United States Office Of Water EPA 841 -B-00-007
Environmental Protection (4503F) January 2001
Agency
TECHNIQUES FOR TRACKING,
EVALUATING, AND REPORTING
THE IMPLEMENTATION OF
NONPOINT SOURCE CONTROL
MEASURES
URBAN
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TECHNIQUES FOR TRACKING, EVALUATING,
AND REPORTING THE IMPLEMENTATION
OF NONPOINT SOURCE CONTROL
MEASURES
I. URBAN
Final
January 2001
Prepared for
Nonpoint Source Pollution Control Branch
Office of Water
United States Environmental Protection Agency
Washington, DC
Prepared under
EPA Contract No. 68-C-99-249
Work Assignment No. 0-25
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CONTENTS
Chapter 1 Introduction
1.1 Purpose of Guidance 1-1
1.2 Background 1-2
1.3 Types of Monitoring 1-3
1.4 Quality Assurance and Quality Control 1-5
1.5 Data Management 1-5
Chapter 2 Methods to Inventory BMP Implementation
2.1 Regulated Activities 2-1
2.1.1 Erosion and Sediment Control 2-1
2.1.2 Septic Systems 2-3
2.1.3 Runoff Control and Treatment 2-6
2.2 Tracking BMP Operation and Maintenance 2-6
2.3 Geographic Information Systems and BMP Implementation/Effectiveness 2-8
2.4 Summary of Program Elements for a Successful BMP Inventory 2-10
Chapter 3 Sampling Design
3.1 Introduction 3-1
3.1.1 Study Objectives 3-1
3.1.2 Probabilistic Sampling 3-2
3.1.3 Measurement and Sampling Errors 3-10
3.1.4 Estimation and Hypothesis Testing 3-12
3.2 Sampling Considerations 3-13
3.2.1 Urbanized and Urbanizing Areas 3-14
3.2.2 Available Resources and Tax Base 3-14
3.2.3 Proximity to Sensitive Habitats 3-14
3.2.4 Federal Requirements 3-14
3.2.5 Sources of Information 3-15
3.3 Sample Size Calculations 3-16
3.3.1 Simple Random Sampling 3-18
3.3.2 Stratified Random Sampling 3-23
3.3.3 Cluster Sampling 3-26
3.3.4 Systematic Sampling 3-26
3.3.5 Concluding Remarks 3-27
Chapter 4 Methods for Evaluating Data
4.1 Introduction 4-1
4.2 Comparing the Means from Two Independent Random Samples 4-2
4.3 Comparing the Proportions from Two Independent Samples 4-3
4.4 Comparing More Than Two Independent Random Samples 4-4
4.5 Comparing Categorical Data 4-4
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Contents
Chapter 5 Conducting the Evaluation
5.1 Introduction 5-1
5.2 Choice of Variables 5-3
5.3 Expert Evaluations 5-6
5.3.1 Site Evaluations 5-6
5.3.2 Rating Implementation of Management Measures and Best
Management Practices 5-11
5.3.3 Rating Terms 5-14
5.3.4 Consistency Issues 5-15
5.3.5 Postevaluation Onsite Activities 5-16
5.4 Self-Evaluations 5-17
5.4.1 Methods 5-17
5.4.2 Cost 5-18
5.4.3 Questionnaire Design 5-21
5.5 Aerial Reconnaissance and Photography 5-23
Chapter 6 Presentation of Evaluation Results
6.1 Introduction 6-1
6.2 Audience Identification 6-2
6.3 Presentation Format 6-2
6.3.1 Written Presentations 6-4
6.3.2 Oral Presentations 6-4
6.4 For Further Information 6-6
References R-l
Glossary G-l
Index 1-1
Appendix A: Statistical Tables A-l
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Contents
List of Tables
Table 3-1 Example applications of four sampling designs for implementation
monitoring 3-3
Table 3-2 Errors in hypothesis testing 3-13
Table 3-3 Definitions used in sample size calculation equations 3-17
Table 3-4 Comparison of sample size as a function of various parameters 3-20
Table 3-5 Common values of (ZB + Z2p)2 for estimating sample size 3-22
Table 3-6 Allocation of samples 3-25
Table 3-7 Number of residences at each site implementing recommended
lawn care practices 3-27
Table 4-1 Contingency table of observed resident type and
implemented BMP 4-5
Table 4-2 Contingency table of expected resident type and implemented BMP 4-5
Table 4-3 Contingency table of implemented BMP and rating of
installation and maintenance 4-6
Table 4-4 Contingency table of implemented BMP and sample year 4-7
Table 5-1 General types of information obtainable with self-evaluations
and expert evaluations 5-4
Table 5-2 Example variables for management measure implementation analysis 5-7
List of Figures
Figure 3-1 Simple random sampling from a list and a map 3-5
Figure 3-2 Stratified random sampling from a list and a map 3-7
Figure 3-3 Cluster sampling from a list and a map 3-8
Figure 3-4 Systematic sampling from a list and a map 3-9
Figure 3-5 Graphical presentation of the relationship between bias,
precision, and accuracy 3-11
Figure 5-1 Potential variables and examples of implementation
standards and specifications 5-5
Figure 5-2 Sample draft survey for residential "good housekeeping" practice
implementation 5-19
Figure 6-1 Example of presentation of information in a written slide 6-3
Figure 6-2 Example written presentation slide 6-4
Figure 6-3 Example representation of data in the form of a pie chart 6-4
Figure 6-4 Graphical representation of data from construction site surveys 6-5
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CHAPTER 1. INTRODUCTION
1.1 PURPOSE OF GUIDANCE
This guidance is intended to assist federal,
state, regional, and local environmental
professionals in tracking the implementation of
best management practices (BMPs) used to
control urban nonpoint source pollution.
Information is provided on methods for
inventorying BMPs, the design and execution
of sampling programs, and the evaluation and
presentation of results. The more regulated
and stable nature of urban areas present
opportunities for inventorying all BMPs versus
the statistical sampling required to assess BMP
implementation for agriculture or forestry.
Inventorying BMP implementation requires
establishing a program that tracks the
implementation or operation and maintenance
of all BMPs of certain types (e.g., septic tanks
and erosion and sediment control practices).
The guidance can help state and local
governments by providing a subset of controls,
both structural and nonstructural, that can be
sampled for:
• inspection programs,
• maintenance oversight, and
• implementation confirmation.
The focus of chapters 3 and 4 is on the
statistical approaches needed to properly
collect and analyze data that are accurate and
defensible. A properly designed BMP
implementation monitoring program can save
both time and money. For example, the cost to
determine the degree to which pollution
prevention activities are conducted by an entire
urban population would easily exceed most
budgets, and thus statistical sampling of a
subset of the population is needed. Guidance
is provided on sampling representative BMPs
This guidance focuses on the methods that
can be used to inventory specific types of
urban BMPs and the design of monitoring
programs to assess implementation of urban
management measures and BMPs, with
particular emphasis on statistical
considerations.
to yield summary statistics at a fraction of the
cost of a comprehensive inventory.
While it is not the focus of this guidance, some
nonpoint source projects and programs
combine BMP implementation monitoring
with water quality monitoring to evaluate the
effectiveness of BMPs in protecting water
quality on a watershed scale (Meals, 1988;
Rashin et al., 1994; USEPA, 1993b). For this
type of monitoring to be successful, the scale
of the project should be small (e.g., a
watershed of a few hundred to a few thousand
acres). Accurate records of all the sources of
pollutants of concern, how these sources are
changing (e.g., new development), and an
inventory of how all BMPs are operating are
vital for this type of monitoring. Otherwise, it
is impossible to accurately correlate BMP
implementation with changes in stream water
quality. This guidance does not address
monitoring the implementation and
effectiveness of individual BMPs. It does
provide information to help program managers
gather statistically valid information to assess
implementation of BMPs on a more general
(e.g., statewide) basis. The benefits of
implementation monitoring are presented in
Section 1.3.
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Introduction
Chapter 1
1.2 BACKGROUND
Because of the past and current successes in
controlling point sources, pollution from
nonpoint sources—sediment deposition,
erosion, contaminated runoff, hydrologic
modifications that degrade water quality, and
other diffuse sources of water pollution—is
now the largest cause of water quality
impairment in the United States (USEPA,
1995). Recognizing the importance of
nonpoint sources, Congress passed the Coastal
Zone Act Reauthorization Amendments of
1990 (CZARA) to help address nonpoint
source pollution in coastal waters. CZARA
provides that each state with an approved
coastal zone management program develop
and submit to the U.S. Environmental
Protection Agency (EPA) and National
Oceanic and Atmospheric Administration
(NOAA) a Coastal Nonpoint Pollution Control
Program (CNPCP). State programs must
"provide for the implementation" of
management measures in conformity with the
EPA Guidance Specifying Management
Measures For Sources Of Nonpoint Pollution
In Coastal Waters, developed pursuant to
Section 6217(g) of CZARA (USEPA, 1993a).
Management measures (MMs), as defined in
CZARA, are economically achievable
measures to control the addition of pollutants
to coastal waters, which reflect the greatest
degree of pollutant reduction achievable
through the best available nonpoint pollution
control practices, technologies, processes,
siting criteria, operating methods, or other
alternatives (all of which are often referred to
asBMPs). Many of EPA's MMs are
combinations of BMPs. For example,
depending on site characteristics,
implementation of the Construction Site
Erosion and Sediment Control MM might use
the following BMPs: brush barriers, filter
strips, silt fencing, vegetated channels, and
inlet protection.
CZARA does not specifically require that
states monitor the implementation of MMs and
BMPs as part of their CNPCPs. State
CNPCPs must however, provide for technical
assistance to local governments and the public
for implementing the MMs and BMPs.
Section 6217(b) states:
Each State program . . . shall provide for
the implementation, at a minimum, of
management measures . . . and shall also
contain ... (4) The provision of technical
and other assistance to local governments
and the public for implementing the
measures . . . which may include assistance
... to predict and assess the effectiveness
of such measures ....
EPA and NOAA also have some responsibility
under Section 6217 for providing technical
assistance to implement state CNPCPs.
Section 6217(d), Technical assistance, states:
[NOAA and EPA] shall provide technical
assistance ... in developing and
implementing programs. Such assistance
shall include: ... (4) methods to predict
and assess the effects of coastal land use
management measures on coastal water
quality and designated uses.
This guidance document was developed to
provide the technical assistance described in
CZARA Sections 6217(b)(4) and 6217(d), but
the techniques can be used for similar
programs and projects. For instance,
monitoring projects funded under Clean Water
Act (CWA) Section 319(h) grants, efforts to
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Chapter 1
Introduction
implement total maximum daily loads
developed under CWA Section 303(d),
stormwater permitting programs, and other
programs could all benefit from knowledge of
BMP implementation.
Methods to assess the implementation of MMs
and BMPs, then, are a key focus of the
technical assistance to be provided by EPA
and NOAA. Implementation assessments can
be done on several scales. Site-specific
assessments can be used to assess individual
BMPs or MMs, and watershed assessments
can be used to look at the cumulative effects of
implementing multiple MMs. With regard to
"site-specific" assessments, individual BMPs
must be assessed at the appropriate scale for
the BMP of interest. For example, to assess
the implementation of MMs and BMPs for
erosion and sediment control (E&SC) at a
construction site, only the structures, areas,
and practices implemented specifically for
E&SC (eg., protection of natural vegetation,
sediment basins, or soil stabilization practices)
would need to be inspected. In this instance
the area physically disturbed by construction
activities and the upslope area would be the
appropriate site and scale.
However, if a state without a centralized
E&SC program were assessing erosion and
E&SC in an area (e.g., coastal) of concern, it
might assess municipal E&SC programs. In
this instance the "site" would be each urban
area and implementation of municipal
regulations, inspection and enforcement
programs, etc. would be checked. For bridge
runoff management, the scale might be bridges
over waterways that carry and average daily
traffic of 500 or more vehicles and the sites
would be individual bridges that meet this
requirement. Site-specific measurements can
then be used to extrapolate to a program,
watershed, or statewide assessment. There are
instances where a complete inventory of MM
and BMP implementation across an entire
watershed or geographic area is preferred.
1.3 TYPES OF MONITORING
The term monitor is defined as "to check or
evaluate something on a constant or regular
basis" (Academic Press, 1992). It is possible
to distinguish among various types of
monitoring. Two types, implementation at a
specific time (i.e., a snapshot) and trend (i.e.,
trends in implementation) monitoring, are the
focus of this guidance. These types of
monitoring can be used to address the
following goals:
• Determine the extent to which MMs and
BMPs are implemented in accordance with
relevant standards and specifications.
• Determine whether there has been a change
in the extent to which MMs and BMPs are
being implemented.
In general, implementation monitoring is used
to determine whether goals, objectives,
standards, and management practices are being
implemented as detailed in implementation
plans. In the context of BMPs within state
CNPCPs, implementation monitoring is used
to determine the degree to which MMs and
BMPs required or recommended by the
CNPCPs are being implemented. If CNPCPs
call for voluntary implementation of MMs and
BMPs, implementation monitoring can be used
to determine the success of the voluntary
program (1) within a given monitoring period
(e.g., 1 or 2 years); (2) during several
monitoring periods, to determine any temporal
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Introduction
Chapter 1
trends in BMP implementation; or (3) in
various regions of the state.
Trend monitoring involves long-term
monitoring of changes in one or more
parameters. As discussed in this guidance,
public attitudes, land use, and the use of
various urban BMPs are examples of
parameters that could be measured with trend
monitoring. Isolating the impacts of MMs and
BMPs on water quality requires trend
monitoring.
Because trend monitoring involves measuring
a change (or lack thereof) in some parameter
over time, it is necessarily of longer duration
and requires that a baseline, or starting point,
be established. Any changes in the measured
parameter are then detected in reference to the
baseline.
Implementation and the related trend
monitoring can be used to determine
(1) which MMs and BMPs are being
implemented, (2) whether MMs and BMPs are
being implemented as designed, and
(3) the need for increased efforts to promote or
induce use of MMs and BMPs. Data from
implementation monitoring, used in
combination with other types of data, can be
useful in meeting a variety of other objectives,
including the following (Hook et al., 1991;
IDDHW, 1993; Schultz, 1992):
• To evaluate BMP effectiveness for
protecting natural resources.
• To identify areas in need of further
investigation.
To establish a reference point of overall
compliance with BMPs.
• To determine whether landowners are
aware of BMPs.
• To determine whether landowners are
using the advice of urban BMP experts.
• To identify any BMP implementation
problems specific to a land ownership or
use category.
To evaluate whether any urban BMPs
cause environmental damage.
• To compare the effectiveness of alternative
BMPs.
MacDonald et al. (1991) describes additional
types of monitoring, including effectiveness,
baseline, project, validation, and compliance
monitoring. As emphasized by MacDonald
and others, these monitoring types are not
mutually exclusive and the distinctions among
them are usually determined by their purpose.
Effectiveness monitoring is used to determine
whether MMs or BMPs, as designed and
implemented, are meeting management goals
and objectives. Effectiveness monitoring is a
logical follow-up to implementation
monitoring, because it is essential that
effectiveness monitoring include an
assessment of the adequacy of the design and
installation of MMs and BMPs. For example,
the objective of effectiveness monitoring could
be to evaluate the effectiveness of MMs and
BMPs as designed and installed, or to evaluate
the effectiveness of MMs and BMPs that are
designed and installed adequately or to
standards and specifications. Effectiveness
monitoring is not addressed in this guide, but
is the subject of another EPA guidance
document, Monitoring Guidance for
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Introduction
Determining the Effectiveness ofNonpoint
Source Controls (USEPA, 1997).
1.4 QUALITY ASSURANCE AND QUALITY
CONTROL
An integral part of the design phase of any
nonpoint source pollution monitoring project
is quality assurance and quality control
(QA/QC). Development of a quality assurance
project plan (QAPP) is the first step of
incorporating QA/QC into a monitoring
project. The QAPP is a critical document for
the data collection effort inasmuch as it
integrates the technical and quality aspects of
the planning, implementation, and assessment
phases of the project. The QAPP documents
how QA/QC elements will be implemented. It
contains statements about the expectations and
requirements of those for whom the data is
being collected (i.e., the decision maker) and
provides details on project-specific data
collection and data management procedures
designed to ensure that these requirements are
met. Development and implementation of a
QA/QC program, including preparation of a
QAPP, can require up to 10 to 20 percent of
project resources (Cross-Smiecinski and
Stetzenback, 1994). A thorough discussion of
QA/QC is provided in Chapter 5 of EPA's
Monitoring Guidance for Determining the
Effectiveness ofNonpoint Source Controls
(USEPA, 1997).
1.5 DATA MANAGEMENT
Data management is a key component of a
successful MM or BMP implementation
monitoring effort. The system
used—including the quality control and quality
assurance aspects of data handling, how and
where data are stored, and who manages the
stored data—determines the reliability,
longevity, and accessibility of the data.
Provided that data collection was well planned
and executed, an organized and efficient data
management system will ensure that the data:
Can be used with confidence by those who
must make decisions based upon it,
• Will be useful as a baseline for similar data
collection efforts in the future,
• Will not become obsolete quickly, and
• Will be available to a variety of users for
myriad applications.
Serious consideration is often not given to a
data management system prior to a collection
effort, which is precisely why it is so important
to recognize the long-term value of a small
investment of time and money in proper data
management. Data management competes
with other priorities for money, staff, and time,
and if the importance and long-term value of
data management is recognized early, the more
likely it will be to receive sufficient funding.
Overall, data management might account for
only a small portion of a project's total budget,
but the return is great, considering that data
can be rendered virtually useless if data are not
managed adequately.
Two important aspects of data that should be
considered when planning the initial collection
and management systems are data life cycle
and accessibility. The cycle has 5 stages:
(1) data are collected; (2) data are checked for
quality; (3) data are entered into a database; (4)
data are used, and (5) data eventually become
obsolete. The expected usefulness and life
span of the data should be considered during
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Introduction
Chapter 1
the initial stages of planning a data collection
effort, when the money, staff, and time
devoted to data collection must be weighed
against its usefulness and longevity. Data with
limited uses and likely to become obsolete
soon after collection are a poorer investment
decision than data with multiple applications
and long life spans.
Data accessibility is a critical factor in
determining its usefulness. Data attains
highest value if widely accessible, if access
requires the least staff effort, and if used by
others conveniently. If date are stored where
obtainable (with little assistance), use and
sharing are more likely. The format for data
storage determines how conveniently data can
be used. Electronic storage in widely available
and used data formats makes for convenience.
Storage as only a paper copy buried in a report,
where any analysis requires entry into an
electronic format or time-consuming
manipulation, makes data extremely
inconvenient and thus unlikely to be used.
The following should be considered for the
development of a data management strategy:
• What level of quality control should the
data be subject to? Data to be used for a
variety of purposes or for important
decisions merit careful quality control
checks.
Where and how will the data be stored?
The options for data storage range from a
printed final report on a bookshelf to an
electronic data base accessible to
government agencies and the public.
Determining where and how data will be
stored requires careful consideration of the
question: How accessible should the data
be?
Who will maintain the data base ? Data
stored in a large data base might be
managed by a professional manager, while
data kept in agency files might be managed
by people of various backgrounds over
time.
How much will data management cost?
As with all other aspects of data collection,
data management costs money and must be
balanced with all other project costs.
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CHAPTER 2. METHODS TO INVENTORY BMP IMPLEMENTATION
Because the potential for serious water quality
degradation is high in urban areas, it is
important to have a means to track the
implementation of BMPs used to control urban
nonpoint source pollution and a means to
measure what is being done to address it. The
activities in urban areas that generate polluted
runoff are usually concentrated in a small area,
discharging to only one or two water bodies,
and diverse, contributing a variety of
pollutants. Although programs exist for
statewide tracking of BMPs for forestry and
agriculture (see Adams, 1994; Delaware
DNREC, 1996) and some studies of BMP
implementation in urban areas have been done
(see Pensyl and Clement, 1987),
comprehensive urban-area BMP tracking
programs are still not the norm. In some ways,
tracking BMPs in urban areas can be easier
than tracking those for forestry or agriculture.
For instance, once an area is developed and
structural BMPs are installed, there is little
change unless problems require retrofits. If an
inventory of BMPs (e.g., stormwater ponds,
swales, buffer strips) is done, the information
can be stored in a database and used for a
variety of purposes. Also, many of the urban
pollutant-generating activities are permitted
(e.g., construction) or regulated in some other
manner (e.g., septic tank operation and
maintenance), providing a paper trail of
information. These advantages can result in a
more complete assessment of urban BMP
implementation. In some instances it is
possible to inventory and track over time the
implementation status of all BMPs of certain
types. For those urban areas that have not
compiled existing codes, regulations, and
permitting requirements, it is recommended
that an inventory be created.
2.1 REGULATED ACTIVITIES
To regulate urban NPS water pollution, states
employ a variety of legal mechanisms,
including nuisance prohibitions, general water
pollution discharge prohibitions, land use
planning and regulation laws, building codes,
health regulations, and criminal laws
(Environmental Law Institute, 1997). Many
states delegate some of these authorities to
units of local government or conservation
districts. Although not all pollutant-generating
activities are covered by these mechanisms, the
applicable mechanisms present opportunities
for inventorying BMP implementation. The
urban activities that are regulated in some
manner include erosion and sediment control,
onsite sewage disposal systems (septic tanks),
runoff from development sites, construction,
and site-specific activities (e.g., oil and grit
separators at gas stations). Perhaps the best
mechanism for collecting information for
tracking BMP implementation is requiring
permits for certain activities. A permitting
system places on the applicant the burden of
obtaining and supplying all necessary data and
information needed to get the permit. Two
types of permits are generally
issued—construction and operating. Issuance
of these permits encourages construction and
operation of BMPs in compliance with local
laws and regulations.
2.1.1 Erosion and Sediment Control
Most urban areas have laws requiring the
control of sediment erosion at construction
sites. These laws are usually implemented as
part of the building permit process. The
material required as part of the building permit
process (including site clearing plans, drainage
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Chapter 2
plans, landscaping plans, and erosion and
sediment control plans) can provide a wealth
of information on proposed BMP
implementation. Because site clearing and
building activities occur in a short time period,
tracking of implementation of BMPs for
erosion and sediment control should be done
on a real-time basis.
Many states and municipalities employ
inspectors to monitor BMP implementation at
building sites. Site inspections are critical to
determining actual BMP implementation.
Paterson (1994), in a survey of construction
practices in North Carolina, found that nearly
25 percent of commonly prescribed
construction BMPs (e.g., storm drain inlet
protection and silt fences) that were included
in erosion and sediment control plans had not
actually been implemented (see Example 1).
Employing adequate numbers of site
inspectors can be expensive. To counter a lack
of BMP implementation and to overcome a
shortage of construction site inspection staff,
the state of Delaware developed an effective
program to monitor implementation of BMPs
This investigation looked at more than 1,000 construction practices that had been included
in 128 erosion and sediment control plans in nine North Carolina jurisdictions. The nine
jurisdictions were selected to be representative of North Carolina's three physiographic
regions (coastal plain, piedmont, and mountain) across three levels of program
administration (municipal, county, and state). Project sites were randomly selected from
lists of permitted construction projects provided by each jurisdiction . The implementation
of erosion and sediment control practices was evaluated in terms of whether the practices
had been installed adequately and whether they were being maintained adequately.
The survey provided information on the following aspects of erosion and sediment control
practices:
• Which practices administrators thought were useful practices and which they
thought were poor performers.
• What administrators thought were the causes of practice failure (e.g., poor
installation, poor maintenance).
• The number of construction practices never installed even though they were on the
erosion and sediment control plan.
• Which practices were poorly installed/constructed/maintained and the installation/
construction/maintenance problem.
• Which practices were prescribed in erosion and sediment control plans.
• Which recommended practices performed worse than less-favored practices.
• What problems were associated with installation of the practices.
The investigators determined that the major problems associated with installation were a
lack of suitable training to install the erosion and sediment control practices properly and
vagueness in the erosion and sediment control plan concerning installation specifications.
The major problems associated with maintenance were neglect of the practices after
installation and initial design flaws.
Example 1... Review of erosion and sediment control plans in North Carolina. (Paterson, 1994)
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Inventory Methods
for erosion and sediment control at
construction sites (Center for Watershed
Protection, 1997) (see Example 2).
2.1.2 Septic Systems
Cesspools, failed septic systems, and high
densities of septic systems can contribute to
the closure of swimming beaches and shellfish
beds, contaminate drinking water supplies, and
cause eutrophication of ponds and coastal
embayments. Onsite sewage disposal systems
(OSDS) are usually locally regulated by
building codes and health officials
(Environmental Law Institute, 1997). A
variety of permit requirements are used to
regulate their siting, installation, and operation
and maintenance.
Several innovative programs have been
developed to track implementation of BMPs
for OSDS (see Examples 3 and 4). Program
The state of Delaware's program requires some builders to hire independent inspectors,
who are officially known as construction reviewers. These reviewers monitor
implementation of erosion and sediment control BMPs at selected construction sites.
The construction reviewers are certified and periodically recertified in erosion and sediment
control by the state of Delaware and provide onsite technical assistance to contractors.
They are required to visit sites at least weekly and to report violations and inadequacies to
the developer, contractor, and erosion and sediment control agency. Their reports are
reviewed by government inspectors. Local or state erosion and sediment control agencies
are still responsible for spot checking sites and issuing fines or other penalties. Reviewers
can lose their certification if spot checks reveal that violations were not reported. Since its
inception in 1991, 340 people have been certified as construction reviewers.
Successful implementation of a program similar to Delaware's would require tailoring it to
regional circumstances and conditions. Key aspects of the program in Delaware include
the following:
• Full-time staff were assigned to administer the program.
• Criteria for selection of appropriate sites for the use of construction reviewers were
established.
• A training program and certification course were developed to support the program.
• Reporting criteria were specified.
• Oversight by a professional engineer was incorporated.
• Specific spot check scheduling was determined.
• Recourse for fraudulent inspection results was incorporated.
• Enforcement actions for contractors who violate erosion and sediment control plans
were included.
• The program was piloted in a test area.
• Objective monitoring criteria were developed to evaluate the program.
• A process for revision to the program based on performance was included.
Example 2... Delaware's construction reviewer program. (CWP, 1997)
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In the Buzzards Bay area a need to track information related to OSDS permitting and
inspection and maintenance was identified. Municipal boards of health in this area are
responsible for implementing and overseeing state regulations for OSDS. The boards of
health lacked the ability to efficiently and effectively monitor permits and inspection and
maintenance information, due to insufficient staffing and information-processing equipment
and systems. They had been overburdened with processing new permits, with the result
that tracking past permits and past orders of noncompliance and reviewing pump-out
reports were tasks often left undone.
Project: The SepTrack Demonstration Project provided computers and specialized
software to communities fringing Buzzards Bay to enable them to better manage
information related to onsite septic systems. This helped to identify patterns of septic
system failure and freed staff time for better design review and enforcement.
Project Goal: To better enable each board of health to track septic system permits and
inspection and maintenance information by reducing information management and retrieval
burdens on boards of health, thereby allowing time to enhance protection of public health
and the environment.
Accomplishments: Computers and specialized software were provided to 11 boards of
health in the Buzzards Bay watershed. Funding was provided to transfer old permit
information and septic pumping records in each community into the SepTrack database.
The project was welcomed with enthusiasm by most municipalities, and many communities
outside the demonstration area have requested copies of the SepTrack software.
Most boards of health receive monthly reports from sewage treatment plants with
information on pumpouts provided by septage haulers. In Massachusetts, the haulers must
report the source of their septage. Frequent pumping at a property is often a sign of a
failing septic system. With SepTrack, a list of frequently pumped systems is provided
automatically. In one town, this listing highlighted a town-owned property as one with a
failing system and revealed inconsistencies in septage hauler information. In another town,
public works water and sewer information in the SepTrack system revealed that 200 homes
along an embayment had never been connected to a sewer line. The board of health
required that this neighborhood connect to the existing sewer.
Example 3... Buzzards' Bay SepTrack System. (USEPA, undated)
features that provide data that can be used to • Periodic inspections for compliance
track implementation include the following: including whenever the system is pumped,
the property is sold, or a complaint is filed.
• Building codes with design, construction, • Requirements that the system be pumped
depth to water table, and soil percolation periodically or if the property is sold and
standards. that the septage hauler file a report with the
• Permitting of systems. local health department.
• Dye testing of systems in areas of concern.
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Chapter 2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^B Inventory Methods
Marin County, California, biennial onsite system inspection program. Marin County, California,
modified its code and established the requirement for a county-administered biennial onsite system
inspection program. Part of the inspection program is a Certificate of Inspection, issued when the
system is built and renewed every 2 years. Every 2 years a letter is sent to inform the property owner
that an inspection is required. The owner must schedule an inspection and pay a renewal fee.
Homeowners have the option of having the inspection performed by a county-licensed septic tank
pumper with supervision by a county field inspector. Should repair or pumping be required, the
homeowner must submit proof of repair or pumping before the certificate is renewed. New
certificates are valid regardless of any change in home ownership prior to the certificate expiration
date. The Certificate of Inspection must be valid and current when home ownership is transferred
(Roy F. Weston, 1979 (draft)).
Wisconsin onsite waste-water treatment system installer certification. Wisconsin requires that onsite
wastewater treatment system installers be certified by the state as either Plumbers or Restricted
Sewer Plumbers. In addition, the state recently replaced the percolation test with a site-specific soil,
drainage, and morphological evaluation that must be performed by a Certified Soil Tester.
Allen County, Ohio, Department of Health Monitoring Program. The Department of Public Health
in Allen County, Ohio, monitors approximately 3,000 onsite disposal systems. Important
components of the monitoring program include the following:
Maintenance of a computerized billing process and paper files of inspection results and
schedules.
Permit issuance for all new systems. Afterward, the annual billing serves as the permit.
Annual inspection of all aerobic systems covered under the program.
Notification sent to property owners in advance of inspections.
Inspections for loan certifications. Inspection is free for systems covered under the permit
program.
Combination visual and chemical monitoring program, Santa Cruz, California. The San Lorenzo
River watershed in Santa Cruz County, California, is encompassed by the Santa Cruz wastewater
management zone. The wastewater management zone monitors the systems in the watershed as
follows (Washington State, 1996):
• Maintaining a database with information on system ownership and locations, permits, loan
certifications, complaints, failure and inspection results, and schedules.
• Assigning to each system a classification that determines the operations requirements, fee
schedules, inspection frequencies, and property restrictions.
• Conducting initial inspections for all systems to assess system condition.
• Inspecting systems that meet standard requirements every 6 years; inspecting other systems
every 1 to 3 years. (The health agency performs all inspections. Property owners are not
notified of upcoming inspections).
• Administering public education programs through direct outreach and distribution of brochures.
• Monitoring surface water quality for fecal coliform and nitrate.
Example 4... Tracking onsite sewage disposal systems.
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Inventory Methods
Chapter 2
2.1.3 Runoff Control and Treatment
It is possible to inventory and track all
structural BMPs in a given geographic area
over time. Such a project requires a large
effort and has been used only when a state or
watershed (e.g., Chesapeake Bay basin) is
trying to reach a specific water quality goal.
Such efforts may become more common in the
future as states implement the Clean Water Act
Section 303(d) total maximum daily load
(TMDL) program for impaired waters. For
example, an entire 94-square mile area of the
Anacostia River watershed in Prince George's
County, Maryland, was inventoried to
• Identify and document water resource
problem areas and potential retrofit sites.
• Evaluated existing storm water
management facilities from water quality
and habitat enhancement perspectives.
• Make recommendations for retrofit.
• Present information derived in a format
useful to public agency personnel.
The investigators collected information on
contributory drainage area, land ownership,
land use/zoning, soils, areas of ecological or
scenic significance, presence of wetland areas,
storm drain outfall size and location, storm
water management facility design
specifications, ownership, maintenance
responsibilities, base flow conditions, stream
channel condition, and canopy coverage and
riparian habitat conditions. The information
was used to make management decisions on
BMP retrofits, stream restoration, and
installation of new BMPs. Another example
of a state inventorying BMPs for the control of
urban runoff is presented in Example 5.
2.2 TRACKING BMP OPERATION AND
MAINTENANCE
In many instances the extent of proper
operation and maintenance of a BMP is as
important as the proper design and installation
of the BMP. Regular inspect!on of BMP
operation and maintenance can provide an
indication of how a nonpoint source control
program is advancing. Such inspections can
also identify BMPs that need repairs or
retrofits as well as identify areas that require
additional management resources. If the right
types of information are collected when a BMP
is installed, the task of tracking operation and
maintenance as well as ascertaining or
monitoring effectiveness is much easier. BMP
operation and maintenance can also be tracked
through review of the BMP maintenance
backlog. A large maintenance backlog
indicates that additional resources are required
to ensure proper operation.
Many of the examples presented earlier in this
chapter contain information on how BMP
operation and maintenance was tracked by the
responsible agency. Lindsey et al. (1992)
investigated the functioning and maintenance
of 250 storm water BMPs in four Maryland
counties and documented a need for improved
inspection and maintenance. They found
excessive sediment and debris in many devices
and growth of woody or excessive vegetation
and the need for stabilization near many.
These problems had led to one-quarter of all
basins (infiltration, wet, and dry) having lost
more than ten percent of their volume and
eroding embankments at more than one-third
of all facilities. The BMPs were assessed as to
the following maintenance criteria:
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Chapter 2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^B Inventory Methods
A comprehensive survey of infiltration devices was conducted in the state of Maryland to quantify
the installation of the devices during the first 2 years after enactment of the Stormwater
Management Act in that state (Pensyl and Clement, 1987). During the survey, state agency
personnel, in cooperation with local county agencies, collected the data through actual site
inspections. A separate inspection form was completed for each site inspection.
The following information was obtained during each site inspection:
• The type of infiltration device in use.
• The number of infiltration devices in use.
• The means of entry of runoff into the infiltration device.
• Whether the infiltration device was functioning.
The data were compiled by county to determine the following:
• The types of infiltration devices in use.
• The total number of infiltration devices installed.
• The total number of infiltration devices in each county.
• The total number of functioning infiltration devices per county.
• The total number of each type of infiltration device per county.
• The total number of each type of infiltration device that was functioning and nonfunctioning in
each county.
• The percentage of functioning infiltration devices in each county.
The data were compiled by infiltration device to determine the following:
• The total number of each type of infiltration device in the survey area.
• The number and percentage of functioning and nonfunctioning infiltration devices of each type.
• Whether functioning infiltration devices were associated with buffer strips and drainage area
stabilization.
• Whether functioning infiltration devices had obvious sediment entry, needed maintenance, or
had standing water.
RESULTS
From the site inspection survey, the following was determined:
• The number of infiltration devices installed in the state.
• The number and percent of functioning infiltration devices.
• The type of infiltration device with the greatest percent of those installed that were functioning.
• The overall success rate of infiltration devices in the state (i.e., 67%).
• Which infiltration practices have a low success rate.
• The likely reasons for the failure of infiltration devices to function properly.
• Recommendations to improve the state storm water management program.
Example 5... Maryland survey of infiltration devices.
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Inventory Methods
Chapter 2
• Facility functioning as designed.
• Quantity controlled as designed.
• Quality benefits produced by facility.
• Enforcement action needed.
• Maintenance action needed.
Several models were used to analyze the
results of the field study, and the inspectors
found that the conditions of the different types
of BMPs varied significantly.
2.3 GEOGRAPHIC INFORMA TION SYSTEMS
AND BMP IMPLEMENTATION/
EFFECTIVENESS
Geographic information systems (GIS) are
useful for characterizing the features of
watersheds in the form of spatial relationships
in a manner that permits gaining much more
information from data than could be obtained
A computer interface between a database,
a GIS, and a storm water model was
created for Jefferson Parish, Louisiana, to
develop a computer simulation model for
studying storm water runoff events,
planning future capital drainage projects,
and developing alternative management
scenarios (Barbe et al., 1993).
The following graphical information was
stored in the GIS: 1-foot contours,
sidewalks, building outlines, aboveground
and belowground public and private
utilities, fences, water features, vegetation,
parcels, political boundaries, and soil
types. Nongraphical data on sewers and
storm drainage were also stored for
reference: pipeline size; pipe construction
material; location of pipelines; and
location, material, and depth of manholes.
Similar information on streets was
incorporated.
from them in the form of separate, unrelated
databases. Spatial relationships among the
locations of pollution sources, land uses, water
quality data, trends in population and
development, infrastructure, climatological
data, soil type and geological features, and any
other data that can be represented graphically
and might be perceived as related to BMP
implementation and water quality management
can be incorporated into a GIS. In addition,
nongraphical data can be incorporated into a
GIS so they can be analyzed with respect to the
graphical data. Nongraphical data include
such things as dates of inspections and BMP
maintenance, types of materials used in
infrastructure, sizes of pipes and storm water
inlets, and so forth.
Robinson and Ragan (1993) note that the
CWA Section 319 requirements—i.e., to
submit reports that detail the amount of
navigable waters affected by nonpoint sources,
the types of nonpoint source affecting water
quality, and the BMP program designed to
control them—will require local governments
Robinson and Ragan (1993) correlated a
nonpoint source model developed by the
Northern Virginia Planning District
Commission with mapping coordinates to
determine the spatial distribution of
nonpoint source constituents. The
nonpoint source model approximates
loading rates of several nonpoint source
constituents from a relationship between
land use and soil type. Robinson and
Ragan developed the GIS themselves
rather than using a vendor-sold system
because the custom-made GIS was easier
to use and did not require specialized
training or modification of a standard GIS
for their particular application.
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Chapter 2
Inventory Methods
Loudoun County, Virginia, grew
enormously from 1980 to 1990. The
primary source of drinking water is ground
water obtained through wells and springs,
so the county enacted regulations to
require hydrogeologic studies to support
proposals for new rural subdivisions. The
county developed a comprehensive
environmental GIS that incorporates a
ground water database with information on
water well yields, well depth, depth to
bedrock, storage coefficients, underground
storage tanks, landfills, sewage disposal
systems, illegal dump sites, sludge
application sites, and chemical analyses of
ground water. The ground water database
is linked to environmental mapping units
(e.g., bedrock) to generate information
such as the distribution of geology and well
yields; the density, type, and status of
potential pollution sources; and ground
water quality as it relates to land use and
geology (Cooper and Carson, 1993).
to integrate information on a regional basis and
relate it through nonpoint source modeling in
order to manage the quantity of data necessary
to achieve the desired results and to conduct
the simulations needed to support the decision-
making process.
Data sets will have to be updated periodically,
particularly with respect to land use,
infrastructure, population, and demographics
in developing areas. Using a GIS interfaced
with nonpoint source pollution models is a
good approach to achieve these ends
(Robinson and Ragan, 1993). For example,
EPA's Better Assessment Science Integrating
Point and Nonpoint Sources (BASINS) is a
system that integrates a GIS, national
watershed data, and environmental assessment
and modeling tools into one package. It allows
users to add customized data layers, such as
BMP implementation information, to existing
data.
A GIS can be an extremely useful tool for
BMP tracking since it can be used to keep
track of and detect trends in BMP
implementation, land treatment (e.g., areas of
high use of fertilizer or pesticides), changes in
land use (e.g., development), and virtually any
data related to BMPs and water quality. An
advantage of using a GIS for BMP tracking is
the ability to update information and integrate
it with existing data in a timely manner. Data
are thereby made extremely accessible.
Through the ability to correlate numerous
types of data with a GIS, changes observed in
data are more easily recognized. This permits
managers to analyze the changes in one set of
conditions with respect to other existing
conditions within a particular geographical
area and to arrive at plausible explanations,
eliminate unplausible ones, and potentially to
predict future problems.
GIS can be used as the basis for sampling for
BMP tracking studies. Criteria for sampling
can be chosen—for instance, age of BMP or
elapsed time since the last inspection—and any
BMPs that fail to meet the criteria can easily
be eliminated from consideration. With all
relevant information on BMPs in a single GIS,
selection criteria for unrelated characteristics
(e.g., retention capacity and most recent
inspection date) can be correlated easily to
arrive at a subset that meets all of the desired
criteria. A GIS used as the basis for a
sampling procedure also provides repeatability.
Random, stratified random, or cluster sampling
can all be accomplished with a GIS.
The powers of GIS extend beyond the data
analysis phase as well. Because of the power
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Inventory Methods
Chapter 2
Louisiana has a statewide discharger
inventory in a GIS. The GIS is a detailed
graphical model of the state that contains
the location of all known discharges. It is
linked to the state Office of Water's
databases and EPA's Toxic Release
Inventory database by discharger
identification number. It includes
information on the segment of the water
body that is discharged into, and the
inventory provides efficient and effective
access to a large quantity of data. Since
the data can be visually portrayed, the GIS
improves comprehension of the impact of
waste discharge on the environment, as
well as understanding of numerous
interrelated waste discharges and their
combined impact on large areas (such as
entire water quality basins). The GIS also
assists in both technical and management
decisions (Richards, 1993).
of the data analysis that is possible with a GIS,
use of one can lead to improvements in data
collection activity design, data tracking
methods, database management, and program
evaluation. The powerful spatial relationships
created through the use of GIS can make data
more accessible to a wider audience, thus
making GIS a valuable tool for the
communication of results of surveys and
analyses, and the ability to select from a
variety of data elements for data analysis
permits customizing the analysis of data for a
variety of audiences.
2.4 SUMMARY OF PROGRAM ELEMENTS
FORA SUCCESSFUL BMP INVENTORY
The essential elements of a successful urban
BMP compliance tracking program include the
following:
• Clear and specific program goals
• Technical guidelines for site evaluation,
design, construction, and operation
• Regular system monitoring
• Licensing or certification of all service
providers
• Effective enforcement mechanisms
• Appropriate incentives
• Adequate records management.
Conversely, the four primary reasons that
urban BMP programs fail are insufficient
funding; programs that are inappropriate for
the specific circumstances under which they
are to be implemented; lack of monitoring,
inspection, and program evaluation; and lack
ofpubliceducation(USEPA, 1997a). An
effective BMP implementation tracking
program will generate considerable data and
information regarding existing, new, and
upgraded BMPs. Essential data management
elements include data collection, database
development, data entry, possibly data
geocoding, and data analysis.
It is not always possible to track the
implementation of every BMP of interest.
Sampling a subpopulation and extrapolating
the findings to the entire population may be
preferred due to time, funding, or personnel
constraints. Lack of adequate legal authorities
might also hinder the collection of data
sufficient to track BMP implementation. If an
inventory of all BMPs of interest is not
possible, care should be taken to prepare a
statistically valid sampling plan as discussed in
Chapter 3.
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CHAPTERS. SAMPLING DESIGN
3.1 INTRODUCTION
This chapter discusses recommended methods
for designing sampling programs to track and
evaluate the implementation of nonpoint source
control measures. This chapter does not
address sampling to determine whether
management measures (MMs) or best
management practices (BMPs) are effective,
since no water quality sampling is done.
Because of the variation in urban practices and
related nonpoint source control measures
implemented throughout the United States, the
approaches taken by various states to track and
evaluate nonpoint source control measure
implementation will differ. Nevertheless, all
statistical sampling approaches should be based
on sound methods for selecting sampling
strategies, computing sample sizes, and
evaluating data. EPA recommends that states
should consult with a trained statistician to be
certain that the approach, design, and
assumptions are appropriate to the task at hand.
As described in Chapter 1, implementation
monitoring is the focus of this guidance.
Effectiveness monitoring is the focus of
another guidance prepared by EPA,
Monitoring Guidance for Determining the
Effectiveness of Nonpoint Source Controls
(USEPA, 1997). The recommendations and
examples in this chapter address two primary
monitoring goals:
• Determine the extent to which MMs and
BMPs are implemented in accordance with
relevant standards and specifications.
• Determine whether there is a change in the
extent to which MMs and BMPs are being
implemented.
For example, local regulatory personnel might
be interested in whether regulations for septic
tank inspection and pumping are being adhered
to in regions with particular water quality
problems. State or county personnel might
also be interested in whether, in response to an
intensive effort in targeted watersheds to
decrease the use of fertilizers and pesticides on
residential lawns, there is a detectable change
in homeowner behavior.
3.1.1 Study Objectives
To develop a study design, clear, quantitative
monitoring objectives must be developed. For
example, the objective might be to estimate the
percent of local governments that require
attenuation of the "first flush" of runoff to
within ±5 percent. Or perhaps a state is
preparing to perform an extensive 2-year
outreach effort to educate citizens on the
impacts of improper lawn care. In this case,
detecting a 10 percent change in resident's
lawn care practices might be of interest. In the
first example, summary statistics are
developed to describe the current status,
whereas in the second example, some sort of
statistical analysis (hypothesis testing) is
performed to determine whether a significant
change has really occurred. This choice has an
impact on how the data are collected. As an
example, summary statistics might require
unbalanced sample allocations to account for
variability such as the type of local
government, whereas balanced designs (e.g.,
two sets of data with the same number of
observations in each set) are more typical for
hypothesis testing.
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Sampling Design
Chapter
3.1.2 Probabilistic Sampling
Most study designs that are appropriate for
tracking and evaluating implementation are
based on a probabilistic approach since
tracking every MM or BMP is usually not
cost-effective. In a probabilistic approach,
individuals are randomly selected from the
entire group (see Example). The selected
individuals are evaluated, and the results from
the individuals provide an unbiased assessment
about the entire group. Applying the results
from randomly selected individuals to the
entire group is statistical inference. Statistical
inference enables one to determine, for
example, in terms of probability, the
percentage of local governments that require
water quality controls for urban runoff without
visiting every community. One could also
determine whether a change in homeowners'
use of lawn care products is within the range
of what could occur by chance or whether it is
large enough to indicate a real modification of
homeowner behavior.
The group about which inferences are made is
the population or target population, which
consists of population units. The sample
A survey of the residential population within three small Baltimore, Maryland
watersheds was conducted in order to:
• Characterize pesticide usage in the residential areas;
• Test the suitability of sampling locations for future monitoring;
• Obtain stream data to correlate with results of the usage survey; and
• Demonstrate the feasibility of characterizing urban nonpoint source pesticide
pollution.
Information for the survey was obtained via door-to-door interviews of randomly
selected residents and mail and telephone surveys of commercial pesticide
applicators that had been hired by the residents that were interviewed. A total of
484 interviews, or 10 percent of the residential population in the three watersheds,
were conducted. The overall response rate to the survey was 69 percent.
The following information was obtained from the survey:
• The percentage of residents that had applied pesticides.
• Where (i.e., indoors and/or outdoors) pesticides were used by the residents.
• The level of use of spray applicators.
• The level of use of fertilizers (i.e., the percentage of residents that used
fertilizers, when they were applied, and their frequency of application).
• The use and disposal of petroleum products (i.e., motor oil and antifreeze).
• A listing of brand names of and active ingredients in the pesticides used by the
residents.
Example . . . Pesticide usage survey (Kroll and Murphy, 1994).
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Chapter 3
Sampling Design
population is the set of population units that
are directly available for measurement. For
example, if the objective is to determine the
degree to which residents are limiting the use
of lawn care products, the population to be
sampled would be residential areas with
single-family homes or multi-family housing
areas with large landscaped areas. Statistical
inferences can be made only about the target
population available for sampling. For
example, if installation of stormwater BMPs is
being assessed and only government facilities
can be sampled, inferences cannot be made
about the management of private lands.
Another example to consider is a mail survey.
In most cases, only a percentage of survey
forms is returned. The extent to which
nonrespondents bias the survey findings
should be examined: Do the nonrespondents
represent those less likely to implement the
MM of interest? Typically, a second mailing,
phone calls, or visits to those who do not
respondent are necessary to evaluate the
impact of nonrespondents on the results.
The most common types of sampling that
should be used for implementation monitoring
are summarized in Table 3-1. In general,
probabilistic approaches are preferred.
However, there might be circumstances under
which targeted sampling should be used.
Targeted sampling refers to using best
professional judgement for selecting sample
locations. For example, state or county
regulatory personnel deciding to evaluate all
MMs or BMPs in a given watershed would be
targeted sampling. The choice of a sampling
plan depends on study objectives, patterns of
Table 3-1. Example applications of four sampling designs for implementation monitoring.
Sampling Design
Example/Applicability
Simple Random
Sampling
Estimate the proportion of homeowners that use herbicides on
their lawn. Applicable when there are major patterns in the group
of homeowners targeted for the survey.
Stratified Random
Sampling
Estimate the proportion of homeowners that use herbicides on
their lawn as a function of subdivision. Applicable when
herbicide use is expected to be different based on the subdivision
or other distinguishing homeowner characteristic (e.g.,
owner/renter, self/lawn service).
Cluster Sampling
Estimate the proportion of homeowners that use herbicides on
their lawn. Applicable when it is more cost effective to sample
groups of homeowners rather than individual homeowners. (See
Section 3.3.3 for a numerical example comparison to simple
random sampling.)
Systematic
Sampling
Estimate the proportion of homeowners that use herbicides on
their lawn. Applicable when working from a (phone or mailing)
list and the list is ordered by some characteristic unrelated to
herbicide use.
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Sampling Design
Chapter
variability in the target population, cost-
effectiveness of alternative plans, types of
measurements to be made, and convenience
(Gilbert, 1987).
Simple random sampling is the most
elementary type of sampling. Each unit of the
target population has an equal chance of being
selected. This type of sampling is appropriate
when there are no major trends, cycles, or
patterns in the target population (Cochran,
1977). Random sampling can be applied in a
variety of ways, including selection of
jurisdictions within a state or BMP sites within
a watershed. Random samples can also be
taken at different times at a single site. Figure
3-1 provides an example of simple random
sampling from a listing of potential inspection
sites and from a map.
If the pattern of MM and BMP implementation
is expected to be uniform across the study
area, simple random sampling is appropriate to
estimate the extent of implementation. If,
however, implementation is homogeneous
only within certain categories (e.g., federal,
state, or private lands), stratified random
sampling should be used.
In stratified random sampling, the target
population is divided into groups called strata.
Simple random sampling is then used within
each stratum. The goal of stratification is to
increase the accuracy of the estimated mean
values over what could have been obtained
using simple random sampling of the entire
population. The method makes use of prior
information to divide the target population into
subgroups that are internally homogeneous.
Stratification involves the use of categorical
variables to group observations into more
units, thereby reducing the variability of
observations within each unit. There are a
number of ways to "select" sites, or sets of
sites (e.g., by type of receiving waterbody, land
use, age of BMP, time elapsed since the last
inspection or maintenance). For example, in
counties with large urban areas and the
resources to develop and implement extensive
urban runoff management programs, there
might be different patterns of BMP
implementation than in counties with smaller
towns that do not have equivalent resources.
Depending on the type of BMPs to be
examined (detention ponds versus household
waste disposal) different stratification might be
necessary. In general, a larger number of
samples should be taken in a stratum if the
stratum is more variable, larger, or less costly
to sample than other strata. For example, if
BMP implementation is more variable in less
developed areas, a greater number of sampling
sites might be needed in that stratum to
increase the precision of the overall estimate.
Cochran (1977) found that stratified random
sampling provides a better estimate of the
mean for a population with a trend, followed in
order by systematic sampling (discussed later)
and simple random sampling. He also noted
that stratification typically results in a smaller
variance for the estimated mean or total than
that which results from comparable simple
random sampling.
If the state believes that there will be a
difference between two or more subsets of
sites, such as between types of development
(commercial, residential, etc.), the sites can
first be stratified into these subsets and a
random sample taken within each subset
(McNew, 1990). to be certain that important
information will not be lost, or that MM or
BMP use will not be misrepresented as a result
of treating all potential survey sites as equal.
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Chapter 3
Sampling Design
BMP Catalog No.
1
2
3
4
5
6
7
8
• • •
118
119
120
121
122
123
124
125
126
127
128
Receiving Waterbody
St ream
Pond
Pond
St ream
River
River
Lake
Lake
• • •
St ream
St ream
Pond
River
Bay
Bay
St ream
Pond
St ream
River
Pond
BMP Type
OSDS
OSDS
Stormwater
Construction
Stormwater
OSDS
Construction
OSDS
• • •
Construction
Construction
Construction
Stormwater
Construction
OSDS
OSDS
Construction
Construction
Stormwater
OSDS
Location Code
N3
S4
S2
E5
S1
S7
W18
E34
• • •
S21
W7
W4
N5
N9
S3
W11
E14
S14
S8
N13
Figure 3-1 a. Simple random sampling from a listing of BMPs. In this listing, all BMPs are
presented as a single list and BMPs are selected randomly from the entire list. Shaded BMPs
represent those selected for sampling.
O
Figure 3-1 b. Simple random sampling from a
map. Dots represent sites. All sites of interest
are represented on the map, and the sites to be
sampled (open dots—O) were selected
randomly from all of those on the map. The
shaded lines on the map could represent
county, watershed, hydrologic, or some other
boundary, but they are ignored for the purposes
of simple random sampling.
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Sampling Design
Chapter
It might also be of interest to compare the
relative percentages of areas with poor, fair, and
good soil percolation that have septic tanks.
Areas with poor or fair percolation might be
responsible for a larger share of nutrient
loadings to ground and surface waters. The
region of interest would first be divided into
strata based on soil percolation characteristics,
and sites within each stratum would be selected
randomly to determine the influence of soil type
on nutrient enrichment in surface and ground
waters. Figure 3-2 provides an example of
stratified random sampling from a listing of
potential inspection sites and from a map.
Cluster sampling is applied in cases where it is
more practical to measure randomly selected
groups of individual units than to measure
randomly selected individual units (Gilbert,
1987). In cluster sampling, the total population
is divided into a number of relatively small
subdivisions, or clusters, and then some of the
subdivisions are randomly selected for
sampling. For one-stage cluster sampling, the
selected clusters are sampled totally. In two-
stage cluster sampling, random sampling is
performed within each cluster (Gaugush, 1987).
For example, this approach might be useful if a
state wants to estimate the areas within
environmentally sensitive watersheds where
additional pretreatment of urban runoff might
be needed. All areas within the watershed with
30 percent or more of the land zoned for
commercial use might be regarded as a single
cluster. Once all clusters have been identified,
specific clusters can be randomly chosen for
sampling. Freund (1973) notes that estimates
based on cluster sampling are generally not as
good as those based on simple random samples,
but they are more cost-effective. Gaugush
(1987) believes that the difficulty associated
with analyzing cluster samples is compensated
for by the reduced sampling cost. Figure 3-3
provides an example of cluster sampling from a
listing of potential inspection sites and from a
map.
Systematic sampling is used extensively in
water quality monitoring programs because it is
relatively easy to do from a management
perspective. In systematic sampling the first
sample has a random starting point and each
subsequent sample has a constant distance from
the previous sample. For example, if a sample
size of 70 is desired from a mailing list of 700
gas station operators, the first sample would be
randomly selected from among the first 10
people, say the seventh person. Subsequent
samples would then be based on the 17th, 27th,
..., 697th person. In contrast, a stratified random
sampling approach for the same case might
involve sorting the mailing list by county and
then randomly selecting gas station operators
from each county. Figure 3-4 provides an
example of systematic sampling from a listing
of potential inspection sites and from a map.
In general, systematic sampling is superior to
stratified random sampling when only one or
two samples per stratum are taken for
estimating the mean (Cochran, 1977) or when is
there is a known pattern of management
measure implementation. Gilbert (1987)
reports that systematic sampling is equivalent to
simple random sampling in estimating the mean
if the target population has no trends, strata, or
correlations among the population units.
Cochran (1977) notes that on the average,
simple random sampling and systematic
sampling have equal variances. However,
Cochran (1977) also states that for any single
population for which the number of sampling
units is small, the variance from systematic
sampling is erratic and might be smaller
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Chapter 3
Sampling Design
BMP Catalog No.
1
2
6
8
• • •
123
124
128
3
5
• • •
121
127
4
7
• • •
118
119
120
122
125
126
Receiving
WaterBody
Stream
Pond
River
Lake
• • •
Bay
Stream
Pond
Pond
River
• • •
River
River
Stream
Lake
• • •
Stream
Stream
Pond
Bay
Pond
Stream
BMP Type
OSDS
OSDS
OSDS
OSDS
• • •
OSDS
OSDS
OSDS
Stormwater
Stormwater
• • •
Stormwater
Stormwater
Construction
Construction
• • •
Construction
Construction
Construction
Construction
Construction
Construction
Location Code
N3
S4
S7
E34
• • •
S3
W11
N13
S2
S1
• • •
S8
E5
W18
• • •
S21
W7
W4
N9
E14
S14
Figure 3-2a. Stratified random sampling from a listing of BMPs. Within this listing, BMPs are
subdivided by BMP type. Then, considering only one BMP type (e.g., OSDS), some BMPs are
selected randomly. The process of random sampling is then repeated for the other BMP types
(i.e., Stormwater, construction). Shaded BMPs represent those selected for sampling.
O
Figure 3-2b. Stratified random sampling from a
map. Letters represent sites, subdivided by type (O
= OSDS, C = construction, S = Stormwater). All
sites of interest are represented on the map. From
all sites in one type category, some were randomly
selected for sampling (shadowed sites). The
process was repeated for each site type category.
The shaded lines on the map could represent
counties, soil types, or some other boundary, and
could have been used as a means for separating
the sites into categories for the sampling process.
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Sampling Design
Chapter
BMP Catalog No.
1
121
122
128
4
8
125
2
3
5
6
118
123
126
127
7
119
120
124
Receiving
Waterbody
St ream
River
Bay
Pond
St ream
Lake
Pond
Pond
Pond
River
River
St ream
Bay
St ream
River
Lake
St ream
Pond
Stream
BMP Type
OSDS
Stormwater
Construction
OSDS
Construction
OSDS
Construction
OSDS
Stormwater
Stormwater
OSDS
Construction
OSDS
Construction
Stormwater
Construction
Construction
Construction
OSDS
Location Code/
Residential Zone
N3/R1 a
N5/R1a
N9/R1a
N13/R1a
E5/R1 b
E34/R1 b
E14/R2a
S4/R2a
S2/R2a
S1/R2a
S7/R2a
S21/R2b
S3/R2b
S14/R2C
S8/R3a
W1 8/R3a
W7/R3b
W4/R3b
W11/R3b
Figure 3-3a. One-stage cluster sampling from a listing of BMPs. Within this listing, BMPs
are organized by residential zone. Some of the residential zones were then randomly
selected and all BMPs in those residential zones were selected for sampling. Shaded BMPs
represent those selected for sampling.
Figure 3-3b. Cluster sampling from a map. All
sites in the area of interest are represented on
the map (closed {•} and open {O} dots).
Residential zones were selected randomly,
and all BMPs in those zones (open dots {O})
were selected for sampling. Shaded lines
could also have represented another type of
boundary, such as soil type, county, or
watershed, and could have been used as the
basis for the sampling process as well.
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Chapter 3
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BMP Catalog No.
1
2
3
4
5
6
7
8
...
118
119
120
121
122
123
124
125
126
127
128
Receiving
Waterbody
St ream
Pond
Pond
St ream
River
River
Lake
Lake
...
St ream
St ream
Pond
River
Bay
Bay
St ream
Pond
St ream
River
Pond
BMP Type
OSDS
OSDS
Stormwater
Construction
Stormwater
OSDS
Construction
OSDS
...
Construction
Construction
Construction
Stormwater
Construction
OSDS
OSDS
Construction
Construction
Stormwater
OSDS
Location Code
N3
S4
S2
E5
S1
S7
W18
E34
• • •
W7
W4
N5
N9
S3
W11
E14
S14
S8
N13
Figure 3-4a. Systematic sampling from a listing of BMPs. From a listing of all BMPs of
interest, an initial site (No. 3) was selected randomly from among the first ten on the list.
Every fifth BMP listed was subsequently selected for sampling. Shaded BMPs represent
those selected for sampling.
Figure 3-4b. Systematic sampling from a map.
Dots (• and O) represent sites of interest. A single
point on the map (a) and one of the sites were
randomly selected. A line was stretched outward
from the point to (and beyond) the selected site.
The line was then rotated about the map and every
fifth dot that it touched was selected for sampling
(open dots—O). The direction of rotation was
determined prior to selection of the point of the
line's origin and the initial site. The shaded lines
on the map could represent county boundaries, soil
type, watershed, or some other boundary, but were
not used for the sampling process.
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Sampling Design
Chapter
or larger than the variance from simple random
sampling.
Gilbert (1987) cautions that any periodic
variation in the target population should be
known before establishing a systematic
sampling program. Sampling intervals that are
equal to or multiples of the target population's
cycle of variation might result in biased
estimates of the population mean. Systematic
sampling can be designed to capitalize on a
periodic structure if that structure can be
characterized sufficiently (Cochran, 1977). A
simple or stratified random sample is
recommended, however, in cases where the
periodic structure is not well known or if the
randomly selected starting point is likely to
have an impact on the results (Cochran, 1977).
Gilbert (1987) notes that assumptions about
the population are required in estimating
population variance from a single systematic
sample of a given size. However, there are
systematic sampling approaches that do
support unbiased estimation of population
variance, including multiple systematic
sampling, systematic stratified sampling, and
two-stage sampling (Gilbert, 1987). In
multiple systematic sampling more than one
systematic sample is taken from the target
population. Systematic stratified sampling
involves the collection of two or more
systematic samples within each stratum.
3.1.3 Measurement and Sampling
Errors
In addition to making sure that samples are
representative of the sample population, it is
also necessary to consider the types of bias or
error that might be introduced into the study.
Measurement error is the deviation of a
measurement from the true value (e.g., the
percent of resident participation in "amnesty
days" for household hazardous waste was
estimated as 60 percent while the true value
was 55 percent). A consistent under- or
overestimation of the true value is referred to
as measurement bias. Random sampling error
arises from the variability from one population
unit to the next (Gilbert, 1987), explaining
why the proportion of homeowners or
developers using a certain BMP differs from
one survey to another.
The goal of sampling is to obtain an accurate
estimate by reducing the sampling and
measurements errors to acceptable levels while
explaining as much of the variability as
possible to improve the precision of the
estimates (Gaugush, 1987). Precision is a
measure of how close an agreement there is
among individual measurements of the same
population. The accuracy of a measurement
refers to how close the measurement is to the
true value. If a study has low bias and high
precision, the results will have high accuracy.
Figure 3-5 illustrates the relationship between
bias, precision, and accuracy.
As suggested earlier, numerous sources of
variability should be accounted for in
developing a sampling design. Sampling
errors are introduced by virtue of the natural
variability within any given population of
interest. As sampling errors relate to MM or
BMP implementation, the most effective
method for reducing such errors is to carefully
determine the target population and to stratify
the target population to minimize the
nonuniformity in each stratum.
Measurement errors can be minimized by
ensuring that interview questions or surveys
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Chapter 3
Sampling Design
(e)
Figure 3-5. Graphical representation of the relationship between bias, precision, and accuracy
(after Gilbert, 1987). (a): high bias + low precision = low accuracy; (b): low bias + low
precision = low accuracy; (c): high bias + high precision = low accuracy; and (d): low bias +
high precision = high accuracy.
are well designed. If a survey is used as a data
collection tool, for example, the investigator
should evaluate the nonrespondents to
determine whether there is a bias in who
returned the results (e.g., whether the
nonrespondents were more or less likely to
implement MMs or BMPs). If data are
collected by sending staff out to inspect
randomly selected BMPs for operation and
maintenance compliance, the approaches for
inspecting the BMPs should be consistent. For
example, a determination that stormwater
ponds are "free of debris" or that swales have
been "properly installed" requires consistent
interpretation of these terms with respect to
actual onsite conditions.
Reducing sampling errors below a certain
point (relative to measurement errors) does not
necessarily benefit the resulting analysis
because total error is a function of the two
types of error. For example, if measurement
errors such as response or interviewing errors
are large, there is no point in taking a huge
sample to reduce the sampling error of the
estimate since the total error will be primarily
determined by the measurement error.
Measurement error is of particular concern
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Sampling Design
Chapter
when homeowner or developer surveys are
used for implementation monitoring.
Likewise, reducing measurement errors would
not be worthwhile if only a small sample size
were available for analysis because there
would be a large sampling error (and therefore
a large total error) regardless of the size of the
measurement error. A proper balance between
sampling and measurement errors should be
maintained because research accuracy limits
effective sample size and vice versa (Blalock,
1979).
3.1.4 Estimation and Hypothesis
Testing
Rather than presenting every observation
collected, the data analyst usually summarizes
major characteristics with a few descriptive
statistics. Descriptive statistics include any
characteristic designed to summarize an
important feature of a data set. A point
estimate is a single number that represents the
descriptive statistic. Statistics common to
implementation monitoring include
proportions, means, medians, totals, and
others. When estimating parameters of a
population, such as the proportion or mean, it
is useful to estimate the confidence interval.
The confidence interval indicates the range in
which the true value lies for a stated
confidence level. For example, if it is
estimated that 65 percent of structural BMPs
are inspected annually and the 90 percent
confidence limit is ±5 percent, there is a 90
percent chance that between 60 and 70 percent
of BMPs are inspected annually.
Hypothesis testing should be used to determine
whether the level of MM and BMP
implementation has changed over time. The
null hypothesis (HJ is the root of hypothesis
testing. Traditionally, H0 is a statement of no
change, no effect, or no difference; for
example, "the proportion of developers that
implement erosion and sediment control (ESC)
BMPs for construction sites after participation
in a certification program is equal to the
proportion of developers that implement ESC
BMPs for construction sites before the
certification program." The alternative
hypothesis (Ha) is counter to H0, traditionally
being a statement of change, effect, or
difference. If H0 is rejected, Ha is accepted.
Regardless of the statistical test selected for
analyzing the data, the analyst must select the
significance level (a) of the test. That is, the
analyst must determine what error level is
acceptable. There are two types of errors in
hypothesis testing:
Type I: H0 is rejected when H0 is really true.
Type II: H0 is accepted when H0 is really
false.
Table 3-2 depicts these errors, with the
magnitude of Type I errors represented by a
and the magnitude of Type II errors
represented by (3. The probability of making a
Type I error is equal to the a of the test and is
selected by the data analyst. In most cases,
managers or analysts will define 1-ato be in
the range of 0.90 to 0.99 (e.g., a confidence
level of 90 to 99 percent), although there have
been applications where I-a has been set to as
low as 0.80. Selecting a 95 percent confidence
level implies that the analyst will reject the H0
when H0 is true (i.e., a false positive) 5 percent
of the time. The same notion applies to the
confidence interval for point estimates
described above:
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Chapter 3
Sampling Design
Table 3-2. Errors in hypothesis testing.
Decision
Accept H0
Reject H0
State of Affairs in the Population
H0 is True
1-a
(Confidence level)
a
(Significance level)
(Type I error)
H0 is False
P
(Type II error)
1-P
(Power)
70 percent range. This implies that if the
decisions to be made based on the analysis are
major (i.e., affect many people in adverse or
costly ways) the confidence level needs to be
greater. For less significant decisions (i.e.,
low-cost ramifications) the confidence level
can be lower.
Type n error depends on the significance level,
sample size, and variability, and which
alternative hypothesis is true. Power (1-0) is
defined as the probability of correctly rejecting
H0 when H0 is false. In general, for a fixed
sample size, a and /?vary inversely. For a
fixed a, ft can be reduced by increasing the
sample size (Remington and Schork, 1970).
3.2 SAMPLING CONSIDERATIONS
In a document of this brevity, it is not possible
to address all of the issues that face technical
staff who are responsible for developing and
implementing studies to track and evaluate the
implementation of nonpoint source control
measures. For example, the best time to
conduct a survey or do onsite visits varies with
BMP and type of study. A single time of the
year that would be best for all BMPs cannot be
identified. Some BMPs can be checked any
time of the year, whereas others have a small
window of opportunity. In areas that have
distinct warm and cold seasons, the warm
season might be the most effective time of year
to assess the implementation of lawn care
BMPs.
The timing of an implementation survey might
also depend on actions taken prior to the
survey. If the goal of the study is to determine
the effectiveness of a public education
program, sampling should be timed to ensure
that there was sufficient time for outreach
activities and for the residents to implement
the desired practices. In such a case, telephone
calls would be time to reach residents when
they are more receptive to participation in a
survey, such as during times when they are
home but not "busy" (e.g., after dinner).
Another factor that must be considered is that
survey personnel must have permission to
perform site visits from each affected site
owner or developer prior to arriving at the
sites. Where access is denied, a replacement
site is needed. Replacement sites are selected
in accordance with the type of site selection
being used, i.e., simple random, stratified
random, cluster, or systematic. This can be
addressed by requiring site access as part of
approval for building codes, permits, etc.
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Sampling Design
Chapter
From a study design perspective, all of these
issues—study objectives, sampling strategy,
allowable error, and formulation of
hypotheses—must be considered together
when determining the sampling strategy. This
section describes common issues that the
technical staff might consider in targeting their
sampling efforts or determining whether to
stratify their sampling efforts. In general, if
there is reason to believe that there are
different rates of BMP or MM implemen-
tation in different groups, stratified random
sampling should increase overall accuracy.
Following the discussion, a list of resources
that can be used to facilitate evaluating these
issues is presented.
3.2.1 Urbanized and Urbanizing Areas
The number and type of BMPs currently in use
is dependent on, among other things, whether
an area is already "built-out" or under
development. In areas that are primarily built
out (i.e., downtown areas of cities and towns),
urban stormwater controls are already in place
in some form, although many only address
water quantity issues (flood control) and not
water quality concerns. There are also space
limitations for installing new BMPs. In areas
that are undergoing development, urban runoff
controls for both quantity and quality can be
installed as development occurs. Therefore,
sampling can be stratified depending on the
level of development in an area. It might be
unreasonable to expect that BMPs that require
retention of stormwater onsite be implement-
ed in larger cities, however, these areas are
very suited to nonstructural controls such as
pet waste ordinances, street sweeping, and
public education campaigns.
3.2.2 Available Resources and Tax
Base
Most structural urban BMPs ultimately fall
under the responsibility of the local
government. A local government's ability to
maintain and operate runoff BMPs depends on
a variety of factors, such as staff available for
inspection and maintenance, and resources for
operation and maintenance. Areas with large
populations and/or higher tax bases might be
more able to develop and implement an urban
runoff control program than urban areas with
small populations and low tax bases. Issues to
be considered include (1) tax base and percent
of tax base dedicated to environmental
protection, and (2) size of local government
and environmental staff.
3.2.3 Proximity to Sensitive Habitats
The types of urban runoff controls used are
often related to the types of resources in need
of protection. For example, areas close to
sensitive coastal habitats (e.g., shellfish
harvesting areas, fish spawning grounds,
endangered species habitats) or public water
supplies, might require stricter runoff control
measures than areas not in the vicinity of such
resources.
3.2.4 Federal Requirements
The 1987 amendments to the Clean Water Act
included a mandate to regulate storm water
point sources. EPA subsequently developed a
comprehensive, phased program for
controlling urban and industrial storm water
discharges. Phase I of the program required
areas with municipal separate storm sewer
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Chapter 3
Sampling Design
systems (MS4s) serving populations greater
than 100,000 to apply for a National Pollutant
Discharge Elimination System (NPDES)
permit for their MS4s. These municipal
permits specify that urban runoff be controlled
to the maximum extent practicable through
implementation of a variety of measures and
include sampling to characterize the discharges
from MS4s as well as ongoing monitoring of
storm water quality to assess program
effectiveness and to ensure compliance. The
Phase I NPDES Storm Water program also
applies to discharges associated with industrial
activity, including construction sites disturbing
5 acres or more.
The Phase II NPDES Storm Water program is
currently under regulatory development. A
proposed regulation was published in 1998 and
the final rule is anticipated in 1999. Based on
the proposed rule, this phase of the program
will identify smaller MS4s and certain
construction sites smaller than 5 acres for
control. At this time, however, in all areas that
are not subject to Phase I, control of urban
runoff is voluntary (except urban coastal areas
subject to CZARA). Therefore, smaller,
noncoastal urban areas might not be
implementing urban runoff BMPs at the same
level as larger and coastal urban areas.
3.2.5 Sources of Information
For a truly random selection of population
units, it is necessary to access or develop a
database that includes the entire target
population. U.S. Census data can help identify
the population, and therefore level of
development, for certain areas.
The following are possible sources of
information for site selection. Positive and
negative attributes of each information source
are included.
County Land Maps. These maps can provide
information on landowners and possibly land
use. County infrastructure maps might have
information on the location of stormwater
utilities.
U.S. Census Bureau. Part of the Department
of Commerce, the Census Bureau is
responsible for compiling data and information
on a variety of topics, including population,
businesses, employment, trade, and tax base.
The data are organized and analyzed in several
different ways, such as by state, county, and
major metropolitan area. The Census Bureau
also performs statistical analyses on the data so
that they can be useful for a variety of
purposes, such as determining rate of change
of population in a specific geographic area.
The Census Bureau also provides information
on areas that are serviced by central sewage
collection and treatment systems and areas that
are unsewered. This information can help state
and local governments focus efforts for
monitoring implementation of the MMs for
onsite disposal systems.
Complaint Records. Complaint records
could be used in combination with other
sources. Such records represent areas that
have had problems in the past, which will very
likely skew the data set.
Local Government Permits Local
governments usually require permits for new
development or redevelopment. The
information required to obtain a permit, the
level of detail contained in the permits, and the
extent to which the permit is monitored varies
among local governments. At a minimum, it
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Sampling Design
Chapter
can be determined whether erosion and
sediment controls are part of the site grading
plan and stormwater management facilities are
included in the overall site development plan.
Local governments might require inspection,
maintenance, and monitoring as conditions of
permit issuance.
Public Health Departments. Local
departments of public health might maintain
records of onsite OSDS inspections, pumping,
and maintenance. These records might contain
information on soil tests, system design,
maintenance history, permit conditions, and
inspection results. In areas where water
quality problems due to septic systems are a
concern, the systems might be monitored on a
watershed basis.
3.3 SAMPLE SIZE CALCULATIONS
This section describes methods for estimating
sample sizes to compute point estimates such
as proportions and means, as well as detecting
changes with a given significance level.
Usually, several assumptions regarding data
distribution, variability, and cost must be made
to determine the sample size. Some
assumptions might result in sample size
estimates that are too high or too low.
Depending on the sampling cost and cost for
not sampling enough data, it must be decided
whether to make conservative or "best-value"
assumptions. Because the cost of visiting any
individual site or group of sites is relatively
constant, it is more economical to collect a few
extra samples during the initial visit rather than
to realize later on that it is necessary to return
to the site(s) to collect additional data. In most
cases, the analyst should probably consider
evaluating a range of assumptions on the
impact of sample size and overall program
cost.
To maintain document brevity, some terms and
definitions that will be used in the remainder
of this chapter are summarized in Table 3-3.
These terms are consistent with those in most
introductory-level statistics texts, and more
information can be found there. Those with
some statistical training will note that some of
these definitions include an additional term
referred to as the finite population correction
term (l-(p), where (pis equal to n/N. In many
applications, the number of population units in
the sample population (N) is large in
comparison to the population units sampled (n)
and (l-(p) can be ignored. However,
depending on the number of units (towns with
populations that fall within a certain range, for
example) in a particular population, N can
become quite small. ,/V is determined by the
definition of the sample population and the
corresponding population units. If (j) is greater
than 0.1, the finite population correction factor
should not be ignored (Cochran, 1977).
Applying any of the equations described in this
section is difficult when no historical data set
exists to quantify initial estimates of
proportions, standard deviations, means,
coefficients of variation, or costs. To estimate
these parameters, Cochran (1977) recommends
four sources:
• Existing information on the same
population or a similar population.
• A two-step sample. Use the first-step
sampling results to estimate the needed
factors, for best design, of the second step.
Use data from both steps to estimate the
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Chapter 3
Sampling Design
Table 3-3. Definitions used in sample size calculation equations.
N = total number of population units
in sample population
n = number of samples
n0 = preliminary estimate of sample
size
a = number of successes
p = proportion of successes
q = proportion of failures (1-p)
x, = ith observation of a sample
x = sample mean
s2 = sample variance
s = sample standard deviation
NX = total amount
u = population mean
o2 = population variance
o = population standard deviation
Cv = coefficient of variation
s2(x) = variance of sample mean
= n/N (unless otherwise stated in
text)
s(x) = standard error (of sample mean)
1- = finite population correction factor
d = allowable error
dr = relative error
p = a/n
q = 1-p
s =
d =
x-\i
C = six
d =
= -1(1
s(p} =
^
Za = value corresponding to cumulative area of
1-a using the normal distribution (see
Table A1).
tadf = value corresponding to cumulative area of
1-a using the student t distribution with df
degrees of freedom (see Table A2).
final precision of the character!stic(s)
sampled.
A "pilot study" on a "convenient" or
"meaningful" subsample. Use the results
to estimate the needed factors. Here the
results of the pilot study generally cannot
be used in the calculation of the final
precision because often the pilot sample is
not representative of the entire population
to be sampled.
• Informed judgment, or an educated guess.
It is important to note that this document only
addresses estimating sample sizes with
traditional parametric procedures. The
methods described in this document should be
appropriate in most cases, considering the type
of data expected. If the data to be sampled are
skewed, as—for example—water quality data
often are, the investigator should plan to
transform the data to something symmetric, if
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Sampling Design
Chapter
not normal, before computing sample sizes
(Helsel and Hirsch, 1995). Kupper and Hafner
(1989) also note that some of these equations
tend to underestimate the necessary sample
because power is not taken into consideration.
Again, EPA recommends that if you do not
have a background in statistics, you should
consult with a trained statistician to be certain
that your approach, design, and assumptions
are appropriate to the task at hand.
Although each agency might have specialized
tracking requirements, there might be core
questions that are common among a number of
agencies. Therefore, it is recommended that
local agencies integrate their tracking effort
with other agencies so that their results can be
compared. Local agencies, initiating a
tracking program, at a minimum should
contact an appropriate state agency to
determine whether the goals and sampling
procedures from the state or another local
agency can be adopted. Note, that even if two
programs have the same goal, sampling
differences could still result in the data be
incomparable.
3.3.1 Simple Random Sampling
In simple random sampling, it is presumed that
the sample population is relatively
homogeneous and a difference in sampling
costs or variability would not be expected. If
the cost or variability of any group within the
sample population were different, it might be
more appropriate to consider a stratified
random sampling approach.
To estimate the proportion of local
governments that implement a certain BMP or
MM such that the allowable error, d, meets the
study precision requirements (i.e., the true
proportion lies between p-d andp+d with a 1-
What sample size is necessary to estimate
the proportion of local governments that
implement pet waste disposal ordinances
to within ± 5 percent?
What sample size is necessary to estimate
the proportion of local governments that
implement pet waste disposal ordinances
so that the relative error is less than 5
percent?
a confidence level), a preliminary estimate of
sample size can be computed as (Snedecor and
Cochran, 1980)
n =
d
(3-1)
If the proportion is expected to be a low
number, using a constant allowable error might
not be appropriate. Ten percent plus/minus 5
percent has a 50 percent relative error.
Alternatively, the relative error, dr, can be
specified (i.e., the true proportion lies between
p-drp andp+drp with a 1 -aconfidence level)
and a preliminary estimate of sample size can
be computed as (Snedecor and Cochran, 1980)
n =
(3-2)
In both equations, the analyst must make an
initial estimate of/? before starting the study.
In the first equation, a conservative sample
size can be computed by assuming/? equal to
0.5. In the second equation the sample size
gets larger as p approaches zero (0) for
constant dr, thus an informed initial estimate of
p is needed. Values of a typically range from
-------
Chapter 3
Sampling Design
Case Study: Delaware Method
The Delaware Department of Natural Resources and Environmental Control (DNREC) has developed a
methodology (referred to as the Delaware Method) to evaluate erosion and sedimentation control
program implementation and effectiveness (Piorko, et al., In press). The method involves a numerical
ranking of construction site conditions, soil erosion practices and land covers, and sediment control
practices, to assess the effectiveness of site controls.
The first step in the Delaware Method involves using a statistical sampling approach to select a valid
representative sample from the total population. For example, in New Castle County, Delaware, 40
sites were randomly chosen from a total population of 453 active construction sites. The sample size
used by DNREC is consistent with the sample size estimated using equations 3-1 and 3-3 of this
guidance. This sample, selected based on the methods from Walpole and Meyers (1972, in Maxted,
1996), yielded a probability estimate of 0.25±0.11 (90 percent confidence interval) of construction
sites implementing ESC BMPs in accordance with county permitting requirements. The sample
compared favorably with the total population: of the 40 sites randomly selected, 47.5 percent were
non-residential, compared with 48.6 percent (220 sites) of the 453 active construction sites that were
non-residential. Using the method, DNREC can estimate countywide implementation of selected
management measures.
Once the representative sample is developed, the Delaware Method uses a series of worksheets to rank
score the sample of construction sites. These worksheets, which are completed in the field, are
designed to specifically evaluate the erosion and sedimentation control practices used at a site. The
information recorded on these worksheets is used to construct a chart that tabulates the final numerical
rating for the total site area and the estimated total tons of soil lost on that site. The results from the
numerical ranking method allow for comparison of the effectiveness of erosion and sedimentation
control practices among construction sites.
0.01 to 0.10. The final sample size is then
estimated as (Snedecor and Cochran, 1980)
where <^is equal to nJN. Table 3-4
demonstrates the impact on n of selecting/?, a,
d, dn and N. For example, 151 random
samples are needed to estimate the proportion
of 500 households that dispose of household
hazardous waste safely to within ±5 percent
(d=0.05) with a 95 percent confidence level
n = '
for (J) > 0.1
(3-3)
no otherwise
assuming roughly one-half of households
dispose of their hazardous waste safely.
Suppose the goal is to estimate the average
storage volume of extended detention ponds.
(This goal might only be appropriate in areas
What sample size is necessary to estimate
the average storage volume of extended
detention ponds to within ± 1,000 ff per acre
of impervious area ?
What sample size is necessary to estimate
the average storage volume of extended
detention ponds to within ±10 percent?
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Sampling Design
Chapter
Table 3-4. Comparison of sample size as a function of p, a, d, dr, and Nfor
estimating proportions using equations 3-1 through 3-3.
Probability
of Success,
P
0.1
0.1
0.5
0.5
0.1
0.1
0.5
0.5
Signifi-
cance
level, a
0.05
0.05
0.05
0.05
0.10
0.10
0.10
0.10
Allowable
error, d
0.050
0.075
0.050
0.075
0.050
0.075
0.050
0.075
Relative
error, dr
0.500
0.750
0.100
0.150
0.500
0.750
0.100
0.150
Preliminary
sample
size, n0
138
61
384
171
97
43
271
120
Sample Size, n
Number of Population Units in Sample
Population, N
500
108
55
217
127
82
43
176
97
750
117
61
254
139
86
43
199
104
1,000
121
61
278
146
97
43
213
107
2,000
138
61
322
171
97
43
238
120
Large N
138
61
384
171
97
43
271
120
that do not have regulations mandating pond
size.) The number of random samples
required to achieve a desired margin of error
when estimating the mean (i.e., the true mean
lies between ~x-d and ~x+d with a I-a
n =
(3-4)
confidence level) is (Gilbert, 1987)
If N is large, the above equation can be
simplified to
n =
(3-5)
Since the Student's t value is a function of n,
Equations 3-4 and 3-5 are applied iteratively.
That is, guess at what n will be, look up tj.^n
! from Table A2, and compute a revised n. If
the initial guess of n and the revised n are
different, use the revised n as the new guess,
and repeat the process until the computed
value ofn converges with the guessed value.
If the population standard deviation is known
(not too likely), rather than estimated, the
above equation can be further simplified to:
n = (Z^a/2a/d)2 (3-6)
To keep the relative error of the mean estimate
below a certain level (i.e., the true mean lies
between ~x-dr ~x and ~x+dr ~x with a
1 -aconfidence level), the sample size can be
computed with (Gilbert, 1987)
Cv is usually less variable from study to study
than are estimates of the standard deviation,
n =-
(3-7)
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Chapter 3
Sampling Design
which are used in Equations 3-4 through 3-6.
Professional judgment and experience,
typically based on previous studies, are
required to estimate Cv. Had Cv been known,
Zi-a/2 would have been used in place of tj.^^j
in Equation 3-7. If A^ is large, Equation 3-7
simplifies to:
n =
(3-8)
Consider a state, for example, where
subdivision developments for single-family
homes typically range in size from 100 to
1,500 lots, although most have fewer than 400.
The goal of the sampling program is to
estimate the average storage volume of
extended detention ponds. However, the
investigator is concerned about skewing the
mean estimate with the few large
developments. As a result, the sample
population for this analysis is the 250
developments with fewer than 400 lots. The
investigator also wants to keep the relative
error under 15 percent (i.e., dr < 0.15) with a
90 percent confidence level.
Unfortunately, this is the first study of this type
that has been done in this state and there is no
information about the coefficient of variation,
Cv. The investigator, however, has done
several site inspections over the last 5 years.
Based on this experience, the investigator
knows that developers typically build ponds
that range in size from 5,000 to 20,000 ft3.
Using this information, the investigator
roughly estimates s as (20,000-5,000)72 or
7,500 (Sanders et al., 1983) and x as 12,500.
Cv is then estimated as 7,500/12,500, or 0.6.
As a first-cut approximation, Equation 3-6 is
applied with Z,.^ equal to 1.645 and assuming
N is large:
n = (1.645 xQ.6/0.15)2
= 43.3 ~ 44 samples
Since n/N is greater than 0.1 and Cv is
estimated (i.e., not known), it is best to
reestimate n with Equation 3-7 using 44
samples as the initial guess of n. In this case,
ti-an,n-i is obtained from Table A2 as 1.6811.
n =
(1.6811xQ.6/0.15)2
1+(1.6811x0.6/0.15)2/250
= 38.3 ~ 39 samples
Notice that the revised sample is somewhat
smaller than the initial guess of n. In this case
it is recommended to reapply Equation 3-7
using 39 samples as the revised guess of n. In
this case, tj.^^j is obtained from Table A2 as
1.6850.
(1.6850x0.6/0.15)2
n =
l+(1.6850xQ.6/0.15)2/250
= 38.5 ~ 39 samples
Since the revised sample size matches the
estimated sample size on which tj.^^j was
based, no further iterations are necessary. The
proposed study should include 39
developments randomly selected from the 250
developments with fewer than 400 lots.
When interest is focused on whether the level
of BMP implementation has changed, it is
necessary to estimate the extent of
implementation at two different time periods.
Alternatively, the proportion from two
different populations can be compared. In
either case, two independent random samples
are taken and a hypothesis test is used to
determine whether there has been a signif-icant
change in implementation. (See Snedecor and
Cochran (1980) for sample size calculations
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Sampling Design
Chapter
for matched data.) Consider an example in
which the proportion of house-holds that
properly dispose of household hazardous waste
will be estimated at two time periods. What
sample size is needed?
To compute sample sizes for comparing two
proportions, p1 andp2, it is necessary to
provide a best estimate forpj andp2, as well as
specifying the significance level and power (1-
= (Za+Z2P)2
(3-9)
(3). Recall that power is equal to the
probability of rejecting H0 when H0 is false.
Given this information, the analyst substitutes
these values into (Snedecor and Cochran,
1980) where Za and Z2/g correspond to the
normal deviate. Although this equation
assumes that N large, it is acceptable for
practical use (Snedecor and Cochran, 1980).
Common values of (Za and Z2j} )2 are
summarized in Table 3-5. To account for p1
andp2 being estimated, Z could be substituted
with t. In lieu of an iterative calculation,
What sample size is necessary to determine
whether there is a 20 percent difference in
household hazardous waste disposal before
and after an education program?
What sample size is necessary to detect a
difference of 2,000 ff per acre of impervious
area in average pond storage volume
between land owners that plan and develop
their own land versus those that hire
independent consultants?
Snedecor and Cochran (1980) propose the
following approach: (1) compute n0 using
Equation 3-9; (2) round n0 up to the next
highest integer,/; and (3) multiply n0 by
(f+3)/(/+!) to derive the final estimate of n.
To detect a difference in proportions of 0.20
with a two-sided test, a equal to 0.05, l-(3
equal to 0.90, and an estimate ofp} andp2
equal to 0.4 and 0.6, n0 is computed as
no = 10.51
= 126.1
[(0.4X0.6) + (0.6X0.4)]
(0.6-0.4)2
Table 3-5. Common values of (Za + Z2p)2 for estimating sample size for use with
equations 3-9 and 3-10.
Power,
1-P
0.80
0.85
0.90
0.95
0.99
a for One-sided Test
0.01
10.04
11.31
13.02
15.77
21.65
0.05
6.18
7.19
8.56
10.82
15.77
0.10
4.51
5.37
6.57
8.56
13.02
a for Two-sided Test
0.01
11.68
13.05
14.88
17.81
24.03
0.05
7.85
8.98
10.51
12.99
18.37
0.10
6.18
7.19
8.56
10.82
15.77
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Chapter 3
Sampling Design
Rounding 126.1 to the next highest integer,/is
equal to 127, and n is computed as 126.1 x
130/128 or 128.1. Therefore 129 samples in
each random sample, or 258 total samples, are
needed to detect a difference in proportions of
0.2. Beware of other sources of information
that give significantly lower estimates of
sample size. In some cases the other sources
do not specify 1-fr otherwise, be sure that an
"apples-to-apples" comparison is being made.
To compare the average from two random
samples to detect a change of d(i.e., *2-*A the
following equation is used:
(3-10)
Common values of (Za and Z2/g / are
summarized in Table 3-5. To account for s}
and s2 being estimated, Z should be replaced
with t. In lieu of an iterative calculation,
Snedecor and Cochran (1980) propose the
following approach: (1) compute n0 using
Equation 3-10; (2) round n0 up to the next
highest integer,/; and (3) multiply n0 by
(f+3)/(/+!) to derive the final estimate of n.
Continuing the extended detention pond
example, where s was estimated as 7,500 ft3,
the investigator will also want to compare the
average pond size between land owners that
plan and develop their own land versus those
that hire independent consultants. The
investigator believes that it will be necessary
to detect a 4,000 ft3 difference to make an
impact on planning decisions. Although the
standard deviation might differ between the
two groups, there is no particular reason to
propose a different s at this point. To detect a
difference of 4,000 ft3 with a two-sided test, a
equal to 0.05, l-(3 equal to 0.90, and an
estimate of s} and s2 equal to 7,500, n0 is
computed as
n =1051
= 73.9
40002
(3-H)
Rounding 73.9 to the next highest integer, /is
equal to 74, and n is computed as 73.9 x
77/75 or 75.9. Therefore, 76 samples
in each random sample, or 152 total
samples, are needed to detect a
difference of 4,000 ft3.
3.3.2 Stratified Random Sampling
The key reason for selecting a stratified
random sampling strategy over simple random
sampling is to divide a heterogeneous
population into more homogeneous groups. If
populations are grouped based on size (e.g.,
lawn size) when there is a large number of
small units and a few larger units, a large gain
in precision can be expected (Snedecor and
Cochran, 1980). Stratifying also allows the
investigator to efficiently allocate sampling
resources based on cost. Information from
preliminary studies (see Section 3.3) can
provide useful sampling cost information. The
stratum mean, ~xh, is computed using the
standard approach for estimating the mean.
The overall mean, xst, is computed as
L
(3-12)
What sample size is necessary to estimate
the average number of households that
carefully monitor their fertilizer applications
when there is a wide variety of lawn sizes?
-------
Sampling Design
Chapter
where L is the number of strata and Wh is the
relative size of the hth stratum. Wh can be
computed as Nf/N where Nh and N are the
number of population units in the hth stratum
and the total number of population units across
all strata, respectively. Assuming that simple
random sampling is used within each stratum,
the variance of ~xst is estimated as (Gilbert,
1987)
n,
h=
(3-13)
n,.
where nh is the number of samples in the hth
stratum and sh2 is computed as (Gilbert, 1987)
(3-14)
There are several procedures for computing
sample sizes. The method described below
allocates samples based on stratum size,
variability, and unit sampling cost. If s2( ~xst) is
specified as V for a design goal, n can be
obtained from (Gilbert, 1987)
n =
(3-15)
=\
where ch is the per unit sampling cost in the hth
stratum and nh is estimated as (Gilbert, 1987)
nh = n
(3-16)
In the discussion above, the goal is to estimate
an overall mean. To apply a stratified random
sampling approach to estimating proportions,
substitute ph, pst, phqh, and s2(pa) for xh, xst, sh\
and s2( ~xst) in the above equations,
respectively.
To demonstrate the above approach, consider a
local government that wishes to determine the
percentage of homeowners in single family
residences that implement recommended lawn
care practices. The investigator anticipated
that there might be a difference in
implementation between home owners that do
their own work versus households that use
lawn care services. Based on some
preliminary work, she determined that
homeowner perform their own lawn care for
7,000 households while lawn services perform
the work for 3,000 households. Table 3-6
presents three basic scenarios for estimating
sample size. In the first scenario, sh and ch are
assumed equal among all strata. That is, the
variability in each of the two groups is
expected to be the same, and the cost to
complete the survey for one household is the
same regardless of group. Using a design goal
of V equal to 0.0025 and applying Equation
3-15 yields a total sample size of 99. Since sh
and ch are equal, these samples are allocated
proportionally to Wh, which is referred to as
proportional allocation. This allocation can be
verified by comparing the percent sample
allocation to Wh. Due to rounding up, a total
of 100 samples are allocated.
Under the second scenario, referred to as the
Neyman allocation, the variability between
strata changes, but unit sample cost is constant.
In this example, sh decreases from 0.40 to 0.75
between strata. (This difference is for
illustrative purposes and might not be realized
-------
Chapter 3
Sampling Design
Table 3-6. Allocation of samples.
Who
Provides
Lawn Care
Number
of Lots
(Nh)
Relative
Size
TO
Standard
Deviation
(sh)
Unit
Sample
Cost
(ch)
Sample Allocation
Number
%
A) Proportional allocation (sh and ch are constant)
Homeowner
Lawn
Service
7,000
3,000
0.70
0.30
0.50
0.50
1
1
70
30
70.0
30.0
Using Equation 3-15, n is equal to 99.0. Applying Equation 3-16 to each stratum yields a
total of 100 samples after rounding up to the next integer.
B) Neyman allocation (ch is constant)
Homeowner
Lawn
Service
7,000
3,000
0.70
0.30
0.40
0.75
1
1
56
45
55.4
44.6
Using Equation 3-15, n is equal to 100.9. Applying Equation 3-16 to each stratum yields a
total of 101 samples after rounding up to the next integer.
C) Allocation where sh and ch are not constant
Homeowner
Lawn
Service
7,000
3,000
0.70
0.30
0.40
0.75
1
1.5
62
41
60.2
39.8
Using Equation 3-1 5, n is equal to 1 01 .9. Applying Equation 3-1 6 to each stratum yields a
total of 103 samples after rounding up to the next integer.
in practice.) The total number of samples
remained roughly the same; however, an
increased number of samples are required for
lawn care services. Using proportional
allocation 30 percent of the samples are taken
from households that use lawn care services
whereas approximately 44.6 percent of the
samples are taken in the same stratum using
the Neyman allocation.
Finally, introducing sample cost variation will
also affect sample allocation. In the last
scenario it was assumed that it is 50 percent
more expensive to evaluate a lot from the
stratum that corresponds to the households that
use lawn care services. (This difference is for
illustrative purposes and might not be realized
in practice.) In this example, roughly the same
total number of samples are needed to meet the
design goal, yet fewer samples are now
-------
Sampling Design
Chapter
required from households that use lawn
services.
3.3.3 Cluster Sampling
Cluster sampling is commonly used when
there is a choice between the size of the
sampling unit (e.g., subdivision versus
individual residences). In general, it is cheaper
to sample larger units than smaller units, but
these results tend to be less accurate (Snedecor
and Cochran, 1980). Thus, if there is not a
unit sampling cost advantage to cluster
sampling, it is probably better to use simple
random sampling. To decide whether to
perform a cluster sample, it will probably be
necessary to perform a special investigation to
quantify sampling errors and costs using the
two approaches.
Perhaps the best approach to explaining the
difference between simple random sampling
and cluster sampling is to consider an example
set of results. In this example, the investigator
did an evaluation of BMP implementation to
evaluate whether certain practices had been
implemented. Since the county was quite large
and random sampling costs would be high due
to travel time, the investigator stopped at 30
sites (locations). At each site he inspected 10
neighboring residences. In addition to
determining whether recommended lawn care
practices were being implemented, the
investigator would probably collect ancillary
data such as whether the household used a
lawn care service. For the purposes of
explaining cluster sampling, the type of lawn
care provider is not critical although it might
be in practice. Table 3-7 presents the number
of residences (out of 10) at each site that were
implementing recommended lawn care
practices. At Site 1, for example, 3 of the 10
households were implementing recommended
lawn care practices. For the 30 sites, the
overall mean is 5.6; a little more than one-half
of the residences have implemented
recommended lawn care practices. Note that
since the population unit corresponds to the 10
residences at each site collectively, thus there
are 30 samples and the standard error for the
proportion of residences using recommended
BMPs is 0.035. Had the investigator
incorrectly calculated the standard error using
the random sampling equations, he would have
computed 0.0287, nearly a 20 percent error.
Since the standard error from the cluster
sampling example is 0.035, it is possible to
estimate the corresponding simple random
sample size to get the same precision using
Is collecting 300 samples using a cluster
sampling approach cheaper than collecting
n =
s(p)2
= 201
= (0.56)(0.44)
0.0352
(3-17)
about 200 simple random samples? If so,
cluster sampling should be used; otherwise
simple random sampling should be used.
3.3.4 Systematic Sampling
It might be necessary to obtain a baseline
estimate of the proportion of residences where
a certain BMP (e.g., reduced lawn fertilization)
is implemented using a mailed questionnaire
or phone survey. Assuming a record of
homeowners in the city is available in a
sequence unrelated to the manner in which the
BMP would be implemented (e.g., in
alphabetical order by the homeowner's name),
a systematic sample can be obtained in the
following manner (Casley and Lury, 1982):
-------
Chapter 3
Sampling Design
Table 3-7. Number of residences at each site implementing recommended lawn care
)ractices pu residences inspected at eacn site;.
Site1:3a Site 2: 9 Site 3: 5
Site 7: 6 Site 8: 3 Site 9: 5
Site 13: 7 Site 14: 4 Site 15: 7
Site 19: 4 Site 20: 6 Site 21: 8
Site 25: 5 Site 26: 3 Site 27: 3
Grand Total = 168 (i.e., 168 of 300
residents used recommended lawn care
practices)
x = 5.6 (i.e., 5.6 out of 10 residents used
recommended lawn care practices)
s = 1.923
Site 4: 7 Site 5: 6
Site 10: 5 Site 11: 5
Site 16: 5 Site 17: 3
Site 22: 4 Site 23: 7
Site 28: 9 Site 29: 9
p= 5.6/1 0 = 0.560
s'= 1.923/10 = 0.1923
Standard error using cluster sampling: s(p) = 0.1923/(30)05 = 0.035
Standard error if simple random sampling assumption had been incorrectly
s(p) = ((0.56)(1 -0.56)7300)° 5 = 0.0287
Site 6: 4
Site 12:7
Site 18: 8
Site 24: 4
Site 30: 7
used:
At Site 1, for example, 3 of the 10 households were implementing recommended lawn care practices.
1. Select a random number r between 1 and
n, where n is the number required in the
sample.
2. The sampling units are then r, r + (N/n), r
+ (2N/n), ..., r + (n-l)(N/n), where Nis
total number of available records.
If the population units are in random order
(e.g., no trends, no natural strata,
uncorrelated), systematic sampling is, on
average, equivalent to simple random
sampling.
Once the sampling units (in this case, specific
residences) have been selected, questionnaires
can be mailed to homeowners or telephone
inquiries made about lawn care practices being
followed by the homeowners.
3.3.5 Concluding Remarks
In the previous examples the type of questions
asked where generally similar yet dramatically
different sample sizes were developed. This
probably leaves the reader wondering which
one to choose. Clearly simple random
sampling is the easiest, but might very well
leave the investigator with numerous
unanswered questions. The primary basis for
selecting a design approach should be based on
a careful review of study objectives and the
discussion in Section 3.1.2 and Table 3-1. As
shown in Section 3.3.3, cluster sampling can
be a good alternative to simple random
sampling when you can demonstrate a
sampling cost savings. In both cases, there is
no stratification or optimization based on your
a priori knowledge about patterns or sampling
-------
Sampling Design
Chapter
costs in the target population. When there are population totals. On the other hand if your
critical factors that you are examining or you
know that there are group differences among
the target population, stratified sampling
(Section 3.3.2) should be used to optimize (in
a less-variance sense) the precision of
objective is to compare implementation among
two groups, then sample size calculations
derived from the Equations 3-9 or 3-10
(Section 3.3.1) should be used.
-------
CHAPTER 4. METHODS FOR EVALUATING DATA
4.1 INTRODUCTION
Once data have been collected, it is necessary
to statistically summarize and analyze the data.
EPA recommends that the data analysis
methods be selected before collecting the first
sample. Many statistical methods have been
computerized in easy-to-use software that is
available for use on personal computers.
Inclusion or exclusion in this section does not
imply an endorsement or lack thereof by the
EPA. Commercial off-the-shelf software that
covers a wide range of statistical and graphical
support includes SAS, Statistica, Statgraphics,
Systat, Data Desk (Macintosh only), BMDP,
and IMP. Numerous spreadsheets, database
management packages, and other graphics
software can also be used to perform many of
the needed analyses. In addition, the following
programs, written specifically for
environmental analyses, are also available:
• SCOUT: A Data Analysis Program, EPA,
NTIS Order Number PB93-505303.
• WQHYDRO (WATER
QUALITY/HYDROLOGY
GRAPHICS/ANALYSIS SYSTEM), Eric
R. Aroner, Environmental Engineer, P.O.
Box 18149, Portland, OR 97218.
• WQSTAT, Jim C. Loftis, Department of
Chemical and Bioresource Engineering,
Colorado State University, Fort Collins,
CO 80524.
Computing the proportion of construction sites
implementing a certain BMP or the average
storage volume per acre of impervious area of
extended detention ponds follows directly
from the equations presented in Section 3.3
and the equations are not repeated here. The
remainder of this section is focused on
evaluating changes in BMP implementation.
The methods provided in this section provide
only a cursory overview of the type of analyses
that might be of interest. For a more thorough
discussion on these methods, the reader is
referred to Gilbert (1987), Snedecor and
Cochran (1980), and Helsel and Hirsch (1995).
Typically, the data collected for evaluating
changes will typically come as two or more
sets of random samples. In this case, the
analyst will test for a shift or step change.
Depending on the objective, it is appropriate to
select a one- or two-sided test. For example, if
the analyst knows that BMP implementation
will only go up as a result of a regulatory or
educational program, a one-sided test could be
formulated. Alternatively, if the analyst does
not know whether implementation will go up
or down, a two-sided test is necessary. To
simply compare two random samples to decide
if they are significantly different, a two-sided
test is used. Typical null hypotheses (H0) and
alternative hypotheses (Ha) for one- and two-
sided tests are provided below:
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Methods for Evaluating Data
Chapter 4
One-sided test
H0: BMP Implementation (Post regulation) <
BMP Implementation (Pre regulation)
Ha: BMP Implementation (Post regulation) >
BMP Implementation (Pre regulation)
Two-sided test
H0: BMP Implementation (Post education
program) = BMP Implementation (Pre
education program)
Ha: BMP Implementation (Post education
program) * BMP Implementation (Pre
education program)
Selecting a one-sided test instead of a two-
sided test results in an increased power for the
same significance level (Winer, 1971). That
is, if the conditions are appropriate, a
corresponding one-sided test is more desirable
than a two-sided test given the same a and
sample size. The manager and analyst should
take great care in selecting one- or two-sided
tests.
4.2 COMPARING THE MEANS FROM Two
INDEPENDENT RANDOM SAMPLES
The Student's t test for two samples and the
Mann-Whitney test are the most appropriate
tests for these types of data. Assuming the
data meet the assumptions of the t test, the
two-sample t statistic with n1+n2-1 degrees of
freedom is (Remington and Schork, 1970)
i, i
n,
(4-1)
where n1 and n2 are the sample size of the first
and second data set and ^ and*2 are the
Tests for Two Independent Random Samples
Test* Key Assumptions
Two-sample t
Both data sets must be
normally distributed
Data sets should have
equal variances1
Mann-Whitney • None
The standard forms of these tests require
independent random samples.
The variance homogeneity assumption can
be relaxed.
estimated means from the first and second data
set, respectively. The pooled standard
deviation, sp, is defined by
0.5
(4-2)
where sf and s22 correspond to the estimated
variances of the first and second data set,
respectively. The difference quantity (A0~) can
be any value, but here it is set to zero. A0 can
be set to a non-zero value to test whether the
difference between the two data sets is greater
than a selected value. If the variances are not
equal, refer to Snedecor and Cochran (1980)
for methods for computing the t statistic. In a
two-sided test, the value from Equation 4-1 is
compared to the t value from Table A2 with
a/2 and n]+n2-2 degrees of freedom.
The Mann-Whitney test can also be used to
compare two independent random samples.
This test is very flexible since there are no
assumptions about the distribution of either
sample or whether the distributions have to be
the same (Helsel andHirsch, 1995). Wilcoxon
(1945) first introduced this test for equal-sized
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Chapter 4
Methods for Evaluating Data
samples. Mann and Whitney (1947) modified
the original Wilcoxon's test to apply it to
different sample sizes. Here, it is determined
whether one data set tends to have larger
observations than the other.
provide detailed examples for both of these
tests.
4.3 COMPARING THE PROPORTIONS FROM
Two INDEPENDENT SAMPLES
If the distributions of the two samples are
similar except for location (i.e., similar spread
and skew), Ha can be refined to imply that the
median concentration from one sample is
"greater than," "less than," or "not equal to"
the median concentration from the second
sample. To achieve this greater detail in Ha,
transformations such as logs can be used.
Tables of Mann-Whitney test statistics (e.g.,
Conover, 1980) can be consulted to determine
whether to reject H0 for small sample sizes. If
rij and n2 are greater than or equal to 10
observations, the test statistic can be computed
from the following equation (Conover, 1980):
rr, •> n I
(4-3)
1 2
n,n^ n 2 n,n~(ri I)2
i / ^ T> 1 ^
n(n 1"
) f i "' 4(n 1)
where
n =
T =
number of observations in sample with
fewer observations,
number of observations in sample with
more observations,
sum of ranks for sample with fewer
observations, and
rank for the z'th ordered observation
used in both samples.
Tj is normally distributed and Table Al can be
used to determine the appropriate quantile.
Helsel and Hirsch (1995) and USEPA (1997)
Consider the example in which the proportion
of site inspection violations has been estimated
during two time periods to bep} andp2 using
sample sizes of n1 and n2, respectively.
Assuming a normal approximation is valid, the
test statistic under a null hypothesis of
equivalent proportions (no change) is
Pi P2
^
1 , 1
(4-4)
where p is a pooled estimate of proportion and
is equal to ( x}+x2 )/( n]+n2 ) and x1 and x2 are
the number of successes during the two time
periods. An estimator for the difference in
proportions is simply p1 - p2.
In an earlier example, it was determined that
129 observations in each sample were needed
to detect a difference in proportions of 0.20
with a two-sided test, a equal to 0.05, and
l-(3equal to 0.90. Assuming that 130 samples
were taken and p1 and p2 were estimated from
the data as 0.6 and 0.4, the test statistic would
be estimated as
0.6' 0.4
^
3.22
0.5(0.5)
1
1
(4-5)
130 130,
Comparing this value to the t value from Table
A2 (a/2 = 0.025, df=258) of 1.96,
H0 is rejected.
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Methods for Evaluating Data
Chapter 4
4.4 COMPARING MORE THAN Two
INDEPENDENT RANDOM SAMPLES
The analysis of variance (ANOVA) and
Kruskal-Wallis are extensions of the
two-sample t and Mann-Whitney tests,
respectively, and can be used for analyzing
more than two independent random samples
when the data are continuous (e.g., mean
acreage). Unlike the t test described earlier,
the ANOVA can have more than one factor or
explanatory variable. The Kruskal-Wallis test
accommodates only one factor, whereas the
Friedman test can be used for two factors. In
addition to applying one of the above tests to
determine if one of the samples is significantly
different from the others, it is also necessary to
do postevaluations to determine which of the
samples is different. This section recommends
Tukey' s method to analyze the raw or rank-
transformed data only if one of the previous
tests (ANOVA, rank-transformed ANOVA,
Kruskal-Wallis, Friedman) indicates a
significant difference between groups.
Tukey's method can be used for equal or
unequal sample sizes (Helsel and Hirsch,
1995). The reader is cautioned, when
performing an ANOVA using standard
software, to be sure that the ANOVA test used
matches the data. See USEPA (1997) for a
more detailed discussion on comparing more
than two independent random samples.
4.5 COMPARING CATEGORICAL DATA
In comparing categorical data, it is important
to distinguish between nominal categories
(e.g., land ownership, county location, type of
BMP) and ordinal categories (e.g., BMP
implementation rankings, low-medium-high
scales).
The starting point for all evaluations is the
development of a contingency table. In Table
4-1, the preference of three BMPs is compared
to resident type in a contingency table. In this
case both categorical variables are nominal. In
this example, 45 of the 102 residents that own
the house they occupy used BMPj. There were
a total of 174 observations.
To test for independence, the sum of the
squared differences between the expected (E;j)
and the observed (Oy) count summed over all
cells is computed as (Helsel and Hirsch, 1995)
X
•ct
m k
i 1 / 1
(O.: E.Y
v ij iy
E..
(4-6)
where Etj is equal to AtC/N. %ct is compared to
the 1-a quantile of the jf distribution with
(m-l)(k-l) degrees of freedom (see Table A3).
In the example presented in Table 4-1, the
symbols listed in the parentheses correspond to
the above equation. Note that k corresponds to
the three types of BMPs and m corresponds to
the three different types of residents. Table 4-
2 shows computed values of Etj and
(O^-E^/Ey) in parentheses for the example
data. Xct is equal to 14.60. From Table A3, the
0.95 quantile of the tf distribution with 4
degrees of freedom is 9.488. H0 is rejected;
the selection of BMP is not random among the
different resident types. The largest values in
the parentheses in Table 4-2 give an idea as to
which combinations of resident type and BMP
are noteworthy. In this example, it appears
that BMP2 is preferred to BMPj for those
residents that rent the house they occupy.
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Chapter 4
Methods for Evaluating Data
Table 4-1. Contingency table of observed resident type and implemented BMP.
Resident Type
Rent
Own
Seasonal
Column Total, C,
BMP,
10(0,0
45 (021)
8 (031)
63 (C,)
BMP,
30 (012)
32 (022)
3 (CU
65 (C2)
BMP3
17(013)
25 (023)
4 (0M)
46 (Ca)
Row Total,
A,
57 (A,)
102(A2)
15(A3)
174(N)
Key to Symbols:
O,y = number of observations for the /th resident and /th BMP type
A, = row total for the /th resident type (total number of observations for a given resident type)
C; = column total for the /th BMP type (total number of observations for a given BMP type)
N = total number of observations
Table 4-2. Contingency table of expected resident type and implemented BMP.
(Values in parentheses correspond to
Resident Type
Rent
Own
Seasonal
Column Total
BMP,
20.64
(5.48)
36.93
(1.76)
5.43
(1.22)
63
BMP,
21.29
(3.56)
38.10
(0.98)
5.60
(1.21)
65
BMP3
15.07
(0.25)
26.97
(0.14)
3.97
(0.00)
46
Row Total
57
102
15
174
Now consider that in addition to evaluating
information regarding the resident and BMP
type, we also recorded a value from 1 to 5
indicating how well the BMP was installed and
maintained, with 5 indicating the best results.
In this case, the BMP implementation rating is
ordinal. Using the same notation as before, the
average rank of observations in row x, Rx, is
equal to (Helsel and Hirsch, 1995)
X 1
R
A: (A; i)/2
(4-7)
where Ai corresponds to the row total. The
average rank of observations in column j, Dp is
equal to
D.
(4-8)
C.
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Methods for Evaluating Data
Chapter 4
where C;- corresponds to the column total. The
Kruskal-Wallis test statistic is then computed
as
K ' (AT 1)
k
m
i 1
.Df N
iR^ N
N^ 1
N
AT l"
N .
(4-9)
where K is compared to the tf distribution with
k-1 degrees of freedom. This is the most
general form of the Kruskal-Wallis test since it
is a comparison of distribution shifts rather than
shifts in the median (Helsel and Hirsch, 1995).
Table 4-3 is a continuation of the previous
example indicating the BMP implementation
rating for each BMP type. For example, 29 of
the 70 observations that were given a rating of
4 are associated with BMP2. The terms inside
the parentheses of Table 4-3 correspond to the
terms used in Equations 4-7 to 4-9. Note that k
corresponds to the three types of BMPs and m
corresponds to the five different levels of BMP
implementation. Using Equation 4-9 for the
data in Table 4-3, K is equal to 14.86.
Comparing this value to 5.991 obtained from
Table A3, there is a significant difference in
the quality of implementation between the
three BMPs.
The last type of categorical data evaluation
considered in this chapter is when both
variables are ordinal. The Kendall rb for
tieddata can be used for this analysis. The
statistic Tb is calculated as (Helsel and Hirsch,
1995)
SSh)
Table 4-3. Contingency table of implemented BMP and rating of installation and
maintenance.
BMP
Implementation
Rating
1
2
3
4
5
Column Total, C,
BMP,
1 (On)
7 (021)
15(031)
32 (041)
8 (0B1)
63 (C,)
BMP,
2 (012)
3 (022)
16(032)
29 (042)
15(0,*)
65 (C2)
BMP3
2 (013)
5 (023)
26 (033)
9 (043)
4 (Os,)
46 (Ca)
Row Total,
A,
5 (A,)
15(A2)
57 (A3)
70 (A4)
27 (As)
174(N)
Key to Symbols:
O,y = number of observations for the /th BMP implementation rating and yth BMP type
A, = row total for the /th BMP implementation rating (total number of observations for a given BMP implementation rating)
Cj = column total for the yth BMP type (total number of observations for a given BMP type)
N = total number of observations
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Chapter 4
Methods for Evaluating Data
where S, SSa, and SSC are computed as
allxy \i>x j>y
where
\
(4-15)
SS
i i
where Zs is zero if S is zero. The values of ai
(4-12) an(j c. are computed as A; /5V and Ci /N,
respectively.
SS
c:
(4-13)
To determine whether rb is significant, S is
modified to a normal statistic using
i
(4-14)
Table 4-4 presents the BMP implementation
ratings that were taken in three separate years.
For example, 15 of the 57 observations that
were given a rating of 3 are associated with
Year 2. Using Equations 4-11 and 4-15, S and
os are equal to 2,509 and 679.75, respectively.
Therefore, Zs is equal to (2509-1)7679.75 or
3.69. Comparing this value to a value of 1.96
obtained from Table Al (
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Methods for Evaluating Data
Chapter 4
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CHAPTER 5. CONDUCTING THE EVALUATION
5.1 INTRODUCTION
This chapter addresses the process of
determining whether urban MMs or BMPs are
being implemented and whether they are being
implemented according to approved standards
or specifications. Guidance is provided on
what should be measured to assess MM and
BMP implementation, as well as methods for
collecting the information, including physical
site evaluations, mail- and/or telephone-based
surveys, personal interviews, and aerial
reconnaissance and photography. Designing
survey instruments to avoid error and rating
MM and BMP implementation are also
discussed.
Evaluation methods are separated into two
types: Expert evaluations and self-evaluations.
Expert evaluations are those in which actual
site investigations are conducted by trained
personnel to gather information on MM or
BMP implementation. Self-evaluations are
those in which answers to a predesigned
questionnaire or survey are provided by the
person being surveyed, for example a local
government representative or homeowner (see
Example) The answers provided are used as
survey results. Self-evaluations might also
include examination of materials related to a
site, such as permit applications or inspection
reports. Extreme caution should be exercised
when using data from self-evaluations as the
basis for assessing MM or BMP compliance
since they are not typically reliable for this
purpose (i.e., most people will not report
failure or non-compliance). Each of these
evaluation methods has advantages and
disadvantages that should be considered prior
to deciding which one to use or in what
combination to use them. Aerial
reconnaissance and photography can be used
to support either evaluation method.
Self-evaluations are useful for collecting
information on the level of awareness that
residents, developers, or local government
representatives of have of MMs or BMPs,
dates of BMP implementation or inspection,
soil conditions, which MMs or BMPs were
implemented, and whether the assistance of a
local or private BMP implementation
professional was used. However, the type of
or level of detail of information that can be
obtained from self-evaluations might be
inadequate to satisfy the objectives of a MM or
BMP implementation survey. If this is the
case, expert evaluations might be called for.
Expert evaluations are necessary if the
information on MM or BMP implementation
that is required must be more detailed or more
reliable than that that can be obtained with
self-evaluations. Examples of information that
would be obtained reliably only through an
expert evaluation include an objective
assessment of the adequacy of MM or BMP
implementation, the degree to which site-
specific factors (e.g., type of vegetative cover,
soil type, or presence of a water body)
influenced MM or BMP implementation, or
the need for changes in standards and
specifications for MM or BMP
implementation. Sections 5.3 and 5.4 discuss
expert evaluations and self-evaluations,
respectively, in more detail.
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Conducting, the Evaluation
Chapter 5
A survey of lawn care practices in the Westmorland neighborhood of the city of Madison,
Wisconsin was conducted by telephone interviews, after advance notice was sent to
homeowners. The objectives of the survey were to:
• Determine the number of people who fertilized their lawn either themselves or
through a professional service.
• Identify the usage of fertilizer (i.e., when it was applied and the quantity applied).
• Determine the brands and types of fertilizers used.
• Identify the pattern of usage of separate weed killers and insecticides.
The survey provided information on:
• The percentage of homeowners that fertilized their lawns themselves.
• The demographic profile (e.g., sex, age, number of children) of the homeowners in
the area that were most likely to use a professional service.
• The annual frequency of fertilizer applications.
• The type of equipment used for fertilizer applications.
• The percentage of homeowners who said they followed manufacturer
recommendations for fertilizer applications.
• The percentage of homeowners who used fertilizer/insecticide combinations.
• The percentage of homeowners that used separate weed killers and insecticides.
• How the homeowners disposed of grass clippings.
Example ... Survey of lawn care practices. (Gene Kroupa & Associates, 1995)
Other important factors to consider when
choosing variables include the time of year
when the BMP compliance survey will be
conducted and when BMPs were installed.
Some urban BMPs, or aspects of their
implementation that can be analyzed, vary with
time of year, phase of construction, or length
of time after having been installed. The
temporary controls for erosion and sediment
control, for instance, would not be inspected
after construction is complete and a site has
been stabilized. Variables that are appropriate
to time-specific factors should be chosen.
Concerning BMPs that have been in place for
some time, the adequacy of implementation
might be of less interest than the adequacy of
the operation and maintenance of the BMP.
For example, it might be of interest to inspect
bridge runoff systems for proper cleaning and
maintenance rather than to determine whether
the number and spacing of runoff drains is
sufficient for the particular bridge. If
numerous BMPs are being analyzed during a
single site visit, variables that relate to
different aspects of BMP installation,
operation, and maintenance might be chosen
separately for each BMP to be inspected.
Aerial reconnaissance and photography is
another means available for collecting
information on urban or watershed practices,
though many of the MMs and BMPs used in
urban areas might be difficult if not impossible
to identify on aerial photographs. Aerial
reconnaissance and photography are discussed
in detail in Section 5.5.
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Chapter 5
Conducting, the Evaluation
The general types of information obtainable
with self-evaluations are listed in Table 5-1.
Regardless of the approach(es) used, proper
and thorough preparation for the evaluation is
the key to success.
5.2 CHOICE OF VARIABLES
Once the objectives of a BMP implementation
or compliance survey have been clearly
defined, the most important factor in the
assessment of MM or BMP implementation is
the determination of which variable(s) to
measure. A good variable provides a direct
measure of how well a BMP was or is being
implemented. Individual variables should
provide measures of different factors related to
BMP implementation. The best variables are
those which are measures of the adequacy of
MM or BMP implementation and are based on
quantifiable expressions of conformance with
state standards and specifications. As the
variables that are used become less directly
related to actual MM or BMP implementation,
their accuracy as measures of BMP
implementation decreases.
Examples of useful variables could include the
change in the quantity of household hazardous
waste collected or the percent of onsite
disposal systems in a subwatershed that are
operating properly, both of which would be
expressed in terms of conformance with
applicable state and/or local standards and
specifications. Less useful variables measure
factors that are related to MM and BMP
implementation but do not necessarily provide
an accurate measure of their implementation.
Examples of these types of variables are the
number of runoff conveyance structures
constructed in a year and the number of onsite
disposal systems approved for installation.
Other poor variables would be the passage of
legislation requiring MM or BMP application
on construction sites, development of an public
information program for lawn management, or
the number of requests for information on
household hazardous waste disposal.
Although these variables relate to MM or BMP
implementation, they provide no real
information on whether MMs or BMPs are
actually being implemented or whether they
are being implemented properly.
Variables generally will not directly relate to
MM implementation, as most urban MMs are
combinations of several BMPs. Measures of
MM implementation, therefore, usually will be
based on separate assessments of two or more
BMPs, and the implementation of each BMP
will be based on a unique set of variables.
Some examples of BMPs related to EPA's Site
Development Management Measure, variables
for assessing compliance with the BMPs, and
related standards and specifications that might
be required by local regulatory authorities are
presented in Figure 5-1. Because developers
and homeowners choose to implement or not
implement MMs or BMPs based on site-
specific conditions, it is also appropriate to
apply varying weights to the variables chosen
to assess MM and BMP implementation to
correspond to site-specific conditions. For
example, variables related to onsite disposal
systems might be de-emphasized—and other,
more applicable variables emphasized
more—in areas where most homes are
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Conducting the Evaluation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^1 Chapter 5
Table 5-1. General types of information obtainable with self-evaluations and expert
evaluations.
Information obtainable from Self-Evaluations
Background Information
• Type of development installed (e.g., residential, commercial, industrial,
recreational)
• Percent impervious area
• Inspection schedule
• Operation and maintenance practices
• Map
Management Measures / Best Management Practices
• Nonstructural practices
• BMPs installed
• Dates of MM / BMP installation
• Design specifications of BMPs
• Type of water body or area protected
• Previous management measures used
ESC Plans (for construction)
• Preparation of ESC plans
• Dates of plan preparation and revisions
• Date of initial plan implementation
• Total acreage under management
• Certification requirements
Information that Requires Expert Evaluations
• Design sufficiency
• Installation sufficiency
• Adequacy of operation / maintenance
• Confirmation of information from self-evaluations
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Chapter 5
Conducting, the Evaluation
Site Development Management Measure
Plan, design, and develop sites to:
(1) Protect areas that provide important water quality benefits and/or are particularly
susceptible to erosion and sediment loss;
(2) Limit increases of impervious areas, except where necessary;
(3) Limit land disturbance activities such as clearing and grading, and cut and fill to reduce
erosion and sediment loss; and
(4) Limit disturbance of natural drainage features and vegetation.
Related BMPs, measurement variables, and standards and specifications:
Management Measure
Practice
Phasing and limiting areas of
disturbance
Preserving natural drainage
features and natural
depressional storage areas
Minimizing imperviousness
Minimum disturbance /
minimum maintenance
Potential Measurement
Variable
Length of time disturbed area
left without stabilization
(temporary or permanent)
Degree to which
postdevelopment landscape
preserves predevelopment
landscape features
Percent impervious surface
Percent increase in
impervious surface
Quantity of land altered by
development from its
predevelopment condition
Example Related Standards
and Specifications
• Maximum time an area may
be left unstabilized
• Maximum area that may be
disturbed at one time,
depending on type of
construction and project
• Site-specific requirements for
preservation of natural
drainage features,
determined during the
permitting process
• Maximum imperviousness,
depending on type of
development
• Maximum percent increase in
imperviousness, based on
type of development
• Guidelines for protection of
natural vegetation and site
characteristics, proposed for
project during project
development
Figure 5-1. Potential variables and examples of implementation standards and
specifications that might be useful for evaluating compliance with the New Development
Management Measure.
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Conducting, the Evaluation
Chapter 5
connected to a sewer system. Similarly, on a
construction site near a water body, variables
related to sediment runoff and chemical
deposition (pesticide use, fertilizer use) might
be emphasized over other variables to arrive at
a site-specific rating of the adequacy of MM or
BMP implementation.
The purpose for which the information
collected during a MM or BMP
implementation survey will be used is another
important consideration when selecting
variables. An implementation survey can
serve many purposes beyond the primary
purpose of assessing MM and BMP
implementation. For instance, variables might
be selected to assess compliance with each
category of BMP that is of interest and to
assess overall compliance with BMP
specification and standards. In addition, other
variables might be selected to assess the effect
that specific circumstances have on the ability
or willingness of homeowners or developers to
comply with BMP implementation standards
or specifications. For example, the level of
participation in a household hazardous waste
collection program could be investigated with
respect to variables for collection locations and
hours of operation. The information obtained
from evaluations using the latter type of
variable could be useful for modifying MM or
BMP implementation standards and
specifications for application to particular
types of developments or site conditions.
Table 5-2 provides examples of good and poor
variables for the assessment of implementation
of the urban MMs developed by EPA
(USEPA, 1993a). The variables listed in the
table are only examples, and local or regional
conditions should ultimately dictate what
variables should be used. The Center for
Watershed Protection (CWP) published a
report, Environmental indicators to assess
stormwater control programs and practices
(Clayton and Brown, 1996), that contains
additional information on this subject. CWP
also recommended that it might be necessary
to evaluate BMP specifications to determine
whether those for "older" structural BMPs are
still appropriate for pollution prevention.
5.3 EXPERT EVALUATIONS
5.3.1 Site Evaluations
Expert evaluations are the best way to collect
reliable information on MM and BMP
implementation. They involve a person or
team of people visiting individual sites and
speaking with homeowners and/or developers
to obtain information on MM and BMP
implementation (see Example). For many of
the MMs, assessing and verifying compliance
will require a site visit and evaluation. The
following should be considered before expert
evaluations are conducted:
• Obtaining permission of the homeowner or
developer. Without proper authorization
to visit a site from the homeowner or
developer, the relationship between the
regulated community and the local
regulatory authority, and any future
regulatory or compliance action, could be
jeopardized.
• The type(s) of expertise needed to assess
proper implementation. For some MMs, a
team of trained personnel might be
required to determine whether MMs have
been implemented properly.
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Chapter 5
Conducting, the Evaluation
Table 5-2. Example variables for assessing management measure implementation.
Management
Measure
Good Variable
Poor Variable
Appropriate
Sampling Unit
URBAN RUNOFF
New Development
Watershed
Protection
Site Development
• Number of county staff trained
in ESC control.
• Width of filter strips relative to
area drained.
• Percent of highly erodible
soils left in an undeveloped
state.
• Percent natural drainage
ways altered.
• Ratio of area of land with
structures to total disturbed
land at a development site
• Area of environmentally
sensitive land to total area of
same disturbed during
construction
• Allocation of funding for
development of education
materials.
• Scheduled frequency of runoff
control maintenance.
• Development of watershed
analysis GIS system.
• Assessed fines for violations
of setback standards.
• Number of erosion and
sediment control plans
developed.
• Subwatershed
• Development site
• Subwatershed
• Subwatershed
CONSTRUCTION ACTIVITIES
Construction Site
Erosion and
Sediment Control
(ESC)
Construction Site
Chemical Control
• Distance runoff travels on
disturbed soils before it is
intercepted by a runoff control
device (relative to slope and
soil type).
• Adequacy of ESC practices
relative to soil type, slope,
and precipitation.
• Proper installation and use of
designated area for chemical
and petroleum product
storage and handling.
• Proper timing and application
rate of nutrients at
development site.
• Number of ESC BMPs used
at a construction site.
• Number of ESC plans written.
• Content and quality of spill
prevention and control plan.
• Number of approved nutrient
management plans.
• Development site
• Development site
EXISTING DEVELOPMENT
Existing
Development
• Proper operation and
maintenance of surface water
runoff management facilities.
• Installation of appropriate
BMPs in areas assigned
priority as being in need of
structural NPS controls.
• Development of a schedule
for BMP implementation.
• Setting priorities for structural
improvements in development
areas.
• Subwatershed
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Chapter 5
Table 5-2. (cont.)
Management
Measure
Good Variable
Poor Variable
Appropriate
Sampling Unit
ONSITE DISPOSAL SYSTEMS
New Onsite
Disposal Systems
Operating Onsite
Disposal Systems
• Proper siting and installation
of newOSDS.
• Density of development with
OSDS in areas with nitrogen-
limited waters
• Increase in proper OSDS
operation and maintenance 6
months after a public
education campaign.
• Average time between OSDS
maintenance visits.
• Number of OSDS installed.
• Reduction in garbage
disposal sales.
• Scheduled frequency of
OSDS inspections.
• Authorization of funding for
public education campaign on
OSDS.
• Subwatershed
• City
• Town
• Subwatershed
• City
• Town
POLLUTION PREVENTION
Pollution
Prevention
• Increase in volume of
household hazardous wastes
collected.
• Miles of roads adopted for
citizen cleanup and volume of
trash collected.
• Number of licenses issued to
lawn care companies offering
"chemical-free" lawn care.
• Development of pollution
prevention campaigns by
nongovernmental
organizations.
• City
• Town
ROADS, HIGHWAYS, AND BRIDGES
Planning, Siting,
and Developing
Roads and
Highways
Bridges
Construction
Projects
• Right-of-ways set aside for
roads and highways based on
projected future growth, and
appropriateness of land set
aside for such use.
• Total distance of bridges in
environmentally sensitive
areas.
• Installation of ESC practices
early in construction project.
• ESC practices installed early
in construction project.
• Miles of road constructed.
• Number of bridges
constructed.
• Number of ESC plans
prepared and approved.
• Number of ESC BMPs used
during construction.
• Subwatershed
• Subwatershed
• Subwatershed
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Conducting, the Evaluation
Table 5-2. (cont.)
Management
Measure
Good Variable
Poor Variable
Appropriate
Sampling Unit
ROADS, HIGHWAYS, AND BRIDGES (cont.)
Construction Site
Chemical Control
Operation and
Maintenance
Road, Highway,
and Bridge Runoff
Systems
• Proper installation and use of
designated area for chemical
and petroleum product
storage and handling.
• Proper timing and application
rate of nutrients at
development site.
• Operating efficiency of NPS
pollution control BMPs.
• Ratio of exposed slopes
and\or damaged vegetated
areas to 1 00 m of roadway
length.
• Frequency of street sweeping.
• Adherence to schedule for
implementation of runoff
controls on roadways
determined to need same.
• Percent of roadway
refurbishment projects that
include runoff control
improvements on roads
needing same.
• Pounds of herbicide applied.
• Purchase of salt application
equipment.
• Purchase of land for location
of treatment facilities.
• Subwatershed
• Subwatershed
• Subwatershed
In Delaware, private construction site
inspectors make at least weekly site
visits to large or significant construction
sites. The private inspectors are trained
by the state and report violations of ESC
regulations and inadequacies in ESC
plans or BMP implementation to the state
or local ESC agency, the developer, and
the contractor. They also offer timely on-
site technical assistance. While not a
comprehensive ESC BMP
implementation inventory program, it can
be used as a model for the development
of such a program.
Example... Delaware construction site
reviews.
The activities that should occur during an
expert evaluation. This information is
necessary for proper and complete
preparation for the site visit, so that it can be
completed in a single visit and at the proper
time.
Inspection reports or certifications
(developed during construction or as the
result of other studies) might exist for some
BMPs. The team of trained personnel should
consider whether the BMP was built to
standards that a "new" BMP would be built
to meet the MMs. (This might require
reviewing the engineering design and
specifications.) If the standards are
comparable, then a previous inspection
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report or certification might be acceptable in
lieu of a detailed site visit and evaluation.
• The method of rating the MMs and BMPs.
MM and BMP rating systems are discussed
below.
• Consistency among evaluation teams and
between site evaluations. Proper training
and preparation of expert evaluation team
members are crucial to ensure accuracy and
consistency.
• The collection of information while at a site.
Information collection should be facilitated
with preparation of data collection forms that
include any necessary MM and BMP rating
information needed by the evaluation team
members.
• The content and format of post-evaluation
discussions. Site evaluation team members
should bear in mind the value of
postevaluation discussion among team
members. Notes can be taken during the
evaluation concerning any items that would
benefit from group discussion.
Evaluators might consist of a single person
suitably trained in urban expert evaluation to a
group of professionals with varied expertise.
The composition of evaluation teams will
depend on the types of MMs or BMPs being
evaluated. Potential team members could
include:
• Civil engineer
• Land use planner
• Hydrologist
• Soil scientist
• Water quality expert
The composition of evaluation teams can vary
depending on the purpose of the evaluation,
available staff and other resources, and the
geographic area being covered. All team
members should be familiar with the required
MMs and BMPs, and each team should have a
member who has previously participated in an
expert evaluation. This will ensure familiarity
with the technical aspects of the MMs and
BMPs that will be rated during the evaluation
and the expert evaluation process.
Training might be necessary to bring all team
members to the level of proficiency needed to
conduct the expert evaluations. State or local
regulatory personnel should be familiar with
urban conditions, state BMP standards and
specifications, and proper BMP
implementation, and therefore are generally
well qualified to teach these topics to
evaluation team members who are less familiar
with them. Local regulatory agency
representatives or other specialists who have
participated in BMP implementation surveys
might be enlisted to train evaluation team
members about the actual conduct of expert
evaluations. This training should include
identification of BMPs particularly critical to
water quality protection, analysis of erosion
potential, and other aspects of BMP
implementation that require professional
judgement, as well as any standard methods
for measurements to judge BMP
implementation against state standards and
specifications.
Alternatively, if only one or two individuals
will be conducting expert evaluations, their
training in the various specialties, such as
those listed above, necessary to evaluate the
quality of MM and BMP implementation
could be provided by a team of specialists who
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Conducting, the Evaluation
are familiar with urban BMPs and nonpoint
source pollution.
In the interest of consistency among the
evaluations and among team members, it is
advisable that one or more mock evaluations
take place prior to visiting selected sample
sites. These "practice sessions" provide team
members with an opportunity to become
familiar with MMs and BMPs as they should
be implemented under different site
conditions, gain familiarity with the evaluation
forms and meanings of the terms and questions
on them, and learn from other team members
with different expertise. Mock evaluations are
valuable for ensuring that all evaluators have a
similar understanding of the intent of the
questions, especially for questions whose
responses involve a degree of subjectivity on
the part of the evaluator.
Where expert evaluation teams are composed
of more than two or three people, it might be
helpful to divide up the various responsibilities
for conducting the expert evaluations among
team members ahead of time to avoid
confusion at the site and to be certain that all
tasks are completed but not duplicated. Having
a spokesperson for the group who will be
responsible for communicating with the
homeowner or developer—prior to the expert
evaluation, at the expert evaluation if they are
present, and afterward—might also be helpful.
A local regulatory agency representative is
generally a good choice as spokesperson
because he/she can represent the county or
municipal authorities. Newly-formed
evaluation teams might benefit most from a
division of labor and selection of a team leader
or team coordinator with experience in expert
evaluations who will be responsible for the
quality of the expert evaluations. Smaller
teams and larger teams that have experience
working together might find that a division of
responsibilities is not necessary. If
responsibilities are to be assigned, mock
evaluations can be a good time to work out
these details.
5.3.2 Rating Implementation of
Management Measures and Best
Management Practices
Many factors influence the implementation of
MMs and BMPs, so it is sometimes necessary
to use best professional judgment (BPJ) to rate
their implementation and BPJ will almost
always be necessary when rating overall BMP
compliance at a site. Site-specific factors such
as soil type, amount of area exposed, and
topography affect the implementation of
erosion and sediment control BMPs, for
instance, and must be taken into account by
evaluators when rating MM or BMP
implementation. Implementation of MMs will
often be based on implementation of more than
one BMP, and this makes rating MM
implementation similar to rating overall BMP
implementation at a site. Determining an
overall rating involves grouping the ratings of
implementation of individual BMPs into a
single rating, which introduces more
subjectivity than rating the implementation of
individual BMPs based on standards and
specifications. Choice of a rating system and
rating terms, which are aspects of proper
evaluation design, is therefore important in
minimizing the level of subjectivity associated
with overall BMP compliance and MM
implementation ratings. When creating overall
ratings, it is still important to record the
detailed ratings of individual BMPs as
supporting information.
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Individual BMPs, overall BMP compliance,
and MMs can be rated using a binary approach
(e.g., pass/fail, compliant/ noncompliant, or
yes/no) or on a scale with more than two
choices, such as 1 to 5 or 1 to 10 (where 1 is
the worst—see Example). The simplest
method of rating MM and BMP
implementation is the use of a binary
approach. Using a binary approach, either an
entire site or individual MMs or BMPs are
rated as being in compliance or not in
compliance with respect to specified criteria.
Scale systems can take the form of ratings
from poor to excellent, inadequate to adequate,
low to high, 1 to 3, 1 to 5, and so forth.
A possible rating scale from 1 to 5 might
be:
5 = Implementation exceeds
requirements
4 = Implementation meets requirements
3 = Implementation has a minor
departure from requirements
2 = Implementation has a major
departure from requirements
1 = Implementation is in gross neglect of
requirements
where:
Minor departure is defined as "small in
magnitude or localized," major departure is
defined as "significant magnitude or where
the BMPs are consistently neglected" and
gross neglect is defined as "potential risk
to water resources is significant and there
is no evidence that any attempt is made to
implement the BMP."
Example...Q\ a rating scale (adapted from
Rossman and Phillips, 1992).
Whatever form of scale is used, the factors that
would individually or collectively qualify a
site, MM, or BMP for one of the rankings
should be clearly stated. The more choices
that are added to the scale, the smaller and
smaller the difference between them becomes
and each must therefore be defined more
specifically and accurately. This is especially
important if different teams or individuals rate
sites separately. Consistency among the
ratings then depends on each team or
individual evaluator knowing precisely what
the criteria for each rating option mean. Clear
and precise explanations of the rating scale
can also help avoid or reduce disagreements
among team members. This applies equally to
a binary approach. The factors, individually or
collectively, that would cause a site, MM, or
BMP to be rated as not being in compliance
with design specifications should be clearly
stated on the evaluation form or in support
documentation.
Rating sites or MMs and BMPs on a scale
requires a greater degree of analysis by the
evaluation team than does using a binary
approach. Each higher number represents a
better level of MM or BMP implementation.
In effect, a binary rating approach is a scale
with two choices; a scale of low, medium, and
high (compliance) is a scale with three choices.
Use of a scale system with more than two
rating choices can provide more information to
program managers than a binary rating
approach, and this factor must be weighted
against the greater complexity involved in
using one. For instance, a survey that uses a
scale of 1 to 5 might result in one MM with a
ranking of 1, five with a ranking of 2, six with
a ranking of 3, eight with a ranking of 4, and
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Conducting, the Evaluation
Case Study: Field Test of a Randomized BMP Sampling Design
Maryland Environmental Service (MES), an organization under contract to the St. Mary's County,
Maryland, Department of Public Works to inventory and inspect all storm water BMPs in the county,
conducted a field test of the sampling techniques in this guidance to determine how well an inspection of a
sample of storm water BMPs could predict the condition of all storm water BMPs in the county (MES,
2000). To do this, MES first conducted its annual inventory and inspection of 30 storm water BMPs, or
approximately one-third of the county's total. The BMPs, which included a variety of types of structural
storm water control facilities, were inspected for deficiencies in 38 areas in accordance with Maryland's
inspection requirements. The inspectors noted any maintenance requirements for the BMPs as well as
their condition and function.
From the results, the total number of areas found to have deficiencies from all 30 BMPs inspected, as well
as the percentage of the BMPs inspected that were deficient in each of the 38 areas was tallied. Then,
MES randomly selected 10 of the 30 storm water BMPs that they had inspected to determine how well the
same analysis performed on these 10 facilities would compare to the analysis of the 30 facilities. The
results are shown in the table below.
BMPs had
deficiencies in:
BMPs with individual
deficiencies ranged from:
Analysis of all 30 BMPs
20 of 3 8 areas
3 to 30 percent
Analysis of a sample of 10 BMPs
7 of 10 areas
10 to 40 percent
Since the 30 BMPs inspected included a variety of types of BMPs, MES also analyzed a subsample of the
BMPs inspected by including only BMPs with detention weirs in another analysis. Of the 30 BMPs
inspected, 24 had detention weirs and were included in the second analysis. Ten of these were selected for
the random sample within the group of 24. MES compared overall inspector ratings for the two groups.
The results are shown in the table below. As can be seen the average inspector rating for all 24 BMPs
with detention weirs easily falls within the 90 percent confidence interval associated with the average
inspector rating associated with the sample of 10 BMPs.
Average inspector
rating was:
Analysis of all 24 BMPs with Detention Weirs
7.92
Analysis of a sample of 10 BMPs from the 24 (± 90%
confidence interval)
7.6±0.9
Lesson: A sample of BMPs can be used to predict the condition of a larger group of BMPs, but results are
more reliable if the random sample design can be used to eliminate sources of error that could result in
erroneous interpretation of the results.
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Chapter 5
five with a ranking of 5. Precise criteria would
have to be developed to be able to ensure
consistency within and between survey teams
in rating the MMs, but the information that
only one MM was implemented poorly, 11
were implemented below standards, 13 met or
were above standards, and 5 were
implemented very well might be more valuable
than the information that 18 MMs were found
to be in compliance with design specifications,
which is the only information that would be
obtained with a binary rating approach.
If a rating system with more than two ratings is
used to collect data, the data can be analyzed
either by using the original rating data or by
first transforming the data into a binomial (i.e.,
two-choice rating) system. For instance,
ratings of 1 through 5 could be reduced to two
ratings by grouping the Is, 2s, and 3s together
into one group (e.g., inadequate) and the 4s
and 5s into a separate group (e.g., adequate).
If this approach is used, it is best to retain the
original rating data for the detailed information
they contain and to reduce the data to a
binomial system only for the purpose of
statistical analysis. Chapter 4, Section 4.5,
contains information on the analysis of
categorical data.
5.3.3 Rating Terms
The choice of rating terms used on the
evaluation forms is an important factor in
ensuring consistency and reducing bias, and
the terms used to describe and define the rating
options should be as objective as possible. For
a rating system with a large number of options,
the meanings of each option should be clearly
defined. It is suggested to avoid using terms
such as "major" and "minor" when describing
erosion or pollution effects or deviations from
prescribed MM or BMP implementation
criteria, or to provide clear definitions for them
in the context of the evaluation, because they
might have different connotations for different
evaluation team members. It is easier for an
evaluation team to agree upon meaning if
options are described in terms of measurable
criteria and examples are provided to clarify
the intended meaning. It is also suggested not
to use terms that carry negative connotations.
Evaluators might be disinclined to rate a MM
or BMP as having a "major deviation" from an
implementation criterion, even if justified,
because of the negative connotation carried by
the term. Rather than using such a term,
observable conditions or effects of the quality
of implementation can be listed and specific
ratings (e.g., 1-5 or compliant/noncompliant
for the criterion) can be associated with the
conditions or effects. For example, instead of
rating a stormwater management pond as
having a "major deficiency," a specific
deficiency could be described and ascribed an
associated rating (e.g., "Structure is designed
for no more than 5-hour attenuation of urban
runoff = noncompliant").
Evaluation team members will often have to
take specific notes on sites, MMs, or BMPs
during the evaluation, either to justify the
ratings they have ascribed to variables or for
discussion with other team members after the
survey. When recording notes about the sites,
MMs, or BMPs, evaluation team members
should be as specific as the criteria for the
ratings. A rating recorded as "MM deviates
highly from implementation criteria" is highly
subjective and loses specific meaning when
read by anyone other than the person who
wrote the note. Notes should therefore be as
objective and specific as possible.
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An overall site rating is useful for
summarizing information in reports,
identifying the level of implementation of
MMs and BMPs, indicating the likelihood that
environmental protection is being achieved,
identifying additional training or education
needs; and conveying information to program
managers, who are often not familiar with
MMs or BMPs. For the purposes of
preserving the valuable information contained
in the original ratings of sites, MMs, or BMPs,
however, overall ratings should summarize,
not replace, the original data. Analysis of
year-to-year variations in MM or BMP
implementation, the factors involved in MM or
BMP program implementation, and factors
that could improve MM or BMP
implementation and MM or BMP program
success are only possible if the original,
detailed site, MM, or BMP data are used.
Approaches commonly used for determining
final BMP implementation ratings include
calculating a percentage based on individual
BMP ratings, consensus, compilation of
aggregate scores by an objective party, voting,
and voting only where consensus on a site or
MM or BMP rating cannot be reached. Not all
systems for arriving at final ratings are
applicable to all circumstances.
5.3.4 Consistency Issues
Consistency among evaluators and between
evaluations is important, and because of the
potential for subjectivity to play a role in
expert evaluations, consistency should be
thoroughly addressed in the quality assurance
and quality control (QA/QC) aspects of
planning and conducting an implementation
survey. Consistency arises as a QA/QC
concern in the planning phase of an
implementation survey in the choice of
evaluators, the selection of the size of
evaluation teams, and in evaluator training. It
arises as a QA/QC concern while conducting
an implementation survey in whether
evaluations are conducted by individuals or
teams, how MM and BMP implementation on
individual sites is documented, how evaluation
team discussions of issues are conducted, how
problems are resolved, and how individual
MMs and BMPs or whole sites are rated.
Consistency is likely to be best if only one to
two evaluators conduct the expert evaluations
and the same individuals conduct all of the
evaluations. If, for statistical purposes, many
sites (e.g., 100 or more) need to be evaluated,
use of only one to two evaluators might also be
the most efficient approach. In this case,
having a team of evaluators revisit a
subsample of the sites that were originally
evaluated by one to two individuals might be
useful for quality control purposes.
If teams of evaluators conduct the evaluations,
consistency can be achieved by keeping the
membership of the teams constant.
Differences of opinion, which are likely to
arise among team members, can be settled
through discussions held during evaluations,
and the experience of team members who have
done past evaluations can help guide decisions.
Preevaluation training sessions, such as the
mock evaluations discussed above, will help
ensure that the first few expert evaluations are
not "learning" experiences to such an extent
that those sites must be revisited to ensure that
they receive the same level of scrutiny as sites
evaluated later.
If different sites are visited by different teams
of evaluators or if individual evaluators are
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Conducting, the Evaluation
Chapter 5
assigned to different sites, it is especially
important that consistency be established
before the evaluations are conducted. For best
results, discussions among evaluators should
be held periodically during the evaluations to
discuss any potential problems. For instance,
evaluators could visit some sites together at the
beginning of the evaluations to promote
consistency in ratings, followed by expert
evaluations conducted by individual
evaluators. Then, after a few site or MM
evaluations, evaluators could gather again to
discuss results and to share any knowledge
gained to ensure continued consistency.
As mentioned above, consistency can be
established during mock evaluations held
before the actual evaluations begin. These
mock evaluations are excellent opportunities
for evaluators to discuss the meaning of terms
on rating forms, differences between rating
criteria, and differences of opinion about
proper MM or BMP implementation. A
member of the evaluation team should be able
to represent the state's position on the
definition of terms and clarify areas of
confusion.
Descriptions of MMs and BMPs should be
detailed enough to support any ratings given to
individual features and to the MM or BMP
overall. Sketching a diagram of the MM or
BMP helps identify design problems, promotes
careful evaluation of all features, and provides
a record of the MM or BMP for future
reference. A diagram is also valuable when
discussing the MM or BMP with the
homeowner or developer or identifying
features in need of improvement or alteration.
Homeowners or developers can also use a copy
of the diagram and evaluation when discussing
their operations with local or state regulatory
personnel. Photographs of MM or BMP
features are a valuable reference material and
should be used whenever an evaluator feels
that a written description or a diagram could be
inadequate. Photographs of what constitutes
both good and poor MM or BMP
implementation are valuable for explanatory
and educational purposes; for example, for
presentations to managers and the public.
5.3.5 Postevaluation Onsite Activities
It is important to complete all pertinent tasks
as soon as possible after the completion of an
expert evaluation to avoid extra work later and
to reduce the chances of introducing error
attributable to inaccurate or incomplete
memory or confusion. All evaluation forms
for each site should be filled out completely
before leaving the site. Information not filled
in at the beginning of the evaluation can be
obtained from the site owner or developer if
necessary. Any questions that evaluators had
about the MMs and BMPs during the
evaluation can be discussed, notes written
during the evaluation can be shared and used
to help clarify details of the evaluation process
and ratings. The opportunity to revisit the site
will still exist if there are points that cannot be
agreed upon among evaluation team members.
Also, while the evaluation team is still on the
site, the site owner or developer should be
informed about what will follow; for instance,
whether he/she will receive a copy of the
report, when to expect it, what the results
mean, and his/her responsibility in light of the
evaluation, if any. Immediately following the
evaluation is also an excellent time to discuss
the findings with the site owner or developer if
he/she was not present during the evaluation.
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5.4 SELF-EVALUATIONS
5.4.1 Methods
Self-evaluations, while often not a reliable
source of MM or BMP implementation data,
can be used to augment data collected through
expert evaluations or in place of expert
evaluations where the latter cannot be
conducted. In some cases, local or state
regulatory personnel might have been involved
directly with BMP selection and
implementation and will be a source of useful
information even if an expert evaluation is not
conducted. Self-evaluations are an appropriate
survey method for obtaining background
information from homeowners or developers
or other persons associated with BMP
installation, such as contractors.
Mail, telephone, and mail with telephone
follow-up are common self-evaluation
methods (see Example). Mail and telephone
surveys are useful for collecting general
information, such as the management measures
that should be implemented on specific urban
land types. Local regulatory agency personnel,
county or municipal planning staff, or other
state or local BMP implementation experts can
be interviewed or sent a questionnaire that
The Center for Watershed Protection in Silver Spring, Maryland conducted a mail survey of
erosion and sediment control (ESC) programs for small (< 5 acres) construction sites. The
survey was sent to 219 jurisdictions located in all EPA regions and CWP received a 52%
(113 surveys) response rate from the survey. The main objective of the survey was to
identify innovative and effective ESC programs.
Through the mail survey, information was collected on the following:
• The age of each program.
• Each program's requirements for permits (i.e., whether a separate process or part of
the site development process).
• The applicability of permit requirements (i.e., whether applicability was based on site
size or other criteria).
• The necessary conditions under which permit wavers would be issued.
• Whether the requirement for a permit was determined on a case-by-case basis, or
whether certain aspects of the development (e.g., proximity to sensitive areas)
would make obtaining a permit necessary.
• The size of populations in jurisdictions with ESC programs.
• Whether the ESC programs were mandated or voluntary.
• The level of detail required in ESC plans.
• Which ESC practices were used commonly.
• Who the enforcement agency was.
• What penalties could be imposed for non-compliance.
• A list of construction-related water quality problems common at small sites.
Example... Mail survey of ESC programs. (Oh re I, 1996)
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Chapter 5
requests very specific information. Recent
advances in and increasing access to electronic
means of communication (i.e., e-mail and the
Internet) might make these viable survey
instruments in the future.
To ensure comparability of results, information
that is collected as part of a self-
evaluation—whether collected through the
mail, over the phone, or during site
visits—should be collected in a manner that
does not favor one method over the others.
Ideally, telephone follow-up and on-site
interviews should consist of no more than
reading the questions on the questionnaire,
without providing any additional explanation
or information that would not have been
available to those who responded through the
mail. This approach eliminates as much as
possible any bias associated with the different
means of collecting the information. Figure 5-
2 presents questions from a residential
questionnaire developed for Prince George's
County, Maryland, to determine residential
"good housekeeping" practices. Questionnaire
design is discussed in Section 5.4.3. It is
important that the accuracy of information
received through mail and phone surveys be
checked. Inaccurate or incomplete responses
to questions on mail and/or telephone surveys
commonly result from survey respondents
misinterpreting questions and thus providing
misleading information, not including all
relevant information in their responses, not
wanting to provide some types of information,
or deliberately providing some inaccurate
responses. Therefore, the accuracy of
information received through mail and phone
surveys should be checked by selecting a
subsample of the homeowners or other persons
surveyed and conducting follow-up site visits.
5.4.2 Cost
Cost can be an important consideration when
selecting an evaluation method. Site visits can
cost several hundred dollars per site visited,
depending on the type of inspection involved,
the information to be collected, and the
number of evaluators involved. Mail and/or
telephone surveys can be an inexpensive
means of collecting information, but their cost
must be balanced with the type and accuracy of
information that can be collected through
them. Other costs also need to be figured into
the overall cost of mail and/or telephone
surveys, including follow-up phone calls and
site visits to make up for a poor response to
mailings and for accuracy checks.
Additionally, the cost of questionnaire design
must be considered, as a well-designed
questionnaire is extremely important to the
success of self-evaluations. Questionnaire
design is discussed in the next section.
The number of evaluators used for site visits
has an obvious impact on the cost of a MM or
BMP implementation survey. Survey costs
can be minimized by having one or two
evaluators visit sites instead of having
multiple-person teams visit each site. If the
expertise of many specialists is desired, it
might be cost-effective to have multiple-
person teams check the quality of evaluations
conducted by one or two evaluators. This can
usually be done at a subsample of sites after
they have been surveyed.
An important factor to consider when
determining the number of evaluators to
include on site visitation teams, and how to
balance the use of one to two evaluators versus
multiple-person teams, is the objectives of the
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Chapter 5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^1 Conducting the Evaluation
Questions about water quality and community activity factors:
1. Do you believe that rainwater runoff from streets, driveways, and parking lots causes
water pollution in nearby streams?
Y N Don't know
2. Do you know where to report water pollution problems? Y N
3. Do you know whether water from your lawn goes into Chesapeake Bay?
Y N Don't know
4. Do the storm drains in your neighborhood have "Drains into the Chesapeake Bay"
stenciled on them? Y N Don't know
5. Please rank the following community issues according to their level of importance (1 •
very important, 10 = not important)
Keeping trash and litter from accumulating in neighborhoods, parking lots, on
main streets, and in commercial areas
Appearance and good maintenance of residential neighborhoods and
commercial facilities
Organized youth programs
Protecting the environment (clean air and drinking water)
Stable property values
Crime
Having clean parks and recreational facilities
Traffic congestion on main roadways
Water pollution (polluted streams and waterways)
Other, please specify
Questions about lawn and garden maintenance activities:
1. Does a professional lawn care company fertilize your lawn? Y N
(If yes, please proceed to question #5)
2. Please identify when and how often you fertilize your lawn:
Times per season Spring Summer Autumn Winter
Once
Twice
Three
Other
3. Indicate, to the best of your knowledge, how much fertilizer you use.
According to the instructions on the bag
pounds of nitrogen per 1,000 square feet
pounds of fertilizer per application
Don't know
Figure 5-2. Sample draft survey for residential "good housekeeping" practice
implementation.
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Conducting the Evaluation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^1 Chapter 5
4. Circle the pesticide/insecticide treatments that you perform on your lawn and/or
garden.
Insects: Spring Summer Autumn Winter Never
Weeds: Spring Summer Autumn Winter Never
Fungi: Spring Summer Autumn Winter Never
5. Please indicate how you dispose of your yard debris. (Mark all that apply)
Compost in backyard Curbside trash pick-up
County/town composting program Take off property to vacant lot or
open space
Other: please specify
6. Please use the space below to provide comments that you may have regarding yard
maintenance and practices that may affect water quality.
Questions about personal vehicle maintenance:
1. Do you know how to report abandoned vehicles? Y N
2. Please identify the number and age of vehicles that you currently own or lease:
Number of vehicles Year
Pre-1980
1980-1990
1990 - present
3. Do you perform minor repairs or maintenance on your vehicle(s) at home ?
Y N
4. How often do you change the oil in your vehicles at home ?
monthly quarterly never
twice a year once a year don't know
5. How often do you change the antifreeze in your vehicle(s) at home?
twice a year once a year
never don't know
6. If you perform minor repairs or maintenance on your vehicle(s), please indicate where
you dispose of the items listed below:
on ground in storm drain gas station home trash other
Engine oil
Antifreeze
Oil filters
Car batteries
Tires
Figure 5-2. (cont.)
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Chapter 5
Conducting, the Evaluation
survey. Cost notwithstanding, the teams
conducting the expert evaluations must be
sufficient to meet the objectives of the survey,
and if the required teams would be too costly,
then the objectives of the survey might need to
be modified.
Another factor that contributes to the cost of a
MM or BMP implementation survey is the
number of sites to be surveyed. Once again, a
balance must be reached between cost, the
objectives of the survey, and the number of
sites to be evaluated. Generally, once the
objectives of the study have been specified, the
number of sites to be evaluated can be
determined statistically to meet required data
quality objectives. If the number of sites that
is determined in this way would be too costly,
then it would be necessary to modify the study
objectives or the data quality objectives.
Statistical determination of the number of sites
to evaluate is discussed in Section 3.3.
5.4.3 Questionnaire Design
Many books have been written on the design
of data collection forms and questionnaires
(e.g., Churchill, 1983; Ferber et al., 1964; lull
and Hawkins, 1990), and these can provide
good advice for the creation of simple
questionnaires that will be used for a single
survey. However, for complex questionnaires
or ones that will be used for initial surveys as
part of a series of surveys (i.e., trend analysis),
it is strongly advised that a professional in
questionnaire design be consulted. This is
because while it might seem that designing a
questionnaire is a simple task, small details
such as the order of questions, the selection of
one word or phrase over a similar one, and the
tone of the questions can significantly affect
survey results. A professionally-designed
questionnaire can yield information beyond
that contained in the responses to the questions
themselves, while a poorly-designed
questionnaire can invalidate the results.
The objective of a questionnaire, which should
be closely related to the objectives of the
survey, should be extremely well thought out
prior to its being designed. Questionnaires
should also be designed at the same time as the
information to be collected is selected to
ensure that the questions address the objectives
as precisely as possible. Conducting these
activities simultaneously also provides
immediate feedback on the attainability of the
objectives and the detail of information that
can be collected. For example, an investigator
might want information on the extent of proper
operation and maintenance of BMPs but might
discover while designing the questionnaire that
the desired information could not be obtained
through the use of a questionnaire, or that the
information that could be collected would be
insufficient to fully address the chosen
objectives. In such a situation the investigator
could revise the objectives and questions
before going further with questionnaire design.
Tull and Hawkins (1990) identified seven
major elements of questionnaire construction:
1. Preliminary decisions
2. Question content
3. Question wording
4. Response format
5. Question sequence
6. Physical characteristics of the
questionnaire
7. Pretest and revision.
Preliminary decisions include determining
exactly what type of information is required,
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Conducting, the Evaluation
Chapter 5
determining the target audience, and selecting
the method of communication (e.g, mail,
telephone, site visit). These subjects are
addressed in other sections of this guidance.
The second step is to determine the content of
the questions. Each question should generate
one or more of the information requirements
identified in the preliminary decisions. The
ability of the question to elicit the necessary
data needs to be assessed. "Double-barreled"
questions, in which two or more questions are
asked as one, should be avoided. Questions
that require the respondent to aggregate several
sources of information should be subdivided
into several specific questions or parts. The
ability of the respondent to answer accurately
should also be considered when preparing
questions. Some respondents might be
unfamiliar with the type of information
requested or the terminology used. Or a
respondent might have forgotten some of the
information of interest, or might be unable to
verbalize an answer. Consideration should be
given to the willingness of respondents to
answer the questions accurately. If a
respondent feels that a particular answer might
be embarrassing or personally harmful, (e.g.,
might lead to fines or increased regulation), he
or she might refuse to answer the question or
might deliberately provide inaccurate
information. For this reason, answers to
questions that might lead to such responses
should be checked for accuracy whenever
possible.
The next step is the specific phrasing of the
questions. Simple, easily understood language
is preferred. The wording should not bias the
answer or be too subjective. For instance, a
question should not ask whether the local
government adequately maintains structural
BMPs (the likelihood of getting a negative
response is low). Instead, a series of questions
could ask whether the local government is
responsible for operation and maintenance
(O&M) of structural BMPs, how much staff
and financial resources are dedicated to O&M,
the frequency of inspection and maintenance,
and the procedure for repair, if repair is
necessary. These questions all request factual
information of which the appropriate local
government representative should be
knowledgeable and they progress from simple
to more complex. All alternatives and
assumptions should be clearly stated on the
questionnaire, and the respondent's frame of
reference should be considered.
Fourth, the type of response format should be
selected. Various types of information can
best be obtained using open-ended, multiple-
choice, or dichotomous questions. An open-
ended question allows respondents to answer
in any way they feel is appropriate. Multiple-
choice questions tend to reduce some types of
bias and are easier to tabulate and analyze;
however, good multiple-choice questions can
be more difficult to formulate. Dichotomous
questions allow only two responses, such as
"yes-no" or "agree-disagree." Dichotomous
questions are suitable for determining points of
fact, but must be very precisely stated and
unequivocally solicit only a single piece of
information.
The fifth step in questionnaire design is the
ordering of the questions. The first questions
should be simple to answer, objective, and
interesting in order to relax the respondent.
The questionnaire should move from topic to
topic in a logical manner without confusing the
respondent. Early questions that could bias the
respondent should be avoided. There is
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Chapter 5
Conducting, the Evaluation
evidence that response quality declines near
the end of a long questionnaire (Tull and
Hawkins, 1990). Therefore, more important
information should be solicited early. Before
presenting the questions, the questionnaire
should explain how long (on average) it will
take to complete and the types of information
that will be solicited. The questionnaire
should not present the respondent with any
surprises.
The layout of the questionnaire should make it
easy to use and should minimize recording
mistakes. The layout should clearly show the
respondent all possible answers. For mail
surveys, a pleasant appearance is important for
securing cooperation.
The final step in the design of a questionnaire
is the pretest and possible revision. A
questionnaire should always be pretested with
members of the target audience. This will
preclude expending large amounts of effort
and then discovering that the questionnaire
produces biased or incomplete information.
5.5 AERIAL RECONNAISSANCE AND
PHOTOGRAPHY
Aerial reconnaissance and photography can be
useful tools for gathering physical site
information quickly and comparatively
inexpensively, and they are used in
conservation for a variety of purposes. Aerial
photography has been proven to be helpful for
agricultural conservation practice
identification (Pelletier and Griffin, 1988);
rangeland monitoring (BLM, 1991); terrain
stratification, inventory site identification,
planning, and monitoring in mountainous
regions (Hetzel, 1988; Born and Van Hooser,
1988); as well as for forest regeneration
assessment (Hall and Aired, 1992) and forest
inventory and analysis (Hackett, 1988).
Factors such as the characteristics of what is
being monitored, scale, and camera format
determine how useful aerial photography can
be for a particular purpose. For the purposes
of urban area BMP implementation tracking,
aerial photography could be a valuable tool for
collecting information on a watershed,
subwatershed, or smaller scale. For example,
it could be useful to assess the condition of
riparian vegetation, level of imperviousness in
a subwatershed, or quantity and location of
active construction sites in a specific area.
Pelletier and Griffin (1988) investigated the
use of aerial photography for the identification
of agriculture conservation practices. They
found that practices that occupy a large area
and have an identifiable pattern, such as
contour cropping, strip cropping, terraces, and
windbreaks, were readily identified even at a
small scale (1:80,000) but that smaller, single-
unit practices, such as sediment basins and
sediment diversions, were difficult to identify
at a small scale. They estimated that 29
percent of practices could be identified at a
scale of 1:80,000, 45 percent could be
identified at 1:30,000, 70 percent could be
identified at 1:15,000, and over 90 percent
could be identified at a scale of 1:10,000.
Camera format is a factor that also must be
considered. Large-format cameras are
generally preferred over small-format cameras
(e.g., 35 mm), but are more costly to purchase
and operate. The large negative size (9 cm x 9
cm) produced using a large-format camera
provides the resolution and detail necessary for
accurate photo interpretation. Large-format
cameras can be used from higher altitudes than
small-format cameras, and the image area
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Conducting, the Evaluation
Chapter 5
covered by a large-format image at a given
scale (e.g., 1:1,500) is much larger than the
image area captured by a small-format camera
at the same scale. Small-scale cameras (i.e.,
35 mm) can be used for identifications that
involve large-scale features, such as riparian
areas and the extent of cleared land, and they
are less costly to purchase and use than large-
format cameras, but they are limited in the
altitude that the photographs can be taken from
and the resolution that they provide when
enlarged (Owens, 1988).
BLM recommends the use of a large-format
camera because the images provide the photo
interpreter with more geographical reference
points, it provides flexibility to increase
sample plot size, and it permits modest
navigational errors during overflight (BLM,
1991). Also, if hiring someone to take the
photographs, most photo contractors will have
large-format equipment for the purpose.
A drawback to the use of aerial photography is
that urban BMPs that do not meet
implementation or operational standards but
that are similar to BMPs that do are
indistinguishable from the latter in an aerial
photograph (Pelletier and Griffin, 1988). Also,
practices that are defined by managerial
concepts rather than physical criteria, such as
construction site chemical control or nutrient
application rate, cannot be detected with aerial
photographs.
Regardless of scale, format, or item being
monitored, it is useful for photo interpreters to
receive 2-3 days of training on the basic
fundamentals of photo interpretation and that
they be thoroughly familiar with the areas
where the photographs that they will be
interpreting were taken (BLM, 1991). A visit
to the areas in photograph is recommended to
improve correlation between the interpretation
and actual site characteristics. Generally, after
a few visits and interpretations of photographs
of those areas, photo interpreters will be
familiar with the photographic characteristics
of the areas and the site visits can be reserved
for verification of items in doubt.
Information on obtaining aerial photographs is
available from the Natural Resources
Conservation Service. Contact the Natural
Resources Conservation Service at: NRCS
National Cartography and Geospatial Center,
Fort Worth Federal Center, Bldg 23, Room 60,
P.O. Box 6567, Fort Worth, TX 76115-0567;
1-800-672-5559. NRCS's Internet address is
http://www.ncg.nrcs.usda.gov.
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CHAPTER 6. PRESENTATION OF EVALUATION RESULTS
6.1 INTRODUCTION
The third, fourth, and fifth chapters of this
guidance presented techniques for the
collection and analysis of information. Data
analysis and interpretation are addressed in
detail in Chapter 4 of EPA's Monitoring
Guidance for Determining the Effectiveness of
Nonpoint Source Controls (USEPA, 1997).
This chapter provides ideas for the
presentation of results.
The presentation of MM or BMP
implementation survey results, whether written
or oral, is an integral part of a successful
monitoring study. A presentation conveys
important information from the
implementation survey to those who need it
(e.g., managers, the public). Failure to present
the information in a usable, understandable
form results in the data collection effort being
an end in itself, and the implementation survey
itself might then be considered a failure.
The technical quality of the presentation of
results is dependent on at least four criteria—it
must be complete, accurate, clear, and concise
(Churchill, 1983). Completeness means that
the presentation provides all necessary
information to the audience in the language
that it understands; accuracy is determined by
how well an investigator handles the data,
phrases findings, and reasons; clarity is the
result of clear and logical thinking and a
precision of expression; and conciseness is the
result of selecting for inclusion only that which
is necessary.
Throughout the process of preparing the results
of a MM or BMP implementation survey for
presentation, it must be kept in mind that the
study was initially undertaken to provide
information for management
purposes—specifically, to help make a
decision (Tull and Hawkins, 1990). The
presentation of results should be built around
the information that was to be developed and
the decisions to be made. The message of the
presentation must also be tailored to that
decision. It must be realized that there will be
a time lag between the implementation survey
and the presentation of the results, and the
results should be presented in light of their
applicability to the management decision to be
made based on them. The length of the time
lag is a key factor in determining this
applicability. If the time lag is significant, it
should be made clear during the presentation
that the situation might have changed since the
survey was conducted. If reliable trend data
are available, the person making the
presentation might be able to provide a sense
of the likely magnitude of any change in the
situation. If the change in status is thought to
be insignificant, evidence should be presented
to support this claim. For example, state that
"At the time that the implementation survey
was conducted, homeowners were using BMPs
with increasing frequency, and the lack of any
changes in program implementation coupled
with continued interaction with homeowners
provides no reason to believe that this trend
has changed since that time." It would be
misleading to state "The monitoring study
indicates that homeowners are using BMPs
with increasing frequency." The validity and
force of the message will be enhanced further
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Presentation of Evaluation Results
Chapter 6
through use of the active voice (we believe)
rather than the passive voice (it is believed).
Three major factors must be considered when
presenting the results of MM and BMP
implementation studies: Identifying the target
audience, selecting the appropriate medium
(printed word, speech, pictures, etc.), and
selecting the most appropriate format to meet
the needs of the audience.
6.2 A UDIENCEIDENTIFICA TION
Identification of the audience(s) to which the
results of the MM and BMP implementation
survey will be presented determines the
content and format of the presentation. For
results of implementation survey studies, there
are typically seven potential audiences:
• Interested/concerned citizens
• Developers/landowners
• Media/general public
• Policy makers
• Resource managers
Scientists
• School groups
These audiences have different information
needs, interests, and abilities to understand
complex data. It is the job of the person(s)
preparing the presentation to analyze these
factors prior to preparing a presentation. The
four criteria for presentation quality apply
regardless of the audience. Other elements of
a comprehensive presentation, such as
discussion of the objectives and limitations of
the study and necessary details of the method,
must be part of the presentation and must be
tailored to the audience. For instance, details
of the sampling plan, why the plan was chosen
over others, and the statistical methods used
for analysis might be of interest to other
investigators planning a similar study, and
such details should be recorded even if they
are not part of any presentation of results
because of their value for future reference
when the monitoring is repeated or similar
studies are undertaken, but they are best not
included in a presentation to management.
6.3 PRESENTATION FORMAT
Regardless of whether the results of a
implementation survey are presented written or
orally, or both, the information being presented
must be understandable to the audience.
Consideration of who the audience is will help
ensure that the presentation is particularly
suited to its needs, and choice of the correct
format for the presentation will ensure that the
information is conveyed in a manner that is
easy to comprehend.
Most reports will have to be presented both
written and orally. Written reports are
valuable for peer review, public information
dissemination, and for future reference. Oral
presentations are often necessary for managers,
who usually do not have time to read an entire
report, only have need for the results of the
study, and are usually not interested in the
finer details of the study. Different versions of
a report might well have to be written—for the
public, scientists, and managers (i.e., an
executive summary)—and separate oral
presentations for different audiences—the
public, developers, managers, and scientists at
a conference—might have to be prepared.
Most information can most effectively be
presented in the form of tables, charts, and
diagrams (Tull and Hawkins, 1990). These
graphic forms of data and information
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Chapter 6
Presentation of Evaluation Results
presentation can help simplify the presentation,
making it easier for an audience to
comprehend than if explained exhaustively
with words. Words are important for pointing
out significant ideas or findings, and for
interpreting the results where appropriate.
Words should not be used to repeat what is
already adequately explained in graphics, and
slides or transparencies that are composed
largely of words should contain only a few
essential ideas each. Presentation of too much
written information on a single slide or
transparency only confuses the audience.
Written slides or transparencies should also be
free of graphics, such as clever logos or
background highlights—unless the pictures are
essential to understanding the information
presented—since they only make the slides or
transparencies more difficult to read.
Examples of graphics and written slides are
presented in Figures 6-1 through 6-4.
Different types of graphics have different uses
as well. Information presented in a tabular
format can be difficult to interpret because the
reader has to spend some time with the
information to extract the essential points from
it. The same information presented in a pie
chart or bar graph can convey essential
information immediately and avoid the
inclusion of background data that are not
essential to the point. When preparing
5 Leading Sources of Water Quality Impairment
in various types of water bodies
RANK ESTUARIES LAKES RIVERS
1
2
3
4
5
Urban Runoff
STPs
Agriculture
Industry Point
Sources
Petroleum
Activities
Agriculture
STPs
Urban Runoff
Other NPS
Habitat
Modification
Agriculture
STPs
Habitat
Modification
Urban Runoff
Resource
Extraction
Figure 6-1. Example of presentation of information in a written slide. (Source: USEPA, 1995)
-------
Presentation of Evaluation Results
Chapter 6
EROSION AND SEDIMENT CONTROLS
• Sediment loading rates from construction sites are 5-500 times greater
than from undeveloped land
• Structural ESC controls can reduce sediment loadings 40-99%
• Structural ESC controls are REQUIRED on all construction sites
Figure 6-2. Example written presentation slide.
information for a report, an investigator should
organize the information in various ways and
choose that which conveys only the
information essential for the audience in the
least complicated manner.
Graphics. Photos, drawings, charts, tables,
maps, and other graphic elements can be
used to effectively present information that
the reader might otherwise not understand.
6.3.1 Written Presentations
The following criteria should be considered
when preparing written material:
• Reading level or level of education
of the target audience.
• Level of detail necessary to make
the results understandable to the
target audience) different audiences
require various levels of
background information to fully
understand the study's results.
• Layout. The integration of text,
graphics, color, white space,
columns, sidebars, and other design
elements is important in the
production of material that the
target audience will find readable
and visually appealing.
6.3.2 Oral Presentations
An effective oral presentation requires special
preparation. Tull and Hawkins (1990)
recommend three steps:
Leading Sources of Pollution
Relative Quantity of Lake Acres Affected by Source
Figure 6-3. Example representation of data in the form of
a pie chart
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Chapter 6
Presentation of Evaluation Results
Enforcement Actions
First Notic
44%
Stop-work
Order
10% Fine
( \l
e \ \ / Second
\^ \^/ Notice
24%
Compliance on Construction Sites
100% T
90%
80% "
70% "
60% "
50%
40%
30%
20% "
10%
Eini
••••i
Grass
County
g— -H
l|H|
1
Sedge
County
n Non-compliant
°> 2 deviations
•0-2 deviations
n Compliant
Management Measures Implemented
2
s
i 60% T
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Presentation of Evaluation Results
Chapter 6
1. Analyze the audience, as explained above;
2. Prepare an outline of the presentation, and
preferably a written script;
3. Rehearse it. Several dry runs of the
presentation should be made, and if
possible it should be taped on a VCR and
the presentation analyzed.
These steps are extremely important if an oral
presentation is to be effective. Remember that
oral presentations of 1A to 1 hour are often all
that is available for the presentation of the
results of months of research to managers who
are poised to make decisions based on the
presentation. Adequate preparation is essential
if the oral presentation is to accomplish its
purpose.
6.4 FOR FURTHER INFORMATION
The provision of specific examples of effective
and ineffective presentation graphics, writing
styles, and organizations is beyond the scope
of this document. A number of resources that
contain suggestions for how study results
should be presented are available, however,
and should be consulted. A listing of some
references is provided below.
The New York Public Library Writer's
Guide to Style and Usage (NYPL, 1994)
has information on design, layout, and
presentation in addition to guidance on
grammar and style.
Good Style: Writing for Science and
Technology (Kirkman, 1992) provides
techniques for presenting technical
material in a coherent, readable style.
The Modern Researcher (Barzun and
Graff, 1992) explains how to turn research
into readable, well organized writing.
Writing with Precision: How to Write So
That You Cannot Possibly Be
Misunderstood, 6th ed. (Bates, 1993)
addresses communication problems of the
1990s.
Designer's Guide to Creating Charts &
Diagrams (Holmes, 1991) gives tips for
combining graphics with statistical
information.
The Elements of Graph Design (Kosslyn,
1993) shows how to create effective
displays of quantitative data.
-------
/?EFE/?E/VCES
Academic Press. 1992. Dictionary of Science and Technology. Academic Press, Inc., San Diego, CA.
Adams, T. 1994. Implementation monitoring of forestry best management practices on harvested sites
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Commission, Columbia, South Carolina. September.
Barbe, D.E., H. Miller, and S. Jalla. 1993. Development of a computer interface among GDS, SCADA,
and SWMM for use in urban runoff simulation. In Symposium on Geographic Information Systems and
Water Resources, American Water Resources Association, Mobile, Alabama, March 14-17, pp. 113-120.
Barzun, J., and H.F. Graff. 1992. The Modern Researcher. 5th ed. Houghton Mifflin.
Bates, J. 1993. Writing with Precision: How to Write So That You Cannot Possibly Be Misunderstood.
6th ed. Acropolis.
Blalock, H.M., Jr. 1979. Social Statistics. Rev. 2nd ed. McGraw-Hill Book Company, New York, NY.
BLM. 1991. Inventory and Monitoring Coordination: Guidelines for the Use of Aerial Photography in
Monitoring. Technical Report TR 1734-1. Department of the Interior, Bureau of Land Management,
Reston, VA.
Born, J.D., and D.D. VanHooser. 1988. Intermountain Research Station remote sensing use for
resource inventory, planning, and monitoring. In Remote Sensing for Resource Inventory, Planning, and
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Casley, D.J., and D.A. Lury. 1982. Monitoring and Evaluation of Agriculture and Rural Development
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Center for Watershed Protection (CWP). 1997. Delaware program improves construction site
inspection. Technical Note No. 85. Center for Watershed Protection, Silver Spring, Maryland.
Watershed Protection Techniques 2(3):440-442.
Churchill, G.A., Jr. 1983. Marketing Research: Methodological Foundations, 3rd ed. The Dryden
Press, New York, NY.
Clayton and Brown. 1996. Environmental indicators to assess stormwater control pro grams and
practices. Center for Watershed Protection, Silver Sprint, Maryland.
Cochran, W.G. 1977. Sampling techniques. 3rded. John Wiley and Sons, New York, New York.
Conover, W.J. 1980. Practical Nonparametric Statistics, 2nd ed. Wiley, New York.
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-------
GLOSSARY
accuracy, the extent to which a measurement approaches the true value of the measured quantity.
aerial photography: the practice of taking photographs from an airplane, helicopter, or other aviation
device while it is airborne.
allocation, Neyman: stratified sampling in which the cost of sampling each stratum is in proportion to
the size of the stratum but variability between strata changes.
allocation, proportional: stratified sampling in which the variability and cost of sampling for each
stratum are in proportion to the size of the stratum.
allowable error: the level of error acceptable for the purposes of a study.
ANOVA: see test, analysis of variance.
assumptions: characteristics of a population of a sampling method taken to be true without proof.
bar graph: a representation of data wherein data are grouped and represented as vertical or horizontal
bars over an axis.
best professional judgment: an informed opinion made by a professional in the appropriate field of study
or expertise.
best management practice: a practice or combination of practices that are determined to be the most
effective and practicable means of controlling point and/or nonpoint pollutants at levels compatible with
environmental quality goals.
bias: a characteristic of samples such that when taken from a population with a known parameter, their
average does not give the parametric value.
binomial: an algebraic expression that is the sum or difference of two terms.
camera format: refers to the size of the negative taken by a camera. 35mm is a small camera format.
chi-square distribution: a scaled quantity whose distribution provides the distribution of the sample
variance.
coefficient of variation: a statistical measure used to compare the relative amounts of variation in
populations having different means.
confidence interval: a range of values about a measured value in which the true value is presumed to lie.
consistency: conforming to a regular method or style; an approach that keeps all factors of measurement
similar from one measurement to the next to the extent possible.
-------
Glossary
cumulative effects: the total influences attributable to numerous individual influences.
degrees of freedom: the number of residuals (the difference between a measured value and the sample
average) required to completely determine the others.
design, balanced: a sampling design wherein separate sets of data to be used are similar in quantity and
type.
distribution: the allocation or spread of values of a given parameter among its possible values.
erosion potential: a measure of the ease with which soil can be carried away in storm water runoff or
irrigation runoff.
error: the fluctuation that occurs from one repetition to another; also experimental error.
estimate, baseline: an estimate of baseline, or actual conditions.
estimate, pooled: a single estimate obtained from combining several individual estimates to obtain a
single value.
finite population correction term: a correction term used when population size is small relative to sample
size.
hydrologic modification: the alteration of the natural circulation or distribution of water by the
placement of structures or other activities.
hypothesis, alternative: the hypothesis that is contrary to the null hypothesis.
hypothesis, null: the hypothesis or conclusion assumed to be true prior to any analysis.
management measure: an economically achievable measure for the control of the addition of pollutants
from existing and new categories and classes of nonpoint sources of pollution, which reflect the greatest
degree of pollutant reduction achievable through the application of the best available nonpoint pollution
control practices, technologies, processes, siting criteria, operating methods, or other alternatives.
mean, estimated: a value of population mean arrived at through sampling.
mean, overall: the measured average of a population.
mean, stratum: the measured average within a sample subgroup or stratum.
measurement bias: a consistent under- or overestimation of the true value of something being measured,
often due to the method of measurement.
measurement error: the deviation of a measurement from the true value of that which is being measured.
-------
Glossary
median: the value of the middle term when data are arranged in order of size; a measure of central
tendency.
monitoring, baseline: monitoring conducted to establish initial knowledge about the actual state of a
population.
monitoring, compliance: monitoring conducted to determine whether those who must implement
programs, best management practices, or management measures, or who must conduct operations
according to standards or specifications, are doing so.
monitoring, project: monitoring conducted to determine the impact of a project, activity, or program.
monitoring, validation: monitoring conducted to determine how well a model accurately reflects reality.
navigational error: error in determining the actual location (altitude or latitude/longitude) of an airplane
or other aviation device due to instrumentation or the operator.
nominal: referred to by name; variables that cannot be measured but must be expressed qualitatively.
nonparametric method: distribution-free method; any of various inferential procedures whose
conclusions do not rely on assumptions about the distribution of the population of interest.
normal approximation: an assumption that a population has the characteristics of a normally distributed
population.
normal deviate: deviation from the mean expressed in units of a.
ordinal: ordered such that the position of an element in a series is specified.
parametric method: any statistical method whose conclusions rely on assumptions about the distribution
of the population of interest.
physiography: a description of the surface features of the earth; a description of landforms.
pie chart: a representation of data wherein data are grouped and represented as more or less triangular
sections of a circle and the total is the entire circle.
population, sample: the members of a population that are actually sampled or measured.
population, target: the population about which inferences are made; the group of interest, from which
samples are taken.
population unit: an individual member of a target population that can be measured independently of
other members.
power: the probability of correctly rejecting the null hypothesis when the alternative hypothesis is false.
-------
Glossary
precision: a measure of the similarity of individual measurements of the same population.
question, dichotomous: a question that allows for only two responses, such as "yes" and "no".
question, double-barreled: two questions asked as a single question.
question, multiple-choice: a question with two or more predetermined responses.
question, open-ended: a question format that requires a response beyond "yes" or "no".
remote sensing: methods of obtaining data from a location distant from the object being measured, such
as from an airplane or satellite.
resolution: the sharpness of a photograph.
sample size: the number of population units measured.
sampling, cluster: sampling in which small groups of population units are selected for sampling and each
unit in each selected group is measured.
sampling, simple random: sampling in which each unit of the target population has an equal chance of
being selected.
sampling, stratified random: sampling in which the target population is divided into separate subgroups,
each of which is more internally similar than the overall population is, prior to sample selection.
sampling, systematic: sampling in which population units are chosen in accordance with a predetermined
sample selection system.
sampling error: error attributable to actual variability in population units not accounted for by the
sampling method.
scale (aerial photography): the proportion of the image size of an object (such as a land area) to its
actual size, e.g., 1:3000. The smaller the second number, the larger the scale.
scale system: a system for ranking measurements or members of a population on a scale, such as
Ito5.
significance level: in hypothesis testing, the probability of rejecting a hypothesis that is correct, that is,
the probability of a Type I error.
standard deviation: a measure of spread; the positive square root of the variance.
standard error: an estimate of the standard deviation of means that would be expected if a collection of
means based on equal-sized samples of n items from the same population were obtained.
-------
Glossary
statistical inference: conclusions drawn about a population using statistics.
statistics, descriptive: measurements of population characteristics designed to summarize important
features of a data set.
stratification: the process of dividing a population into internally similar subgroups.
stratum: one of the subgroups created prior to sampling in stratified random sampling.
subjectivity: a characteristic of analysis that requires personal judgement on the part of the person doing
the analysis.
target audience: the population that a monitoring effort is intended to measure.
test, analysis of variance (ANOVA): a statistical test used to determine whether two or more sample
means could have been obtained from populations with the same parametric mean.
test, Friedman: a nonparametric test that can be used for analysis when two variables are involved.
test, Kruskal-Wallis: a nonparametric test recommended for the general case with a samples and «,
variates per sample.
test, Mann-Whitney: a nonparametric test for use when a test is only between two samples.
test, Student's t: a statistical test used to test for significant differences between means when only two
samples are involved.
test, Tukey's: a test to ascertain whether the interaction found in a given set of data can be explained in
terms of multiplicative main effects.
test, Wilcoxon's: a nonparametric test for use when only two samples are involved.
total maximum daily load: a total allowable addition of pollutants from all affecting sources to an
individual waterbody over a 24-hour period.
transformation, data: manipulation of data such that they will meet the assumptions required for
analysis.
unit sampling cost: the cost attributable to sampling a single population unit.
variance: a measure of the spread of data around the mean.
watershed assessment: an investigation of numerous characteristics of a watershed in order to describe
its actual condition.
-------
INDEX
accuracy, ID-10
aerial photography, V-23, V-24
aerial reconnaissance, V-l-V-3, V-23
allowable error, IE-14
alternative hypothesis, HI-12
analysis of variance (ANOVA), IV-4
assumptions, ni-1
audience identification, VI-2
balanced designs, III-2
baseline estimate, ni-26
best management practices (BMPs), I-1-1-5,
II-l,n-2, II-4, H-5, II-8-II-11
essential elements, 11-10
implementation of, II-1, D-2,11-10
operation and maintenance, D-4, II-5
tracking, II-1, H-4, II-9, H-10
best professional judgment (BPJ), V-12
bias, HI-10
binomial, V-13
camera format, V-23
categorical data, IV-4
nominal, IV-4
ordinal, IV-4
cluster sampling, ni-6, ni-25
Coastal Nonpoint Pollution Control Program
(CNPCP), I-2-I-4
Coastal Zone Act Reauthorization
Amendments of 1990 (CZARA), 1-2,
1-3
coefficient of variation, ffi-21
confidence interval, HI-12
consistency, V-10, V-15
cost, V-20
cumulative effects, 1-3
data
management, 1-5,1-6
data accessibility, 1-6
data life cycle, 1-6
degrees of freedom, IV-2
descriptive statistics, ffi-12
dichotomous questions, V-22, V-23
erosion and sediment control (E&SC), 1-3
erosion potential, V-l 1
error, HI-10
accuracy, IE-10
measurement bias, ID-10
measurement, III-lO-ni-12
precision, ID-10
sampling, IE-10
estimated mean, in-3
estimation, point, HI-12
evaluation methods, V-l
evaluations
expert, V-l
site, V-l, V-6, V-10
variable selection, V-6
federal requirements, IE-15
municipal separate storm sewer systems
(MS4s), IH-15
National Pollutant Discharge Elimination
System (NPDES), IE-15
finite population correction term, HI-17
Friedman test, IV-4
geographic information systems (GIS), II-
8-H-10
hydrologic modifications, 1-2
hypothesis testing, ffi-12
alternative hypothesis, ffi-12
confidence interval, ffi-12
null hypothesis, ffi-12
significance level, ffi-12
information sources, ffi-15
complaint records, ffi-16
county land maps, ffi-15
local government permits, ffi-16
public health departments, ffi-16
U.S. Census Bureau, ffi-16
Kruskal-Wallis, IV-4, IV-5
management measures (MMs), I-2-I-5
Mann-Whitney test, IV-2-IV-4
measurement bias, ffi-10
measurement error, ffi-10
-------
medians, HI-12
monitoring, 1-3
baseline, 1-4
compliance, 1-4
effectiveness, 1-4,1-5
implementation, 1-3-1-5
project, 1-4
trend, 1-4
validation, 1-4
multiple-choice questions, V-22
National Oceanic and Atmospheric
Administration (NOAA), 1-2,1-3
navigational errors, V-24
Neyman allocation, 111-24
nominal, IV-4
normal approximation, IV-3
normal deviate, in-22
null hypothesis, III-12
one-sided test, IV-1
open-ended questions, V-22
operation and maintenance, HI-14
ordinal, IV-4
overall mean, in-23
photography, V-l-V-3, V-23
point estimate, IE-12
pooled estimate, IV-3
population units, ffi-3
precision, ID-10
presentation, VI-2
oral, VI-3
format, VI-2
written, VI-3
probabilistic approach, ffi-2
probabilistic sampling, ni-2
cluster sampling, ni-6
probabilistic approach, in-2
simple random sampling, ffi-3
statistical inference, ffi-2
stratified random sampling, ffi-3
systematic sampling, ffi-4, ffi-6
targeted sampling, ffi-3
proportional allocation, ffi-24
quality assurance and quality control
(QA/QC), 1-5
quality assurance project plan (QAPP), 1-5
questionnaire, V-20, V-21
rating, V-12
pass/fail system, V-12
scale system, V-12
implementation, V-12
site, V-14
site rating, V-14
terms, V-13
resolution, V-23
resources, ffi-14
sample population, ffi-3
sample size, ffi-1, ffi-16
finite population correction term, HI-17
point estimates, ffi-16
standard deviations, ffi-17
sampling, II-9-II-11
cluster sampling, n-10
random sampling, 11-10
stratified random, II-10
sampling error, ffi-10
sampling strategy, ffi-14
scale, V-23
scale systems, V-12
self-evaluations, V-l, V-16
sensitive sabitats, HI-15
septic systems, II-2
significance level, ffi-12
simple random sampling, ffi-3, ffi-18
standard deviations, ffi-17
standard error, ffi-26
statistical inference, ffi-2
stratification, ffi-3
stratified random sampling, ffi-3, ffi-23
stratum, ffi-3
Student'st test, IV-2
subjectivity, V-ll
surveys
accuracy of information, V-20
systematic sampling, ffi-4, ffi-6, ffi-26
-------
target audience, V-21
target population, ni-3
tax base, HI-14
total maximum daily load, 1-3, D-2
Tukey' s method, IV-4
two-sided test, IV-1
U.S. Environmental Protection Agency
(EPA), 1-2,1-3,1-5
unit sampling cost, ni-24
urbanizing areas, III-14
variables, V-2, V-3, V-6, V-14
variance, ni-4
watershed assessments, 1-3
Wilcoxon'stest, IV-3
"double-barreled" questions, V-22
-------
APPENDIX A
Statistical Tables
-------
Appendix A
Table Al. Cumulative areas under the Normal distribution (values of p corr
toZp)
ZP
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
— ^
0.00 0.01
0.5000 0.5040
0.5398 0.5438
0.5793 0.5832
0.6179
0.6217
0.6554 0.6591
0.6915
0.6950
0.7257 0.7291
0.7580 0.7611
0.7881
0.8159
0.7910
0.8186
0.8413 0.8438
0.8643 0.8665
0.8849
0.9032
0.9192
0.9332
0.9452
0.8869
0.9049
0.9207
0.9345
0.9463
0.9554 0.9564
0.9641
0.9649
0.9713 0.9719
0.9772
0.9821
0.9861
0.9778
0.9826
0.9864
0.9893 0.9896
0.9918 0.9920
0.9938 0.9940
0.9953 0.9955
0.9965
0.9966
0.9974 0.9975
0.9981
0.9982
0.9987 0.9987
0.9990 0.9991
0.9993 0.9993
0.9995
0.9995
0.9997 0.9997
A
0.02
0.5080
0.5478
0.5871
0.6255
0.6628
0.6985
0.7324
0.7642
0.7939
0.8212
0.8461
0.8686
0.8888
0.9066
0.9222
0.9357
0.9474
0.9573
0.9656
0.9726
0.9783
0.9830
0.9868
0.9898
0.9922
0.9941
0.9956
0.9967
0.9976
0.9982
0.9987
0.9991
0.9994
0.9995
0.9997
I
0.03
0.5120
0.5517
0.5910
0.6293
0.6664
0.7019
0.7357
0.7673
0.7967
0.8238
0.8485
0.8708
0.8907
0.9082
0.9236
0.9370
0.9484
0.9582
0.9664
0.9732
0.9788
0.9834
0.9871
0.9901
0.9925
0.9943
0.9957
0.9968
0.9977
0.9983
0.9988
0.9991
0.9994
0.9996
0.9997
k
0.04
0.5160
0.5557
0.5948
0.6331
0.6700
0.7054
0.7389
0.7704
0.7995
0.8264
0.8508
0.8729
0.8925
0.9099
0.9251
0.9382
0.9495
0.9591
0.9671
0.9738
0.9793
0.9838
0.9875
0.9904
0.9927
0.9945
0.9959
0.9969
0.9977
0.9984
0.9988
0.9992
0.9994
0.9996
0.9997
•£.
0.05
0.5199
0.5596
0.5987
0.6368
0.6736
0.7088
0.7422
0.7734
0.8023
0.8289
0.8531
0.8749
0.8944
0.9115
0.9265
0.9394
0.9505
0.9599
0.9678
0.9744
0.9798
0.9842
0.9878
0.9906
0.9929
0.9946
0.9960
0.9970
0.9978
0.9984
0.9989
0.9992
0.9994
0.9996
0.9997
a = a
0.06
0.5239
0.5636
0.6026
0.6406
0.6772
0.7123
0.7454
0.7764
0.8051
0.8315
0.8554
0.8770
0.8962
0.9131
0.9279
0.9406
0.9515
0.9608
0.9686
0.9750
0.9803
0.9846
0.9881
0.9909
0.9931
0.9948
0.9961
0.9971
0.9979
0.9985
0.9989
0.9992
0.9994
0.9996
0.9997
0.07
0.5279
0.5675
0.6064
0.6443
0.6808
0.7157
0.7486
0.7794
0.8078
0.8340
0.8577
0.8790
0.8980
0.9147
0.9292
0.9418
0.9525
0.9616
0.9693
0.9756
0.9808
0.9850
0.9884
0.9911
0.9932
0.9949
0.9962
0.9972
0.9979
0.9985
0.9989
0.9992
0.9995
0.9996
0.9997
0.08
0.5319
0.5714
0.6103
0.6480
0.6844
0.7190
0.7517
0.7823
0.8106
0.8365
0.8599
0.8810
0.8997
0.9162
0.9306
0.9429
0.9535
0.9625
0.9699
0.9761
0.9812
0.9854
0.9887
0.9913
0.9934
0.9951
0.9963
0.9973
0.9980
0.9986
0.9990
0.9993
0.9995
0.9996
0.9997
0.09
0.5359
0.5753
0.6141
0.6517
0.6879
0.7224
0.7549
0.7852
0.8133
0.8389
0.8621
0.8830
0.9015
0.9177
0.9319
0.9441
0.9545
0.9633
0.9706
0.9767
0.9817
0.9857
0.9890
0.9916
0.9936
0.9952
0.9964
0.9974
0.9981
0.9986
0.9990
0.9993
0.9995
0.9997
0.9998
-------
Appendix A
Table A2. Percentiles of the t a^f distribution (values oft such that 100(1-a
distribution is less than t)
df
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
35
40
50
60
80
100
150
200
inf.
a = 0.40
0.3249
0.2887
0.2767
0.2707
0.2672
0.2648
0.2632
0.2619
0.2610
0.2602
0.2596
0.2590
0.2586
0.2582
0.2579
0.2576
0.2573
0.2571
0.2569
0.2567
0.2566
0.2564
0.2563
0.2562
0.2561
0.2560
0.2559
0.2558
0.2557
0.2556
0.2553
0.2550
0.2547
0.2545
0.2542
0.2540
0.2538
0.2537
0.2533
^
a = 0.30
0.7265
0.6172
0.5844
0.5686
0.5594
0.5534
0.5491
0.5459
0.5435
0.5415
0.5399
0.5386
0.5375
0.5366
0.5357
0.5350
0.5344
0.5338
0.5333
0.5329
0.5325
0.5321
0.5317
0.5314
0.5312
0.5309
0.5306
0.5304
0.5302
0.5300
0.5292
0.5286
0.5278
0.5272
0.5265
0.5261
0.5255
0.5252
0.5244
/"
a = 0.20
1 .3764
1 .0607
0.9785
0.9410
0.9195
0.9057
0.8960
0.8889
0.8834
0.8791
0.8755
0.8726
0.8702
0.8681
0.8662
0.8647
0.8633
0.8620
0.8610
0.8600
0.8591
0.8583
0.8575
0.8569
0.8562
0.8557
0.8551
0.8546
0.8542
0.8538
0.8520
0.8507
0.8489
0.8477
0.8461
0.8452
0.8440
0.8434
0.8416
\
_p
t
a =0.10
3.0777
1.8856
1 .6377
1 .5332
1.4759
1.4398
1.4149
1 .3968
1 .3830
1 .3722
1 .3634
1 .3562
1 .3502
1.3450
1 .3406
1 .3368
1 .3334
1 .3304
1 .3277
1.3253
1 .3232
1.3212
1.3195
1.3178
1.3163
1.3150
1.3137
1.3125
1.3114
1.3104
1 .3062
1.3031
1.2987
1.2958
1.2922
1.2901
1.2872
1.2858
1.2816
Area
a = 0.05
6.3137
2.9200
2.3534
2.1318
2.0150
1.9432
1.8946
1.8595
1.8331
1.8125
1.7959
1.7823
1 .7709
1.7613
1.7531
1.7459
1 .7396
1.7341
1.7291
1.7247
1 .7207
1.7171
1.7139
1.7109
1.7081
1 .7056
1 .7033
1.7011
1.6991
1 .6973
1.6896
1 .6839
1 .6759
1 .6706
1.6641
1 .6602
1.6551
1.6525
1.6449
= a
a = 0.025
12.7062
4.3027
3.1824
2.7765
2.5706
2.4469
2.3646
2.3060
2.2622
2.2281
2.2010
2.1788
2.1604
2.1448
2.1315
2.1199
2.1098
2.1009
2.0930
2.0860
2.0796
2.0739
2.0687
2.0639
2.0595
2.0555
2.0518
2.0484
2.0452
2.0423
2.0301
2.0211
2.0086
2.0003
1.9901
1.9840
1 .9759
1.9719
1 .9600
a =0.010
31.8210
6.9645
4.5407
3.7469
3.3649
3.1427
2.9979
2.8965
2.8214
2.7638
2.7181
2.6810
2.6503
2.6245
2.6025
2.5835
2.5669
2.5524
2.5395
2.5280
2.5176
2.5083
2.4999
2.4922
2.4851
2.4786
2.4727
2.4671
2.4620
2.4573
2.4377
2.4233
2.4033
2.3901
2.3739
2.3642
2.3515
2.3451
2.3264
a = 0.005
63.6559
9.9250
5.8408
4.6041
4.0321
3.7074
3.4995
3.3554
3.2498
3.1693
3.1058
3.0545
3.0123
2.9768
2.9467
2.9208
2.8982
2.8784
2.8609
2.8453
2.8314
2.8188
2.8073
2.7970
2.7874
2.7787
2.7707
2.7633
2.7564
2.7500
2.7238
2.7045
2.6778
2.6603
2.6387
2.6259
2.6090
2.6006
2.5758
-------
Appendix A
Table A3. Upper and lower percentiles of the Chi-square distribution
df
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
35
40
50
60
70
80
90
100
200
0.001
0.002
0.024
0.091
0.210
0.381
0.599
0.857
1.152
1.479
1.834
2.214
2.617
3.041
3.483
3.942
4.416
4.905
5.407
5.921
6.447
6.983
7.529
8.085
8.649
9.222
9.803
10.391
10.986
1 1 .588
14.688
17.917
24.674
31 .738
L
0.005
0.010
0.072
0.207
0.412
0.676
0.989
1.344
1.735
2.156
2.603
3.074
3.565
4.075
4.601
5.142
5.697
6.265
6.844
7.434
8.034
8.643
9.260
9.886
10.520
11.160
1 1 .808
12.461
13.121
13.787
17.192
20.707
27.991
35.534
39.036 43.275
46.520
54.156
61.918
143.84
51.172
59.196
67.328
152.24
\
0.010
0.020
0.115
0.297
0.554
0.872
1.239
1.647
2.088
2.558
3.053
3.571
4.107
4.660
5.229
5.812
6.408
7.015
7.633
8.260
8.897
9.542
10.196
10.856
11.524
12.198
12.878
13.565
14.256
14.953
18.509
22.164
29.707
37.485
45.442
53.540
61.754
70.065
156.43
>
__p.
0.025
0.001
0.051
0.216
0.484
0.831
1.237
1.690
2.180
2.700
3.247
3.816
4.404
5.009
5.629
6.262
6.908
7.564
8.231
8.907
9.591
10.283
10.982
11.689
12.401
13.120
13.844
14.573
15.308
16.047
16.791
20.569
24.433
32.357
40.482
48.758
57.153
65.647
74.222
162.73
. Area
*
^^M_
0.050
0.004
0.103
0.352
0.711
1.145
1.635
2.167
2.733
3.325
3.940
4.575
5.226
5.892
6.571
7.261
7.962
8.672
9.390
10.117
10.851
11.591
12.338
13.091
13.848
14.611
15.379
16.151
16.928
17.708
18.493
22.465
26.509
34.764
43.188
51.739
60.391
69.126
77.929
168.28
= ^P
P
0.100
0.016
0.211
0.584
1.064
1.610
2.204
2.833
3.490
4.168
4.865
5.578
6.304
7.041
7.790
8.547
9.312
10.085
10.865
11.651
12.443
13.240
14.041
14.848
15.659
16.473
17.292
18.114
18.939
19.768
20.599
24.797
29.051
37.689
46.459
55.329
64.278
73.291
82.358
174.84
0.900
2.706
4.605
6.251
7.779
9.236
10.645
12.017
13.362
14.684
15.987
17.275
18.549
19.812
21.064
22.307
23.542
24.769
25.989
27.204
28.412
29.615
30.813
32.007
33.196
34.382
35.563
36.741
37.916
39.087
40.256
46.059
51.805
63.167
74.397
85.527
96.578
107.57
118.50
226.02
0.950
3.841
5.991
7.815
9.488
1 1 .070
12.592
14.067
15.507
16.919
18.307
19.675
21.026
22.362
23.685
24.996
26.296
27.587
28.869
30.144
31.410
32.671
33.924
35.172
36.415
37.652
38.885
40.113
41 .337
42.557
43.773
49.802
55.758
67.505
79.082
90.531
101.88
113.15
124.34
233.99
0.975
5.024
7.378
9.348
11.143
12.832
14.449
16.013
17.535
19.023
20.483
21.920
23.337
24.736
26.119
27.488
28.845
30.191
31.526
32.852
34.170
35.479
36.781
38.076
39.364
40.646
41.923
43.195
44.461
45.722
46.979
53.203
59.342
71 .420
83.298
95.023
106.63
118.14
129.56
241.06
0.990
6.635
9.210
1 1 .345
13.277
15.086
16.812
18.475
20.090
21 .666
23.209
24.725
26.217
27.688
29.141
30.578
32.000
33.409
34.805
36.191
37.566
38.932
40.289
41 .638
42.980
44.314
45.642
46.963
48.278
49.588
50.892
57.342
63.691
76.154
88.379
100.43
112.33
124.12
135.81
249.45
0.995
7.879
10.597
12.838
14.860
16.750
18.548
20.278
21.955
23.589
25.188
26.757
28.300
29.819
31.319
32.801
34.267
35.718
37.156
38.582
39.997
41.401
42.796
44.181
45.558
46.928
48.290
49.645
50.994
52.335
53.672
60.275
66.766
79.490
91.952
104.21
116.32
128.30
140.17
255.26
0.999
10.827
13.815
16.266
18.466
20.515
22.457
24.321
26.124
27.877
29.588
31.264
32.909
34.527
36.124
37.698
39.252
40.791
42.312
43.819
45.314
46.796
48.268
49.728
51.179
52.619
54.051
55.475
56.892
58.301
59.702
66.619
73.403
86.660
99.608
112.32
124.84
137.21
149.45
267.54
------- |