques are suitable for exploring issues re-
lating to household or non-household surveys. For ex-
ample, sometimes it is essential for the sponsors to
know the record-keeping practices of the industries they
intend to survey so they can determine what kinds of
questions the respondents may reasonably be expected to
answer. Either of these exploratory research techniques
is likely to add two to six weeks to the overall develop-
ment process. If an OMB clearance is necessary, it may
take somewhat longer. However, because the final ques-
tionnaire undoubtedly will require fewer refinements and
less testing, you may well be able to recover this time
before the main survey begins.
Be sure to check with your office's Information Manage-
ment Coordinator regarding the need for an OMB clearance
for this preliminary interviewing work. Sometimes a
clearance is necessary.
 Step 4: Prepare First Draft
of the Questionnaire
Building on (a) the data and analytic requirements you
formulated in Steps 1 and 2; (b) the findings of the
exploratory interviews, if any (Step 3); and (c) other
specifications in the work plan concerning the data
collection, processing, and analysis procedures, the
contractor can begin to draft the questionnaire. A
structured questionnaire typically consists of --
Introductory information explaining the objectives
of the survey and the reasons the respondent's
cooperation is solicited. (In a self-administered
questionnaire, this information is usually stated
in a cover letter.)
= Identification and control information showing the
name of the survey sponsor, the name of the organi-
zation collecting the data, the authority for col-
lecting the data (e.g., any applicable statutes),
the OMB control number and expiration date of the
clearance, various code numbers identifying the
individual response unit (the household, business,
individual, etc.) and where the unit is located,
and any additional information needed for control

A set of standardized questions addressing the re-
search problem;
Instructions to the person filling in the data;
Definitions of all technical and unusual terras.(An
EPA-sponsored survey of businesses or industries
frequently will include an entire section on defi-
nitions .)
In most cases, once you have formulated the basic con-
tent of the questionnaire and approved the work plan,
it is best to let the contractor construct the ques-
tionnaire. The content and wording of the individual
items as well as the overall organization and format
of the questionnaire will be major factors in deter-
mining whether the survey ultimately produces timely,
reliable, useful information.
The questions must be worded so they can be clearly
understood, arranged in the best possible order, and
capable of eliciting objective, unbiased answers. If
the questionnaire is to be self-administered, it has
to be designed in a way that will motivate the re-
spondents to make the necessary effort to retrieve,
organize, or report the required information in the
specified format. If it is to be administered by a
trained interviewer, the design and format should
facilitate the work of the interviewers in asking
questions and recording responses. The format should
also expedite the coding and data entry operations
during the processing phase.
Step 5: Review and Approve First
Draft of the Questionnaire
Extensive reviews of the first draft of the question-
naire (and all subsequent drafts) submitted for Agency
approval are vital to ensure that --
= The content is relevant to and focused on the re-
search objectives;
= The wording is clear and unambiguous: and
The overall organization and format of the ques-
tionnaire will facilitate the data collection,
processing, and analysis activities.
As project officer, one of your principal responsi-
bilities during the development process is to ensure

that the questionnaire is constructed so that it will
achieve the objectives of the study. Criteria for a
systematic review of draft questionnaires are given
in section C; therefore, we will not elaborate further
In addition to circulating drafts to key people on the
project staff, you should have computer programmers,
systems analysts, and statisticians review them as
well as people outside EPA who are knowledgeable about
the subject matter or the intended uses of the data.
After the contractor incorporates changes in the
draft, make sure the comments of all reviewers are
accounted for.
 Step 6: Prepare
Flan for Pretest
While the Agency is reviewing the initial draft of
the questionnaire, the contractor should prepare a
plan to pretest it informally on one or more sub-
groups of the target population.
The pretest plan should cover (a) the scope of the
test (whether the entire questionnaire or only cer-
tain questions will be evaluated); (b) the size and
composition of the test sample; (c) the techniques
to be used in administering the test (e.g., face-
to-face or telephone interviews); (d) procedures for
training the interviewers and observers; (e) proce-
dures for conducting and evaluating the test; and
(f) the kinds of tabulations and analyses that will
be done.
Pretesting is essential for all structured question-
naires, regardless of the data collection method pro-
posed for the survey proper. The techniques used to
pretest an interview survey and a mail survey are
quite different, however.
= For a face-to-face or telephone survey, one or more
informal pretests are mandatory. Rigorous analytic
techniques normally are not used, however. Instead,
interviewers, observers, and respondents subjec-
tively evaluate various aspects of the question-
naire. At a relatively low cost, pretests can
determine whether changes in the wording of the
questions, their sequence, or the length of the
questionnaire are likely to improve the quality
of the survey data. Pretests also may indicate a
need for adding or eliminating certain questions.

Usually the contractor will do a few informal
tests; then, when the wording and format of the
questionnaire have been refined, they will conduct
a formal test, called a "pilot test," to evaluate
the data collection procedures as well as the
questionnaire. For a major interview survey, a
full-scale pilot test should be done. (Step 12.)
Some of the techniques used to evaluate pretests
of an interview survey are (a) observations by
trained supervisory staff; (b) discussions with
respondents immediately after the questionnaire
is administered; (c) daily interviewer debriefings;
(d) interviewer records of call-back rates and the
duration of the interviews; (e) tape recordings
of a few test interviews; (f) written reports by
interviewers on the difficulties encountered in
collecting the data, and suggestions for improving
the questionnaire, control forms, or the interview-
ing procedures; (g) debriefings at the conclusion
of the pretest with the interviewers, questionnaire
designers, field supervisors, and observers; and
(h) preliminary tabulations of the pretest data.
= Techniques for pretesting a mail survey tend to be
more formal. Usually, a draft of the questionnaire
is mailed to a small subset of the target popula-
tion. The results are then tallied and evaluated.
A less formal method of testing a mail question-
naire is to mass-administer it to a group of re-
spondents "classroom-style," with a moderator and
several observers in attendance. Some face-to-face
interviews also may be used for testing mail ques-
tionnaires at an early stage of their development.
When the contractor submits the pretest plan for
Agency review, make sure (a) the pretest sample ade-
quately represents all important subgroups of the tar-
get population, (b) the size of the sample is adequate
for a valid test, (c) the test conditions approximate
those of the actual survey, and (d) enough time has
been allowed to analyze the test results and incor-
porate any necessary revisions in the questionnaire.
Submit the plan along with the questionnaire to ap-
proval authorities in your office. If you need to
apply for an OMB clearance for the pretest, also have
the Information Management Branch of the Office of
Standards and Regulations review the plan and the
questionnaire at this time.

 Step 7: Initiate Clearance
Request for the Pretest
Obtaining OMB clearance(s) for all pretests and the
main survey in a timely way is a major responsibility
of the project officer if data are to be collected
from ten or more members of the public. Clearance is
mandatory per the Paperwork Reduction Act. The pur-
pose of the OMB review is to ensure that (a) the in-
formation that agencies propose to collect is in the
public interest, (b) the reporting "burden" (the
length of time it takes a respondent to complete a
questionnaire or be interviewed) is reasonable, and
(c) certain statistical standards are met.
You may submit a clearance request for the pretest
(or a series of pretests) along with one for the main
survey. Allow two weeks for each Agency office that
must review the clearance package before it goes to
OMB. Allow a minimum of two months to obtain OMB
clearance after you secure all necessary internal
approvals. (See Chapter 7 of Volume I for more in-
formation on OMB clearance procedures.)
Step 8: Pretest on a Sample
of the Target Population
While awaiting the OMB clearance, the contractor some-
times will organize and train the interviewers and
other staff to be used for the pretest, but usually
it is best to wait until the clearance is granted.
The contractor's principal responsibilities in prepar-
ing for the pretest are --
Selecting the agreed number of respondents from the
target population. For an informal pretest, 20 to
50 respondents usually will suffice. Generally,
a "purposive" sample rather than a probability sam-
ple is drawn so that all subgroups in the target
population or specific subgroups of concern are
Choosing interviewers for the test. Some survey
research firms maintain an experienced team of
interviewers solely for pretests. Others use only
supervisors so they can gain experience that will
be useful in training and overseeing the inter-
viewers picked for the main survey. Still others
use interviewers with education and experience
similar to that of the interviewers to be used for

the main survey. In all cases, it is best to use
as many interviewers as possible, provided each of
them has a sufficient workload to justify the cost
of their training and travel.
Selecting and training one or more field supervi-
sors to oversee the interviewing.
Training the interviewers in the general purposes
of the survey and the specific objectives of the
pretest. This kind of training is vital for all
the interviewers who participate in the test --
even the most experienced. If the interviewers
do not have a thorough understanding of the ques-
tions, it will be impossible for the questionnaire
designers to determine whether problems with the
questionnaire are due to poor interviewing or to
the data collection instrument itself.
The interviewers also must be thoroughly trained
in the proper way to administer the questionnaire
(e.g., not to arbitrarily reword questions; how to
probe and ask other questions when respondents'
first answers are inappropriate, inaccurate, or
The pretest itself frequently is conducted under con-
ditions similar to that planned for the main survey.
As for the project staff's responsibilities once the
pretest is in progress, we recommend that --
- You or members of your staff observe several pre-
test interviews to gain first-hand experience in
how the questionnaire works in practice. Discus-
sions with respondents following each pretest in-
terview -- a major feature of informal pretests --
provide important feedback to questionnaire design-
ers on how respondents interpreted various ques-
tions; difficulties they experienced in replying to
certain items; how they would ask certain questions;
or their feelings about questions to which they
responded "Don't know," etc.
You attend some of the daily debriefings with the
interviewers. The purpose of these debriefings is
to get immediate feedback from field personnel on
problems they have had with the questionnaire so
the contractor can make on-the-spot refinements
for testing during the next day's interviewing.
The interviewers may discuss (a) difficulties they

encountered in locating respondents; (b) questions
that embarrassed respondents or otherwise made them
feel uncomfortable; (c) which items respondents re-
fused to answer and the reasons given for the refus-
als; (d) difficulties they had in maintaining rap-
port with respondents; (e) whether the respondents
became impatient or bored; (f) whether respondents
seemed to want to rush through any part of the ques-
tionnaire, particularly the ending; (g) whether the
format of the questionnaire was particularly hard
to follow; (h) whether any items required further
explanation;(i) how long the interviews took; and
(j) if there was enough space to record answers,
especially to open questions.
(See sectionCfor suggestions on monitoring pretests.)
	Step 9: Debrief Interviewers
and Assess Pretest Findings
When the pretest is over, the contractor generally
will hold one or more debriefing sessions with all
the interviewers, supervisors, and observers who have
participated in the pretest.
You and other members of your staff should attend
these sessions so that any necessary changes in the
questionnaire or training procedures can be jointly
agreed to and quickly implemented. The format of
these 9es9ions generally is similar to that of focus
group discussions (see section A of Chapter 2).
Based on the outcome of the final debriefings and any
preliminary tabulations, the contractor will be in a
position to determine if further revisions or tests
of the questionnaire are needed.
The contractor should revise the questionnaire after
each pretest until all problems are resolved. In a
major survey, another pretest should be done after
each revision because the revisions may cause new
Note: Steps 10-13 may be omitted if no further tests are
	Step 10: Revise Questionnaire and
Prepare Plan for the Pilot Test
If you propose to survey more than 500 people (or
units), the la9t step in the testing process for an

Agency-sponsored survey should be a full-scale pilot
test -- a more formal type of pretest. A pilot test
is, in effect, a "dress rehearsal" for the main sur-
vey. Normally, it should duplicate the field proce-
dures as closely as possible, and the questionnaire
should appproximate the one that will be used in the
main survey.
The first step in preparing for the pilot test is to
develop a planning document clearly delineating the
objectives of the test. Pilot tests can be used
to --
= Evaluate the wording, content, and format of the
questionnaire, and test alternative versions, if
 Identify and correct weaknesses in the proposed
interviewing procedures -- the interviewer's in-
structions and training manuals, the length of
the interviews, and the logistics of the field
= Provide a realistic body of data to test the pro-
posed processing procedures -- the specifications
and instructions for coding, data entry, computer
editing, and tabulation operations.
If the test is carried through to the analysis phase,
the preliminary tabulations can provide a final check
on the analysis plan.
The time required to conduct, process, and evaluate
the results of a pilot test is considerably longer
than for an informal pretest. From five to ten
months may be required for the pilot -- after the
Agency approves the questionnaire. This includes
the time required to obtain OMB approval (up to 90
days) .
In a pilot test of a face-to-face survey, usually
at least 50 respondents and several interviewers at
different skill levels are used. It is not unusual
to have up to 300 respondents and as many as 20
interviewers. Potentially "difficult" respondents or
"hard-to-reach" population groups should be included.
The interviewers also must be selected and trained
in the specifics of the test and one or more field
supervisors appointed to keep track of the inter-
viewers' workload and evaluate their performance.

	Step 11: Review Revised Questionnaire
and Pilot Test Plan
You and your staff should critically review the pilot
test plan, giving special attention to the proposed
tabulations and analyses. Circulate it to computer
programmers and system analysts, if necessary.
The contractor should allow enough time to analyze
the data and apply the findings before the main sur-
vey begins. Important benefits of pilot tests fre-
quently are not realized because the analysis is not
planned in enough detail, and insufficient time and
resources are committed to it.
If you have not yet applied for OMB clearance of the
pilot test, you must do so at this time. We recom-
mend that you combine it with the clearance request
for the main survey so the contractor can proceed
with the main survey as soon as the pilot test
results are analyzed.
	Step 12: Recruit Interviewers
and Prepare Training Materials
The quality of the interviewing in the pilot test and
the survey proper will be greatly influenced by the
amount of care taken in selecting and training the
intervi ewers.
As we have seen, a great deal of effort typically
goes into the development of the questionnaire so it
will effectively yield valid, unbiased data. To
achieve satisfactory results in an interview survey,
the data must be collected in a systematic, uniform
manner from all the respondents.
The interviewers selected for the pilot test usually
work in the main survey as well. If the contractor
has a permanent field staff in the sampling areas,
there probably will be no need to recruit new inter-
viewers. Most large survey research firms maintain
a permanent cadre of interviewers located throughout
the United States. Having a permanent interviewing
staff does not guarantee the quality of the fieldwork
will be high, but experienced interviewers are far
more likely to collect good data than a group of new
interviewers recruited solely for one survey.
In addition to selecting the interviewers, the con-
tractor must (a) develop procedures and materials for

training the interviewers and a field supervisor, (b)
determine how many training sessions will be needed,
and (c) where the session will be held. This can be
done while awaiting the OMB clearance for the pilot.
Interviewer training for the pilot test should cover
the objectives of the survey, the content and concepts
of the questions, interviewing techniques, the proce-
dures to be used to control the quality of the field
work, and practice interviews. Instruction manuals
and other training materials also should be prepared
so their effectiveness can be assessed before the in-
terviewers for the main survey are trained. (See
section A of Chapter 5 for detailed information on
	Step 13: Pilot Test Questionnaire
and Assess Results
Once the interviewers are recruited and trained, the
interviewing phase of the pilot test should proceed
much like any other data collection operation using a
structured questionnaire. The techniques used to ob-
serve and evaluate the test are similar to those used
in informal pretests (see Steps 8 and 9) with one
major difference -- a greater focus on statistical
evaluation of the data.
For example, debriefing sessions with all the inter-
viewers and observers are held following the test.
The debriefings may alert the analysts to problems
with specific questions, the order of the questions,
or the length of the questionnaire. As a result, it
may be necessary to change or discard certain ques-
tions. If the average length of the interviews is
too great, some questions may be dropped to stay
within the established time and budget constraints
-- even if nothing is wrong with the questions.
To carry a pilot test to its logical conclusion, the
analysis of the pilot test data should be sufficient
to allow the contractor to assess the validity of
the analysis plan,
	Step 14: Revise Questionnaire and
Collection Procedures for Main Survey
When the pilot test is concluded, the questionnaire
ordinarily should require few revisions. By gradually
fine-tuning the data collection instrument -- through
discussions with respondents, interviewer debriefings,

observation and monitoring of the interviews, inter-
viewer reports and assessments, data validation, and
the analysis of the pilot test data -- the contractor
should be in a position to begin the main survey with
clear assurance that the resulting data will meet the
Agency's objectives.
In addition to modifying the questionnaire, the con-
tractor should submit a revised data collection plan
to the Agency for approval before the survey proper
begins. The plan should include (a) provisions for
training and supervising the interviewers, (b) "rules"
for respondent eligibility (respondent rules) , (c)
rules for following up the initial contacts with re-
spondents, (d) rules for verifying and evaluating the
interviews, and (e) the quality-control measures that
will be used to ensure that the target response rate
for the survey and the response rates established for
individual items are achieved. (See section A of
Chapter 5 for detailed information on preparing for
the interviews.)
	Step 15: Review and Approve
Procedures for the Main Survey
The final draft of the questionnaire and the proposed
data collection procedures should be critically re-
viewed by the project staff, data processing special-
ists, and systems analysts. We strongly recommend
that you have a survey expert review these materials
(whatever collection method is planned) before grant-
ing approval to proceed with the survey. A success-
ful field operation requires close coordination and
monitoring by the contractor and the EPA project
staff, effective interaction between the interviewers
and the respondents, and careful training and super-
vision of dozens of interviews at several field
If you have not submitted the OMB clearance request
for the main survey, do so at this time after clear-
ing it with your Office Director and OSR's Informa-
tion Management Branch.
	Step 16: Print
The questionnaire for the main survey should not be
printed until the results of the pilot test indicate
there are no more serious problems. In no case should
it go to the printer until you have received an OMB

control number. Both the number and the expiration
date of the clearance must appear on the form.
Make sure that the contractor orders enough question-
naires. It is best to get 50-1 00 percent more than
the number of respondents. The extra copies can be
used for training purposes and practice interviews.
Copies sometimes are lost during the distribution
process and others are wasted in the field.
Check proofs of the questionnaire received from the
printer for spelling and typographical errors. When
the printed version arrives, batches should be spot
checked for poor print quality, missing pages, etc.
This section provides instructions for systematically re-
viewing a survey questionnaire. The instructions are
intended to help you critique drafts submitted by the
contractor for Agency approval during the development
process, as shown in Exhibit 2.
The instructions are presented in three parts. We recom-
mend that you first review (1)the form, content, and word-
ing of each question individually; then (2)the content and
organization of the questionnaire as a whole; and, lastly,
(3jthe overall format^
A checklist of the suggested criteria for this three-stage
review is given in Exhibit 3. Use it, along with a copy of
the analysis plan (see Chapter 1), to guide your reviews.
Also, be sure to circulate review drafts to others with
expertise in questionnaire design, data processing, and
statistical analysis, as appropriate.
1. Reviewing Individual Questions
Begin your review of the questionnaire by critically
examining each question. Review the --
You'll want to look first at the
the form -- the answer format -
e appropriateness of
-- or each question.

* Relevance
= Reasonableness
= Sensitivity
Word ing
Absence of (unintentional)
leading or "loaded" terms
Scope of the questions
Order of the questions
Explanatory and control
informat ion
= Introductory explanations
= Instructions
= Interviewing aids
ID and control information
= Data processing directives
General appearance
Of the questions
= Of the instructions

There are three reasons: (a) Survey questions are
classified by their answer format, (b) the form is
the most immediately visible aspect of a question,
and (c) the proposed form of the question may
affect your review of the content and wording.
To assist you, we'll briefly outline (1) the basic
types of survey questions and (2) the advantages
and limitations of each.
= Types of survey questions.
There are three basic types of survey questions:
(1)	Closed (or closed-ended) questions offer re-
spondents a choice of two or more response
options, the most common of which are "Yes/
No" and "Agree/Disagree." Sometimes a third
option, "Don't know" or "Undecided," is used.
Closed questions are sometimes called "fixed
alternative," "fixed choice," or "poll" ques-
tions. Also classified as closed questions
are so-called "multiple-choice" questions,
which permit respondents to choose their an-
swers) from several response categories.
(2)	Open (or open-ended) questions give respond-
ents a frame of reference but permit them to
reply in their own words. Traditional open
questions allow respondents to give their
opinions fully, in language comfortable to
them, without restriction. However, open
questions do not necessarily call for a
lengthy response. They are often used when
very short numerical answers are sought --
age in years, expenditures in dollars, volume
in cubic feet, etc.
Open questions are further classified as
*" 11	; traditional open question) or
open, the interviewer simply records the re-
ply" verbatim. The questionnaire will include
a blank space for the interviewer to write in
the respondent's answer. If the interviewers
find it necessary to use probes to encourage
a more complete answer, they are expected to
indicate directly on the questionnaire where
they intervened to seek clarification
usually by placing an "X" after the respond-
ent's reply.
When a question is fully-

Partially-open questions, on the other hand,
are more like closed questions. They appear
to be open to the respondent, but they actu-
ally provide a fixed set of response options.
The interviewer selects the response op-
tion(s) closest to the respondent's answers
or, sometimes, will guide the respondent to
an answer within certain limits. Partially-
open questions on self-administered question-
naires provide several fixed response options
as well as an "Other-Specify" category.
(3) Scale (or ranking) questions permit respond-
ents to rank their responses according to
(a) preference or interest, (b) degree of
agreement or disagreement, or (c) some other
scale of measurement. Scale questions are
actually a special form of closed questions.
Advantages and Limitations.
Many survey research firms have a decided pref-
erence for closed questions. There are three
reasons: (1) closed questions tend to be more
reliable; (2) they are easier for interviewers,
coders, and analysts to deal with; and (3) un-
like open questions, they generate no irrele-
vant, unintelligible responses to complicate the
data processing and analysis phases.
Nevertheless, closed questions have certain dis-
advantages. The major problem is their superfi-
ciality. A questionnaire containing only closed
questions doesn't get to the heart of issues.
Closed questions also tend to force replies.
Sometimes respondents choose any answer to con-
ceal their ignorance about the topic or they may
pick a response that does not reflect their
true opinion just because they feel compelled
to check or circle one of the fixed responses.
Carefully constructed and used in combination
with open questions, however, closed questions
can be very effective.
Open questions have many advantages. They put
a minimum of restraint on respondents' replies
and the manner in which they express them. The
open format permits interviewers to probe the
respondents' knowledge of a subject and their

frames of reference, and to clarify or ascertain
the reasons for the answers they give. To learn
the respondents' true intentions, beliefs, feel-
ings, or attitudes, some open questions should
be used. The open format is an invaluable tool
for exploring a topic in depth. It is an abso-
lutely essential tool if you are beginning work
on a new research topic and need to explore all
aspects of the subject.
But open questions are also appropriate when the
potential responses are both nominal in nature
and sizeable in number, e.g., questions asking
for a single-word response such as the respond-
ent's age or income.
The richness of data the open format yields,
however, can be a disadvantage when it comes
time to summarize the data in concise form.
Reducing a large number of varied responses to
a few categories that can be treated statisti-
cally is a major challenge for coders during
the processing. Coding a complex set of open
responses is not only time-consuming and costly,
but also introduces some amount of (coding)
error. If the data categories are extensive,
the contractor must develop complex coding in-
structions, train staff in the proper use of
the codes, and make periodic reliability checks
to estimate the amount of coding error. (See
Chapter 6 for more information on coding.)
There are other disadvantages. Open questions
take more time to answer than closed questions.
This tends to increase the response burden of
the survey. They also require greater inter-
viewer skill in recognizing response ambigui-
ties, and in probing or drawing out respondents,
particularly those who are reticent or not
highly verbal, to make sure their answers are
codable. This aspect of the open format has
made some researchers wary about using it except
in situations where they are sure of getting
well-trained, well-supervised interviewers.
Scale questions are good for measuring attitudes
and values because they allow researchers to
identify the intensity of respondents' feelings,
beliefs, or preferences. You might devise an
intensity scale, for instance, to measure a com-
munity's preference of air quality strategies.

To help you assess question forms, we conclude
our discusssion with a few tips from Sudraan and
Bradburn's Asking Questions (see list of refer-
ences at the end of this chapter.)
(1)	Open questions should be used sparingly --
for developmental work, to explore a topic
in depth, and to obtain quotable material.
Closed questions are more difficult to con-
struct but easier to analyze and less sub-
ject to interviewer and coder variance.
(2)	When lists are used, complete information
can be obtained only if each item is re-
sponded to with a "Yes/No," "Applies/Does
not apply," "True for me/Not true for me,"
and the like, rather than with instructions
such as "Circle as many as apply."
(3)	Rating-scales with more than four or five
verbal points should not be used. Numeri-
cal scales are preferable if more detailed
measurement is desired.
(4)	Respondents should not be asked to rank
their preferences among a number of options
unless they can see or remember all the
options. In face-to-face interviews where
prompt cards are used, respondents can rank
no more than four or five options. If many
options are present, respondents can rank
the three most desirable and the three least
desirable. In a telephone interview, rank-
ings can be obtained by a series of paired-
comparison questions. However, respondent
fatigue limits the total number of alter-
natives that can be ranked.
Next, you'll want to review the content of the indi-
vidual items. Each question should be (a) relevant
to the Agency's informational or analytical objec-
tives, (b) reasonable, given the respondents' proba-
ble knowledge and experience, (c) sens itive to the
respondent's self-interest, and (d) complete. More
specifically --
Each question should be clearly relevant to the
informational and analytical objectives of the

survey, as defined in the analysis plan. Except
for the first one or two questions, which may be
designed simply to orient the respondents or
put them at ease, each item on the questionnaire
should yield a particular piece of data that
will contribute to the informational objectives
of the survey. Of course, more than one question
may be needed to get a complete perspective on a
single research question or variable.
The question should ask for information the re-
spondents can reasonably be expected to provide,
given their probable knowledge and experience.
The extent to which people can respond to the
question will affect both the quality and quan-
tity of their responses. Rather than admit their
ignorance, respondents may give a false reply or
no reply at all.
In reviewing the question, therefore, consider
the difficulty of the question from the respond-
ent's perspective.
For example,is the respondent required to recall
events or transactions that happened weeks or
months ago? According to the Sudman and Bradburn
reference mentioned earlier, periods of a year
(or sometimes even more) can be used for highly
salient topics such as the purchase of a new
house, the birth of a child, or a serious auto
accident. Periods of a month or less should be
used for items with low saliency such as the
purchase of clothing or minor appliances.
If detailed information on frequent behavior of
low salience is required, respondents can be
asked to keep diaries. Diaries will provide
more accurate results than memory. In a busi-
ness survey, the use of records (if available)
and direct observation by interviewers will im-
prove reporting of the desired information. In
addition to diaries, records, and direct obser-
vation, other techniques can be used to motivate
respondents to supply accurate data, e.g., (a)
probes or follow up questions, (b) verbal re-
inforcement by interviewers, and (c) interview-
ing aids such as pictures, calendars, checklists
or prompt cards.

Sens itivity.
In addition to being unable to answer, the re-
spondents may not want to reply to a particular
question because they feel some harm may come
to them, or they will be embarrassed, or that
the information is too personal to divulge to
others. The net result is the same as for un-
reasonable items -- many inaccurate or missing
respons es.
Therefore, in reviewing the content of individ-
ual questions, it is important to consider the
sensitivity of each question. Topics many people
regard as sensitive are income, assets, profit,
religion, political affiliation, and beliefs.
Any question dealing with such topics must be
well justified. (OMB, in fact, requires addi-
tional justification for questions that are
likely to be considered intrusive or damaging
to respondent self-esteem.)
If the question is not essential, it may be best
to drop it. If it ^s essential, there are ways
of minimizing the possibility of inaccurate or
missing responses:
(1)	Careful placement helps. Locating a ques-
tion on a sensitive subject towards the
end of the questionnaire or grouping it
with related questions of a non-threatening
nature tends to improve the reliability of
the response. (See Placement at the end
of this section.)
(2)	For obtaining information on frequencies of
socially-undesirable behavior, open ques-
tions are better than closed questions,
and long questions are better than short
(3)	If respondents are being asked to rank atti-
tudes or behavior, the scale should start
with the least socially-desirable response
options. Otherwise,the respondent may choose
a socially-desirable answer without hearing
or reading the entire set of responses.
(4)	In asking about socially-undesirable behav-
ior, it is better to ask respondents whether
they have ever engaged in the behavior

before asking them about their current be-
havior. Also, it is better to ask about
"current" rather than "usual" behavior.
Obviously each question should have all the
elements necessary to get the desired informa-
tion. There are several tests you can apply to
each question to determine whether it is com-
plete. For example --
(1)	If the respondent is to check only one of a
set of fixed response categories, the cate-
gories must be exhaustive, i.e., they must
cover all possible alternatives. If not,
then an "Other-specify" category should be
added. Response categories also must be
mutually exclusive, i.e, there should be no
overlap to confuse the respondent.
(2)	If the question contains a time reference,
the period or date should be specified.
(3)	By the same token, if you want the respond-
ent to reply with a numerical amount, clear-
ly indicate the desired units, such as days,
tons, or dollars.
(4)	If the respondent is asked to give an opin-
ion on a particular issue, a "Don't know" or
"No opinion" response category may be need-
ed. Including such a category frequently
will have an effect on the results. Whether
or not to include an additional response
option of this type depends on how desir-
able you believe it is to get the respond-
ent's opinion -- even though he or she may
have little knowledge of the issues.
(5)	Questions should be phrased so that the
analysts can distinguish between no response
and a response of "Zero" or "None." For
example, if an item such as --
Annual volume of chemical waste products
	 (metric tons)
is left blank, it will not be clear to the
analysts whether the firm's waste products

total zero tons or whether they simply did
not answer the question. This can be reme-
died by changing the item to --
Annual volume of chemical waste products
[ | None or 	 .
(metric tons)
The last set of review criteria for individual ques-
tions concerns wording. Each question should be
(a) clear and unambiguous, (b) simple and specific,
and (c) free of any unintended leading or "loaded"
In reviewing the wording, read each question slowly,
preferably aloud, and assess its --
= Clarity.
To keep response errors and biases to a minimum,
each question should be clearly and unambigu-
ously worded so there is no way for anyone in
the sample to misinterpret it.
Words that can change the entire meaning of a
question if they are not correctly interpreted
should be italicized or underscored. For exam-
ple, any change in the frame of reference from a
previous question should be clearly indicated --
a request for "total gross sales last month,"
rather than a request earlier in the question-
naire for "total gross sales last year"; or
or "monthly net income," rather than "monthly
fross income."71 If necessary, the question should
e reworded to eliminate any chance of misin-
Words with multiple meanings are especially
problematic. For example, in a question like
"Do you think EPA has treated the chemical in-
dustry fairly?" -- "fairly" could mean "justly,"
"equitably," "not too well," "impartially," or
"obj ect ively."
Any unusual words should be defined. (See
Definitions later in this section.) Slang and
colloquialisms should be avoided -- not because

they violate good usage, but because many re-
spondents may not know what they mean.
Simply worded questions also help to reduce the
number of inaccurate and missing responses. Com-
pound questions giving two or more frames of
reference -- so called "double barreled" items
-- confuse respondents and result in many in-
valid responses. A question like "Do you feel
that air pollution is a serious problem and that
dust from construction sites is the major cause?"
would confound many respondents, who may agree
with only half the question. The classic example
of a double-barreled question is "Have you stop-
ped beating your wife?"
Making questions as specific as possible tends
to make the respondent's task easier, which, in
turn, results in fewer invalid replies. Nor-
mally, a question should tap a specific opinion,
not a general attitude. Items should be direc-
ted to specific rather than general concerns.
Absence of leading or "loaded" terms.
Respondents generally want to be thought of as
good people. Even in circumstances where they
might be expected to be strongly opposed to
something or someone, respondents tend to choose
an answer that is most favorable to their self-
esteem, that they think makes them look intelli-
gent or thoughtful, that they think the inter-
viewer would like them to give, or that is in
accord with social norms. A further factor lead-
ing to bias is a desire to be polite to an in-
terviewer, who usually is a stranger. In being
polite, respondents will hesitate to say unkind
things they believe might offend the interviewer.
Therefore, any question asking about socially-
desirable or socially-undesirable behavior or at-
titudes tends to produce bias and must be word-
ed with care. One of the most common traps
questionnaire designers fall into, in fact, is
to use leading or "loaded" words, particularly
words that are loaded with "social desirability."
At the same time, there are instances where
it may be desirable to use leading questions.

For example, you might ask the question, "When
was the last time your exhaust filtration equip-
ment failed to function properly?" The equip-
ment actually may never have failed. On the
other hand, if the researchers believe the
respondents may have a tendency to underreport
such failures, asking the question this way
may result in more accurate statistics.
2. Reviewing the Overall Content and Organization
Next, examine the questionnaire as a whole, specifi-
cally looking at the --
	Scope of the Questions
The questionnaire should, of course, cover all as-
pects of the problem. Since you, as the survey
sponsors, undoubtedly will have contributed the
basic substance of the questionnaire, your review
of the overall content at this point should be a
simple matter of making sure that the draft encom-
passes all the Agency's data requirements. The
analysis plan will be invaluable for guiding this
part of your review.
	Order of the Questions
Questions should be logically ordered and grouped
into coherent categories. The categories do not
necessarily have to be labeled, but similar items
should be grouped together. A transition statement
should mark significant change in topics.
Whether respondents complete the questionnaire on
their own or in the presence of an interviewer, they
are less likely to become fatigued and will make
fewer mistakes if they don't have to shift mental
gears constantly. Most respondents are not experts
at questionnaire design, but they certainly can dis-
tinguish between a questionnaire that is well organ-
ized and one that is poorly ordered, duplicative,
and repetitive, and are likely to be less coopera-
tive in responding to a poorly constructed form.
	Scope of the questions
	Order of the questions
	Explanatory and control

The order of the questions should consider, first,
the respondent; then the interviewer (if any); then
the processing personnel; and, lastly, the analyst.
Sequencing questions in favor of the respondents
tends to improve the quality of their answers. The
least sensitive, most general,and simplest questions
should be placed first. Beginning the questionnaire
with a few non-threatening or easy-to-answer items
tends to promote a more positive attitude on the
part of the respondent. Moreover, if at all possi-
ble, demographic questions should not be located
at the beginning of the questionnaire since some
respondents may find them threatening, e.g., ques-
tions about age, income, employment status. Usually
it is best to place them close to the end, so refus-
als won't affect answers to earlier questions.
Explanatory and Control Information
In addition to the questions themselves, survey
questionnaires contain a variety of explanatory and
control items to guide people who will be handling
the forms -- respondents, interviewers, and data
processing personnel. Don't neglect these items in
your review.
Below are suggestions for critiquing the following
"special" questionnaire items: (a) introductory ex-
planations to respondents or interviewers; (b) in-
structions to whoever completes the questionnaire;
(c) definitions; (d) interviewing aids such as
show cards, calendars and scales, which interviewers
sometimes use to prompt replies; (e) control numbers
to identify the questionnaires and control their
flow through the collection and processing opera-
tions; and (f) codes and directives for processing
= Introductory explanations.
Virtually all questionnaires contain a few ex-
planatory remarks at the beginning of the ques-
tionnaire, either for the respondent or to sug-
gest the interviewer's opening remarks.
Introductory informacion on a mail questionnaire
is very important because no interviewer will
be present to tell respondents (a) what the
study is about, (b) its objectives, (c) why their
cooperation is important, (d) how their replies

will be used and who will have access to them,
and, (e) how to get help if they have any
Respondents also should be told at the outset
that accurate and complete answers are desired
and that they should think carefully, search
their memory, and, if appropriate, take time to
check their records. If any questions are par-
ticularly sensitive or threatening, a few addi-
tional comments may be necessary.
Introductory information should be included in a
one-page letter accompanying the questionnaire.
The letter should be individually addressed, if
possible. (The mail merge capability of most
word processors makes this feasible at little
extra cost.)
A mail questionnaire also should advise respond-
ents what to do with the questionnaire when they
have completed it. Should the questionnaire be
returned in a self-addressed envelope? What's
the deadline for completing it? (Note that
deadlines will increase the response rate.) A
return address should appear on both the cover
letter and the questionnaire proper.
Suggestions for the interviewer's opening re-
marks are usually stated at the top of the
questionnaire. These should be brief. Long
explanations tend to make respondents uncomfort-
able. The interviewers should simply identify
themselves and the organization they represent,
and state the purposes of the survey in one or
two sentences.
Ins truct ions.
Instructions to respondents or interviewers on
how to complete the questionnaire must be care-
fully phrased to prevent errors and omissions.
Review the instructions as attentively as you
do the questions.
All instructions should be uniform in style
and clearly distinguishable from other material
on the questionnaire, e.g., set off in capital
letters. Only instructions applicable to all
interviewing situations normally should appear
on the questionnaire. (See "Item Placement"

later in this section for additional review
cons iderations.)
There are two basic kinds of instructions:
(1)	Directions on how to answer the individual
(2)	So-called "skip instructions," which in-
struct the person completing the form where
to go next, depending on how they answer
the current question.
Skip instructions should be (a) worded positive-
ly and (b) reference a later question. They
should inform the person completing the form
where to skip to when a particular reply is
given, not where to go when no answer is given.
Skip instructions should never ask the respond-
ent to skip backwards to a previous question.
Complex skip patterns should be avoided, espe-
cially on mail questionnaires. (They are easily
managed in a computer-assisted telephone inter-
view, however, because the system can be pro-
grammed to present the next question correctly,
based on the last answer keyed in by the inter-
Note that, in addition to the instructions
printed on the questionnaire, interviewers are
given separate question-by-question written in-
structions. These are usually more detailed and
cover unusual interviewing situations. Usually
they are incorporated in a manual and used both
for training and reference purposes.
In the interest of clarity, any unusual terms
on the questionnaire should be defined. For
example, if manufacturers are asked to estimate
the "value of goods" sold last year, the ques-
tionnaire should indicate whether answers should
be expressed in current dollars, the depreciated
book value, or some other method of calculating
the value.
Definitions of technical terms often are a major
component of questionnaires for Agency-sponsored
surveys. It is not unusual for an entire section

to be devoted to definitions. Be sure to have
the project personnel most knowledgeable about
the subject matter review all definitions.
Interviewing aids.
Although the visual aids that interviewers show
respondents to encourage more accurate replies
are not strictly a component of the question-
naire, you should review them along with the
questionnaire to make sure they contain an
appropriate range of alternative answers.
ID and control information.
Every questionnaire should contain information
to identify it and control its flow through the
collection and processing stages. At a minimum,
the first page or cover page should include the
title of the study, the name of the organization
conducting the study, the OMB control number and
expiration date, and a space to insert whatever
multi-digit code numbers the contractor plans to
use to identify the response units for follow-
up, evaluation, or cross-referencing purposes
and for determining what sample weights to apply
(see Chapter 4). (Since it is possible for the
questionnaire to come apart, each page should
be numbered and include some information identi-
fying the form.)
In addition, in face-to-face or telephone sur-
veys , there should be a space to record the date
and time the interview began and ended. The
contractor also may include a place to rate the
performance of the interviewer or processors.
Make sure that proper identification and control
information is included on the final draft of
the questionnaire. Check these items again when
you review proofs of the final questionnaire.
Data processing provisions.
If at all possible, the format of the question-
naire should be arranged so it is easy for the
transcribers or the data entry clerks to proceed
from one item to the next. Certain formats and
coding schemes can simplify the processing oper-
ations and, at the same time, facilitate the
tasks of the respondents or the interviewers.

Closed questions can be "precoded" to facilitate
processing and ensure that the data are in proper
form for analysis. Precoding involves assigning
a code number to every response option. The re-
sponse options are either explicitly stated in
the question or are printed on a card handed to
the respondent. When they appear on the ques-
tionnaire, the respondents select their replies
by checking a box, circling a coded answer, un-
derlining a preprinted response option, or writ-
ing in a code or a number. Provisions also may
be made for "Mo answer" or "Don't know" replies.
When the completed questionnaires are processed,
the data entry clerks simply key the appropriate
numerical codes directly into the computer. This
eliminates one step in the processing because
the replies do not have to be coded or tran-
scribed onto a coding or keying sheet before
being entered into the computer.
Reviewing the Format
The last step in your review should be devoted to the
the general format of the questionnaire, specifically
to the --
	General appearance
0 Length
	Placement of questions and
Although the contractor should have designers experi-
enced in the proper formatting of questionnaires, a
final review by Agency subject-matter and data process-
ing specialists may suggest revisions that will improve
the questionnaire's response-getting power.
A well-formatted questionnaire can significantly reduce
response errors. If the questionnaire is designed to
be self-administered, your review of the format should
have high priority. The format should give primary
consideration to the respondents, then the interview-
ers, and lastly the data processors.
General Appearance
The general appearance of the questionnaire, the kind
of paper it is printed on, the size and style of the

type, and the amount of open space all influence how
well the respondents or the interviewers are able
to follow instructions and complete the question-
naire. Appearance is very important in a self-
administered questionnaire and will influence the
response rate.
The questionnaire form should look professionally
designed and easy to answer. If the form is more
than four pages long, a booklet format is desirable.
It should be printed on good stock because it will
be subjected to a great deal of handling during the
course of the collection and processing operations.
Colored paper or color-shaded sections may be help-
ful in a complex questionnaire. Shading can be used
to direct attention to answer spaces, to highlight
certain topics, to indicate transitions between sec-
tions, and to reserve space for office use. The re-
duction in respondent and clerical errors is well
worth the small additional expense for two-color
Large, clear type should be used throughout. Dif-
ferent type styles should be used for questions,
instructions, and data processing notations. In-
structions should be in bold type so they are clear-
ly distinguishable from the questions.
Above all, the questionnaire should not look crowd-
ed. Ample white space should be allowed because
it will make the questionnaire look easier to com-
plete, and generally will result in fewer errors
by both interviewers and respondents. Response
formats should be consistent, and adequate space
should be allowed for replies to open questions,
arithmetical calculations, and general remarks (by
respondents or interviewers).
Survey literature abounds with recommendations on
questionnaire length. The general consensus is that
setting an arbitrary limit on length is unnecessary
and unrealistic. Much depends on the method of ad-
ministration, tEe respondent's obligation to reply,
the subject matter, and the way the questionnaire
is constructed.
Let's first look at the length of self-administered
questionnaires. Since no social interaction is

involved, mail questionnaires sent out to the gen-
eral public are directly affected by length. If
the subject matter is interesting and relevant and
the respondents are generally well-educated, the
questionnaire may be 12-16 pages long and there
will be no serious loss of cooperation. If the
topics are likely to be of little interest to the
respondents, however, the questionnaire should not
exceed four pages. Anything longer is likely to
induce fatigue and result in a considerable number
of response errors and a lower completion rate.
Even poorer response can be expected if efforts to
cut down on length include crowding questions, using
oversize paper, or reducing the print size.
The length of a mail questionnaire is not as impor-
tant in a business survey. In fact, EPA relies
heavily on long, complex, self-administered ques-
tionnaires for obtaining detailed technical infor-
mation from business and industry. Whether replies
are voluntary or mandatory, a long mail question-
naire is often less burdensome than a lengthy face-
to-face interview. It is less disruptive of office
routines and gives the organizations time to discuss
the questions with other people and search their
records, as necessary.
As for interview surveys, if the topics are inter-
esting and important to the respondents, face-to-
face interviews of two or three hours can be con-
ducted with little difficulty, regardless of the
type of respondent. Telephone interviews lasting
over an hour also can be conducted successfully
provided they deal with highly salient topics. On
the other hand, unless responses are mandatory,
telephone or face-to-face interviews have to be
considerably shorter -- 20 to 45 minutes at most,
as a rule.
Remember that the length of the data collection in-
strument directly affects the total response burden
of the survey. "Response burden" is the time it
takes to complete the data collection instrument.
The estimated amount of time it takes to complete
the proposed questionnaire multiplied by the number
of respondents in the sample is the total response
burden you must report to 0MB in your clearance
request. The burden should not exceed the allowance
provided for the survey in your office's Information
Collection Budget.

 Item Placement
The placement of the questions, instructions, and
other items on the questionnaire can make the task
of respondents and interviewers easier and more
enjoyable. The placement of response categories
also should be consistent. In some cases, good
placement helps to minimize response errors, refus-
als, and incompletions.
Below we discuss some general rules for the place-
ment of (a) questions and (b) instructions. Place-
ment "rules" for other items (introductory material,
definitions, and ID and control information) were
covered earlier in this section.
= Questions.
The questionnaire should start with a few short
items that are relevant, interesting, non-
threatening, and necessary. As we mentioned
earlier, placing questions the respondent may
perceive as threatening at the beginning of the
questionnaire may result in defensive -- and
frequently invalid -- responses. It is best to
put them close to the end but not at the end of
the questionnaire. Important questions should
be placed towards the beginning. The last items
in a questionnaire rarely get the same degree of
attention as earlier ones, hence the least sig-
nificant, items should be placed last.
It is generally best to start a mail question-
naire with a few short, simple, closed questions.
Never begin with an open question requiring a
lengthy response. Writing long answers may be
difficult and embarrassing for some respondents,
who may worry about making spelling and grammat-
ical errors. Also, include space at the end
for general comments.
Questions should never be split between two
pages because the person completing the form may
think the question is complete and inadvertently
provide a premature, inaccurate response.
Instructions on how to answer a question or a
series of questions should be placed before
items, not at the beginning of the questionnaire.

Instructions for responding to individual items
should be placed either immediately before the
question or immediately after it, prior to the
space provided for the answer.
Skip instructions should be placed immediately
after the answer space allowed for the question.
Sometimes words and sometimes arrows are used
to advise respondents or interviewers which
question they should answer or ask next, depend-
ing on how the current question was answered.
Coding or probing instructions for interviewers
should be placed after the question. Notations
for coding personnel should be in small type and
located so they will be as unobtrusive as possi-
ble to respondents or interviewers.
In addition to reviewing all questionnaire drafts, the
project officer should take an active role in both the
exploratory research and testing activities. Time spent
in testing the questionnaire before collecting data for the
main survey may eliminate problems that would be costly if
not impossible to correct later. For this reason, a pre-
test and a pilot test are vital for a major survey.
Before the contractor is hired, be sure to review the pre-
test provisions of the offerors' proposals. (See section
B-3 of Chapter 6, Volume I, for more information.)
After the contractor is aboard --
(1)	Participate as an observer in any exploratory inter-
views that many be conducted. This will help you eval-
uate response problems, some of which may be serious
enough to require changes in the data requirements
of the survey.
(2)	Critically review the contractor's plans for testing
the questionnaire. Make sure that (a) the pretest sam-
ple adequately represents all important subgroups of
the target population, including any types of respond-
ents for which special problems are anticipated; (b)
the size of the test sample is adequate for a valid
test; (c) the test conditions approximate those of the
actual survey; and (d) enough time has been allowed to
analyze the test results and incorporate any necessary
revisions in the questionnaire before the survey starts.

(3)	Facilitate and coordinate all internal approvals re-
quired by your office for pretest(s) as well as the
OMB clearance request (if more than nine respondents
will be used) with the EPA's Statistical Policy Branch
and Information Management Branch of the Office of
Standards and Regulations and your office's information
management coordinator.
Clearance requests for pretests may be submitted sepa-
rately or in combination with the clearance request you
submit for the main survey. (See section B of Chapter
7, Volume I, for details.)
(4)	Participate in all pretests. Go along on a few of the
interviews to get a first-hand view of respondents' re-
actions to the questions, and attend the debriefing
sessions at the conclusion of the tests.
(5)	Review all pretest reports carefully. They should in-
clude a list of the proposed refinements to the ques-
tionnaire and an analysis of the pretest data. The
pilot test report also should propose any refinements
to the field procedures the contractor deems necessary.
When you review subsequent drafts of the questionnaire or
plans for further tests, make sure the contractor has
taken into account all reviewers' suggestions.

	Approaches to Developing Questionnaires, Statistical
Policy Working Paper 10, Statistical Policy Office,
Office of Information and Regulatory Affairs, OMB,
Washington, DC, 1983.
	Asking Questions: A Practical Guide to Questionnaire
Design, S. Sudman and N. Bradburn, Jossey-Bass, San
Francisco, CA, 1982.
	"Questionnaire Construction and Interviewing Proce-
dures," Research Methodology in Social Relations,
Fourth Edition, A. Kornhauser, P^ Sheatsley, et al;
Holt, Rinehart, and Winston, New York, NY, 1981.
	The Art of Asking Questions, S. Payne, Princeton
University Press, Princeton, NJ, 1951.
	Basic Background Items for U.S. Household Surveys,
R~! Van Dusen and N. Zil 1, Social Science Research
Council, Washington, DC, 1975.
	General Social Surveys, 1972-1982: Cumulative Codebook,
National Opinion Research Center,University of Chicago,
Chicago, IL, 1982.
	Measures of Social Psychological Attitudes, Revised
Edition, J. Robinson and P. Shaver, Institute for
Social Research, University of Michigan, Ann Arbor, MI,

Sampling is selecting some portion of a target population,some-
times called a study population or simply population,and inves-
tigating just this portion, which is called a sampTe.
A half century ago, many statisticians felt that collecting in-
formation about every member of a population they wanted to in-
vestigate was the only acceptable way of conducting a survey.
Today, as a result of technical advances in sampling theory and
its applications, sample surveys are now widely accepted as an
efficient and reliable.way of studying individuals, land areas,
or even extremely unstable environmental media such as surface
water or air. Thus, specimens of blood or urine constitute a
sample of a patient's body fluids. Specimens of soil taken from
a lawn comprise a sample from that lawn. Specimens of water
from a swimming pool form a sample of the water in that swimming
pool. And so forth.
In this chapter we'll give you an overview of the basic concepts
of sampling theory and some practical tips on monitoring the sam-
pling activities of a survey contractor. We shall consider two
general types of sampling: Probability sampling, which refers to
the selection of sample members by chance, and non-probability
sampling, where the units selected for study are chosen according
to some purposive or convenient scheme, often by expert judgment.
Specifically, we'll look at --
	The advantages of using sampling for an
Agency-sponsored survey;
	The relationship between sampling errors
and sample size;
	The methods used to design survey samples;
	The major components of a sampling plan; and
	Ways the sponsoring office can ensure the
quality of the sampling activities.
Almost all statistical surveys the Agency sponsors use sam-
pling to select the members of the population they want to

study. Why collect information from only a sample rather
than everyone in the population?
In most research situations, taking a census of the study
population is both impractical and inefficient. The most
important reason for investigating only a sample of the
population generally is to hold down costs. Obviously it
is cheaper to collect information about 500 people, land
areas, processes, etc., than about 5,000, say. Fewer staff
are needed to collect the information and process it in a
form suitable for analysis. Using sampling for studies of
human populations also reduces the burden on those from
whom information must be collected. Sampling also gives
faster and more accurate results because fewer data have
to be collected and processed.
Let's expand on these four main advantages of sampling.
1	. Lower Costs
If the population of the proposed study is very large
-- national in scope, say -- collecting information
about the entire population is simply out of the ques-
tion from a cost standpoint. The cost of taking a
census of the U.S. population in 1980 was $1 billion,
for example. A good quality sample survey of a large
human population requires a small fraction of the re-
sources needed to collect data from everyone in the
study area. The per-unit cost of a sample normally
is higher than complete enumeration of the population
because more highly trained staff and more stringent
quality control throughout every phase of the survey
are required.
Similarly, if you plan to use an expensive measurement
procedure to collect certain environmental data, study-
ing a sample of the population often is the only feasi-
ble way of keeping costs within reasonable bounds. The
cost of using an expensive monitoring device to measure
ambient air quality in more than a small number of com-
munities may be prohibitively expensive -- as well as
unnecessary, given the state of the art of sampling.
2	. Reduced Paperwork Demands
The Office of Management and Budget, in accordance with
the Paperwork Reduction Act of 1980, imposes limits on
all Federally-sponsored information collections. Using
sampling to study a population of interest helps to
minimize the paperwork demands Federal agencies impose
on the public, particularly on business and industry.

3. More Timely Results
The Agency often needs the results of their survey re-
search projects quickly. Because fewer respondents or
specimens have to be investigated in a sample survey,
the time required to collect and process the data gen-
erally is substantially lower.
A. More Accurate Results
Since survey researchers use carefully controlled pro-
cedures to collect and process sample data, it is not
unreasonable or unlikely for a well chosen sample to
produce more accurate results. Although sampling in-
troduces a source of error in the data -- called
sampling error -- that would not occur if all members
of the population were studied, sampling error is
identifiable and measurable.
At the same time, because the investigators focus the
available resources only on a portion of the popula-
tion, there is less chance for human error and, there-
fore, the data quality tends to be higher. Human
errors can creep in at any stage of a survey -- during
the data collection phase, during the editing and
coding of the questionnaires, and during the tabulation
and analysis operations. Because there are fewer
data to deal with in a sample survey, greater quality-
control can be exercised throughout each stage to
guard against all kinds of errors.
Given these advantages, are there any research situations
where sampling may not be appropriate for collecting envi-
ronmental and health data which EPA needs to effectively
fulfill its Mission?
In some cases, only sampling is possible -- air or water
monitoring, for example. In studies of human populations,
if the study population is small or if separate detailed
data for small subsets of the population are desired, col-
lecting data for the entire population may be appropriate
-- at least for some parts of the investigation. If your
target population is all U.S. chemical manufacturers, for
instance, it probably would be feasible to study only a
sample of them to get the information you need. However,
if you were interested in a specific chemical produced
at only ten plants in the United States, it probably would
be best to collect data from all these plants. Similarly,
if you were interested in all the chemical manufacturing
plants in a single county, it might be best to survey all
of them.

In Volume I we recommended that, in establishing the mini-
mum design criteria for your survey, you include an accept-
able level of sampling error for the key statistics you
need to achieve your research objectives. Since this is
a task that should be done in the planning stage, before
a contractor is hired, we'll discuss sampling errors be-
fore considering other aspects of sampling. We'll also
show you how sampling errors are measured, and the rela-
lationship between sampling errors and sample size.
The purpose of most surveys is to measure certain charac-
teristics of a population. When only a portion of a pop-
ulation is used for study purposes, survey statisticians
need a way of estimating the extent to which this portion
-- the sample	and the entire population differ from
each other. Studying a sample rather than every member of
a population means abandoning mathematical certainty and
entering the realm of inference and probability. The val-
ues of the estimates (statistics) derived from the data
collected from the sample, by the same token, also will be
different than the actual mathematical values that would
have resulted had data been collected for everyone in the
population. The difference in these two sets of values
for every statistic is called the sampling error. Col-
lectively, sampling errors are errors that statisticians
can measure and take into account in reporting the survey
findings. Other sources of data errors in a survey are
(a) estimation biases, (b) systematic errors caused by
defective measuring devices, (c) exclusion of part of the
population due to a faulty sampling frame, and (d) failure
of the interviewers to ask all the questions -- all of
which produce errors that are much more difficult to
measure than sampling errors and which can significantly
affect the survey results.
1. Sampling Errors
Sampling errors, we have seen, are measures of the ex-
tent to which the values estimated for the sample such
as means, totals, or proportions differ from the values
that would be obtained if the entire population were
surveyed. Since there are inherent differences among
the members of any population, and since data are not
collected for the whole population, we cannot know
the exact values of these differences for a particular
sample. Moreover, different samples give different re-
sults. To compute sampling errors, therefore, statis-
ticians measure the average differences between sample
estimates and population values, i.e., averages of the

differences for a hypothetical set of sample surveys
using the same sample design and measurement procedures.
When a probability sample is used, sampling errors can
be estimated with a certain degree of precision. A
probability sample is one in which each member of the
target population has a known, positive probability of
being selected. In fact, the main reason we have re-
commended that probability sampling be used for all
Agency surveys, whenever feasible, is that statements
based on sample results are always probability state-
ments -- always estimates, not statements of fact. If
probability methods are not used to select the survey
sample, there is no way of knowing how much error there
is in the data and hence how much confidence one can
place in the survey findings.
2. Measuring and Expressing Sampling Errors
Let's look now at the ways statisticians measure and
report sampling errors when probability methods have
been used to select the study population.
Suppose you have contracted for a survey to determine
how many families in a particular city -- we'll call it
City X -- are getting their drinking water from contami-
nated sources. Now, after completing the survey, let's
say the contractor estimates that 40 percent of all
families in City X are using contaminated sources. The
contractor tells you that the standard error, or stand-
ard deviation, of this estimate is 2 percentage points.
Moreover, the contractor says that this estimate is
likely to be within 4 percentage points of the true pro-
portion of families in City X using contaminated water.
What does this mean?
The standard error is a measure of the probable accu-
racy or precision of any one estimate derived from sam-
ple data. To relate the standard error of this parti-
cular statistic -- that 40 percent of all families in
City X are using contaminated sources -- to the true
value, the contractor formed a 95 percent confidence
interval, which is approximately defined as --
Sample estimate + twice the standard error (S.E.)
The confidence interval in this example is the interval
from 36 to 44 percent, i.e., 40 percent +2x2 percen-
tage points.
Provided the contractor has used a reasonably large
sample of families in City X to collect data on the

quality of the drinking water, you could give 19 to 1
odds that this 95 percent confidence interval would
include the value you would get if you surveyed all
the families in City X. If you were willing to accept
lower odds or if you wanted higher odds, other multi-
ples of the standard deviation could be used to attain
other confidence levels, such as --
Confidence	Approximate
Interval	Level of Confidence
Est imate
Est iraate
9 5%
Let's turn to another aspect of reporting sampling
errors. Sampling errors may be expressed either in ab-
solute or relative terms. To illustrate the differ-
ence , let's suppose that City X has a total of 5,000
families. The 40 percent estimate of families using
contaminated drinking water translates to a total of
2,000 families.
Exhibit 4 on the next page shows the absolute and rela-
tive sampling error of this estimate expressed in three
ways. Relative standard error (relative to the esti-
mate) is often called the coefficient of variation. It
is always expressed as a percentage of the sample esti-
mate. As you can see, the relative standard error (or
the coefficient of variation) is the same for each type
of estimate, even though the estimates themselves and
their standard errors are expressed in different units.
When you establish the Agency's minimum design specifi-
cations, therefore, be sure to state whether you are
referring to absolute or relative sampling errors.
This is especially important for estimates of percents
or proportions.
3. Determining Sample Size
How large a sample is needed for a particular survey?
Questions about sample size seem to be simple ones,
but answering them is not so simple.
In Chapter 3 of Volume I, we recommended that you
exclude the size of the sample when you specify the

Type of
Coeff ic ient
of Variation
2,000 families
100 families
0.40 of families
0.02 of families
40% of families
2% of families
survey design criteria in the statement of work for
the RFP. The level of sampling error (or level of pre-
cis ion, as it is sometimes called) and sample s ize
are closely related. When probability sampling is
used, it is relatively easy to determine how many mem-
bers of the target population have to be included in
the sample to achieve results with the level of preci-
sion you have specified. For a particular sample de-
sign, it is primarily the number of units in the sam-
ple not the percentage of the population the sample
represents that affects the precision of the sample
estimates. For example, in estimating percents or
proportions, the sampling error associated with a sam-
ple of 1,000 units taken from a population of 100,000
is almost the same as the error for a sample of the
same size from a population of 100,000,000.
We recommend that you specify the level of precision
you need for the key estimates (statistics) and leave
it to the offerors to propose a sample design that
meets this specification at the lowest possible cost.
If you specify both precision and sample size, the
offerors may find it impossible to meet both your
To achieve the most efficient sample design, the con-
tractor must determine a sample size that --
(1) Will achieve a fixed level of precision for mini-
mum cost; or

(2) For a fixed cost, will achieve the greatest esti-
mation precision.
In virtually all EPA survey contracts (1) will apply.
In other words, the contractor starts with a require-
ment to attain a given level of accuracy (precision)
and must satisfy this requirement at minimum cost.
Alternatively, the contractor may be given a particu-
lar budget and must make a sample allocation that will
provide the most accurate results. By "allocating the
sample," we mean dividing the sampling units (the
units of the population from which the sample is
drawn) among various components of the sample such as
strata, regions, counties, cities, and so on.
How many sample members are taken from where? An ex-
ample of the difficulty a contractor may encounter in
allocating a sample in a study of environmental media
is the following: If we have the capacity to chemi-
cally analyze 1,000 specimens of lake water, how many
sample lakes and how many specimens per lake are most
efficient in answering our questions?
When you draft the statement of work for the survey,
be sure to consult a sampling specialist to ensure
that the precision levels you set are reasonable given
the resources you have available.
In addition to the levels of precision you specify in
the statement of work for the key statistics, the of-
ferors also have to take the following design factors
into account in determining the sample size.
	The level of geographic detail for which estimates
are needed. If the target population is the entire
U.S. population, getting estimates at a specified
level of precision for each State would require a
sample roughly 50 times larger than that required
to get estimates with the same level of precision
for all 50 States collectively.
	Variability of	the characteristics of the target
population. The greater the differences between
the units in the target population, the larger the
sample has to be to achieve a specified level of
precision. The level of precision in sample surveys,
in fact, is based on sample variance. It measures
the lack of homogeneity among the data collected
from the sample.
	The methods used to design the sample. Survey de-
signers use many different sampling methods and

combinations of methods to design a survey sample.
The levels of precision for a sample of a given size
will vary, depending on the sample design.
Cluster sampling.a method of choosing a survey sam-
ple in which ail the sampling units are clustered
in one or more geographic areas rather than across
the entire area in which the population is located,
has perhaps the greatest impact on the precision of
the statistics. (See section C below for more about
cluster sampling.) Obviously, estimates derived from
a sample of 1,000 households chosen at random from a
city directory would give a considerably higher level
of precision than those derived from a sample of
only 50 households chosen from each of 20 randomly-
selected city blocks.
	Expected level of non-response. In almost all sample
surveys, regardless of what method of collection is
used, researchers will not succeed in obtaining re-
sponses for every unit in the sample. There are
many reasons for this, which we'll discuss in Chap-
ter 5. For example, a respondent may refuse to be
interviewed, or an interviewer may fail to contact
an acceptable respondent, or the person designing
the sample may include ineligible units (such as a
business that is no longer active) in the sampling
frame. The sampling frame is the list of units from
which the sample is drawn.
Often, survey designers increase the sample size to
compensate for the anticipated rate of non-response.
This will reduce sampling errors, but it will not
reduce the bias in the estimates that arises because
eligible units provide no data or incomplete data.
	Cost and time. As we indicated above, the resources
the Agency has available to do the survey place con-
straints on the size of the sample -- generally, the
larger the sample, the more the survey will cost.
Moreover, if there is a deadline for obtaining the
results, the time it will take to collect and process
the sample data also may limit the size of the
In this section we will describe briefly the methods most
commonly used to design survey samples. To illustrate the

different methods, we will continue with the City X exam-
ple used in section B. Knowing something about the differ-
ent methods used to construct a sample will give you a
better understanding of sample designs you may have to
Our focus in this section is on probability sampling meth-
ods, which we recommend for virtually all Agency surveys.
Probability sampling, also called random sampling, is an
objective process recognized and accepted as standard pro-
cedure by knowledgeable survey specialists throughout the
world. We will also describe three types of non-probabil-
ity samples. Non-probability samples are selected accord-
ing to some purposive or convenience method, often by an
expert or specialist on the basis of his or her considered
1. Probability Sampling Methods
Probability samples are those in which the members of
the population (or the sampling units) are selected at
random -- solely by chance. "Random" is not equivalent
to "haphazard." A true random selection must be inde-
pendent of human judgment. The two distinctive fea-
tures of probability sampling are --
(1)	The use of some random device (such as a table of
random numbers) "to determine which units in the
population (or the frame) are included in the
sample. This prevents the person designing the
sample from biasing the selection (consciously or
unconsciously) towards a sample that will produce
some desired result.
(2)	The sample can be used to make estimates of the
sampling errors associated with the survey find-
ings. Hence, anyone using the survey data can
determine how accurate the data are and how much
confidence to place in any conclusions based on
the sample data.
Let's look at six of the most common methods of proba-
bility sampling used today:
Simple random sampling
Stratified sampling
Cluster sampling
Systematic sampling
Sampling with probability
proportionate to size
 Multi-stage sampling

 Simple Random Sampling
In simple random sampling each unit in the target
population has an equal chance of being selected, a
characteristic shared by many probability sampling
methods. Simple random sampling is also known as
"sampling with equal probabilities," or "equal prob-
ability selection." However, simple random sampling
is unique in that every possible sample of a given
size has the same probability of being selected.
Simple random sampling is particularly appropriate
for very small studies where the sampling units are
approximately the same size or there is no useful
measure of size for the survey. A sample of medi-
cal records in a hospital (to review diagnoses for
possible cases of pesticide poisoning, say) is an
example of a situation where simple random sampling
may be appropriate. Simple random sampling is sel-
dom used by itself in designing Agency surveys,
but it is frequently used in combination with one
or more of the other sampling methods described
in this section.
Let's see how we would draw a simple random sample
from the 5,000 families in City X. First, we would
need to prepare a list of all 5,000 families. We
might get this from a city telephone directory, or
it may be necessary to create a list by canvassing
the area or some other means. We would then list
all the families by name and number them in sequence
from 1 to 5,000.
To begin the selection of the sample, we would pick
a random number between 1 and 5,000 -- 254 say. The
family with that number would be the first unit in-
cluded in the sample. We would continue to randomly
select numbers until we had chosen the desired num-
ber of sample units -- 500 families, perhaps.
What if the same random number comes up more than
once? Usually, numbers that have already been
picked are set aside so that no number (the number
'254," for example) shows up more than once. This
is known as simple random sampling without replace-
ment, i.e., a number, once selected, is not returned
to the sampling frame. (Note that sampling with re-
placement, where the numbers are returned to the
frame, Ts sometimes used for probability samples,
including simple random sampling.)

 Stratified Sampling
It i9 often useful to divide the population into
exhaustive and mutually exclusive subgroups for
sampling purposes. If we propose to sample from
every subgroup, then the subgroups are termed stra-
ta. In stratified sampling, the population (or the
frame froin which the sample is drawn, if they are
not equivalent) is divided into two or more strata,
and the selection of the sample is carried out sep-
arately for each subgroup or stratum. Stratifica-
tion does not imply any departure from probability
selection. It only means that before any units
are selected, the population is divided into one
or more strata. Then a random sample is selected
within each stratum.
Continuing with our City X example, let's suppose
we have reason to suspect that contamination is more
likely to occur in some parts of City X than in
others. If so, we could use a geographic stratifi-
cation to select the survey sample. For example,
we could draw a separate sample from each of the
city's seven wards. This would ensure the selection
of some sampling units in each ward, whereas if we
did not stratify, the sample could -- purely by
chance -- be heavily concentrated in one or two
How should the overall sample be allocated among the
strata, or wards? If we had no clue as to the like-
lihood of contamination in different strata, we would
probably use the same sampling fraction, say, 1 in
10, in each of the wards. This is called propor-
tional stratified sampling because the distribution
of the sample families in each ward would be propor-
tional to the distribution of families in each ward
in the population.
It is not necessary to use the same sampling fraction
in each stratum. If we had information indicating
that the drinking water contamination problems were
much more serious in three of the seven wards, we
could sample at a higher rate in those three wards.
The primary reason for using stratified sampling is
to make the sample more efficient, i.e., to produce
estimates with smaller sampling errors. How well
this objective is met depends on the criteria used
to define the strata.

 Cluster Sampling
In cluster sampling, groups or "clusters" of adja-
cent units in the population are formed and a ran-
dom sample of the clusters is selected. In other
words, within a particular stratum, rather than
selecting individual units one by one, whole clus-
ters of units are selected.
To illustrate cluster sampling, one way of select-
ing a probability sample of families in City X
would be first to select a sample of city blocks
at random and then construct a sample of some or
all of the families living in those blocks. If
City X has a total of 100 blocks, we might use sim-
ple random sampling to choose 10 blocks and then
interview some or all the families in only these 10
Estimates derived from a cluster sample are likely
to have considerably larger sampling errors than
estimates from a simple random sample having the
same number of families. The reason is that adja-
cent sampling units tend to have similar charac-
teristics. This similarity, or correlation, re-
duces precision by producing a degree of redundancy
in the data collected from members of the same
Why, then, should we use cluster sampling? It is a
practical necessity to use clusters in large sur-
veys. First, there is a considerable savings of
time and expense in compiling a frame that lists
only the units in the clusters rather than all
the units in the population. Second, if face-to-
face interviews will be used to collect the data,
by concentrating them in a smaller geographic
area, the overall cost savings can be enormous --
especially in a national sample.
 Systematic Sampling
In systematic sampling, researchers first list
the sampling units (which may or may not be indi-
vidual members of the population) in some specific
order. Then, they select units for the sample by
computing an appropriate sampling interval (I) and
and taking every Ith unit in the sampling frame.
The starting point is chosen at random from
the first I unit. This is called the random start.

To select a systematic sample of 500 families in
City X from the 5,000 families in the frame, we
might use a sampling interval of 10 (5,000 divided
by 500) and a random start between 1 and 10. If our
random start were "7" for example, the families in-
cluded in the sample would be those numbered 7, 17,
27, and so on, up to the family with the number
Systematic sampling is widely used in survey re-
search, especially in combination with other meth-
ods. It has two main advantages --
= Only one random number need be picked during the
selection process, rather than one for each unit
needed to complete the sample.
If the sampling units are listed in some mean-
ingful order -- by block in City X, say -- the
effect of using systematic sampling is essential-
ly the same as using stratified sampling, i.e.,
certain types of units are assured adequate rep-
resentation in the sample.
Another version of systematic sampling is sampling
based on the ending digits of identification num-
bers . In this method, the last digit of a set of
serial numbers that constitute the sampling frame
is chosen at random, and all the units in the frame
with ID numbers ending in those digits are included
in the sample.
For example, suppose we listed the social security
number (SSN) of the head of each family in City X.
We could select our l-in-10 sample by including all
families with SSNs ending in "4." This method
would give us a sample of approximately 500 fami-
lies, although the exact size would depend on which
ending digit was chosen as the random start.
Caution must be used in selecting any series of ID
numbers for sampling purposes. SSNs frequently are
used for sampling based on ending digits. For busi-
ness surveys, IRS employer identification numbers
(EINs) may be appropriate; however, because of cer-
tain peculiarities in the way EINs were initially
issued, they are not suitable for serial sampling
until the ending digits are assigned a more nearly
random distribution.

 Sampling with Probability Proportionate to Size
Up to now, all the methods we have looked at have
involved sample designs where every member of the
population, or the sampling frame, or at least the
stratum has an equal chance of being chosen as part
of the sample. However, in some sample designs, all
the sampling units do not have the same selection
probability. If the population characteristics in
which the researchers are interested are related
to the size of the sampling unit, and it is possible
to obtain some measure of the size of the units,
greater precision usually can be achieved by giving
larger units a greater probability of selection.
This is sampling with probability proportional to
size (PPS).
For example, in sampling the U.S. population, re-
searchers typically select Standard Metropolitan
Sampling Areas (SMSAs), counties, or other sam-
pling units with probability proportional to the
number of individuals residing there. In a soil
study, counties may be selected PPS with crop
acreage as the size measure. Or for a study of
rivers, hydrologic units may be selected with prob-
ability proportional to the miles of river they
To illustrate, suppose we wanted to select a sample
of 10 of the 100 blocks in City X. We could simply
select 10 blocks with equal probability using either
a simple random sample or a systematic sample. How-
ever, if we had a count of the number of families in
each city block (from a recent census, a local tele-
phone directory, or some other source) , and the
blocks varied quite a bit in size (number of fami-
lies) , a more efficient sample design might result
if we gave the more populous blocks a greater chance
of selection. (By "more efficient" sample design, we
mean one in which the statistics will have a smaller
margin of sampling errors.)
The selection procedure would be as follows;
(1)	First, we would list all 100 blocks in some order
and, alongside each block, list the count (the
number of families residing there) and the cumu-
lative total of these families, as in the
table below.
(2)	Then, we would divide the total number of fami-
lies in City X (5,000) by the number of blocks

to be chosen -- 10 in this case. The result --
500 -- is the sampling interval we would use for
selection purposes.
(3)	Next, we would select a random start number be-
tween one and the sampling interval. Let's use
213 for illustration purposes. We would then
form a series of sample-selection numbers begin-
ning with the random start and add the interval
as many times as needed, i.e.,
213, 713, 1213, ... 4713
(4)	Finally, for each sample selection number (213
or 713, say) we would choose the first block
whose cumulative total equals or exceeds that
number until 500 units are chosen for the
sample. Note that each block ultimately will
be represented in the sample. The table below
shows how the first two blocks were selected,
e.g., blocks 2 and 6.
Block No. of Families	Sample
No.	in Block	Cumulative Selection No.
1	120	120
2	220	340	213
3	50	390
4	170	560
5	90	650
6	130	780	713
7	310	1090
Sampling with probability proportionate to size
(PPS) is especially applicable for selecting the
first-stage units of a multi-stage design (discussed
next). To use PPS sampling, it is necessary to have
"measures of size" for all the units in the target
population or frame, e.g., counts of families by
block in City X. The measures of size need not be
exact. It is sufficient for them to be reasonably
close to, or correlated with, their actual sizes.
 Multi-Stage Sampling
Earlier we discussed a sampling method called "clus-
ter sampling," where groups of units rather than
individual units are used to form the sample.
Multi-stage sampling refers to the process of
selecting subgroups within the clusters chosen at
a previous stage. All multi-stage designs are, in

fact, cluster samples. For practical purposes,
virtually all Agency-sponsored surveys use some
form of multi-stage selection. Multi-stage designs
are essential for any national survey, face-to-face
survey, or survey using a widely dispersed sample.
Continuing with our City X example, suppose we did
not have a current listing of the 5,000 families in
the city. We might decide to use a multi-stage
design to select our sample. Let's start by illus-
trating a two-stage sample design. In the first
stage, we might select a sample of blocks using
probability proportionate to size, as discussed
above, based on approximate block counts from the
best available source. Next we would prepare lists
of all the families in the sample blocks. Then, by
simple random sampling or systematic sampling, we
would select a sample of families from the list of
families residing in each of the blocks selected
in the first stage. Briefly put, the sample design
would be as follows:
= Stage 1 : Selection of sample blocks.
= Stage 2: Selection of sample families within the
sample blocks.
The most important advantages of multi-stage sam-
pling are --
(1)	Researchers can concentrate on a smaller number
of areas, with a consequent reduction in time,
staff, and dollars; and
(2)	Researchers need only listings of the sampling
units chosen at the previous stage, rather than
a complete list of the population, e.g., in the
above example, the families in the blocks se-
lected in the first stage instead of a list of
all 5,000 families in City X.
Most multi-stage samples involve four or five stages
of selection. Exhibit 5 shows the stages of selec-
tion for a multi-stage household survey conducted by
the University of Michigan's Survey Research Center.
The stages of selection shown in the exhibit are --
= Stage 1: Selection of "primary areas," usually
counties or groups of adjacent counties. In the
Survey Research Center's design, 74 primary
areas were selected (see any U.S. map).

(Reproduced from "Interviewer's Manual,"
Survey Research Center, University of Michigan)

~	DC G ~ P D ~ ~ 3
4 Segment
~ ~ ~ ~ ~
UJ' 'httJsn u)i

= Stage 2: Selection of "sample locations" (cit-
ies, towns, and rural areas) within primary
= Stage 3: Selection of "chunks" (areas such as
city blocks or rural townships, each containing
from 16 to 40 housing units) from each sample
= Stage 4: Selection of "segments" of from 4 to
16 housing units in each sample chunk.
Stage 5: Selection of "housing units" from the
sample segments.
Our discussion of probability sampling methods has
merely scratched the surface of the techniques survey
statisticians use to construct samples and the ways
they apply them to investigate various populations.
Frequently, complex combinations of the methods we
have described are used, along with variations such
as double or sequential sampling, replicated sampling,
and controlled selection.
There are several references at the end of this chapter
that will help you expand your knowledge of probability
sampling methods.
2 . Non-Probability Sampling Methods
Non-probability sampling methods are characterized by a
subj ective selection procedure. Unlike probability
sampling, the choice of the sample members is not ran-
dom but, consciously or unconsciously, is influenced
by human choice -- usually by expert judgment -- in
accordance with some purposive or convenience scheme.
The problem with all non-random selection schemes is
that even the most conscientious individuals make
unconscious errors of judgment that may be of consider-
able magnitude. These errors, which are very difficult
to measure, are called "biases."
Because non-probability samples do have applications
in some environmental research situations, we will
briefly examine several types. Non-probability sam-
ples are also used sometimes in the final stage of
selection of some environmental studies where strict
probability sampling is not feasible, such as obtaining
specimens for chemical analysis (house dust from a
sample household, or water specimens from a small sam-
ple stream segment). They also are sometimes suitable

for small-scale qualitative exploratory studies, and
for pretests or pilot tests of EPA-sponsored surveys
where the intent is to use probability methods to
select the sample for the survey proper. Note that
when non-random methods are used to select pretest or
pilot test samples, the choice should not be restric-
ted to "easy-to-get" units. If pretest samples include
only units for which it is easy to collect information,
it will be difficult to anticipate the kinds of prob-
lems that may occur in the main survey and how much the
survey proper is likely to cost in time and dollars.
In any research situation where non-probability sam-
pling is used, keep in mind that the results only per-
tain to the sample itself. The findings should not
be used to make quantitative statements about any
population, including the population from which the
sample was selected.
Let's look now at the most common non-probability
samples --
9 Haphazard or convenience
s amples
 Purposive or judgment
s amples
 Quota samples
 Haphazard or Convenience Samples
Haphazard or convenience samples are samples select-
ed from populations for which it is relatively easy
to collect information on a particular topic. Another
feature of these samples is that the population
groups from which they are selected do not reflect,
with any measurable degree of error, the character-
istics of some larger, well-defined group of which
they are a part.
To illustrate, the following are examples of con-
venience samples of human populations --
= Voters interviewed in a shopping center;
-	Volunteer subjects for experiments (e.g., fami-
lies responding to a radio or newspaper appeal
for volunteers to try out a new kind of water
purification equipment in their homes);
-	People answering a reader opinion questionnaire;

= People writing to their congressmen or senators
about a particular issue.
	Purposive or Judgment Samples
Purposive or judgment samples are samples that an
investigator or another subject-matter expert con-
siders to be "representative" of some study popu-
lation. Like convenience samples, judgment samples
are often used by EPA for pretesting purposes.
For example, to pretest a survey of chemical plants
that manufacture sulfuric acid, an expert researcher
in the field might arbitrarily choose for preliminary
investigation a few plants where all the manufactur-
ing processes commonly used in the industry are
Judgment sampling is most usefully applied to early,
exploratory phases of research involving extremely
small samples. In environmental studies, judgment
sampling and probability sampling are sometimes
combined in a multi-stage sample, the final stage
being a judgment sample.
	Quota Samples
In some national surveys, investigators use proba-
bility sampling to choose the first one or two
stages of a sample, and use quota sampling for
subsequent stages. Quota sampling, therefore, is
a version of stratified sampling in which the
selection within strata is non-random.
Quota samples also are frequently used in marketing
and opinion research. For example, in an opinion
survey, the interviewers will each be given a
quota of interviews to conduct with various classes
of individuals, households, businesses, etc. An
interviewer's quota might consist of a specified
number of individuals in each of six age-sex cate-
gories. Within these categories, and in the assigned
area, the interviewer is free to decide how to
locate and interview the specified number of indi-
viduals. However, since the selection process is
subject to human judgment, there is no guarantee
that biases will not occur. An interviewer may fill
his or her quota in the top age group mainly with
people 65 or 66, thus the very old will be under-
represented .
Quota sampling has two main advantages:

(1)	It is less costly than random sampling -- perhaps
one-third as much; and
(2)	There is no need to develop a frame for selecting
respondents in the sampled area, which means that
call-backs are avoided. If an eligible respondent
i9 not available at a dwelling when the interviewer
calls, the interviewer simply proceeds to the next
As with all other non-probability samples, the non-
randomness in the selection of the sampling units is
the main disadvantage of quota sampling. Thus, it is
impossible to estimate the sampling variability from
the sample and to know the possible biases, which may
be sizeable.
The starting point for developing a sampling plan is the
five minimum survey design specifications we recommended
for all Agency surveys in Chapter 3 of Volume I. These
design specifications, which the sponsoring office should
clearly define in the statement of work, are (a) the re-
search objectives, (b) the target population and coverage,
(c)	the required level of precision (sampling error), and
(d)	the target response rate. The fifth design specifica-
tion is that (e) probability sampling be used throughout
the selection process whenever feasible.
In a contract survey, offerors normally will submit a draft
of the sampling plan in their technical proposals. The
plan may undergo several refinements before the final
selection of the sample for the survey proper occurs.
The main components of a sampling plan, which are discussed
in the remainder of this section are --
1. Sampling Frames
A sampling frame is a listing of population elements --
geographic areas, manufacturing plants, crop acreage,
telephone numbers, city blocks, households, factories,
	Sampling frame(s)
	Sample selection procedures
	Estimation procedures
	Procedures for calculating
sampling errors

etc.-- from which the survey sample is drawn. The frame
is the most important component of the overall sam-
ple design because it identifies the population ele-
ments from which the sample is chosen. The population
elements listed on the frame are called the sampling
units. Often these are groups or clusters of units
rather than individual units. The sampling units for
which data are ultimately collected are known as the
units of observation.
The choice of sampling frames and the steps taken to
assure their completeness and accuracy affect every
aspect of the sample design. Ideally, a sampling frame
should --
	Fully cover the target population;
	Contain no duplication;
	Contain no "foreign" elements (elements that are not
members of the population);
	Contain information for identifying and contacting
the units selected for the sample; and
	Contain other information that will improve the
efficiency of the sample design and the estimation
If the sample design calls for a multi-stage selection,
a separate frame must be prepared for each stage (or
stratum) of the sample design. For example --
	In the two-stage sample design for City X that we
used earlier to illustrate multi-stage sampling, the
frame for the first stage would be a listing of the
blocks in City X. The frame for the second stage
would be listings of all the families living in each
sample block. In this two-stage design, the first-
stage sampling units are the city blocks; the
second-stage sampling units are the families for
which the data will be collected. The families also
are the units of observation.
	In a survey of plants manufacturing sulfuric acid,
the sampling frame of the first stage might consist
of a list of all U.S. chemical companies that manu-
facture sulfuric acid at one or more of their plants.
After selecting a sample of these companies, we
could make a listing of all or only a sample of the
sulfuric acid plants belonging to the companies

chosen at the first stage. This listing would
serve as the frame for the second stage of selection.
The development of the frame can be a major undertak-
ing involving substantial effort and expense. Complete,
current frames do not always exist. Many frames have
missing units and some frames contain duplicate list-
ings. Both of these frame imperfections cause biases
if they are not detected before the selection is done.
To illustrate some of these points, a city telephone
directory is a poor frame for a telephone survey of all
local households. Studies show that as many as 20 per-
cent of U.S. households have unlisted numbers or no
telephones. Using the telephone directory, therefore,
would result in undercoverage of the population. More-
over, some households would be overrepresented because
they have more than one listed number. Finally,
most directories also include business and other non-
residential numbers, some of which are hard to distin-
guish from residential numbers.
For surveys of businesses, it is especially difficult
to obtain complete and current lists. Probably the
best lists are those maintained for Federal programs
like social security, income taxes, unemployment in-
surance, and the economic censuses. Unfortunately,
these lists generally are not available to EPA and
other Federal agencies, so other sources must be used --
commercial business lists or lists that EPA maintains
of organizations that are required to comply with
certain Agency regulatory requirements.
In general, perfect or ideal frames are seldom avail-
able. The sampling plan should always specify what
steps the contractor will take to evaluate the frames
and deal with any deficiencies such as missing or
inaccurate elements.
2. Sample Selection Procedures
The sampling plan must provide complete specifications
for the procedures to be used to select units from the
frame at each stage of sampling.
Most sampling is done at a central location -- usually
at the contractor's main office. However, for some of
the later stages of sampling, the selection may be done
in the field. For example, in a face-to-face survey
the field supervisors may select sample housing units
from block or segment listings prepared by the main

office. Similarly, in a mail survey, if the contractor
intends to conduct follow-up interviews with some of
the people who do not send back questionnaires, pro-
cedures for selecting the follow-up sample should be
described in the sampling plan.
The selection procedures in the sampling plan should
specify --
	Any tasks necessary for reorganizing or otherwise
refining the frame prior to selection, such as --
= Screening to eliminate units that clearly are
not in the target population; and
= Transforming information about individual units
into measures of size (necessary for sampling
with probability proportionate to size).
	Whether the selection of sampling units (at each
stage) will be with equal probability or with vari-
able probability. If variable probability is to be
used, the basis for assigning selection probabili-
ties to individual units must be included.
	The sample sizes or intervals. If stratified sam-
pling is used, sizes or intervals may vary by
stratum. For some designs it may be necessary to
obtain preliminary counts or other tabulations from
the sampling frame to determine the most appropriate
size or intervals.
	The specific probability mechanism to be used to
select the individual sampling units or, for system-
atic sampling, the random starting point. If
selection is manual, the use of random-number
tables is recommended. If the selection is done
by a computer, most systems will have access to a
random-number generator.
	Any steps that will be taken to screen out ineli-
gible sampling units, obtain better addresses, etc.,
after the initial selection is made.
3. Estimation Procedures
Estimation procedures are the methods used to convert
sample data into estimates -- totals, means, propor-
tions, and other statistics -- for the population.
The actual preparation of the estimates (and the

calculation of sampling errors, discussed below) is
done towards the end of the data processing phase of
a survey, but the procedures that will be used to ob-
tain the estimates should be included in the sampling
plan. The approach used for the estimations also plays
a role in determining the size of the sample -- another
reason for determining the estimation procedures early
on. In addition, some kinds of estimates require the
capture of certain data when the sample is selected,
during the data collection phase, or during the proc-
essing phase of the survey.
The estimation procedures should specify how the con-
tractor proposes to derive the most precise estimates
possible from the sample data using statistical tech-
niques such as (1) applying "weights" to give greater
relative importance to some sampled elements than to
others; (2) making adjustments to reduce the bias
caused by eligible sampling units for which no data
were collected; and (3) using auxiliary information
obtained from the questionnaires, the sampling frames,
or other sources such as administrative records, other
surveys, etc.
We'll elaborate briefly on these three methods of en-
hancing data quality.
 Application of Weights
When analyzing complex samples, statisticians assign
weights (or multipliers) to adjust for (a) sampled
elements for which the probability of selection was
in some way unequal, (b) eligible units for which
no data were collected (total non-response units),
and (c) sampling units not included in the sampling
frame (non-coverage errors).
To explain --
If all the sampled elements had the same probabil-
ity of selection (sometimes called a self-weighting
sample), survey analysts can obtain valid estimates
of some statistics such as proportions, means,
percents, and medians without weighting the data
obtained from the sample. However, to estimate
totals for the sample, all units must be weighted
by the reciprocal of the sampling fraction. For
If a simple random sample of 1 in 10 housing units
has been selected, population totals could be

estimated by applying a weight of 1 0 to the data for
each housing unit sampled, or, similarly, by tabu-
lating the sample data and multiplying the sample
counts or aggregate by 10.
To illustrate how weights would be applied to adjust
for unequal probabilities of selection, if a multi-
stage sample were used and a sample of 1 0 city blocks
were selected from a total of 50 blocks, and then
every tenth family in these 10 blocks were selected
for interviewing, the over-all selection probability
for these families would be 1 in 50 --
10 x 1 =1
50	10	50
A uniform sampling weight of 50 would then be used
to estimate totals from the sample data.
 Adjusting for Missing Data
The techniques that will be used to adjust for total
non-response (eligible members of the sample that
provide no data) are usually incorporated in the
estimation procedures. The techniques used to make
these kinds of adjustments are --
Reweighting the sampled units by the inverse of
the proportion of units that did respond. For
example, if the proportion of the sample that
responded was 0.80, a reweighting factor of 1 .25
(1.00 divided by 0.80) would be used to adjust
for the non-response. Reweighting factors often
are computed separately by stratum or for each
member chosen at the first-stage of selection.
This allows for variations in the proportions of
different categories or areas of the sample that
Duplicating the values reported by the sampled
units to compensate for eligible units that did
not respond. Information from all sampled units
can be used in selecting the units that are
duplicated. For example, the units to be dupli-
cated could be selected from the same size or
industry category or from the same geographic
area as the non-responding units.
These kinds of non-response adjustments will reduce
non-response biases but will not eliminate them en-
tirely. The use of non-response adjustments is not

an acceptable substitute for diligent efforts to
collect data for all eligible units in the sample.
Note that other different techniques are used to
adjust for missing data from single questionnaire
items (called item non-response), (These adjustment
techniques are discussed in s"tep 7 of Chapter 6.)
 Using Auxiliary Information
Survey analysts often can improve sample estimates
by taking advantage of auxiliary information about
the population, which may be taken directly from
the sample (from the questionnaires, for example),
from the sampling frames, or from independent sourc-
es. Auxiliary information is most often used to
construct ratio estimates.
Suppose, for example, that we want to estimate the
number of unemployed individuals in a national
household survey. One way to do this is to tabu-
late the unemployed people in the sample and assign
them appropriate weights based on their selection
probabilities, a procedure known as simple unbiased
est imating.
For example, suppose we have an estimate of the
total population from an independent source at the
time of the survey (the U.S. census, say). We could
use this independent estimate to construct a ratio
estimate of unemployed individuals as follows:
Ratio estimate
of unemployed =
estimate of X
estimate of total
Unbiased estimate
of total
populat ion
In other words, we would use the sample data to
estimate the proportion of unemployed individuals
and apply that figure to an independent estimate of
the total population to derive a more precise esti-
mate of the number of unemployed individuals in the
population. If we had independent estimates of the
population by age and sex, we could make separate
ratio estimates of the number of unemployed individ-
uals in each age-sex group and total them to get
an estimate of the total number of unemployed in-
dividuals in the population.

Several different kinds of ratio estimation proce-
dures are available, as are other procedures that
make use of auxiliary information such as regres-
sion estimation. The choice of procedures will re-
flect the survey designer's judgment about how all
relevant data from the sample itself, the sampling
frames, and other sources can be used to develop the
most precise survey estimates, i.e., how to make the
best use of all available information.
In practice, weighting can be a complex task because a
combination of adjustments is often necessary. Weights
first may be assigned to adjust for unequal selection
probabilities. These weights then may be revised to
adjust for varying levels of response within the sam-
ple. Still further revisions may have to be made later
to adjust the sample to known distributions in the
populat ion.
The sampling plan, therefore, should fully describe the
estimation methods, formulas, or procedures the con-
tractor plans to use to produce the survey estimates.
4. Calculation of Sampling Errors
Of all aspects of sampling, calculating (or estimating)
sampling errors is the most technically complex. Most
surveys collect data on a large set of variables and
produce estimates for both the variables and their
relationships to each other. It is impractical and
usually impossible to calculate standard errors for
all the estimates. Survey analysts, therefore, nor-
mally compute standard errors only for the key statis-
tics and a few other selected estimates. From these
calculations, they develop generalized models from
which other standard errors can be inferred.
The sampling plan should specify --
	The estimates for which sampling errors will be
calculated. (Standard errors should be computed
for all key variables and a selection of other
	The approach that will be used to calculate the
sampling errors (formulas, methods, or software
packages) .
	Any assumptions or approximations implicit in the
proposed approach.

The extent of sampling error depends on the design of
the sample. The formula for calculating standard er-
ror found in most over-the-counter software packages
is applicable only to simple random sampling with re-
placement designs. It will produce overestimates or,
more often, underestimates of sampling error if applied
indiscriminately to other sample designs.
The sample designs for most of the surveys EPA spon-
sors are complex, often involving a combination of
multi-state and stratification sampling methods. For
these complex designs, survey designers use a variety
of approaches for calculating sampling errors such as
the "Taylor expansion method," "balanced repeated rep-
lications," "jackknife repeated replications," etc.
(See Kalton, 1983, for more information.)
In addition, several software packages have been de-
veloped recently for calculating sampling errors of
estimates that are based on complex sample designs.
The selection of suitable software poses difficulties
because most packages treat the sampling units chosen
at the first stage as being sampled with replacement,
when, in fact, this is rarely the case.
(See Step 8 in Chapter 6 for more information on the
application of these approaches to the calculation of
sampling errors after the data are processed.)
The sponsoring office's greatest impact on the development
and faithful execution of a sound sampling plan occurs in
the design stage of the survey. Therefore, with the help
of other Agency offices, we suggest that you, as project
officer, do the following before the contract is awarded --
(1)	Specify in the statement of work for the RFP the kinds
of information offerors should include in their pro-
posed sampling plans. The main components of a sam-
pling plan -- the selection and development of the
sampling frame, the sample selection procedures, esti-
mation procedures, and the procedures for calculating
sampling errors -- are discussed in section D of this
chapter. (For further information on preparing the
statement of work, see Chapter 5, Volume I.)
(2)	Make sure the technical evaluation panel reviewing
the responses to the RFP includes someone qualified

to evaluate the proposed sampling plans. Expertise
in sampling theory and its applications to surveys
is necessary to spot defects such as --
	Any (unnecessary) departures from probability
	Imprecise descriptions of the sample selection
	Sample sizes or sampling allocation rates that
will not achieve the levels of precision you
specified in the RFP;
	Incorrect estimation formulas or methods; and
	Inappropriate formulas or methods for calculating
sampling errors.
(For additional information on what to look for in
reviewing the sampling plan, see "Evaluating the
Technical Proposals" in Chapter 6, Volume I.)
After EPA awards the contract, there are several things
you can cJo to monitor the execution of the sampling plan.
(3)	Sampling, perhaps more than any other aspect of survey
methodology, is an area where expertise is vital for
effective monitoring and control. Most statisticians
are not experts in sampling theory. We recommend,
therefore, that you have an expert in this special
branch of statistics review the sampling plan before
giving the contractor permission to proceed with the
development of the frame, the selection of the sample,
and other sampling operations. If a sampling expert
is not available in your office, you may request
assistance from the Statistical Policy Branch of the
Chemicals and Statistical Policy Division within
the Office of Standards and Regulations. Afterwards,
make sure the contractor revises the sampling plan to
incorporate any changes you or other review authori-
ties suggest.
(4)	Be sure the contractor tests the validity of the sam-
pling frames before beginning the selection of the
sample for the survey proper. Missing and duplicative
sampling units can cause difficulty if they are not
detected. Frame counts, broken down by geographic
area and other characteristics, should be checked
against information about the population that may be

available frora other sources. The accuracy of totals
for various kinds of industrial establishments may be
cross-checked with the most recent economic census,
for example. Sometimes, especially when using commer-
cial business lists, it may be desirable to contact a
small sample of the units in the frame to determine
what proportion are currently active members of the
population, and to check the accuracy of names, ad-
dresses, and other identifying information. While
the contractor normally will perform the validity
tests, the results should be fully documented for
Agency review.
Compare the sample selection procedures in the work
plan with the results of the sample selection opera-
tions actually carried out at each stage of sampling
for the survey proper.
If any sampling is to be done in the field, the con-
tractor should pretest the selection procedures and
provide counts of the number of units selected at
each stage, broken down by categories for which frame
information is available. Agency experts or the con-
tractor should check these counts against the antici-
pated sample sizes. Frame totals can be checked by
(a) applying appropriate sampling weights to the sam-
ple counts, and then (b) using tolerances based on
estimated sampling errors, comparing them with actual
frame totals. Make sure these checks are made before
giving the contractor authority to start collecting
data for the main survey.
Review the specifications for preparing the sample
estimates. Later, when the contractor has completed
the preliminary tabulations, check the key statistics
against (a) data frora prior surveys or other sources
and (b) known totals from the sampling frames that
were used. (For further details, see "Preparation of
the Outputs" in section A of Chapter 6.)
Review the specifications for calculating sampling
errors. Check the actual estimates of sampling errors
for reasonableness as soon as they are available. An
easy way to check estimates of sampling errors for
population counts as well as proportions or percents
based on these counts is to compare them with the sam-
pling errors that would have been obtained if a sim-
ple random sample had been used. The ratios of the
contractor's estimates to the corresponding values
of the sampling errors for the simple random sample
generally range from slightly less than 1 to about 2

or 3, depending on the sample design used. If all the
ratios are much larger or smaller, there is likely to
be a programming error or an error in the estimation
formula (or method).
Another way of checking the reasonableness of the sam-
pling errors is to plot the estimated sampling errors
against the corresponding estimates obtained from the
sample data (totals, percents, means, etc.). The
values usually will follow a fairly regular pattern.
Any extreme values may indicate processing errors for
the items in question. If the plotted values for a
particular class of estimates do follow a regular pat-
tern, a curve can be fit to these calculated values.
This curve can be used to estimate sampling errors of
items for which sampling errors were not actually
	Basic Ideas of Scientific Sampling, Second Edition,
~K~. Stuart, Charles Griffin and Co7 Ltd., 1976.
	Introduction to Survey Sampling, Quantitative Applica-
tions in the Social Sciences, No. 35, G. Kalton, Sage
Publications, Beverly HilIs, CA, 1983. A few equations,
but straightforward and clearly written.
A Sampling in a Nutshell, M. J. Slonim, Simon and Shuster,
New York, NY, 1960.
	Survey Sampling: A Non-Mathematical Guide, A. Satin and
W. Shastry, Statistics Canada, 1983,

A survey interview is a conversation between an interviewer and
a respondent for the purpose of obtaining certain information
from the respondent. Coupled with a well-designed, well-tested
questionnaire, personal interviews are a powerful, indispensable
survey research tool. Whether conducted at the respondent's
home or place of business, or over the telephone in a centralized,
supervised environment, interviews have been used effectively to
collect survey data for more than 30 years. They are especially
appropriate for sounding out people's opinions, future inten-
tions, feelings, attitudes, and reasons for behavior, and are
adaptable to a wide variety of research situations.
In this chapter we will look at --
	The kinds of quality-assurance procedures the
contractor should establish to ensure that
their interviewers collect good data from the
survey sample;
	The tasks the contractor typically performs
to organize and effectively manage the inter-
viewing activities;
	The role of the interviewers in a face-to-
face survey, and
	The things the sponsoring office can do to
foster the collection of high-quality data.
Our emphasis throughout this chapter is on face-to-face surveys.
However, much of the text is relevant to telephone interviewing
and, to the extent that interviews are used for follow-up or
quality-control purposes, to mail surveys as well.
It is vital for the contractor to establish a set of
procedures to assure the quality of the work done through-
out the data collection phase. The quality-assurance
procedures should cover --

(1)	Who specifically is to be interviewed at each sampling
unit (or unit of observation). These are called "re-
spondent rules."
(2)	How much effort the interviewers should exert to secure
an interview. This is established in the so-called
"follow-up procedures."
(3)	The strategies that are to be used to ensure the col-
lection of high-quality data. These "quality-control
strategies" are intended to reduce data errors for
which interviewers are primarily responsible and, in-
sofar as possible, to detect and correct these errors.
The respondent rules, follow-up procedures, and quality-
control strategies should be incorporated into the work
plan and approved by the sponsoring office before any
data for the main survey are collected. They should be
revised as necessary following any pretests or pilot tests.
The contractor should highlight these procedures and strat-
egies in all training programs and instructional materials
prepared for the interviewers, supervisors, and support
Let's examine the three types of quality-assurance proce-
dures in greater detail.
1. Respondent Rules
Respondent rules specify the individual or individuals
who are eligible, acceptable, or most desirable as
respondents for each unit of observation. These rules
also specify whether the respondents are to be inter-
viewed alone or with other respondents at the same
unit, and whether individuals who are not respondents
may be present.
How stringent or flexible the respondent rules should
be depends on the kinds of questions to be asked and
the conditions under which the interviews are to be
conducted (where, when, and the length of the question-
naire). Obviously, the more inflexible the respondent
rules, the more "call-backs" the interviewers will have
to make to reach the designated respondents. Con-
versely, the more flexible the rules, the higher the
interviewers' completion rates will be.
Respondent rules usually include eligibility criteria
such as age (in household surveys) and title or type
of responsibility (in business surveys). Sometimes
the rules designate only one person in the sampling

unit as an acceptable respondent for the unit, e.g.,
the head of the household, the board chairman, the
supervisor of public works. In other cases, anyone
who meets the eligibility criteria may be designated
as the respondent. For some surveys, the interviewers
may be required to talk with several individuals at
each unit (all responsible adults, say), with each
respondent supplying answers to different parts of
the questionnaire. In other surveys, a particular
type of respondent may be identified as the "most
desirable" respondent, but the interviewer may be
allowed to interview any other responsible adult if
this person is not available.
Respondent rules also specify whether interviewers may
talk with an alternate respondent -- a "proxy" -- after
they have made a certain number of unsuccessful attempts
to interview the designated respondent(s) . Using prox-
ies may produce a marked deterioration in data quality,
however. Usually, some information about the units of
observation is best supplied by a particular person
(the head-of-household, the plant manager). If data
are obtained from someone other than the designated
respondents, there are likely to be serious gaps, inac-
curacies, and biases in the information the interviewer
gets. Nevertheless, if it is imperative to obtain some
information about the unit of observation, the rules may
allow the interviewer to collect data from neighbors,
co-workers, or others if the designated respondents
cannot be reached.
2. Follow-Up Rules
Follow-up rules prescribe the amount of effort that must
be expended to complete an interview with the designated
respondent(s) for each sampling unit. Follow-up rules
should specify --
	The number of attempts that must be made to secure
an interview from a single unit or a cluster of
un i t s ;
	The time of day the interviewers are to make the ini-
tial visit and subsequent visits to each unit; and
 Any allowable deviations from these rules (e.g., to
hold down costs, the interviewer may make fewer per-
sonal visits to units in sparsely populated areas,
if necessary).
For a particular survey, the stringency of the follow-
up rules will depend on (a) how vital the researchers

believe it is to obtain information directly from the
designated respondents rather than proxies; (b) the
survey budget (call-backs are costly); (c) how soon
the data are needed (inflexible follow-up rules may un-
necesarily delay the project); (d) the characteristics
of the target population (some types of respondents are
difficult to reach during the day); and (e) the charac-
teristics of the areas to be surveyed (e.g., widely
dispersed units, inner-city neighborhoods).
3. Quality Control
Guarding against missing and inaccurate data is a major
objective in any survey. Strategies must be developed
to control three principal types of non-sampling er-
rors that occur during the data collection phase, all
of which can seriously compromise the statistics:
	Coverage errors, which result from interviewing
ineligible units or failing to interview eligible
un i t s ;
	Non-response errors, which result when no data or
incomplete data aTe obtained from eligible units
(units that should be surveyed); and
	Response errors, which are incorrect reports by the
interviewer or the respondent, whether inadvertent
or deliberate.
Our concern here is with the effects that interviewing
may have on the quality of the data collected in a
survey. The fewer errors there are in the data, the
higher the data quality will be. Data errors that
result from the use of sampling can be measured, and
reports of sampling errors included in any reports of
the survey results can alert users, so they can take
them into account. Non-sampling errors are much more
difficult to measure and, therefore, can seriously
compromise the survey results.
Non-sampling errors can occur in any survey, regard-
less of the collection method. Moreover, they do not
result solely from poor interviewing. For example,
some coverage errors may be directly attributable to
the use of incomplete frames, and some non-response
and response errors may be the result of poor question-
naire design. In a mail survey where no follow-up
interviewing is done, they may be directly attributable
to the questionnaire.

However, poor performance by the interviewers or inef-
fective interaction with respondents can seriously in-
fluence the quality of the raw data the interviewers
collect, and hence affect the validity of the resultsT
If the interviewers 3o not adhere to the respondent
rules and follow-up procedures, and do not properly ad-
minister the questionnaire, the number of non-sampling
errors is likely to be very large. Many of these
errors may be systematic errors, which no increase in
sample size can reduce or eliminate.
Let's examine the sources of (1) coverage errors, (2)
non-response errors, and (3) response errors. Then,
in the last subsection, look at the principal quality-
control strategies survey researchers have developed
to reduce these errors during the interviewing.
	Coverage Errors
The main sources of coverage errors in an interview
survey are poorly constructed or outdated sampling
frames. For example, the interviewers may be given
incorrect listings of the households or businesses
they are to cover, so some of the units they attempt
to contact are unacceptable, non-existent, or other-
wise ineligible. These errors cannot be attributed
to the interviewers.
In some cases, however, the interviewers may be re-
sponsible for coverage errors. They may interview
the wrong unit by mistake -- because the street num-
ber is not clearly marked on the house, for instance.
They may even go so far as to make up the answers to
a questionnaire for a difficult-to-reach unit, in-
stead of getting data from the designated respondent
in that unit.
	Non-Response Errors
Non-response errors occur
the interviewer gets no
response") or incomplete
"item non-response") from
Let's look at the sources
response errors.
= Total non-response.
Total non-response occurs when an interviewer
does not obtain any data (or less than the mini-
mum amount required to count as a completed
, as we said earlier, when
data (called "total non-
data for an item (called
an eligible sampling unit,
of these two kinds of non-

interview) from a sample unit that is eligible
for an interview.
Frequently, not all sample units assigned to
interviewers are eligible for interviewing. In
a household survey, for example, units that turn
out to be vacant or demolished are ineligible
and will not be treated as non-response cases.
On the other hand, where interviews are not ob-
tained for eligible units because of refusals or
inability to contact designated respondents,
the units will be counted as non-response cases.
It is important that the contract specify in
some detail what kinds of units should be de-
fined as ineligible for interview. For example,
should households with no English-speaking mem-
bers be considered ineligible? What about house-
holds where all of the eligible respondents are
deaf, senile, or otherwise in no condition to be
interviewed? These points should be clearly
spelled out in the survey contract to avoid later
disputes about whether the contracting organiza-
tion has achieved the target response rate set in
the contract. You will recall that we said in
Volume I that a response rate lower than 75
percent usually is unacceptable for an Agency-
sponsored survey.
Experienced, well-trained interviewers can do a
lot to minimize the number of non-responses for
eligible units. (See "Locating Respondents" and
"Securing Interviews" in section C.) Keep in
mind that whatever probability sampling method
the contractor uses, every member of the sample
must be accounted for if the statistics are to
reflect characteristics, opinions, attitudes,
etc., of the target population. Therefore, the
interviewers must try to complete interviews
with all the units or individuals in the sam-
ple assigned to them in accordance with the re-
spondent rules and follow-up procedures estab-
lished for the survey. The level of difficulty
they face depends largely on how stringent the
respondent rules are, i.e., whether they may
interview any responsible adult at the unit or
must interview one or more specific individuals.
In addition to total non-response, a partial
non-response may occur. Cases are class if ied
as partial non-response if the interviewer fails

to obtain acceptable responses to one or more
questions but does obtain enough data so the
unit need not be counted as a case of total
The definition of "total non-response" should be
included in the contract. This classification
normally is assigned to units where responses
are missing for any one of certain specified
questions or more than a specified number of
other items.
= Item non-response.
In what is called "item non-response," the inter-
viewer fails to obtain data for a single item on
the questionnaire. Either the respondent or the
interviewer may be at fault. For example --
(1)	The respondent may remain silent or refuse
to answer the question;
(2)	The respondent may give an irrelevant an-
swer; or
(3)	The interviewer may fail to ask one of the
questions or skip to the wrong question,
which in either case results in a missing
Interviewers are trained to handle the first two
kinds of item non-response with techniques such
as pausing briefly to give the respondent time
to answer, using words of encouragement to elic-
it a reply or a more complete reply, repeating
questions, probing adequately, and reading ques-
tions exactly as they are worded. (See "Asking
Questions" in section C for more information.)
 Response Errors
Response errors may be caused by either the respond-
ent or the interviewer. For example --
= Respondents may give inaccurate replies when
they do not understand a question and are reluc-
tant to ask the interviewer to repeat or explain
it. Or the respondents simply may not know the
answer, and rather than appear uninformed or
stupid will give a false reply. Or, respondents
may deliberately give an inaccurate reply to a

question they consider overly sensitive. For
example, a 51-year-old man may underreport his
age as 47, or overstate his income to impress
the interviewer.
= Interviewers may misrecord a respondent's reply
(e.g., the same respondent truthfully states his
age as 51 but, out of carelessness, the inter-
viewer records it as 41.) Or, interviewers may
misread a question, not probe sufficiently when
a respondent seems confused or tentative, skip
certain questions altogether in the belief they
will be able to fill in the answers themselves
later when they edit the questionnaire.
Although we said earlier that response errors are
caused by the respondent or the interviewer, the un-
derlying cause is the interaction of the two. Other
sources contributing to response errors which are
not entirely independent of the interviewing process
are the conditions of the interview such as the
form, content, and wording of the questionnaire;
the training and instructions given to the inter-
viewer; and the location of the interview.
The principal things the interviewers can do to min-
imize response errors are to (a) make an effort to
establish a good interaction with the respondent,
(b) be faithful to the questionnaire, and (c) main-
tain an open, neutral position on the questionnaire
topics. (See "Asking Questions" and "Recording and
Editing the Responses" in section C for details.)
 Quality-Control Strategies
Survey researchers have developed numerous "quality-
control" strategies to detect and eliminate or re-
duce non-sampling errors for which interviewers are
primarily responsible. The principal strategies
used during the data collection phase to control
so-called interviewer effects" are --
9 Monitoring interviewer
completion rates
m Observation of interviews
	Preliminary screening of
ques tionnaires
6 Validation of interviews

Each of these control strategies serves a different
purpose. All five should be used in every Agency-
sponsored survey where interviewing is the primary
collection method, resources permitting. The Agency
should require the contractor to specify in the work
plan (a) the quality-control strategies that will be
used, (b) what each strategy is expected to accom-
plish, (c) how it will be applied and when, and (d)
the procedures that will be used to make sure it is
implemented properly.
Let's look briefly at how the five quality-control
strategies listed above typically are used to detect
and reduce coverage, non-response, and response er-
rors while the interviewing is going on, (Note that
in some surveys quality-evaluation strategies may
be used at the end of the survey in an attempt to
measure the extent of the non-sampling errors.
These additional measures are beyond the scope of
this Handbook, however.)
= Monitoring interviewer completion rates.
Often a small proportion of the interviewers is
responsible for a disproportionate share of the
non-response errors in a survey. To help super-
visors track the number of these errors each
interviewer makes, the interviewers are required
to record the specific outcome of each call.
For example, to report a (total) non-response
for any unit, interviewers must record exactly
why they were unable to secure an interview. If
a unit is found to be ineligible for interview,
the reason must be given.
Interviewers are usually required to prepare a
weekly summary of their work, showing the number
of assigned cases in four categories: (1) eli-
gible, interview completed; (2) eligibleP non-
response; (3) ineligible; and (4) pending. Fur-
ther breakdowns of non-response and ineligible
cases, by reason, are often required. Alterna-
tively, these reports may be prepared by supervi-
sors or office clerks, based on the question-
naires turned in by the interviewers.
In either case, these weekly reports should be
used by supervisors to monitor the quality and
quantity of each interviewer's work, A key
indicator of quality is the completion rate
the percent of all eligible cases for which

completed interviews are obtained. Another in-
dicator is the proportion of ineligible cases.
A high proportion may indicate that interview-
ers are misclassifying some eligible units. The
average number of call-backs per completed case
(those in categories 1 , 2, and 3 above) may
serve as an indicator of how carefully inter-
viewers are scheduling their calls. Careful re-
view of these and other indicators will allow
supervisors to concentrate their attention on
interviewers whose work is substandard. (See
also the discussion of "Preliminary screening"
Observation of interviews.
Observation of interviews in both face-to-face
and telephone surveys is widely used to train
and assess interviewers, and to evaluate re-
spondent reactions in pretest interviews or in
exploratory studies.
Direct observation of face-to-face interviews
during the survey proper is relatively uncommon,
however, because of its high cost. If resources
are available for some direct observation of
interviewers in the field, supervisors should
observe the work of less experienced interview-
ers and those with below-average performance, as
shown by their activity reports and the failure
rates of field screenings of their completed
questionnaires (see below). A possible substi-
tute for direct observation of face-to-face
interviews is to ask each interviewer to tape
record one or more of their interviews at speci-
fied intervals.
Direct observation of telephone interviews, on
the other hand, is relatively inexpensive and
therefore a valuable tool for controlling all
types of non-response and response errors. It
is widely used to monitor and assess telephone
interviewers. Throughout the data collection
phase, supervisors can easily monitor the inter-
viewer's side of the conversation, quickly cor-
rect deficiencies in the way interviewers ask
questions, and make sure they ask all the ques-
tions. Moreover, with the proper equipment and
the permission of the respondent, supervisors
can monitor both sides of the conversation and
give interviewers valuable feedback on how to
improve their skills.

The contractor should develop written evaluation
criteria for whatever observation techniques are
planned. The criteria are needed to guide the
supervisors in which aspects of the interviews
they need to look at. Supervisors also should
be instructed in how to use the results of their
observations to help interviewers improve their
= Preliminary screening of questionnaires.
An initial "field screening" of the question-
naires turned in by the interviewers is an
effective way of detecting and correcting many
types of non-sampling errors. The terra "field
screening" is more properly applied to face-to-
face surveys, but similar procedures are used by
supervisors in conventional telephone surveys
to control the quality of the interviews.
Questionnaires may be screened by supervisors
or their office assistants. Whoever does the
screening should look for (a) missing entries
(which may indicate failure to follow skip
patterns correctly), (b) inadmissible or ques-
tionable entries, (c) unnecessary entries, and
(d) illegible entries. The supervisor should
record all errors and discuss them with the
Field screening may reveal systematic procedural
errors by the interviewers, or even faulty in-
structions or training materials. It is impor-
tant to detect systematic errors of this type
early in the data collection phase, so super-
visors can alert the interviewers to their mis-
takes before too many additional interviews are
done. Once the screening has shown that an
interviewer is doing good work, it may not be
necessary to review all their completed ques-
tionnaires -- occasional spot checks may be
Validation of the interviews.
Another important quality-control strategy is
for the field staff to verify whether interview-
ers are actually making all the interviews they
claim to have made. Verification is usually
accomplished by mailing respondents a card ask-
ing (a) if they were interviewed, (b) how long

the interview took, (c) if they would be willing
to participate again, and (d) if they have any
comments or questions about the interview or the
interviewer. If a respondent does not return
the card within ten days, the supervisor con-
tacts them by phone to verify the interview.
Generally, 10 percent of each interviewer's com-
pleted questionnaires are verified each week.
Although professional interviewers rarely forge
an interview, if any questionnaire fails the
verification test the contractor should verify
all the interviewer's previous worke
Reinterviews may be an effective method of mea-
suring response errors. They should be done
soon after the initial interviews because the
longer the interval between the initial review
and the reinterview, the more changes in the
respondents' characteristics and availability
there are likely to be. Sometimes an inter-
viewer with similar training and experience will
reinterview the original unit; in other cases,
supervisors or more experienced interviewers are
used. To minimize the burden on the respondents
selected for a "second" interview, usually just
a few questions are asked.
The cost of reinterviews is high, however, and
the time required to conduct them and process
the results -- especially if complete reinter-
views are done -- make them unsuitable as a
quick, early strategy for measuring interviewer
performance. They can be especially useful in
continuing surveys, however.
Reinterviews sometimes are used to determine
whether units interviewers have called "ineli-
gible" have been correctly classified. Super-
visors may reinterview all the housing units
in a particular area which interviewers had
reported as "vacant." The reinterviews would
reveal whether any of these units were actually
occupied at the time of the survey. Interview-
ers sometimes are tempted to raisclassify occu-
pied housing units where interviews are incon-
venient or difficult to obtain as "vacant,"
thereby eliminating the requirement to obtain
interviews for these units.

In addition to establishing strategies to assure the qual-
ity of the data, in a face-to-face or telephone survey the
contractor must organize and oversee the work of dozens,
perhaps hundreds, of interviewers as well as supervisory
and administrative staff.
Although managing the data collection phase of a mail sur-
vey is less complex, the contractor must still set up a
system to coordinate and control the flow of the question-
naires to and from the respondents. In addition, since
mail surveys usually entail some telephone or face-to-face
follow-up interviews, staff must be instructed in the
proper procedures for these interviews.
In this section we continue our focus on face-to-face in-
terviews, and examine the organizational and administrative
tasks a survey contractor typically performs to set up a
successful field operation for collecting data in the
sampling areas. The four main tasks are --
	Preparing instructions and
training materials
	Staffing the field operations
	Training the interviewers
	Coordinating and controlling
the field work
Organizing the "field" operations of a telephone survey is
similar in many ways, but less complex. There is no need
to set up a far-flung field operation as in a face-to-face
survey, for example. Usually the interviewers work in one
centralized location, supervised by a few members of the
contractor's permanent staff. However, instructions and
training materials for the supervisors and interviewers
must be prepared; the interviewers must be selected and
trained; and a system must be set up to coordinate and
control the interviewing activities.
The contractor should fully document these procedures in
the work plan well before any of the preparatory tasks
are initiated. The sponsoring office should review them
at the same time as the quality-assurance procedures that
we discussed in section A.
1. Preparing Instructions and Training Materials
Once the Agency approves the quality-assurance proce-
dures that will be used to guide the interviewing,

the contractor should document them in instructions
and training materials for the interviewers, super-
visors, and other field staff. How extensive these
materials have to be depends largely on the method of
collection. Obviously, face-to-face surveys require
the greatest number of written materials and mail
surveys the least.
Let's look at the three basic guidance documents pre-
pared for a major face-to-face survey: (a) instructions
for the supervisors, (b) an interviewer's manual, and
(c) a training guide.
	Instructions for the Supervisors
It is almost impossible to over-emphasize the impor-
tance of the field supervisor in controlling the
quality of interviewers' work. Yet all too fre-
quently written guidance materials for supervisors
concentrate on logistic and administrative matters
-- receipt and shipment of materials, payment and
allowances for interviewers, etc. These subjects
are important, but they do not deal directly with
the supervisor's central responsibility, which is
to see that the work is done on schedule and that
standards of quality are met.
The instructions to the supervisors should clearly
specify --
The kinds of quality-related problems requiring
communication with the central survey staff, and
a well-defined procedure for resolving problems
that arise;
= The quality-control strategies that will be
used to assess the work done by the interview-
ers, and the supervisor's responsibilities in
implementing them and evaluating their effec-
tiveness; and
A description of the criteria that higher-level
field staff or central staff will use to evaluate
the supervisor's performance.
	Interviewer's Manual
A detailed written instruction manual for the inter-
viewers is essential for every survey. Supervisors
will also use this manual in their training and for
oversight purposes.

If the contractor has developed a standard training
manual covering record-keeping, interviewing tech-
niques, and other features common to all surveys,
it may be sufficient to prepare a supplement to
their standard manual which will cover only the
special features of the Agency's survey such as --
How the respondents were, or are to be selected,
and the procedures for locating them;
= The respondent rules;
= The follow-up procedures, especially how to deal
with various non-response situations;
= The quality-control strategies to be used;
 The objectives, purpose, and scope of the survey;
Question-by-question specifications explaining
the intent of each question; and
= Any special administrative matters, e.g., the
length of the data collection period, who to
contact in case of problems, what to do with the
completed questionnaires.
 Training Guide
A formal training guide for supervisors and others
conducting interviewer training sessions is often a
desirable supplement to the interviewer's manual.
The guide should include topics the trainers should
cover, the order in which they are to be taken up,
and practice exercises, quizzes, etc., for each
training session. The guide can be in outline form
or it may be a verbatim guide.
To supplement the training guide, the contractor may
develop other materials such as --
= Test exercises, to be completed at various points
in the training;
= Written instructions for "mock" interviews;
= Audio-visual materials such as taped demonstra-
tion interviews; and
= Slides and other visual aids showing maps of the
sampling areas, questionnaire forms, etc.

2. Staffing the Field Operations
Once the instructions and training materials are ready,
the contractor must assign existing staff or recruit
new staff to carry out the data collection activities.
To complete the fieldwork for a major face-to-face sur-
vey, normally several dozen interviewers located in 50-
100 sampling points (cities or counties), several field
supervisors and support personnel, staff for overall
project supervision, and a full-time central office
will be needed. There should be enough supervisors so
they all will have adequate time to monitor the per-
formance of the interviewers assigned to them.
The staff people most directly involved in the field-
work are (1) the field supervisors and (2) the inter-
viewers themselves. Let's briefly examine their re-
spective responsibilities.
Some supervision of the interviewers is essential in
every survey to detect poor work and assure that the
fieldwork proceeds smoothly. Sometimes, centrally-
located supervisors direct the work of a mobile
field staff, which moves into the various sampling
areas. Some survey research firms prefer a network
of perhaps a dozen supervisors, who work on a re-
gional basis and move with the field staff from area
to area. Whether the field supervisors are centrally
located or dispersed, they are the main link between
the head office and the interviewers in the field.
The contractor should establish some equitable ratio
of interviewers (and other field staff) to super-
visors. The ratio should be small enough so the
supervisor is able to spend sufficient time both in
the field and in the regional (or central adminis-
trative) unit to regularly review and evaluate the
work of the interviewers for whom they are respon-
sible. The appropriate ratio for any specific sur-
vey will depend on factors such as the experience
of the interviewing staff, the size of the assign-
ment area, the type of transportation and communi-
cation facilities available, and the amount of time
the supervisors are required to spend on matters
not directly related to the survey.
Each field supervisor is responsible for hiring,
training, and maintaining a staff of interviewers
in the areas assigned to them. They should be in

constant communication with interviewers through
personal visits, mail, and telephone contacts.
The field supervisors, along with a support staff of
clerical personnel who usually work in the areas
where the interviewing is going on, are responsible
(1)	Arranging travel and lodging for staff and
(2)	Preparing specific work assignments for the
interviewers -- areas, times, lists of house-
holds -- or, in the case of a business survey,
coordinating and scheduling interview sessions;
(3)	Logging in the completed questionnaires and
control forms (the interviewers' evaluations,
notes, weekly activity reports, etc.);
(4)	Scanning the questionnaires for completeness
and accuracy, and forwarding them for editing
and coding;
(5)	Regularly evaluating the interviewers' work,
using the quality-control strategies disussed
in the previous section; and
(6)	Preparing detailed reports on the field activi-
ties. These will be used to prepare periodic
progress reports for the Agency showing the
number of interviews completed or partially
completed, the number of refusals, the number
of verifications, etc., and the overall re-
sponse rate.
In any face-to-face or telephone survey, interview-
ers play a major role in the quality of the re-
sponses and hence in the quality of the results. In
some EPA-sponsored surveys, the interviewer is the
only link between the contractor's central office
staff and the respondents.
No matter what size sample is to be surveyed, the
contractor must establish policies and procedures
for selecting and training the interviewers and
maintaining their morale. A relatively small face-
to-face survey of 500 respondents may involve hiring
and training as many as 30 interviewers. Keeping

interviewer workloads on each survey small will help
to (a) keep interviewer travel costs low; (a) mini-
mize the time needed to complete the fieldwork; (c)
avoid making the interviewers' job too repetitive
and monotonous; and (d) Minimize the effects of
systematic errors by individual interviewers.
There is a wide range of practices among survey re-
search firms regarding the hiring of interviewers.
Most reputable survey research firms maintain a net-
work of skilled interviewers they can call upon on
an as-needed basis. Interviewers usually are re-
cruited on the basic of written applications, fol-
lowed by a lengthy personal interview and a written
test to evaluate the basic clerical skills needed
to record, summarize, and edit respondents' answers.
At the end of the project, interviewers generally
are rated on their productivity, accuracy, coopera-
tion, and dependability.
Firms typically maintain a file of the names, cap-
abilities, and performance ratings of those who have
passed the initial screening. In addition, the file
contains detailed information on the interviewers'
geographic location, hours available for work, edu-
cational background, special skills, current avail-
ability, and the results of performance evaluations
on previous surveys.
Before hiring interviewers for a specific project,
it is important to make sure that they are able
to work at the necessary level during specific
hours; are able to get to the interview locations;
and are willing to work in the assigned areas.
People become interviewers for many reasons. They
are motivated by the flexible working hours, the
chance to interact with others, and the opportunity
to satisfy their curiosity about a variety of re-
search topics.
While there is no such thing as an "ideal" inter-
viewer -- much depends on the nature of the sur-
vey, the most sought after qualities typically are
intelligence, dedication, honesty, dependability,
attention to detail, a professional attitude (nei-
ther overly social nor overly aggressive), and
an ability to adapt to a variety of interviewing
situations (different types of people, different
areas, etc.).

Once interviewers are hired, maintaining morale is
vital. Good working conditions, a reasonable sched-
ule of assignments, equitable pay rates, and bonuses
for high quality work and difficult assignments all
contribute to their efficiency.
3. Training the Interviewers
One of the contractor's most important tasks in pre-
paring for a survey is to train the interviewers. The
contractor should begin training those who will be used
for the main survey shortly after the Office of Manage-
ment and Budget approves the clearance request.
No matter how skilled or experienced an interviewer or
how simple the questionnaire, the interviewers must be
	Thoroughly instructed in the specific objectives,
the rules, and procedures of the survey;
	Taught all quality-assurance procedures they will
be responsible for, and the procedures for reporting
their progress to the supervisor; and
	Taught a standard format for recording respondent
repli es.
If the interviewers are inexperienced, they also should
be instructed in basic interviewing skills (techniques
for gaining entry, probing), and be taught how to plan
and update their calling schedules so as to make the
best use of their time and travel.
Survey research firms use a variety of techniques to
train or retrain interviewers -- interactive lectures,
home study programs, practice interviews, and practice
in the field. Often a final exam on the field proce-
dures is given as well.
Most face-to-face surveys are complex enough to require
interviewers to attend a two-to-five day training con-
ference. These are sometimes held at several different
locations around the country. A field supervisor and
several professional trainers generally lead the train-
ing. Training is guided by the interviewer's manual,
the training guide, and various other training aids the
contractor has prepared.
The supervisor should evaluate both the effectiveness
of the training sessions and, by rating the trainees'
performance in practice exercises, quizzes, and exams

of various kinds, the extent to which each interviewer
has mastered the essentials. Interviewers who are
clearly incapable of doing work in the field should
be eliminated from consideration, reassigned, or given
additional training.
Once the interviewing is in progress, the field staff
may provide training for new interviewers or conduct
special sessions to reinforce the initial training.
The intent of these combined training techniques is to
ensure that the interviewers are capable of collecting
complete and accurate data and are fully prepared to
elicit respondent cooperation.
Coordinating and Controlling the Fieldwork
In addition to hiring and training interviewers, super-
visors, and administrative support staff, the contrac-
tor must set up a system to coordinate and control the
fieldwork. For most surveys, this means establishing
procedures for --
0 Scheduling and tracking the work of several dozen
interviewers for several weeks, or perhaps months.
Once the contractor has determined how many inter-
viewers will be needed, either the central adminis-
trative unit or the field supervisors will prepare
a schedule of the units each interviewer must cover.
The assignments are based on the interviewer's
availability and experience, and often the special
characteristics of the sampling areas that have to
be covered. For example, although most interviewers
are women, if high-crime areas are to be surveyed
(particularly at night), male interviewers should
be assigned to those areas.
For both economic and administrative reasons, it is
necessary to limit the length of the interviewer's
assignments. However, from a practical standpoint,
the field supervisors should allow the interviewers
enough time to cover all their assigned units and
to make whatever number of call-backs were estab-
lished in the follow-up procedures.
 Controlling the flow of materials to and from the
Once the data collection begins, the pace of the
administrative work accelerates rapidly. Unless the

contractor establishes close control over the flow
of materials to and from the field, chaotic condi-
tions may result. Often a central administrative
unit at the contractor's main facility will be
given the responsibility of sending instructions and
training materials, blank forms and questionnaires
and other necessary supplies to field personnel.
This same unit also can receive and screen the ques-
tionnaires and other such materials completed in the
field. A regional field organization frequently is
incorporated into the loop. Each unit in the commu-
nications chain must maintain accurate records of
its own, particularly regarding the response status
of each sample unit.
 Resolving problems in the field.
The contractor must develop a system for the field
supervisors to report problems encountered in the
field to the regional supervisors or the central
administrative unit. If the resolution of these
problems affects the existing procedures, all staff
should immediately be notified of the changes.
Let's turn now from methodological and organizational con-
cerns, for which the researchers, analysts, and administra-
tors on the contractor's staff are responsible, to the
practical aspects of interviewing -- the actual conduct of
the interviews. We will examine the four principal tasks
of the interviewers in a face-to-face survey, which are --
We'll focus our discussion on formal interviews, where the
interviewer's goal is to obtain full and accurate answers
to a fixed set of items and record them on a standardized
survey questionnaire. When a structured questionnaire is
administered in a uniform way, the researchers and analysts
can be reasonably confident that all the answers are com-
parable. For this reason, formal interviewing is the norm
for statistical surveys. This does not mean that formal
	Locating the respondents
	Gaining respondents' cooperation
	Asking the questions
	Recording and editing the

interviewing allows no flexibility. The interviewer can
explain and probe and adjust the speed of the interview --
but within some predetermined limits. Rarely are the
interviewers permitted to change the wording or order of
the questions, and probing may be allowed only for certain
1.	Locating Respondents
In most face-to-face surveys, only about one-third of
the interviewers' time is actually spent interviewing.
Their most time-consuming pursuit is simply finding the
respondents. Approximately 40 percent of an interview-
er's time, studies show, is spent traveling and loca-
ting respondents. The remainder is devoted to clerical
and editing tasks. (Note that in a telephone survey,
no time is lost in travel and comparatively little is
wasted in searching for the respondents. This is why
the cost of a phone survey is about half that of a
a face-to-face survey of comparable size.)
How much of the interviewers' time is spent locating
the respondents depends largely on the respondent rules.
In a household survey, usually less than half of the
interviewer's initial contacts result in completed
interviews -- either because no acceptable respondent
is hone or none of them will agree to be interviewed
at the time. Interviewers often have to make several
return visits before they secure an interview with an
acceptable respondent. If the respondent rules require
an interview with one or more specific individuals in
the household, a still greater number of call-backs
are likely to be necessary. Since the sample units
assigned to any one interviewer are often spread over
a broad geographic area (a town or county, perhaps),
a lot of travel -- and frustration -- are not uncommon.
Locating non-household respondents poses somewhat dif-
ferent problems. Physically locating them usually is
not difficult. The main problem in business or indus-
trial surveys is finding the people most qualified to
answer the questions. Several call-backs may be neces-
sary before the interviewer locates the right people,
and is able to schedule interviews with them.
2.	Gaining Respondents' Cooperation
Once the interviewer has located a respondent, the next
task is to secure an interview. The way interviewers
introduce themselves, the identification they carry,

what they say about the survey, how they dress and
behave, and the courtesy they show to all the people
they come in contact with -- not simply the respondents
-- all have a bearing on how successful they are in
getting respondents' cooperation. The person the in-
terviewer talks to initially may not be an acceptable
respondent, but that person may be able to provide
information on when the desired respondent will be at
home and ultimately may influence the person's willing-
ness to cooperate.
The interviewer should present a positive, pleasant,
relaxed, professional image, and offer the respondent
proper credentials -- a picture ID showing the name
of the survey research firm they represent, possibly
a calling card, and other materials that will demon-
strate the integrity of the firm and the importance
of the research effort.
The interviewer should briefly explain the nature of
the study, the purpose of survey research, and the
reasons they want to talk with the respondent. The
interviewer also may explain how the data will be
used, and who will be permitted access to the data.
Explanations about the extent of disclosure of indivi-
dual responses are especially important to business or
industrial respondents, who frequently have strong
concerns about revealing trade-sensitive or confiden-
tial information.
Most household respondents will agree to be interviewed
if approached properly. They do so because they are
curious about the subject matter or surveys in general,
or because they are pleased to have an opportunity to
express their views to someone. Sometimes they agree
just because it is harder to say "No" than "Yes" to a
skillful interviewer.
Some respondents are willing to be interviewed with
only a brief explanation of the purpose of the visit;
for others it will be necessary to go into some detail.
Respondents have various concerns and questions
why they were selected, what good will the survey do,
why isn't the person next door being interviewed in-
stead -- and the interviewers must give correct and
courteous answers.
In no case should an interviewer exert undue pressure
to obtain an interview from a reluctant respondent.
Responses given reluctantly are likely to be less

accurate than those of a more willing respondent.
Faced with a persistent refusal, it is best to make no
further attempts to get an interview. Sometimes a
second approach by the supervisor or a more experienced
interviewer will succeed in "converting" a refusal to
a completed interview.
Respondents may refuse to be interviewed for any number
of reasons -- they are reluctant to break their daily
routine; they have other obligations; they are afraid
or suspicious of the interviewer; or they are indiffer-
ent or hostile to the Federal government, the subject
matter, or research in general. Studies show that the
respondent's attitude towards surveys in general, based
on their own experience and what they have heard from
others, is the overriding factor in their decision to
grant or refuse an interview.
Asking Questions
Once the respondent agrees to be interviewed, the in-
terviewer should immediately try to establish a good
interaction so the respondent will be cooperative in
supplying the required data. Ideally, the interviewer
will have an opportunity to talk with the respondent
in private long enough to complete the questionnaire
with no disturbances.
As we said at the beginning of this section, the goal
of a formal interview is to obtain full and accurate
answers to a fixed set of questions. In addition to
reading the questions slowly and deliberately so there
is no chance they can be misinterpreted, the interview-
er should do whatever is necessary to get satisfactory
answers. An important part of the interviewer's task,
in fact, is to assess the adequacy of the respondent's
answers and, if necessary, to take steps to get more
Whenever necessary, the interviewer should --
 Ask the respondent if they would like the question
clarified or repeated;
 Provide feedback to indicate that an adequate reply
has been given or that something else the respondent
said has been noted or understood;
 Clarify aspects of the respondent's task which seem
to be problematic or confusing; for example, confirm
the frame of reference of a particular question;

	Check with the respondent to make sure that a parti-
cular response was correctly heard or interpreted;
	Motivate the respondent to complete the question-
naire by interjecting a few words of encouragement
from time to time; and
e Control the direction and extent of the respondent's
replies, by keeping the respondent from digressing
or by reading the next question as soon as a satis-
factory answer is recorded, for example.
4. Recording and Editing Responses
Although asking questions well is a critical aspect of
a formal interview, the information the respondents
provide will be lost if it is not recorded accurately
and fully. All interviewers should use the same meth-
ods and conventions for recording responses and for
editing the questionnaire after the interview is over.
Recording answers may seem to be a relatively simple
task, but interviewers sometimes make serious errors.
The reason is that interviewing is a fairly tiring,
repetitive activity and often a lengthy and complex
one as well. In recording replies, interviewers often
must follow complex skip instructions and coding rules,
and, at the same time, listen carefully to the respond-
ent so they can be ready to take whatever action is
necessary to deal with a vague or inadequate reply.
To minimize recording errors, interviewers are trained
to check the questionnaire for omissions, ambiguities,
illegible entries, and clerical errors before conclud-
ing the interview and while the respondent is still
available. The interviewer also should note where
probes were used, and make a few comments on the inter-
view situation. If a tape recorder is used as a back-
up in a long interview, the interviewer should tran-
scribe and edit any new information onto the question-
naire .
As project officer, there are several things you can do,
both before and after the fieldwork begins, to foster the
collection of high quality data.
Before hiring a contractor, pay particular attention to the
following items in the offerors' proposals:

(1)	The firm's experience in managing surveys where inter-
views were used to collect a similar volume of data.
Selecting a survey research firm with a good track
record in conducting surveys of similar size and scope
is usually the best guarantee of getting high-quality
data from your survey.
(2)	The proposed interviewing activities. Proposals should
include clear-cut plans for: (a) quality assurance; (b)
selecting, training, and supervising the interviewers
and administrative staff; and (c) organizing and over-
seeing the interviewing activities. We strongly recom-
mend that you have a survey expert review these plans,
regardless of what primary collection method the con-
tractor plans to use. Even in a mail survey, some
interviewing normally must be done to follow up non-
response and response errors.
The quality of the data gathered in a face-to-face survey
depends largely on the work done by the interviewers.
Inaccuracies, omissions, and biases in the data they col-
lect can be kept to a minimum by good training; rigorous
use of the quality-assurance procedures established for
the data collection; attentive oversight by the contractor
throughout the data collection phase; and close monitoring
by the sponsoring office.
Therefore, after the contractor is retained --
(3)	Have a survey expert review the quality assurance pro-
cedures and the procedures for controlling the field
operations, as described in the work plan (see sections
A and B).
(4)	Participate in the pilot test. Go along on some of the
interviews as an observer. Attend the interviewer de-
briefing sessions during and following the pilot test.
Work with the contractor on revising the interviewing
procedures for the survey proper, if necessary. This
will expedite any changes in the questionnaire or the
interviewing procedures that require Agency approval.
Circulate the pilot test report to survey experts, and
make sure the contractor takes proper account of all
comments and suggestions before any data are collected
in the main survey. (See section A of Chapter 3 for
wore information on pilot tests.)
(5)	Review drafts of all instructions and training mater-
ials the contractor prepares for the interviewers and
supervisors. Attend as many interview training ses-
sions as possible. You can explain the study goals,

emphasize the Agency's interest in obtaining high
quality data, and answer any questions.
(6)	Once the data collection begins, make occasional visits
to field sites or the facility where the phone inter-
views are being conducted. If the interviewing is not
proceeding according to plan, advise the contracting
officer so the Agency can take whatever steps are
necessary to correct the problems.
(7)	Have a survey expert review the contractor's progress
reports during the data collection phase to make sure
the contractor is (a) maintaining the schedule, (b)
achieving the response rates specified in the work
plan, and (c) using the quality-control procedures
established in the plan.
	Interviewer's Manual, Survey Research Center, Revised
Edition, Survey Research Center, Institute for Social
Research, University of Michigan, Ann Arbor, MI,
1976. Excellent guide to the practical aspects of
	Interviewing, Richardson, Dohrenwend and Klein; Basic
Books, New York, NY, 1965.
	"Questionnaire Construction and Interview Procedures,"
Research Methodology in Social Relations, Fourth
Edition, A. Kornhauser, Sheatsley, and Kidder,et al;
Holt, Reinhart, and Winston, New York, NY, 1981.
	Survey Methods in Social Investigation, Second Edition,
C. Moser and G. Kalton, Basic Books, New York, NY,
1972. Chapter 12, "Interviewing."
	The Dynamics of Interviewing: Theory, Technique, and
Cases, R. L. Kahn and C. F. Cannell, John Wiley & Sons,
New York, NY, 1957.

In most EPA surveys, the contractor is required to process the
"raw" data collected from the sample into usable information.
Processing involves a series of manual and computerized opera-
tions to reduce responses on the questionnaires to machine-
readable form so they can be stored, retrieved, summarized, and
The desired end-product of these processing operations is a
"clean" -- virtually error-free -- data file, usually preserved
on magnetic tape. The data file is then programmed by the con-
tractor or the Agency to produce a variety of "output" reports,
ranging from simple tables summarizing the characteristics
of the data base to highly sophisticated statistical analyses.
In this chapter we discuss --
	The eight fundamental steps in processing
survey data; and
	How to monitor the contractor's data proc-
essing activities so that the end-product
is a clean data file, suitable for preparing
tabulations and analyses that will reveal
the salient features of the data base.
This section examines the eight steps involved in process-
ing the data collected in a typical statistical survey to
produce the results for the final report.
	Development of the
processing procedures
 Staff selection and training
	Receipt and control of the
completed questionnaires
	Manual review and edit
	Coding of open questions
	Data entry
	Error detection and resolution
	Preparation of the outputs
-1 39-

The complexity of these steps in any particular survey
depends on three factors:
(1)	The extensiveness of the outputs defined in the anal-
ysis plan. The analysis plan, which specifies the
preliminary tabulations and the types of analyses to
be prepared from the data file, not only influences
the design of the questionnaire, the sampling plan,
and the data collection procedures, but also guides
the processing operations. (See Chapter 1 for more
information on the analysis plan.)
(2)	The size and complexity of the questionnaire. The na-
ture of the questionnaire profoundly influences the
processing procedures. If there are many open ques-
tions, which require respondents to frame answers in
their own words, editing and coding the raw data on
the questionnaires will necessarily be more complex.
Conversely, if most of the questions offer a fixed
range of pre-coded responses, or if a CATI-programmed
questionnaire is used, several processing steps may
be bypassed.
(3)	The size of the sample and the complexity of the sam-
pling procedures. These determine how many question-
naires have to be processed and how much weighting and
other treatment of the data are needed to produce
results for the final survey report. (See Chapter 4.)
Let's turn now to the tasks the contractor typically will
perform during each of the processing steps listed above.
 Step 1: Develop the
Processing Procedures
The first step in transforming the raw data that have
have been collected from the respondents into usable
information is to develop a set of procedures for
processing the questionnaire data.
The processing procedures are one of the six components
of the work plan. The contractor should develop them
after major decisions on the questionnaire, the sampling
plan, and the analysis plan have been made.
The data processing procedures should specify --
= The specific tasks the contractor will perform after
the completed questionnaires arrive at the central
processing facility to produce a clean, virtually
error-free data file from which the contractor or

the Agency can produce the descriptive tabulations
or analytic interpretations of the data base to meet
the objectives of the survey;
= The software, hardware, and personnel to be used for
each of these tasks;
Provisions for training processing personnel in the
special procedures developed for the survey;
= The quality control techniques that will be used to
minimize errors at each step of the processing;
A flow chart for the tasks to be completed at each
step; and
= A complete listing and schedule of the tabulations
and other output reports that will be generated in
preparation for the analysis.
The sponsoring office may establish some preliminary
specifications for the processing operations during the
design phase of the survey, particularly the form and
content of the tabulations (or desired outputs). Once
hired, the contractor will have to work with Agency
data processing experts, systems analysts, and subject
matter specialists to make sure the computerized output
reports are clearly defined. This should be done be-
fore any computer programs to generate these reports
are written. Normally, existing statistical software
packages can be modified to accommodate the Agency's
tabulation and analysis requirements. However, if the
contractor has to develop any new software, sufficient
time and resources must be allowed.
Be sure to have appropriate Agency experts review the
final processing procedures before giving the contrac-
tor the go-ahead to process any data. If the contrac-
tor pretests these procedures -- usually in a pilot
test or "dry run" of the main survey -- these experts
also should review the adequacy of the preliminary
outputs generated from the pilot test data. The con-
tractor should incorporate any modifications they rec-
ommend at least two weeks before processing any data
collected in the survey proper.
 Step 2: Select and
Train Staff
Most of the people who will be involved in the data
processing operations will be permanent members of the

contractor's staff with experience in processing sur-
vey data. For most surveys the staff also will include
a data processing manager; a computer center manager;
operations personnel; clerical, coding, and editing
personnel; an operational control unit; data entry
personnel; systems analysts; and programming personnel.
Usually a supervisor will be assigned to oversee each
step of the processing, e.g., the initial screening of
the completed questionnaires, the manual edit and
coding, the transfer of the data to machine-readable
form, the final computer edit and "treatment" of the
data, and the preparation of the tabulations.
No matter how experienced the contractor's profession-
al staff, all processing personnel, especially the
editors and coders, should receive formal training in
the special procedures developed to screen, edit, and
code the survey data. Data entry personnel also need
a short training course. The systems analysts and
programmers, too, should be thoroughly oriented in
the informational and analytical objectives of the
survey before their work on the project begins.
For most surveys, the contractor will have to prepare
instructional and reference materials to train and
guide the editors and coders. These materials typi-
cally include procedures for coding each open question
and for dealing with omissions, inaccuracies, and in-
consistencies in the data (item non-response). They
should be updated throughout the data processing phase.
The actual processing of the data (Steps 3 thru 7) begins
shortly after the first few batches of completed question-
naires arrive at the processing facility. Appropriate mem-
bers of the contractor's staff will first check in and
screen the questionnaires (Steps 3 and 4) and code any
open questions (Step 5). Next, other staff will manually
key the data either onto cards or directly into the comput-
er (Step 6). Then comes the final "cleaning" of the data
file and the classification and sorting of the data, all
of which are operations usually performed by a computer
(Step 7) . The last task is the preparation of various
tabulations and analyses which summarize and interpret
the content of the file, along with a report fully docu-
menting the processing procedures (Step 8).
Note that if computer-assisted telephone interviewing is
used as the primary collection method, several steps are
bypassed because the respondents' answers are keyed direct-
ly into an on-line terminal during the interviews. Despite
CATI's advantages, it should be used only for large surveys

-- over 300 respondents, say -- because of the high cost
of the initial programming.
	Step 3: Screen the
Since all members of the sample must ultimately be ac-
counted for, strict control of the questionnaires (and
other paperwork generated during the data collection
phase) is essential. The contractor should assign a
control number to each questionnaire. The number is
usually placed on the title page. The purpose of the
control number is to permit the processing staff to
identify data from each questionnaire at any point in
the processing.
During this step, clerks at the main processing facil-
ity log in the questionnaires soon after they are re-
turned by the respondents (in a mail survey) or the
field supervisors (in a face-to-face or telephone
	Step 4: Review and Edit
the Questionnaires
After logging the control numbers, the clerks will
batch the questionnaires and forward them to an editing
and coding supervisor for screening. The amount of
screening done at this stage of the survey depends on
the method of collection and how much screening was
done in the "field."
In face-to-face and conventional telephone surveys,
questionnaires often receive a preliminary screening
by the field supervisors to rectify obvious problems
and errors. However, an additional review by the proc-
essing staff is almost always done to check for legi-
bility, completeness, and internal consistency. This
is especially critical for the first few batches of
questionnaires. The hand screening is an effective
way of detecting systematic errors the interviewers or
other field staff may be making before the interviewing
is too far along. Any questionnaires containing major
problems generally are returned to the field supervisor
for action.
Errors on mail questionnaires, on the other hand, are
referred to other staff for follow-up action to fill
in the missing or inconsistent entries -- usually
via phone interviews -- before further processing is
done. The purpose of this screening is to isolate
questionnaires that 

= Are ready for further processing;
= Contain omissions and inconsistencies requiring
some follow-up (usually in short face-to-face or
telephone interviews) before further processing is
= Will be counted as "non-response" cases because
there are too many omissions or illegible answers
to warrant follow-up; or
Are deemed "unacceptable" for processing for other
reasons, e.g., the questionnaire was completed for
an ineligible unit.
It is essential that you and the contractor fully agree
on the precise criteria to be used for the screening
operations. Usually, to be considered acceptable for
processing, a questionnaire must contain legible and
complete responses for all key variables and no more
than a specified number of omissions for other items.
The clerks doing the screening also may do a thorough
review and edit of the questionnaires or, depending on
the complexity of the questionnaires, may forward them
to editing or coding specialists.
The purpose of a manual review and edit at this stage
of the processing is to catch errors before the data
are transferred to punch cards or computer tape.
Hand editing is relatively slow and inefficient for
catching errors, but in a small survey where the data
are relatively complete, it plays a major role in the
processing. A subsequent computer edit (also called
"machine edit") involving a more detailed and complete
application of the editing "rules" is vital (see Step
7). The computer edit also serves to detect and cor-
rect human errors introduced during the coding and data
entry stages, discussed next.
 Step 5: Code Open
Many EPA survey questionnaires include one or more open
questions. These questions may generate a large number
of different yet acceptable responses, which must be
grouped into a reasonable number of manageable response
categories so they can be counted and analyzed. This
process is called coding.
Closed questions are usually "pre-coded" (coded before-
hand directly on the questionnaire). The codes are often

very simple. For example, "Yes" is coded "1" and "No"
is coded "2." The codes are printed on the question-
naire in machine-readable form. Fully pre-coded ques-
tionnaires thus bypass the manual coding step and
the replies are entered directly into the computer.
Codes for open questions often require a lengthy devel-
opment process. First, the investigators tentatively
define a few codes for a set of plausible responses to
each open question. The coded response categories then
are matched against the answers actually given by re-
spondents in the pretest. Usually the initial codes
have to be redefined to fit the pretest responses, and
perhaps tested again. After the first 50 to 100 ques-
tionnaires in the main survey are edited and coded,
the codes may be further refined. Still further adjust-
ments may be made later if the coders have difficulty
fitting existing codes to actual responses on new
batches of questionnaires that arrive for processing.
The actual coding of the replies to open items may be
done by the interviewers (partially-open questions
are coded during the interview); their supervisors
(shortly after the interviewers turn in the completed
questionnaires); or, most frequently, by experienced
coders at the processing facility. Whoever does the
coding uses a special coding manual listing the codes
defined for each open question.
Quality control of the coders is vital. The work of
each coder must be checked periodically for accuracy and
consistency with the codes defined in the manual. Proc-
essing supervisors normally check 100 percent of each
coder's work at the start. Because coding errors tend
to decrease as the clerks become more familiar with the
subject matter, a random sample -- usually 10 percent of
the coded questionnaires -- is checked after the
coders' errors decline to an acceptable level.
To control consistency among the coders, supervisors
periodically run tests on a sample of the coded ques-
tionnaires and establish a "rate of agreement" for each
question. Typically the rate is based on the number of
times pairs of experienced coders select the same code
for a particular response.
 Step 6:
Enter Data
The next step in the processing is to transfer the
edited and coded data from the questionnaires onto a

computer tape, a disk, or some other machine-readable
The two most common methods of transferring (entering)
data are (1) to keypunch them onto cards or (2) to key
them directly onto tape or disk through on-line termi-
nals connected to a computer. Both methods involve
manual keying and are, therefore, subject to human
When the keypunch method is used, two different opera-
tors keypunch one or more cards containing the data
from a single questionnaire. Quality control is a-
chieved by a computer-assisted comparison of the cards
to spot and reconcile any differences.
Direct keying (key-to-tape or key-to-disk) is rapidly
replacing keypunching as the preferred method of data
entry because direct keying is more efficient and more
convenient. In direct keying, experienced operators
type data from the questionnaires at entry stations
that have a keyboard similar to a typewriter. Quality
control is achieved through periodic checks of the
operators' output as well as the data entry equipment
and software. Some of the newer key-to-disk equipment
can be programmed to identify (and in some cases
correct) inadmissible values or codes.
There is another still more sophisticated method of
data entry called optical scanning. A scanner "reads"
the data on the questionnaire and enters them directly
to the computer medium. Optical scanning still is
not widely used for processing survey data, but it
undoubtedly will enjoy broad application in the future.
 Step 7: Detect and Resolve
Errors in the Data File
The next step is to "clean" the data to enhance its
quality and facilitate the subsequent production of
tabulations and analyses. Data cleaning is the process
of detecting and resolving inaccuracies and omissions
in the data file. Often it is the most complicated and
time-consuming step of the processing.
In almost all surveys today, the bulk of the work of
detecting and resolving data errors is performed by
a computer. First an intensive machine edit is per-
formed to identify inaccuracies and omissions, and
then various techniques are used to correct or convert

unacceptable entries into a form suitable for tabula-
tion and analysis.
Computer edit.
In a computer edit, the first step is to program the
computer to check for inconsistent or "impossible"
entries, some of which may have been introduced in
the previous processing steps. For example, the
computer may be programmed to identify errors such
(1)	Inadmissible codes -- the code attributed to an
item does not correspond with the permissible
replies in the coding manual (a code "4" has
been entered for an item to which only codes "1"
and "2" have been assigned);
(2)	Out-of-range entries -- the amount that has been
entered is below or above the permissible values
programmed for that item;
(3)	Omissions -- no entry has been made;
(4)	Inconsistencies -- entries for two or more items
are not consistent with each other (a respondent
is reported to be 14 years old and a physician);
(5)	Math errors -- the total for a list of items
should be equal to the sum of the amounts shown
for individual items on the list.
The computer may be further programmed to print an
error message indicating the nature of the failure,
or even to correct certain errors and log them.
Decisions on how much editing should be done by hand
and how much by machine depend on many factors. For
some surveys, several manual checks as well as com-
puter runs using special check-and-edit programs may
be necessary to achieve an acceptable error rate.
Generally speaking, the more complex the question-
naire, the more difficult it is to develop computer
programs for detailed edit-checks; thus considerable
manual editing may have to be done. Larger sample
sizes tend to make computer editing a more cost-
effective option.
= Error resolution.
The computer edit detects errors but does not re-
solve them. Several techniques are used to deal

with the errors the computer has identified. Sur-
vey researchers use several techniques to deal with
data omissions and inaccuracies in individual ques-
tionnaire items (so-called "item non-response").
The principal ones are (1) returning to the orig-
inal questionnaires to see if errors were made in
entering the data or if it is possible to infer
correct responses from other information on the
questionnaires, (2) having the computer impute val-
ues for missing responses, and (3) creating sepa-
rate categories to report all missing replies. More
specifically --
(1)	Consulting Questionnaires
Generally, the most reliable procedure for re-
solving omissions and inconsistencies in the
data file is to consult the questionnaires. Data
entry clerks sometimes pick up data from the
questionnaires incorrectly. Or, if the respond-
ent has left an answer-space blank, it sometimes
is possible to infer the correct answer from
other information on the questionnaire. Foot-
notes or write-in comments also may provide
helpful information.
For instance, if respondents fail to state their
ages, researchers may be able to infer their cor-
rect ages from other information on the ques-
tionnaires such as dates of birth or school
attendance. Inconsistent responses sometimes
can be resolved by considering the whole range
of information supplied by a respondent and de-
ciding which of the conflicting entries is most
plausible, e.g., from information on the income,
education, and marital status of the "14-year-
old physician" in the example on the previous
page, it might be reasonable to assume that the
respondent is really 41 years old.
Consulting questionnaires as a means of resolv-
ing errors, however, is time-consuming and not
always productive.
(2)	Imputating Missing Values
Another method of error-resolution is to try and
compensate for the non-response bias by having
the computer impute values for the omitted and
inconsistent replies. Imputation involves as-
signing values for missing or unusable responses

by drawing on information from other sources
such as answers to other items on the same ques-
tionnaire, another questionnaire from the same
survey, or external sources (administrative rec-
ords or another survey). Imputation corresponds
to the weighting adjustments for total non-
response, which we will discuss in Step 8.
Imputation generally is a faster and less costly
error-resolution technique than consulting ques-
tionnaires, but it must be used with discretion.
Imputed items should be flagged in the data file
so that tabulations and analyses can be prepared
with and without the imputations, if desired.
Also, any reports about the survey should indi-
cate the extent of the imputation so that anyone
using the data later can distinguish between
real and imputed values.
The extent to which the contractor intends to
impute values for missing or omitted replies
should be specified in the data processing
procedures submitted with the work plan.
Note that the contractor should aim to get
good data from the respondents in the first
place, and make data adjustments judiciously
and strictly as a back-up measure. Imputation
can be kept to a minimum by instructing inter-
viewers to carefully check the questionnaires
immediately after each interview; regular, thor-
ough, and timely checks of the interviewers'
work during the data collection phase; and
follow-ups of respondents in mail surveys.
(3) Creating Categories for Unreported Responses
If attempts to resolve omissions and inconsis-
tencies in the data file using the above two
techniques are unsuccessful, the researchers may
allow the errors to stand and report them as
such in the tabulations. For example, they may
report a total for all respondents who provid-
ed no valid income data in a new category called
"income unknown."
Decisions on whether to impute values for omitted
and inconsistent replies or to add "not reported"
categories in the tabulations depend on a number
of circumstances. Using a "not reported" category
for tabulating data on such basic characteristics

of the sample as "sex" and "age" creates serious
problems in the analysis. Analysts sometimes han-
dle this by imputing values for fundamental demo-
graphic variables for which considerable related in-
formation is available, and creating "not reported"
categories for describing and relating information
on all others.
 Step 8: Prepare
the Outputs
The final step in processing survey data is to prepare
the tabulations and other outputs called for in the
work plan. The contractor's main tasks at this step
are to (1) weight the sampled elements to produce the
estimates (the results), (2) prepare the preliminary
tabulations describing the data base (the content of
the data file) and finalize the analysis plan, (3)
apply the procedures described in the sampling plan for
calculating the sampling errors, and (4) document the
procedures used in preparing the data file.
Let's examine these tasks more closely.
(1) VJeighting the Sampled Elements
The first task in generating the tabulations is to
weight the virtually error-free data file prepared
in the previous step. Except for simple lists of
data items, these preliminary reports summarizing
the content of the file should be based on weighted
data. Weights (or multipliers) are assigned to
survey data for three reasons --
= To account for the probabilities used in se-
lecting the sample from the target population.
If all units in the sample have the sane proba-
bility of being chosen, the survey analysts can
obtain valid estimates of some statistics such
as proportions, percents, means, and medians
without weighting the data. However, to esti-
mate totals, all units must be weighted by the
reciprocal of the sampling fraction. For ex-
ample, if the sampling fraction was 1 in 200,
all sample values or totals must be multiplied
by 200. If the selection probabilities were
not the same for all the units, appropriate
weights must be applied to estimate any sta-
tistic. (See section D of Chapter 4 for more
information on weighting.)

= To adjust for sampled units for which no data
were obtained (non-response).
There are two methods of making adjustments for
One way is to increase the weights applied to
individual units that did respond and are simi-
lar (based on data available for all the sample
units) to those for which no data were obtained.
For example, if one sample household in a block
did not respond, one of the households for
which data were obtained would be selected at
random and given an additional weight of "2."
The other way is to apply a uniform weight to
all the units in the sample or to those in a
particular subgroup. For example, in a busi-
ness survey, if 20 percent of the sample estab-
lishments with fewer than 10 employees did not
respond, a weight of 1.25 (100 divided by 80)
would be applied to all establishments that did
= To apply more sophisticated estimation proce-
dures such as ratio or regression estimates.
These procedures require a determination of
relationships between variables or the intro-
duction of independent data from other sources,
e.g., current population estimates.
The overall weights the analysts ultimately assign
to the data will reflect the combined effects of
these three types of adjustments. Deciding on the
sequence and procedures for weighting the data in
a particular survey requires a good technical grasp
of the sample design and the data processing sys-
tem. Sampling and data processing experts at the
Agency and on the contractor's staff should work
out the weighting and estimation procedures long
before the processing starts. These procedures
should be critically reviewed by systems analysts
at the Agency before the contractor processes any
data collected in the survey proper.
Preparing the Preliminary Tabulations
After the weighting and estimation procedures are
completed, a data file suitable for generating the
preliminary tabulations should result.

Using a standard computer software package or soft-
ware specially designed for the survey, the con-
tractor then can program the data file to generate
a set of preliminary tabulations, which normally
will include --
= Frequency distributions(sometimes called "mar-
ginal tabulations") of responses for categori-
cal variables (those based on questions with
fixed response categories);
= Some simple cross tabulations;
= Estimated totals, ranges, and means (or medi-
ans) for the entire target population and for
various subgroups;
= Listings of individual responses for selected
items, especially for large sample units; and
= Tabulations of key variables showing the num-
ber of units for which an item was imputed and
how much of the total was imputed, where appli-
cable .
The preliminary tabulations will give you and the
contractor an opportunity to review the data base
in an organized fashion, and thereby get an idea
of its structure and quality before the contractor
prepares the final tabulations.
Subject-matter specialists should carefully study
these preliminary tabulations before the contractor
prepares a revised list of the final tabulations
to include in the analysis plan. The list should
include the computerized output reports (tables and
graphs) that will be prepared to fully describe the
content of the data base.
There is no clear line between the output reports
generated at the conclusion of the processing phase
and those developed for the analysis. However, the
analysis of the data base usually goes beyond
simple descriptive summaries and explores the un-
derlying relationships among the study variables.
A host of sophisticated analytic techniques may be
used to reveal the full informational content of
the data base.
Usually, the final tabulations include --

= Detailed descriptive statistics (frequency
distributions and cross-tabulations);
= "Measures of central tendency" (means, medi-
ans , and modes);
= "Measures of variability" (standard deviations,
ranges); and
= Other analytical statistics such as correla-
tions and regression coefficients.
The revised analysis plan should specify for each
tabulation (a) the data sources to be used, (b)
the variables to be cross-classified, (c) the sub-
populations to be included, (d) the statistics to
be shown, (e) how the data are to be weighted, (f)
the title, subheadings, and footnotes; and (g) the
layout. The analysis plan should also include 
A full description of the methods for quanti-
fying all relevant variables;
= Values of sample weights and all necessary
formulas for estimating population means, med-
ians, and variances;
= A list of hypotheses and the tests to be used
to evaluate them;
= Descriptions of the variables and respondent
groups that may be interrelated, and recommen-
dations for regression and discrimination
analyses based on the relationships; and
= Suggested methods for handling problems during
the subsequent analysis, that arise from miss-
ing data or non-response problems.
You should work with data processing and systems
analysts both at the Agency and on the contractor's
staff in defining these specifications for the fi-
nal analysis plan.
(3) Finalizing the Computations of Sampling Errors
The actual calculation of sampling errors for vari-
ous estimates should be an integral part of the
processing operations. Making these calculations
after the preliminary tabulations are generated
is generally much more difficult, time-consuming,
and costly.

The estimates of sampling errors (variances) serve
two purposes --
= They may help evaluate the data base. For ex-
ample, unusually large sampling errors for some
items may indicate processing errors; and
= They are essential for determining whether ob-
served relationships are statistically signifi-
cant or may be due to random variation intro-
duced by the use of sampling.
As discussed in Chapter 4, sampling errors usually
are not calculated for all the statistics produced
from the survey. This is usually unnecessary and
often too costly. The contractor's analysts and
sampling specialists should select the items for
which sampling error estimates are needed, making
sure to include all key statistics and a represent-
ative set of other types of statistics that are to
be tabulated from the data file. (For more details
on calculating sampling errors, see section D of
Chapter 4.)
Documenting the Processing Operations
Once the final tabulations are completed, the con-
tractor should create a file documentation manual
describing the procedures used to edit, code, and
weight the data. The manual should identify the
source of each data item (on the questionnaire or
other document used during the data collection
phase) and its position on the file.
If EPA is to analyze the content of the data file,
the contractor should submit the documentation man-
ual , the final analysis plan, and whatever other
materials (computer cards, for example) Agency
analysts will need to study and interpret the data
f ile.
On the other hand, if the contractor is to do the
analysis, the documentation manual should be sub-
mitted for EPA review and approval along with the
final analysis plan before the data are analyzed.
A discussion of data analysis is beyond the scope
of this Handbook. To assist you in this regard,
we have provided a list of excellent sources at
the end of this chaper, along with a number of
selections offering additional guidance on data
processing issues.

The final step of the survey, the presentation of the
results, any necessary background information, and the
conclusions drawn from the results, is covered in Chapter 8
of Volume I. Often the survey contractor is required to
prepare both a non-technical report for the public and a
detailed account of the technical findings. Remember that
any report about the survey should be issued by the Agency,
not the contractor.
Throughout this Handbook we have emphasized that EPA's major
impact on the successful outcome of a contract survey comes
long before the data collection and data processing activi-
ties are under way. Achieving a clean data file on which
to base the analytic work is largely dependent on the pro-
fessional, clerical, and management capabilities of the
firm the Agency hires to conduct the survey. As in the
data collection phase, the sponsoring office has only lim-
ited control over the data processing activities.
Therefore, before the contractor is hired, you should --
(1)	Require the offerors to specify in their proposals --
= The formal quality-control procedures they intend
to use at each step of the processing;
= How they intend to keep coding and other errors to
a minimum; and
= How they will report production and error rates for
each step of the processing.
(2)	Specify the format and any special requirements for
the completed data file to ensure compatibility with
other EPA data files and otherwise facilitate the
analys is.
(3)	Require Agency approval of the key deliverables of the
data processing phase (the data file, the tabulations,
the estimated sampling errors, and the documentation
of the processing procedures). If the Agency is to do
the analysis,specify that EPA must approve these deliv-
erables before the contract is closed out. If the
contractor is to do the analysis, do not let the con-
tractor begin it until you have reviewed and approved
the above products of the data processing phase.
Other things you can do after the contractor is aboard to

help assure the quality of the data file and the other
deliverables are --
(4)	Make sure the questionnaire is designed to facilitate
the processing operations.
(5)	Before data for the main survey are collected, care-
fully review the processing procedures and tabulations
specified in the work plan. If necessary, work with
the contractor on specifications for the content and
format of the final tabulations. If a pilot test is
done, review the procedures and tabulations and make
sure the contractor makes any necessary modifications
before processing any data from the survey proper.
(6)	Participate in the development of response codes and
procedures for treating non-response and "unacceptable"
(7)	Scrutinize all progress reports submitted during the
processing to make sure the contractor is (a) adhering
to the schedule and budget and (b) following the veri-
fication and quality-control procedures specified in
the work plan.
(8)	Have Agency statisticians, project personnel, and data
processing experts review the preliminary tabulations
and the file documentation manual. All tables should
be reviewed to be sure that (a) they are internally
consistent; (b) the estimates appearing in more than
one table agree; (c) significant changes from compar-
able data in earlier surveys are adequately explained;
and (d) the estimates are reasonable" based on expec-
tations and data from other sources.
(9)	Finally, if the Agency is to do the analytic work, make
sure that all deliverables are in good order before
the contract is closed out.

c National Household Survey Capability Programme,
Survey Data Processing: A Review of Issues and
ocedureTj United Nations, Department of Technical
Cooperation for Development and Statistical Office,
New York, NY, 19 82.
	Survey Methods in Social Investigation, Second Edi-
tion, C. A. Moser and G. Kalton, Basic Books, Inc.,
New York, NY, 1972. Chapter 16, "Processing of
the Data," and Chapter 17, "Analysis, Interpretation
and Presentation."
	Survey Research Practices, G. Hoinville, R. Jowell,
and associates, Heinemann Educational Books, London,
England, 1978. Chapter 8, "Data Preparation."
e The Sample Survey: Theory and Practice, D. P. Warwick
and C. A. Lininger, McGraw-Hill, New York, NY, 1975.
Chapter 9, "Editing and Coding," and Chapter 10,
"Preparation for Analysis."
	A Guide for Selecting Statistical Techniques for
Analyzing Social Science Data^ Second Edition, F. M.
Andrews, et al, Institute for Social Research,
University of Michigan, Ann Arbor, MI, 1981.
	Applied Regression Analysis, Second Edition, N.
Draper and H. Smith, John Wiley & Sons, New York,
NY, 1983.
	Searching for Structure, Revised Edition, J. A.
Songuist, E. L. Baker, and J. N. Morgan, Institute
for Social Research, University of Michigan,
Ann Arbor, MI, 19 74.
	"Standards for Discussion and Presentation of Errors
in Survey Census Data," Journal of the American
Statistical Association, Vol. 70, No. 351, Part II,
M. Gonzalez et al, September 1975.
	Understanding Robust and Exploratory Analysis,
D. Hoaglin et al, John Wiley & Sons, New York, NY,

BIAS - The difference between the survey estimate, averaged
over repeated samples, and the true value. Sampling bias
can result from use of a non-probability sample or from
errors in the execution of a probability sample design. Non-
sampling bias can result from many factors such as use of
an incomplete sampling frame (coverage bias) , non-response
in the survey (see NON-RESPONSE BIAS), a poorly designed
questionnaire, respondent errors, interviewer errors, or
processing errors.
BURDEN - In the 1980 Paperwork Reduction Act, "burden" is
defined as the amount of time required to collect data
from the public using a particular data collection instru-
ment (a questionnaire). The response burden of a particu-
lar survey questionnaire is the estimated number of hours
each respondent needs to complete the instrument, multi-
plied by the total number of people to be surveyed. The
total number of burden hours for a survey questionnaire
must be reported to the U.S. Office of Management and Bud-
get (0MB) if data are to be collected from more than nine
members of the public. 0MB is responsible for overseeing
Agency compliance with the PRA.
CATI (computer-assisted telephone interviewing) - A relatively
new method of telephone interviewing in which a structured
questionnaire is programmed into a computer, rather than
printed on a form. The interviewer sits before a video ter-
minal and asks the questions as they appear on the screen.
The interviewer then enters the respondent's replies direct-
ly into the computer via a keyboard attached to the terminal.
CLOSED QUESTIONS - Questions offering respondents two or more
alternative answers, either explicitly or implicitly, e.g.,
Yes/No, Male/Female, Strongly Agree/Agree/Disagree/Strongly
disagree. When more than two choices are offered, closed
questions are sometimes called "multiple choice questions."
CODING - The processing of survey answers into numerical form
for entry into a computer, so that statistical analysis can
be performed. Coding of alternative responses to closed
questions (see CLOSED QUESTIONS) can be performed in advance
so that no additional coding is required. This is called
"precoding." If some items are precoded or keyed directly
(numerical amounts), then coding refers only to the coding
of open questions (see FIELD CODING).

DEBRIEFING - A meeting of interviewers, supervisors, research
analysts, etc., immediately after a pretest or during the
early stages of the data collection phase of the main sur-
vey. Debriefings alert project personnel to problems with
the questionnaire, so they can be corrected before the rest
of the interviews are done.
DEMOGRAPHIC CHARACTERISTICS - The basic variables used by sur-
vey researchers to classify population groups, e.g., sex,
age, marital status, race, ethnic origin, education, income,
occupation, religion, and residence.
iables are the behaviors or attitudes whose variance the
researchers are attempting to explain. Independent vari-
ables are those variables used to explain the variance in
the dependent variables. Variables such as "occupation" or
"income" may be dependent or independent, depending on the
purposes of the research and the model used. In more com-
plex models, variables may be interdependent; that is, vari-
able A is affecting variable B while, simultaneously, vari-
able B affects variable A.
DIARIES - Written records kept by respondents to keep track of
events that may be difficult to recall accurately later.
Diary-keepers are requested to make entries immediately
after an event occurs. Sometimes they are compensated with
money or gifts for their efforts.
FACE-TO-FACE INTERVIEWS - One of the three traditional inter-
viewing methods used to collect statistical data. In face-
to- face interviewing, a trained interviewer poses questions
in the presence of the respondent.
FIELD CODING - The coding of responses to open questions by the
interviewer during the interview. When this technique is
used, the questionnaire includes a set of preprinted, coded
replies. Instead of writing down the respondent's answer
verbatim, the interviewer checks the preprinted reply that
most nearly matches the respondent's reply.
FOCUS GROUPS - An exploratory interviewing technique involving
small, informal group discussions "focused" on selected
topics of concern to the researchers. The discussions are
led by a moderator knowledgeable about the subject matter.
The participants are selected from the target population or
a specific subgroup of the target population.
FRAME - The source or sources from which the survey sample is
drawn. The sampling frame may consist of one or more lists

of individuals or organizations, but it also may be a set of
city blocks, a set of telephone exchanges, etc.
IMPUTATION - The process of replacing missing or unusable in-
formation with usable data from other sources such as
responses to other items on the same questionnaire, another
questionnaire from the same survey, or external sources
(another survey or administrative record). The use of
imputation techniques is rapidly expanding in scope and
sophistication due to advances in computer technology.
viewers regarding which questions to ask or skip, how to
enter responses, and when to probe (see PROBES). Interview-
er instructions are printed on the questionnaire but not
read to respondents.
LOADED QUESTION - A question worded in a way that increases the
likelihood of a particular kind of response. Loaded ques-
tions may legitimately be used to overcome respondent reluc-
tance to report sensitive information. Poorly written ques-
tions using "loaded" words or expressions may inadvertently
produce biased responses.
NON-RESPONSE BIAS - Non-response bias results when units who
do not respond to the survey differ significantly from those
who do respond. It can also result from non-response to
individual items on the questionnaire.
OPEN (OR OPEN-ENDED) QUESTIONS - Questions allowing respondents
to answer in their own words. The open format encourages
respondents to express themselves in language that is com-
fortable to them. Some open questions are coded during the
interview using a fixed set of response categories (see
PILOT TEST - A small field test replicating the field proce-
dures proposed for the main survey. Usually a purposive
sample of 10 to 50 members of the target population is used
for the test. A pilot test is more elaborate than a pretest
(see PRETEST) in that the proposed collection procedures
as well as the questionnaire are tested. Its purpose is
to alert the researchers to any operational difficulties
not anticipated during the planning and pretesting stage.
(Note that some researchers use "pretest" and "pilot test"

PRETEST - A small field test of the questionnaire proposed for
the main survey. Usually a purposive sample drawn from var-
ious subgroups of the target population is used. Pretests are
vital for all Agency-sponsored surveys involving new topics
or populations. (Also, see PILOT TEST.)
PROBABILITY SAMPLE - A sample drawn in such a way that each
unit (person, household, organization, etc.) in the target
population (see TARGET POPULATION) has a known, non-zero
probability of being included in the sample. This method of
selecting the survey respondents makes possible statistically
valid inferences about the entire population the sample is
designed to represent.
PROBES - Questions or statements used by the interviewer to
obtain additional information from the respondent when the
initial answer appears incomplete. Examples of probes are:
"How do you mean?" "In what way?" or "Could you explain
that a little?"
QUESTIONNAIRE - The complete data collection instrument used by
an interviewer or respondent during a survey. The question-
naire includes not only the questions and spaces for the an-
swers, but also interviewer or respondent instructions and an
introduction. The questionnaire usually is printed, but
recently nonpaper versions are being used on computer termi-
nals (see CATI).
RANDOM DIGIT DIALING (RDD) - A method used to select samples
for telephone surveys by random selection of telephone num-
bers within working exchanges. This method permits cover-
age of both listed and unlisted telephone numbers.
RANDOM SAMPLE/NON-RANDOM SAMPLE - In practice, the term "ran-
dom sample" is often used loosely to mean any kind of prob-
ability sample. "Simple random sample" is a technical term
for a sample in which each unit in the population has the
same probability of selection and in which all possible
samples of a given size are equally likely to be selected.
The terra "non-random sample" is used to mean any sort of
non-probability sample such as a quota sample, a conven-
ience sample, or a judgment sample.
RECORDS - Documents used to reduce memory error on factual
questions. Memory errors are unintentional errors in re-
spondent reports caused by forgetting or incorrectly recall-
ing events or details of events. Examples of records are
bills, checkbook records, cancelled checks, and inventory

RESPONSE EFFECTS - Variations in the quality of data resulting
from the process used to transmit information from the re-
spondent to the interviewer (where applicable) and ultimately
to the data user. The principal sources of variation in
quality are the interviewer's performance, the respondent's
performance, and the nature of the data requirements and
collection methods established by the survey designers.
SAMPLING - Selection of some of the units (a sample) from a
population (see TARGET POPULATION) to otain information that
that can be used to characterize or describe the whole popu-
lation. Probability sampling is the prescribed method for
Agency surveys. See PROBABILITY SAMPLE.
SCALE QUESTION - A multiple-choice question that asks respond-
ents to rate a particular quality in themselves or some
other person or thing. For example, they may be asked whe-
ther they agree or disagree with a statement of opinion,
about the frequency of a type of behavior, or whether they
like or dislike a certain product. Some scales are entirely
verbal (sometimes referred to as "fully-anchored scales"),
e.g., "excellent," "very good," "fair," "poor."
respondents to read and answer the questions themselves.
Self-administered mail questionnaires are one of the three
traditional methods of collecting survey data. Note that a
questionnaire can be considered to be self-administered
even if an interviewer is present to hand it out, collect
it, and clarify questions.
SKIP INSTRUCTIONS - Directions on the questionnaire to show the
person completing the form which question to ask or answer
next, based on the answer to the previous question. Skip
instructions make it possible to use a single questionnaire
for many different types of respondents because they need
answer only those items that are relevant.
perception by respondents that the answer to a question will
enhance or hurt their self-image in the eyes of the inter-
viewer. Examples of socially-desirable behavior are voting,
being well informed, and fulfilling moral and social respon-
sibilities. Examples of socially undesirable behavior in-
clude alcohol and drug abuse, deviant sexual practices, and
traffic violations.
STATISTIC - A summary measure derived from sample data. "Sta-
tistics" (plural), in everyday language, refers to a collec-
tion of numerical data. "Statistics" (singular) is an
academic discipline concerned with methods of converting

numerical data into information useful for scientific re-
search, business decision-making, and other similar purposes.
naires specify the wording of the questions or items and the
order in which they are asked. They are used for all sta-
tistical surveys, regardless of whether the questionnaire is
administered by interviewers (in person or by telephone) or
by the respondents themselves. Unstructured questionnaires
are essentially topic outlines in which the wording and
order of the questions are left to the interviewer's discre-
tion. Unstructured survey questionnaires are used primarily
in exploratory research for in-depth individual interviews
or focus group studies.
SENSITIVE QUESTIONS - These are questions that are likely to
make respondents feel uneasy or threatened and to which they
may be reluctant to respond. They include questions about
socially desirable and socially undesirable activities (see
sensitive questions include those covering information which
they may not want to reveal to their competitors or to
government regulatory authorities.
TARGET POPULATION - The complete set of people, households,
organizations, businesses, or other units that is of inter-
est and from which the samples for pretests and the main
survey are drawn.
TELEPHONE INTERVIEWS - One of the three major methods of
collecting statistical data. Data are obtained using a
structured telephone interview. As in face-to-face inter-
viewing, the interviewer both asks the questions and records
the responses. A relatively recent innovation in telephone
interviewing is computer-assisted telephone interviewing,
(See CATI.)
VALIDATION - The process of recontacting respondents to deter-
mine whether an interview was actually conducted. In a
broader sense, "validation" also refers to the process of
obtaining data from other sources to measure the accuracy of
respondent reports. Validation may be at either the indivi-
dual or group level. Examples include the use of financial
or medical records to check on reports of assets or health
care expenditures. Unless public records are used, valida-
tion of individual responses usually requires the consent of
both the respondent and the custodian of the records.
VARIABILITY/VARIANCE - Used in reference to a population, vari-
ability refers to differences between individuals or groups

in the population, usually measured as a statistical vari-
ance or simply by observing the distribution of values for
the group. In samples, variability has the same meaning
with respect to members of the sample. For estimates based
on samples, variance refers to differences between estimates
from repeated samples selected from the same population
using the same selection procedures. For statistical defi-
nitions of variance, see any statistics textbook.

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