TANF Sampling and Statistical Methods Manual Part 265.5

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TANF
SAMPLING
AND
STATISTICAL
METHODS
MANUAL

May 2007
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TANF
SAMPLING AND STATISTICAL METHODS MANUAL
TABLE OF CONTENTS

1100 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1110 Purpose of the Manual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1200 BASIC STATISTICAL CONCEPTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1210 Sampling and Non-Sampling Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1220 Common Types of Scientific Sampling Techniques . . . . . . . . . . . . . . . . . . 5
1221 Simple Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1222 Systematic Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1223 Stratified Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1224 Allocation of Stratified Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1224.1
Proportional Allocation . . . . . . . . . . . . . . . . . . . . . . . 8
1224.2
Optimal Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1230 Validity and Reliability of Statistical Data . . . . . . . . . . . . . . . . . . . . . . . . . 9
1231 Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1232 Precision -- Computation of the Confidence Interval . . . . . . . . . . . 10
1232.1
Computation of Sample Size to Obtain a Desired
Precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1232.2
Computation of Levels of Precision for Stratified
Samples From State (Tribal) Sample . . . . . . . . . . . . 14
1232.3
Relative Efficiency of Stratified Random and Simple
Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1300 SAMPLING PLAN REQUIREMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1310 Criteria for Plan Approval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1320 Sample Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1330 Sample Selection Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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17
18
18
19

1400 SAMPLE SIZES AND PROCEDURES FOR SELECTING SAMPLE CASES
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1410 Annual Sample Size Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1411 Sample Size Requirements for the TANF Active Sample . . . . . . . 21
1412 Sample Size Requirements for the TANF Sample of Closed Cases
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1413 Sample Size Requirements for the SSP-MOE Active Sample . . . . 23
1414 Sample Size Requirements for the SSP-MOE Sample of Closed
Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1415 Adjustment to the Sample Size for States and Tribes with Small
Caseloads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1416 Average Monthly Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1420 Sample Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1421 Sampling Frame for the TANF Active Case Sample . . . . . . . . . . . 26
1422 The Treatment of Special Groups With Respect to TANF Reporting
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1422.1
Newly Approved Applicant (aka, Initial Assistance
Cases) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1422.2
Non-Custodial Parents . . . . . . . . . . . . . . . . . . . . . . . 29
1422.3
Members of Indian Tribes Not Eligible under a Tribal
Family Assistance Plan . . . . . . . . . . . . . . . . . . . . . . 30
1422.4
Members of Indian Tribes Receiving Assistance under
a Tribal Family Assistance Plan . . . . . . . . . . . . . . . . 30
1422.5
Cases Selected For More Than One Sample Month
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1422.6
Cases Receiving Assistance Under the State's TANF
Program and Separate State Programs for the Same
Month . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1422.7
Cases With a Child Not Living With a Parent or Adult
Caretaker Relative . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1422.8
Cases for Which State Changes Funding Stream
State must make all changes in funding streams to cases for a
report month prior to formation of the sample frame(s) and
sample selection for the report month. Changes in funding
stream after sample selection are not permitted because such
changes will destroy the representativeness of the sample and
result in invalid samples. This would make the State liable
for a data reporting penalty.

- ii -

1423 Sample Frame for the Sample of Closed TANF Cases
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1424 Sample Frame for the Sample of Active SSP Cases . . . . . . . . . . . . 32
1425 Sample Frame for the Sample of Closed SSP Cases . . . . . . . . . . . 33
1430 Procedures for Selecting Sample Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
1440 Procedures for Selecting Sample Cases Using a Simple Random Sample
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1450 Retention of Sampling Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
1500 CORRECTION FOR UNDERSAMPLING AND EXCESSIVE
OVERSAMPLING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
1510 Standard Method to Correction for Undersampling or Oversampling when
Sample Selected Using Systematic Random Sampling . . . . . . . . . . . . . . . 42
1510.1
Correction for Oversampling . . . . . . . . . . . . . . . . . . . . . . . . 42
1510.2
Correcting for Undersampling . . . . . . . . . . . . . . . . . . . . . . . 43
1520 Alternate Method of Correcting for Undersampling or Oversampling When
Sample Selected Using Systematic Random Sampling . . . . . . . . . . . . . . . 44
1530 Correcting for Undersampling Using a Reserve Sample Pool . . . . . . . . . 46
1531 Procedure for Setting Up a Reserve Sample Pool . . . . . . . . . . . . . 47
1532 Procedure for Obtaining Cases from a Reserve Sample Pool . . . . 49
1540 Correction for Undersampling or Oversampling When Sample Was
Selected Using Simple Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . 49
1540.1
Correcting for Undersampling . . . . . . . . . . . . . . . . . . . . . . . 50
1540.2
Correcting for Oversampling . . . . . . . . . . . . . . . . . . . . . . . . 51
1600 WORK PARTICIPATION RATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
1610 Work Participation Rate Standards and Caseload Reduction Credit . . . . . 53
1620 Definitions of Annual and Monthly Work Participation Rates . . . . . . . . . 55
1630 Countable Work Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
1640 Required Hours of Work to be "Engaged in Work" . . . . . . . . . . . . . . . . . 58
1641 Deeming Core Hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
1642 The Thirty (30) Percent Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
1650 Methodology Used in Calculating the Monthly Work Participation Rate
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
1651 Calculation of the Monthly Work Participation Rate from Universe
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
1652 Calculation of the Monthly Work Participation Rate from Sample
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
1653 Calculation of the Monthly Work Participation Rate from Stratified
Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

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1654 Adjusting the Monthly Work Participation Rate for Exceeding the
30% Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
1700 STATISTICAL METHODS IN DATA ANALYSIS . . . . . . . . . . . . . . . . . . . . 75
1710 Statistical Tests of Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
1711 Testing the Representativeness of the Sample with the Caseload
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
1711.1
Comparison of Sample and Total Caseload When
Proportions Are Not Used . . . . . . . . . . . . . . . . . . . . 76
1711.2
Comparison of Sample and Total Caseload When
Proportions Are Used . . . . . . . . . . . . . . . . . . . . . . . . 77
1711.3
One Sample Chi-Square () Test . . . . . . . . . . . . . . . . 78
1712 Testing Differences of Proportions Between Samples . . . . . . . . . . 83
1713 Testing Differences Within the Same Sample -- Chi-Square () . . . 85
1720 Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
1721 Moving Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
1722 Individual Monthly Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
1723 Computation of a Regression Line by "Least Squares" Method . . 93
1723.1
Practical Uses of Trend Line and Trend Values . . . . 98
1723.2
Testing Trend for Statistical Significance . . . . . . . . 98
1723.3
Relationship Between Time Sequence and Error Rates
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
1730 Statistical Procedures for Developing Profiles of Error-Prone Cases . . . 101
1731 Criteria for Setting Up Error-Prone Profile Models . . . . . . . . . . . 102
INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

FIGURES
Figure 1.

Normal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 2.

Six-Month Moving Averages of Completed Sample Cases
By the Month of Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Figure 3.

Six-Month Moving Averages of Completed Sample Cases
Regardless of Month of Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
- iv -

Figure 4.

Trend Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

APPENDICES
Appendix A

Table of Random Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Appendix B

Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

Appendix C

Standard Error of Percentages Based on Selected
Sample Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Appendix D

TANF Sample Plan Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Appendix E

Tribal Codes for the TANF Program . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Appendix F

FIPS County Codes - Alphabetical List . . . . . . . . . . . . . . . . . . . . . . . . . 169

-v-

TANF
SAMPLING AND STATISTICAL
METHODS MANUAL

1100

INTRODUCTION

Title I of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996
(PRWORA) establishes the Block Grants for Temporary Assistance for Needy Families
(TANF) Program by amending Titles IV-A and IV-F of the Social Security Act. The
purpose of this welfare reform initiative, which replaced the Aid to Families with
Dependent Children Program and the Jobs Opportunity and Basic Skills Program, is to
increase the flexibility of States and Tribal grantees in operating a program designed to:
1.

Provide assistance to needy families (cases) so that children may be cared
for in their own homes or in the homes of relatives;

2.

End the dependence of needy parents on government benefits by promoting
job preparation, work, and marriage;

3.

Prevent and reduce the incidence of out-of-wedlock pregnancies and
establish annual numerical goals for preventing and reducing the incidence
of these pregnancies; and

4.

Encourage the formation and maintenance of two-parent families.

While the TANF provisions allow States and Tribal grantees discretion as to the
mechanisms used in meeting these goals, they place on States and Tribal grantees a
responsibility for measuring, tracking, and reporting on their reform initiatives.
The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 requires
States and Tribes to collect on a monthly basis and report to the Secretary of the
Department of Health and Human Services (DHHS) on a quarterly basis a wide variety of
disaggregated case record information on the families receiving assistance, families no
longer receiving assistance, and families applying for assistance from programs funded

under the TANF program. State or Tribal grantee may comply with this requirement by
collecting and submitting case record information for its entire caseload or by collecting
and submitting the case record information for a portion of the caseload which is obtained
through the use of scientifically acceptable sampling methods.

1110

Purpose of the Manual

Sampling is the selection of a part of a whole for the purpose of drawing conclusions
about the population, or universe. It permits the administrator to cut costs; reduce
manpower requirements; gather vital information more quickly; obtain data not available
otherwise; obtain more comprehensive data; and, in some instances, actually increase
statistical accuracy. The manual explains statistical techniques in sufficient detail for
careful observance of sound sampling procedures and other basic statistical principles.
Theory is included to the extent necessary to provide working rules for application of the
more commonly used techniques as well as for recognizing the limitation of such
techniques. Because many users of the Manual are not statisticians, mathematical
exposition and technical language have been kept to a minimum.
This sampling manual contains the broad framework and procedures to be used by each
State or Tribes, that opts to file its TANF Data Report (or Tribal TANF Data Report)
based on a sample of its caseload. In developing its more specific sampling plans, States
and Tribal grantees have considerable latitude in designing samples that are consistent
with the principles described herein. The manual should provide the user with a basic
understanding of the TANF program sample requirements and statistically valid sampling
methods, which are essential to the successful reporting on the TANF program.
Section 1200 describes common types of sample designs (e.g., simple random sampling
and systematic random sampling) and basic statistical concepts, which are applicable in
any sample survey setting. It is intended to provide a general background to nonstatisticians who use the manual. Section 1300 contains sampling plan requirements: a
State or Tribal sampling plan must include a detailed description of the sample frame and
the procedures that are to be employed in constructing the sample frame, i.e., the list from
which the sample is to be selected. Also, the plan must describe in detail the sample
selection procedures for identifying the sample cases (families) for which data are to be
reported. For guidance on developing a sampling plan, see Appendix D. Section 1400
contains the sample size requirements, sample selection procedures for systematic random
sampling and simple random sampling, and special sampling problems associated with
the TANF program. Section 1500 describes procedures to be using in the event an
adjustment to the sample size is needed. Section 1600 describes the methodology for
calculating the monthly and annual work participation rates. Section 1700 contains
general information on basic statistical techniques that can be used for an effective

analysis of the TANF program data. States and Tribes should use the sampling plan
requirements specified in Sections 1300, 1400, and 1500, along with the outline contained
in Appendix D (Page 147), to develop their detailed sampling plans. If they need further
assistance to develop sampling plans, they can contact the Administration for Children
and Families (ACF) Regional TANF Manager for assistance.

1200

BASIC STATISTICAL CONCEPTS

Probability sampling is an acceptable alternative to providing 100% counts of the TANF
caseload each month. Probability sampling has two properties: (1) every unit in the
entire population has a known, non-zero chance (called a probability) of being selected in
the sample, and (2) there is an element of "randomness" used to select the particular
sample for which data are to be collected. These two principles --measurability and
randomness -- distinguish probability samples from haphazard, judgment, or quota
samples.

1210

Sampling and Non-Sampling Errors

When a sample is selected through a random procedure, the estimates of a population
characteristic from that sample will generally be different from the true value of the
population characteristic simply because the estimates are based on a sample. Thus, a
sampling error may be defined as the difference between the value of the characteristic as
estimated from the sample and the true population value of the characteristic. Although
such errors cannot be avoided, they can be controlled and measured (in probability
samples).
Non-sampling errors, on the other hand, are generally not measurable (except by the use
of special auxiliary sample checks). Examples of non-sampling errors include: (1)
careless errors in coding responses, (2) errors attributable to the imperfect design of
measurement tools, e.g., I.Q. tests are only an approximate measure of intelligence, and
(3) errors due to inability to obtain relevant information for all sample members, i.e.,
non-response bias.
The design of any study should be examined carefully in order to determine the presence
and impact of such errors.

1220

Common Types of Scientific Sampling Techniques

It is impossible to specify a single sampling procedure that would be best suited to all
State agencies for all samples. There are many different ways of selecting scientific
(probability) samples from populations with items of equal importance. The simplest and
most widely used methods are: simple random sampling, systematic random sampling,
stratified simple random sampling, and stratified systematic random sampling. These
four widely used methods are acceptable methods of sampling for the purpose of
collecting and reporting the disaggregated TANF and separate State program -

maintenance of errort (SSP-MOE) data.

1221

Simple Random Sampling

Simple random sampling is a method of selecting a sample in such a way that each unit of
the frame has an equal and independent chance of being included in the sample. For
samples of any given size
from a population of size N, all possible combinations of
units that could form samples of that size must have the same probability of selection.
A table of random numbers (see Appendix A, page 103) or a computer program with a
random number generator is generally used to choose the sample units. This method is
relatively easy to administer and is responsive to variations in caseload size over the
course of the sample period.

1222

Systematic Random Sampling

Systematic random sampling method provides a system or pattern of selection of
individual units from a sample frame (which may be a hardcopy list or computer file of all
the individual units in the population) at equally spaced intervals (such as every 10th,
140th, 850th, etc., as required to obtain the total of a given sample size) with the starting
point within the first interval being determined by random selection.
In using the systematic random sampling method, one needs to be aware of a major pitfall
that exists when the cases on the sample frame are arranged in some kind of repetitive or
cyclical pattern. In such an ordered list, the sample interval might sometimes be the same
as the cycle and could, therefore, yield a sample of cases with similar characteristics
which may not be typical of the caseload. It is, therefore, important not to use a
systematic sample with a listing that is cyclical in nature.

1223

Stratified Random Sampling

Stratified random sampling is random sampling of a population that is divided into a
number of sub-populations according to some pre-determined criterion (geographic
location, characteristic, etc.). In order to produce estimates with a given precision while
minimizing the total sample size required, the population is divided into several
homogeneous groups so that the units in the same group are more alike than the units in
different groups. Each group is called a "stratum" and the process of dividing the
population into groups is referred to as "stratification." The strata do not overlap and
together comprise the entire population. Sample cases can be selected independently
from each stratum using either systematic random sampling, simple random sampling, or

an alternative approved sampling procedure. If the percent of the sub-population selected
from each sub-population are equal, i.e., proportional sampling, no weighting is required.
The sample is "self-weighting." Otherwise, individual weighting factors for each subpopulation must be taken into account before the sub-population sample results can be
combined.
There are various purposes for stratification. It may be that information is desired on the
strata separately; that more accurate estimates of the population parameters are needed
than can be obtained by a non-stratified sample; or that costs and administrative
constraints must be considered. To achieve these purposes, optimum allocation of the
sample size among the strata is usually required. Because a disproportionate number of
cases can be drawn from particular strata, some strata may be sampled more intensively
than others. For example, a State may find it administratively efficient to give a higher
probability of being sampled to urban areas than to rural areas.
The following points should be considered in using the stratified sampling method:
1.

Stratified sampling requires advance knowledge of the proportion of the
population in each stratum;

2.

Stratification by one characteristic does not ensure an efficient stratification
by other characteristics that may be of interest;

3.

Gains in precision for population estimates will be negligible unless it is
known that there are substantial differences between the strata and
relatively small differences within each stratum;

4.

The cost and effort of creating the strata may outweigh the potential gains
in precision;

5.

The weighting procedures required for calculating population estimates and
confidence levels for stratified samples in which the strata units are
disproportionately allocated can be complex and time consuming (see
Section 1232.2 (page 14); and

6.

Over stratification (i.e., creating too many strata) for a given size of sample
can result in some small strata that may adversely affect the precision of
estimates.

1224

Allocation of Stratified Sample

If a State selects a stratified sample, the State must decide how to allocate the sample
among the strata and describe the allocation procedures in the sampling plan. Two
common methods for sample allocation are allocation proportional to stratum caseload
size and optimal allocation with respect to an important program characteristic (e.g.,
participation rate).

1224.1

Proportional Allocation

Proportion allocation means that the size of the samples from the different strata are
proportional to the size of the caseload for the strata. In general, this allocation method is
desirable because it produces a self-weighting sample. For proportional allocation
calculate the stratum sample size by multiplying the total sample size by the ratio of the
stratum's caseload to the total caseload.

1224.2

Optimal Allocation

Optimal allocation of a given size sample means that the sizes of the samples from the
different strata are determined so that the overall variance is minimized. This is done by
taking into consideration several characteristics, e.g., caseload size as well as the
estimated standard error for the value of the program characteristic of interest to the
program administrator. Because strata differ in both caseload size and the program
characteristic, it is reasonable to take larger samples from the strata with greater value of
the program characteristic of interest and smaller samples from the strata with less value
of the program characteristic of interest. Optimal allocation with respect to the program
characteristic produces a disproportionate stratified sample that minimizes the estimated
standard error of the program characteristic. Because the sample is disproportionally
allocated, the sample results will have to be weighted to generate State program

characteristics. The equation for the optimal allocation of a sample is:

where:
is the sample size for the

stratum;
is the total State sample size;
represents the strata, in which the State's
caseload is grouped for sampling;

is the estimated standard error of program characteristic for the
stratum; and
is the TANF caseload for the

1230

stratum.

Validity and Reliability of Statistical Data

Sampling and statistical procedures, by themselves, cannot assure validity (or freedom
from bias) of the collected data -- that is, that case record information is actually correct
and is reported correctly. The validity of the statistical data depends upon the adequacy
of the coding schedule in relation to the scope, detail, and significance of the data
collected; the accuracy and completeness of the data in the case record; and the degree to
which case record reviews are carried out effectively.
Sound sampling procedures can assure a known degree of reliability (also referred to as
precision) of statistical data. If sampling procedures are soundly based, the results
obtained from one sample taken from the total caseload will be the approximate results
obtained if the whole caseload was reviewed.
The TANF sample is designed so that the reliability of the sample results is measurable
and can be shown to be relatively high. These results can be made more reliable through
proper application of statistical methods, as well as through an increase in sample size.

Because of their importance, examples of sources of bias (which affect validity) and
explanations of the formulas involved in measuring precision (reliability) are discussed in
some detail.

1231

Bias

A biased sample is one that does not represent the population from which it was selected,
i.e., an infinite number of selected samples would not yield the characteristics of the
population from which they were selected. For example, suppose that an opinion survey
was conducted in the middle of the day by interviewing everyone on a busy street willing
to stop for ten minutes for the interview. If 90 percent of those persons interviewed had a
favorable opinion on the issue involved, it would not necessarily follow that about 90
percent of the city residents have a favorable opinion. People on a particular street at a
particular time of day would more than likely be unrepresentative of the total city
population. Also, the fact that the sample consisted only of individuals who could spare
ten minutes in the middle of the day makes the sample even more unrepresentative. Such
a sample could contain bias.
One source of bias deals with cases for which data cannot be collected. "Data not
collected" or non-response cases fall into several categories. Such cases should have
been included in the sample but could not be for reasons such as the case record could not
be located or contains incomplete information.
If the number of non-response cases is small, the bias resulting from their non-response
will generally also be small. If the number of such cases is large, a considerable bias may
be introduced. In effect, a segment of the total caseload is unrepresented if the sample
cases for that segment are not reviewed. If a substantial number of sample cases are not
included, there is no assurance that conclusions drawn from the sample apply to the total
caseload. The number of such cases can be anticipated and should be compensated for by
oversampling. Even if the correct number of cases is compensated by oversampling,
non-response bias may still be present.

1232

Precision -- Computation of the Confidence Interval

Population values, which can normally be estimated from a sample, are often referred to
as population "parameters." A single valued estimate of a population parameter is called
a "point estimate." In order to predict the actual proportion of the population with a given
caseload characteristic (i.e. the proportion of the caseload with an adult participating in a
work program) with any degree of certainty, a range of possible values (confidence
interval) is computed. The first step is to compute the "variance" (also called the "mean

square deviation") of the point estimate. Variance is the quantity that is used to measure
the extent of fluctuations around the mean (simple average) while mean square deviation
is used to measure the dispersion around the mean or some arbitrary origin.
For systematic random samples, when simulating simple random selection, the estimated
variance of a proportion is computed approximately by the following equation 1 :

where:

=

estimated proportion (for item being estimated) in the sample, and

=

sample size

The precision of a sample estimate is measured by the standard error of the
estimate,
, which is the square root of the variance. The standard error, like the
variance, is normally unknown, and can be estimated from the sample.

If

is small relative to

, then

can be ignored.

The precision specification consists of two elements. First, the administrative decision on
the desired degree of reliability determines the sample size necessary to meet the
specified probability level and precision range. For example, the administrator might
specify that the estimate of the proportion of two-parent families in the caseload is to be
within 1 percentage point of the figure that would be obtained by a complete review of
the entire caseload. This is called the tolerance specification or limit.
Secondly, since the administrator is dealing with a sample, a certain degree of risk is also
assumed. Thus, in the example given above, if the sampling error had been computed so
that the estimate plus or minus 1 percent would include the true value in 95 out of 100
samples selected from the same population, the estimate plus or minus 1 percent would be
called the 95 percent confidence interval.
1

It can be shown that, if the units are randomly ordered, the variance of a systematic
sample is equivalent to the variance of a simple random sample.

In general, the 95 percent confidence interval is equal to the point estimate plus or minus
1.96 times the standard error of the normal distribution (or its approximation) and is
expressed as follows:

This confidence interval will cover the true value of "p" about 95 percent of the time
when sampling repetitively. Expressed in another way, we can be reasonably confident
that about 95 percent of the sample proportions will be within 1.96 standard errors of
their corresponding population proportion. A visual representation of this statement is
shown in the following figure. (The standard normal deviate, 1.96, is associated with the
exact 95 percent confidence interval. In practice, however, 2 is sometimes conveniently
used to replace 1.96 for constructing a 95 percent confidence interval. The actual
probability is 95.46 percent if 2, instead of 1.96, is used.)

Figure 1.
Normal Distribution

If " " is the sample proportion, then there is a 95 percent probability that the population
value lies between
and
. Thus, the population value is within 2
standard errors. (If 99.7 percent confidence was desired, the appropriate universe value
would be within 3 standard errors.) This is called two-tailed probability and is used when
interest is in both the upper and lower limits of an estimate.

If however, only one limit is of interest, a one-tailed limit can be used. The standard error
(SE) units and probabilities are different for one-tailed limits. The 95 percent confidence
interval for the one-tailed lower limit is
. If p represents the sample
estimate of the participation rate, there is a 95 percent probability that the true
participation rate is greater than
. Similarly, the 95 percent confidence
interval for the one-tailed upper limit is
. There is a 95 percent probability
that the true participation rate is less than
.

1232.1

Computation of Sample Size to Obtain a Desired Precision

By algebraic rearrangement, it is possible to compute the minimum sample size needed to
obtain a desired precision. For example, to obtain the sample size required for 95 percent
confidence, that a sample proportion " " will be within plus or minus 2 percent of the
true proportion " " when " " is assumed to be 50 percent. The computation is as
follows:

where " " is the desired precision level (2 percent in this example).
Substituting:

=

2,401 or approximately 2,400 cases 2

It should be noted that, for a proportion, precision is primarily a function of sample size.
Larger samples will generally yield more precise estimates. In many cases, the size of the
population from which the sample is drawn is not important. As the population size
increases, and the ratio
approaches 1.00 (where " " is the population size
and " " is the sample size), the effect of population size on precision diminishes and can
usually be disregarded.
The specification of precision and confidence are both administrative decisions that are
generally the responsibility of those who will use the data. The uncertainty associated
with sampling can be reduced by taking larger samples or using superior measurement
techniques, but only at some expense. Therefore, these decisions also must take account
of the resources available to collect the sample data.

1232.2

Computation of Levels of Precision for Stratified Samples From State (Tribal)
Sample

In a stratified sample, population and variance estimates are computed from information
in each stratum or group, appropriately weighted and combined.

Precision for Proportions
If in each stratum ( ) a systematic sample (approximating a simple random sample) is
selected, the equations for estimating the overall proportion
and its variance are as
follows:

2/ The 2,400 figure is based on the assumption that the population rate is 50 percent
and that the sample is a small fraction of the caseload so that the finite population
factor can be ignored. If the same fraction is large, the finite population factor
should be included; the sample size can be modified using the equation

where N is the population size.

and
3

where:
= number of strata;
= population size in stratum

;

=

= total population size;

=

= stratum weight;

= sample size in stratum
=

= total sample size of all strata; and

= proportion in stratum
The standard error of

;

.

is estimated by the square root of its estimated variance and, as

mentioned earlier, is used in the calculation of confidence intervals. These intervals are
calculated in the same manner as for a non-stratified sample.
For example, assume a sample is drawn from three strata. The population sizes in each
stratum are 1,000, 2,000, and 4,000; the sample sizes are 50, 200, and 200; and the
stratum proportions are .05, 0.1, and 0.2 respectively. The overall proportion is
estimated as:

3/ If the finite population factor is included, the equation is given as follows:

and the variance of the proportion is:

The standard error of the proportion is:

The 95 percent confidence interval of the proportion is:

1232.3

Relative Efficiency of Stratified Random and Simple Random Sampling

The frequently adopted definition of relative efficiency ( ) of an estimator having a
variance, for example, of
to another having a variance of
is:

Thus, the smaller the variance of an estimator, the more efficient the estimator. If a State
proposes to change its sample design, e.g., from a systematic random sample to a
stratified random sample, it should check to see if the estimator (for a variety of
characteristics being measured) based on the new sample design has a variance that is
equal or smaller than that of the present sample design.

1300

SAMPLING PLAN REQUIREMENTS

The sampling plan serves as the foundation for the Administration for Children and
Families (ACF) review of the integrity of the State agency's and Tribal grantee's TANF
sampling procedures and SSP-MOE sampling procedures. The State or Tribe that elects
to submit case record information for a sample of families (also known as, cases) must
select its TANF sample (and, if applicable, SSP-MOE sample) for data reporting purposes
under a sampling plan approved by the ACF TANF Manager. All sampling procedures
used by the State agency or Tribal grantee, including frame composition and construction,
must be fully documented and available for review by the ACF Regional Office. This
requirement includes all data processing specifications and automated routines used to
select the samples.
The sampling plan documentation must describe the list(s) of families from which the
samples are selected, the sample selection procedures, and the methodology for
estimating caseload characteristics and sampling errors. Referencing sub-sections of this
manual in the sampling plan does not constitute acceptable compliance with the
requirements set forth for sampling plan documentation without further explication of the
specific procedures the State or the Tribe will use. Detailed descriptions of the sample
frames, sample selection, and estimation procedures used by the State or Tribe must be
included in the sampling plan documentation.
If a State or Tribe opts to report the required case record information for a sample of
families (as opposed to for the entire caseload), a State shall have an approved sampling
plan in effect for a full sample period. A State or Tribe may not implement a new sample
design without prior approval. A revised sampling plan must be submitted to the ACF
Regional Administrator with specific documentation of any substantive modification of a
previously approved sample design at least 60 days before the start of the annual sample
period, i.e., no later than August 1. The State is not required to resubmit the sampling
plan if it is unchanged from the previous year. Changes in random start numbers, sample
intervals, or caseload estimates are not to be submitted as a revision of the sampling plan.
They should, however, be sent to the ACF Regional Office.

1310

Criteria for Plan Approval

The sampling plan must meet the following criteria:
1.

Conformance to principles of probability sampling, i.e., each case (family)
in the population must have a known, non-zero probability of selection and
computational methods of estimation must lead to a unique estimate;

2.

Documentation of methods for constructing and maintaining the sample
frame(s), including assessment of frame completeness and any potential
problems associated with using the sample frame(s);

3.

Documentation of methods for selecting the sample cases from the sample
frame(s); and

4.

Documentation of methods for estimating case characteristics and their
sampling errors, including the computation of weights, where appropriate.

1320

Sample Frame

Samples are selected from a list of families called a "sample frame." The sampling plan
must describe in detail the master file, the payroll file, or other list(s) from which the
sample of families is actually selected. The plan must explicitly describe the following
sample frame characteristics:
1.

Date(s) when the sample cases (both regular and supplemental, if
applicable) for the sample month are selected, e.g., first workday of the
month following the sample month);

2.

Source, components, accuracy, and completeness of the sample frame in
relation to the total caseload; if not accurate or complete, explanation of
why not and how State (Tribe) plans to correct for the problems with the
sample frame;

3.

Procedures for ensuring that the sample frame contains complete coverage
of the applicable caseload (e.g., the active TANF sample frame include all
families receiving assistance under the State's or Tribe's TANF Program,
including all newly approved applicants for the sample month and the
closed TANF sample frame includes all families no longer receiving
assistance under the State's TANF Program, i.e., assistance terminated
effective for the sample month);

4.

Whether or not the frame is constructed by combining more than one list (if
more than one list, explanation of how lists are identified and how
duplication of cases on lists are prevented);

5.

Whether the frame is compiled entirely in the State office, entirely in local
offices, in the State office based on information supplied by local offices,
etc.;

6.

Form of the frame, e.g., a computer file, microfilm, hard copy; OR OTHER
(specify), if parts of the frame are in different forms, specifications for each
part;

7.

Frequency and length of delays and method used in updating the frame or
its sources;

8.

Procedures for estimating the proportion of sample cases for which the
State (Tribe) will not be able to collect and report case record information
(e.g., dropped as "listed-in-error" because the family (case) did not receive
TANF assistance for the reporting month);

9.

Methods of locating and deleting "listed-in-error" cases from the frame;

10.

Structure of the frame, i.e., the order of cases within each list and the data
elements on the frame, including definitions of coded values;

11.

Treatment of special populations under TANF (e.g., individuals under a
tribal family assistance plan, a non-custodial parent who participates in
work activities); and

12.

Criteria for stratifying sample (if applicable).

1330

Sample Selection Procedures

The sampling plan must describe in detail the procedures for selecting the sample cases.
The plan must explicitly describe the following characteristics:
1.

Procedures for estimation of caseload size, if applicable to sampling
method;

2.

Procedures for determination of an appropriate allowance for sample cases
for which the review may not be complete because the sample case was
"listed-in-error" (e.g., family did not receive TANF assistance for the
sample month);

3.

Procedures for determining the required monthly sample size and indication
of the sample size;

4.

If stratified sample design is used, procedures for sample allocation;

5.

Procedures for the computation of sample intervals and the determination of

random starts if the State (Tribe) used systematic random sampling or
stratified systematic random sampling;
6.

Application of selection procedures to identify sample cases;

7.

Procedures to compensate for excessive oversampling or undersampling;
and

8.

Time schedule for each step in the sampling procedure.

1400

SAMPLE SIZES AND PROCEDURES FOR SELECTING
SAMPLE CASES

1410

Annual Sample Size Requirements

State agencies and Tribal grantees should consider their own management information
needs relative to desired reliability of characteristic data broken out for specific
groupings, geographic areas, or by monthly or quarterly time periods in deriving the
TANF and the SSP-MOE sample sizes. While this section of the manual specifies the
minimum required annual sample sizes for completed case reviews, States and Tribes are
encouraged to select larger size samples in order to increase the precision of the resulting
estimates and to meet their own information needs.
For TANF data collection and reporting purposes, there are two sampling frames from
which cases are to be sampled. The sampling frames are for families receiving assistance
(i.e., active cases, including all newly approved applicants) and families no longer
receiving assistance (i.e., closed cases).
If a State has one or more SSP-MOE programs, it must collect and report a limited
amount of data on TANF families receiving assistance, as defined in Appendix B (page
121) and no longer receiving assistance under the SSP-MOE programs. For the SSPMOE data collection and reporting purposes, there are two sampling frames from which
cases are to be sampled. The sampling frames are for families receiving assistance (i.e.,
active SSP-MOE cases, including all newly approved applicants) and families no longer
receiving assistance (i.e., closed SSP-MOE cases).

1411

Sample Size Requirements for the TANF Active Sample

The minimum required annual sample size for the active TANF sample is 3000 completed
cases, of which approximately 2400 are ongoing cases and 600 are newly approved
applicants. Of the 2400 ongoing cases, approximately 600 cases are two-parent TANF
families. Approximately, one-twelfth of the annual sample must be selected each month
of the annual sample period. The minimum required sample sizes are designed to provide
reasonably precise estimates for such proportions as the work participation rates for all
families (e.g., a precision of about plus or minus 2 percentage points at a 95% confidence
level) and for two-parent families (e.g., a precision of about plus or minus 2.3 percentage
points at a 95% confidence level), as well as for demographic and case characteristics of
newly approved TANF families and all TANF families. In addition, these sample sizes
will permit us to detect real changes in certain proportions over time (e.g., changes in the
proportion of child-only cases).

The midpoint estimate (from which the confidence limits are constructed) of the overall
and two-parent work participation rates will be used in determining if States have met the
statutory requirements. If the State is unwilling to accept the precision levels obtained
from the minimum required annual sample sizes for the purpose of assessing penalties for
failing to met the work participation rates, it is the State's responsibility to increase its
sample size to what the State determines is an acceptable level of precision for this
purpose.
To meet these sample size requirements, States and Tribes may select one of the
following options:
1.

Use a simple or systematic random sampling methodology (or other
acceptable method) and use an overall sample size that is sufficiently large
enough to obtain the 600 cases needed to meet the two-parent family
required sample size, the 600 required to meet the newly approved
application sample size, and the 3000 cases required to meet the overall
sample size.

2.

Stratify the sample by newly approved applications; two-parent families;
and all other families, and use a random sampling method within each
stratum to select the sample. Taking into consideration the fact that twoparent families are included in the calculation of the all family work
participation rate, compute the sample size for each stratum based on the
600-case requirement for the two-parent stratum and 600 for the newly
approved applications stratum, and 1800 cases for the remaining families.

Under option 2, each stratum is sampled separately, and the monthly all families work
participation rate is a weighted rate, reflecting the representation of two-parent families
and other families with at least one adult or a minor child head-of-household to the total
all family population. If a State or Tribe uses a stratified sample design, the State (or
Tribe) must submit the monthly caseload for each stratum. These monthly caseload sizes
by stratum are due 45 days after the close of each quarter (i.e., the same due dates as for
the quarterly TANF Data Report, Sections one, two and three).
If a State or Tribe does not have enough newly approved applicants or two-parent
families to meet the required annual sample sizes of 600 families (i.e., the average
monthly sample size of approximately 50 newly approved applicant families or 50 twoparent families), the State or Tribe must select 100% of such families and select from the
other ongoing stratum enough additional cases to meet the overall required annual sample
size of 3000 families. If a State or Tribe does not have enough families to meet the
overall sample requirement (i.e., 3000 families for the active TANF sample for an
average monthly sample of 250 families), the State or Tribe must report on 100% of their
families each month.

States and Tribes are not limited to these two methods for meeting the sample size
requirements. However, alternative methods should be discussed with Regional statistical
staff to ensure the reliability of the work participation rates and any other statistic used to
award a bonus or assess a penalty is not severely affected.

1412

Sample Size Requirements for the TANF Sample of Closed Cases

The minimum required annual sample size for the sample of closed cases is 800 cases.
Approximately one-twelfth of the annual sample must be selected each month of the
annual sample period. An 800-case sample will permit us to obtain a precision of plus or
minus 3.5 percentage points for an attribute of 0.50 at a 95% confidence level. This result
is obtained from the formula in Section 1232.1 of this manual.
If a State or Tribe does not have enough closed cases to meet the required minimum
annual sample size of 800 families (i.e., an average monthly sample size of approximately
67 families), the State or Tribe must collect data for and report on 100% of the closed
cases.

1413

Sample Size Requirements for the SSP-MOE Active Sample

The minimum required annual sample size for the active SSP-MOE sample is 3000 cases,
of which approximately 2400 are ongoing cases and 600 are newly approved applicants.
Of the 2400 ongoing SSP-MOE cases approximately 600 cases are two-parent families.
Approximately, one-twelfth of the annual sample must be selected each month of the
annual sample period. The minimum required annual sample sizes are designed to
provide reasonably precise estimates for such proportions as the work participation rates
for all families (e.g., a precision of about plus or minus 2 percentage points at a 95%
confidence level) and for two-parent families (e.g., a precision of about plus or minus 2.3
percentage points at a 95% confidence level), as well as for demographic and case
characteristics of State SSP-MOE families. In addition, these sample sizes will permit us
to detect real changes in certain proportions over time (e.g., changes in the proportion of
child-only cases).
If a State does not have enough newly approved applicants or two-parent families to meet
the required annual sample size of 600 newly approved applicant families and 600 twoparent families (i.e., the average monthly sample size of approximately 50 newly
approved applicant families and 50 two-parent families respectively), the State must
select 100% of such families and select from the other ongoing stratum enough additional
cases to meet the overall required annual sample size of 3000 families. If a State does not
have enough families to meet the overall sample requirement (i.e., 3000 families for the
active SSP-MOE sample for an average monthly sample of 250 families), the State must

collect data for and report on 100% of its families.

1414

Sample Size Requirements for the SSP-MOE Sample of Closed Cases

The minimum required annual sample size for the SSP-MOE sample of closed cases is
800 cases. Approximately one-twelfth of the annual sample must be selected each month
of the annual sample period. An 800-case sample will permit us to obtain a precision of
plus or minus 3.5 percentage points for an attribute of 0.50 at a 95% confidence level.
This result is obtained from the formula in Section 1232.1 of this manual.
If a State does not have enough closed cases to meet the required annual SSP-MOE
sample size of 800 families (i.e., an average monthly sample size of approximately 67
families), the State must collect data for and report on 100% of the closed cases.

1415

Adjustment to the Sample Size for States and Tribes with Small Caseloads

If a State or Tribe has a small average monthly caseload, it may use the following
procedures in applying the finite correction factor to adjust the minimum annual sample
size. The formula for obtaining an adjusted sample size using the finite correction factor
is:

where

1.

=

Total number of case months for the annual sample period (i.e., the
average monthly caseload times twelve months)

=

minimum required annual sample (e.g., active case sample is 3000
cases and closed case sample is 800 cases)

Compute the estimated number of case months for the annual reporting period.
For example, it a State or Tribe has an estimated average monthly active
TANF caseload of 1,000 cases, then the total number of case months is 12,000
case months (i.e., N = 1,000 cases per month times 12 months = 12,000 case
months).

2.

Use the above formula and round up to determine the adjusted overall sample
size requirement.

For our example, the adjusted overall minimum required active TANF sample
size would be:

3.

In computing the adjusted minimum annual sample size for the State's or
Tribe's active TANF sample or the active State SSP-MOE sample, prorate the
overall adjusted sample size to determine the required number of two parent
families, the required number of newly approved applicants and the required
number of other ongoing cases.

For our example, the sample size requirement for two-parent families is 480 cases (i.e.,
600 times 2400 divided by 3000 ), for newly approved applicant families is 480 (i.e., 600
times 2400 divided by 3000) and for other ongoing cases is 1440 cases (i.e., 1800 times
2400 divided by 3000).

1416

Average Monthly Sample Size

A State agency or Tribal grantee must select approximately one-twelfth of its annual
sample size each sample month. The average monthly sample size is determined by
dividing the required annual sample size by 12 and rounding the result up to the nearest
whole number. For the active TANF sample and SSP sample, the average monthly
sample sizes are 250 cases, of which 50 are two-parent families, 50 are newly approved
applicants, and 150 are other ongoing cases. For TANF and SSP samples of closed cases,
the average monthly sample sizes are about 67 cases. The following additional
procedures apply to the TANF samples and to the State's SSP samples:
1.

State agencies and Tribal grantees should select additional cases (use the
rate for "listed-in-error" cases based on historical data or, if unknown, use
five percent) of each sample to compensate for cases that may be reported
as "listed-in-error";

2.

A State or Tribe may increase its sample size above the minimum (and we
encourage them to do so) but may not reduce its sample size below the
minimum; and

3.

A State or Tribe has the option of collecting and reporting data for the entire
TANF population and a State has the option of collecting and reporting data
for its entire SSP population. However, we encourage States and Tribes to
take advantage of their option to use sampling, when appropriate.
Sufficiently large samples can produce reasonably precise estimates, while

saving substantial administrative staff resources and funds.

1420

Sample Frame

Creating a frame or list of cases from which the monthly samples are to be selected and
determining the sample size are preliminary steps applicable to any probability sample
design. Careful study of the structure of the sample frame is always essential in
probability sampling, especially in systematic random sampling. The choice of a frame
depends upon the criteria of timeliness, completeness, and administrative burden. The
structure of the sample frame should provide for an unduplicated list of cases comprising
the target population or otherwise allow for all units to have a known, non-zero chance of
selection into the sample. In systematic random sampling, cases should be randomly
ordered with respect to the variables being measured, e.g., case characteristics data,
earnings, participation in work activities, etc. This random order is usually achieved if
cases are arranged by case number or by county and then alphabetically within county, or
by any other file organization that is not directly related to the measurement of critical
variables. In stratified sampling, each family must be assigned to one (and only one)
stratum. The structure of the sampling frame must be fully documented in the sampling
plan and may not be changed without an approved revision of the sampling plan.

1421

Sampling Frame for the TANF Active Case Sample

The monthly TANF sample frame consists of all families who receive assistance under
the State (Tribal) TANF Program for the sample month by the end of the sample month.
The term "assistance", defined in §260.31 of the final rule, includes cash, payments,
vouchers, and other forms of benefits designed to meet a family's ongoing basic needs
(i.e., for food, clothing, shelter, utilities, household goods, personal care items, and
general incidental expenses). It includes such benefits even when they are provided in the
form of payments by a TANF agency, or other agency on its behalf, to individual
recipients and conditioned on their participation in work experience, community service,
or other work activities (i.e., under §261.30).
Except where excluded as indicated in the following paragraph, it also includes
supportive services such as transportation and child care provided to families who are not
employed.
The term "assistance" excludes:
1.

Nonrecurrent, short-term benefits (such as payments for rent deposits or
appliance repairs) that:

a.

Are designed to deal with a specific crisis situation or episode of
need;

b.

Are not intended to meet recurrent or ongoing needs; and

c.

Will not extend beyond four months.

2.

Work subsidies (i.e., payments to employers or third parties to help cover
the costs of employee wages, benefits, supervision, and training);

3.

Supportive services such as child care and transportation provided to
families who are employed;

4.

Refundable earned income tax credits;

5.

Contributions to, and distributions from, Individual Development Accounts;

6.

Services such as counseling, case management, peer support, child care
information and referral, transitional services, job retention, job
advancement, and other employment-related services that do not provide
basic income support; and

7.

Transportation benefits provided under an Access to Jobs or Reverse
Commute project, pursuant to section 404(k) of the Act, to an individual
who is not otherwise receiving assistance.

The exclusion of nonrecurrent, short-term benefits under (1) of this paragraph also covers
supportive services for recently employed families, for temporary periods of
unemployment, in order to enable continuity in their service arrangements.
The TANF active case sample frame could be a master file; a payroll file; an eligibility,
activity, or other caseload file; or a combination of such files depending on how the State
or Tribe defines its range of benefits/assistance. If such a list cannot be constructed based
on the above definition of the sampling universe, it may be necessary to use a special
procedure to ensure that all families receiving assistance have a known, non-zero chance
of being included in the sample. The sampling plan should contain the State or Tribe's
objective criteria for the delivery of assistance and determination of eligibility as set forth
in the State or Tribe's family assistance plan. State agencies and Tribal grantees should
verify the receipt of assistance for all selected cases, and all such cases discovered not to
have received assistance for the reporting month should be reported as "listed-in-error."
For all other cases selected into the sample, the data collection must be completed and the
data must be submitted to ACF by the specified time frames.

States or Tribes that use regular first-of-the-month payroll or eligibility listings as the
frame for selection of sample cases must extend that frame at the end of the report month
and continue sampling all cases for which assistance was initiated during the report
month that were not on the first-of-the-month payroll/eligibility listing. Care must be
taken to ensure that the sample frame consists of unduplicated cases. A distinction is
made between cases already receiving TANF and cases in which assistance is initiated
during the month. For example, a case receiving a regular payment on October 1 and a
supplemental payment on October 12 should only be subject to selection once for the
month of October. Procedures for accomplishing this must be specified in the sampling
plan. Normally, this will be accomplished by running a computer sort/merge routine at
the end of the report month in order to establish the list of supplemental cases to be added
to the frame.
States or Tribes that use simple random sampling should form the sample frame at the end
of the sample month, ensuring all families that received assistance for the month by the
end of the month are on the sample frame. Then the sample is selected after the end of
the sample month.

1422

The Treatment of Special Groups With Respect to TANF Reporting

There are a number of family circumstances that merit special attention. These are
described below.

1422.1

Newly Approved Applicant (aka, Initial Assistance Cases)

A newly-approved applicant or an "initial payment/assistance" case for a sample month
means the family is newly added to the TANF caseload and the current reporting month is
the first month in which the TANF family receives TANF assistance (and thus has had a
chance to be selected into the TANF sample). This may be either the first month that the
TANF family has ever received assistance or the first month of a new spell on assistance.
The initial payment/assistance case should be included on the sample frame for the initial
month in which it received assistance and for all subsequent months for which assistance
is issued. For States that provide assistance back to the date of application, these cases
may, at State option, be included on the frames for prior months, as assistance was not
received by the end of such months.
A family that moves back and forth between receipt of assistance to receipt of only nonassistance in a subsequent month while remaining in the TANF program will be a newly
approved applicant each time it moves to receipt of assistance for a reporting month.

1422.2

Non-Custodial Parents

A non-custodial parent is defined in §260.30 as a parent of a minor child who: (1) lives in
the State and (2) does not live does not live in the same household as the minor child.
The State must report information on the non-custodial parent if the non-custodial parent:
(1) is receiving assistance as defined in §260.31; (2) is participating in work activities as
defined in section 407(d) of the Act; or (3) has been designated by the State as a member
of a family receiving assistance. In reporting non-custodial parents, States or Tribes
should not treat the non-custodial parent as a separate case. Rather, when the family unit
containing his/her child(ren) is selected into the sample, code the type and amount of
assistance received by the non-custodial parent as part of that case. The non-custodial
parent's person level data must also be provided. States and Tribes have the option to
include or exclude the non-custodial parent from the work participation rate on a case-bycase basis. If an individual is both a custodial parent for a TANF family receiving
assistance and a non-custodial parent for another TANF family receiving assistance, the
State or Tribe should report the individual only with the family for which (s)he is the
custodial parent.

1422.3

Members of Indian Tribes Not Eligible under a Tribal Family Assistance Plan

The State sample frame must include each member of an Indian tribe otherwise meeting
the definition of the sampling unit who is domiciled in the State and is not eligible for
assistance under a Tribal family assistance plan.

1422.4

Members of Indian Tribes Receiving Assistance under a Tribal Family
Assistance Plan

The State should not include members of an Indian tribe receiving assistance under a
Tribal family assistance plan, even if the State selected the option to include such families
in the calculation of its participation rate as provided for in section 407(b)(4) of the Social
Security Act.

1422.5

Cases Selected For More Than One Sample Month

If a family is selected into the sample for more than one month during the annual
reporting period, the State or Tribe should collect data for and report on the family for
each month for which it is selected.

1422.6

Cases Receiving Assistance Under the State's TANF Program and Separate

State Programs for the Same Month
A TANF eligible family may receive some form of assistance under both the State's
TANF Program and its SSP during the reporting month. If this occurs, the family should
be included on the active sample frame for both the TANF and the SSP. If such a family
is selected into the sample, the State should collect data for and report on the family for
each program for which it was selected.

1422.7

Cases With a Child Not Living With a Parent or Adult Caretaker Relative

Many activities are covered under section 401(a) of the Social Security Act (Act) (the
purposes of the TANF program). However, some activities are not permissible under the
purposes of the TANF program, but had been included in a State's approved AFDC plan,
JOBS plan, or Supportive Services plan as of 9/30/95, or at State option, 8/21/96. Section
404(a)(2) "grandfathers in" States whose prior programs had such expenditures. Thus,
this section allows States to use Federal TANF funds for specific activities that had been
previously authorized based on an approved plan, using the same financial eligibility
criteria contained in the approved prior plan. Examples of such activities are juvenile
justice and foster care activities that were included in some States' approved plan.
The legislative history makes it clear that the State may elect to continue to provide the
service or benefit under section 404(a)(2) of the Act, notwithstanding the prohibitions in
section 408 of the Act. For example, if a State's approved AFDC plan enabled it to
provide "assistance" or services to children in the juvenile justice system that does not
constitute TANF "assistance", then it may continue to use TANF funds for such activities
even though the child is not living with his parent or other adult caretaker relative.
Nonetheless, if the child is receiving "assistance" funded under the State TANF program,
the child is a child-only family for data collection and reporting purposes. For a State that
reports on its entire caseload, the State must collect data on and report data for all such
child-only families for each month that the families receive assistance. For the State that
reports its data for a sample of families, the State must include all such child-only
families on its monthly sample frame for each month that the family receives assistance.
If the child-only family is selected in a monthly sample, the State must collect data for
and report data on the child-only family for that month.

1422.8

Cases for Which State Changes Funding Stream

State must make all changes in funding streams to cases for a report month prior to
formation of the sample frame(s) and sample selection for the report month. Changes in
funding stream after sample selection are not permitted because such changes will destroy
the representativeness of the sample and result in invalid samples. This would make the
State liable for a data reporting penalty.
1423

Sample Frame for the Sample of Closed TANF Cases

For closed cases, the monthly TANF sample frame must consist of all families whose
assistance under the State TANF Program was terminated for the reporting month (do not
include families whose assistance was temporarily suspended), but received assistance
under the State's TANF Program in the prior month. A family that moves from receipt of
assistance under the TANF program for a month to receipt of benefits that are not
assistance under the TANF program for the subsequent month is a closed case for
reporting purposes. Also, a TANF eligible family that is transferred to the State's SSP or
Tribal TANF program is usually closed for the State TANF Program.

1424

Sample Frame for the Sample of Active SSP Cases

The monthly active SSP sample frame must consist of all families who receive assistance
under the separate State programs for the reporting month by the end of the reporting
month. The term "assistance" for separate State programs has the same meaning as for
TANF Programs. See Section 1421 (page 27) for the definition.

1425

Sample Frame for the Sample of Closed SSP Cases

For closed cases, the monthly SSP sample frame must consist of all families whose
"assistance" under the SSP was terminated for the reporting month (do not include
families whose assistance was temporarily suspended), but received assistance under the
SSP in the prior month. A family that is transferred to a State's TANF Program is usually
a closed case for the SSP.

1430

Procedures for Selecting Sample Cases

States and Tribal grantees have flexibility to choose from a wide variety of sampling
methods, including systematic random sampling, simple random sampling, and stratified
(systematic or simple) random sampling. For illustrative purposes, the following
procedures are based on the systematic random sampling design and, if used, are repeated

each month during the annual sample period. In illustrating the procedures, a State or
Tribe with an estimated average monthly active TANF caseload of 42,600 is used. These
same procedures could be used to select the sample of closed TANF cases or the sample
of the active and closed SSP cases. Note, these procedures could be used to select a
sample within each stratum for a stratified systematic random sample design.
1.

Estimate Caseload Size
The TANF average caseload is an estimate of the average monthly number
of cases that will receive assistance for the forthcoming annual sample
period. The average caseload size should be estimated on the basis of past
caseload sizes and trends. Any known circumstances, such as policy
changes that would appreciably change caseload sizes, also should be taken
into account in making the estimate.
Since the average monthly caseload must be estimated before the beginning
of the annual sample period, unanticipated changes can result in the need
for adjusting the sample interval. Recognizing the difficulty of forecasting
caseloads over a 12-month period, States and Tribes should re-evaluate the
estimated caseload before the end of each quarterly reporting period. If the
caseload estimate is changed, a new sample interval for the 12-month
period and adjustments to the number of sample cases already selected may
be needed. The procedures in Section 1510 (page 42), or 1520 (page 44,)
can be used depending on whether the sample requires correction for
oversampling or undersampling. If no correction is required for the
remaining quarterly reporting period(s), using these procedures will result
in a self-weighting annual sample.

2.

Determine Sample Size
The minimum required annual sample sizes of completed cases are shown
in Section 1410 (page 21). In our illustration, the minimum sample size is
used.

An estimate of the percent of cases that may possibly be reported as listedin-error during the TANF data collection process will need to be made in
order to arrive at the required completed sample size. For example,
assuming that 5 percent of the selected cases will be reported as listed-in-

error, the number of cases to be selected can be computed as follows:

3.

Establish Frame
As mentioned in Section 1421 (page 27), a listing of all TANF cases that
received assistance for the sample month by the end of the month (including
initial assistance cases and cases that are reinstated) comprises the frame
from which the sample is selected.

4.

Establish Average Monthly Sample Size
The average monthly sample size is obtained by dividing the sample size
for the sample period by the number of months in the period. In our
illustration, the average monthly sample size is 3,158 ÷ 12, which is
263.167 cases.

5.

Compute Sample Interval
The sample interval is obtained by dividing the estimated average caseload
in the annual sample period (Step 1) by the unrounded average monthly
sample size (Step 4). In our example, the sample interval is 42,600 ÷
263.167, or 161 (rounded down). This means that each month, data will be
collected for 1 out of every 161 TANF cases.

6.

Select Random Start Number
The random start number can be as large as the number of cases contained
in the sample interval and is used only to determine the first selected sample
case for each month's sample. Since the sample interval in our example is
161, we must select a random start number between 001 and 161
(inclusive). Assume the number selected is 103.

7.

Select Monthly Sample
The sequential position of the first selected sample case on the frame is the
starting point for selection of all subsequent cases. (If the frame is in
several parts, it will be desirable to assemble the parts so that one
continuous list is created. Every "
" case will then be selected from a
list in which all cases are present.)
It is important in selecting the monthly sample to apply the same sample

interval to the entire list of cases each month. This is an important part of
the sampling design and should not be violated in order to obtain a specific
number of cases each month.
In our illustration, if the sample interval was a whole number, the 103rd
case on the list would be selected and every 161st case thereafter, i.e.,
103rd, 264th, 425th, etc. In each of the remaining eleven months of the
sample period, assuming no adjustment in estimated caseload size is
necessary after the sixth month, new random starts would be obtained as the
first case of each month and then multiples of 161 added to obtain the other
cases for data collection and reporting.
It should be understood that the numbers selected for the sample cases
relate to specific cases; substitutions or approximations are not acceptable.
For example, only the 103rd case must be selected, not the 102nd, or 104th,
etc. Once the random start and sample interval are determined, the specific
cases to be selected are identified.
There are several methods of selecting sample cases when the sample
interval is not a whole number. In one method, the sample case to be
selected is determined by rounding the number obtained after the sample
interval is added to the previous sample interval. For example, since in our
illustration the sample interval number was actually 161.87 instead of 161,
the following sample cases would be selected from the sample frame of
eligible cases (assuming a random start number of 163):

Selected Cases

Selection Procedure

# 103

- random start case

# 265

103
+ 161.87
264.87 = 265

- random start case
- interval
- rounded

# 427

264.87
+ 161.87
426.74 = 427

- previous total
- interval
- rounded

# 589

426.74
+ 161.87
588.61 = 589

- previous total
- interval
- rounded

# 750

588.61
+ 161.87
750.48 = 750

- previous total
- interval
- rounded

etc.

etc.

For TANF purposes, an acceptable method for selecting sample cases when
the sample interval is not a whole number is to round down to the next
lower whole number and use that number in selecting the sample cases. For
example, using the same sample interval of 161.87 and random start number
of 103, the interval would be rounded down to 161 and the sample cases
selected would be the 103rd, the 261th, the 425th, the 586th, 747th, etc.

8.

Submission of Caseload Size, Sample Interval and Sample Cases Selected
If a State or Tribe opts to use systematic random sampling or stratified
systematic random sampling, the State or Tribe should send the estimated
average monthly caseload and the computed sample interval(s) to be used
for the 12-month sample period to the ACF Regional TANF Manager thirty
(30) calendar days before the October sample selection.
If a State or Tribe uses a stratified sample design, it must submit the
monthly caseload sizes by stratum (see the TANF Data Report - Section
four and the SSP-MOE Data Report - Section four) for each month of the
quarter within 45 days after the end of the quarter. These data are needed
for weighting purposes.
Regardless of the method used to select the sample cases, each State and
Tribe that opts to collect data for and report on a sample of cases must
submit the monthly list of selected sample cases (including reserve pool
cases, if applicable, under Section 1531 page 47), within 10 days of the date
of selection specified in the State or Tribe sampling plan.

1440

Procedures for Selecting Sample Cases Using a Simple Random Sample

States and Tribal grantees may want to use simple random sampling or stratified simple
random sampling because there are a number of computer software packages that contain
programs that use this method of sampling. For illustrative purposes, the following
procedures are based on the simple random sampling design and, if used, are repeated
each month during the annual sample period. These same procedures could be used to
select the sample of closed TANF cases or the sample of the active and closed SSP cases.
Note, these procedures could be used to select a sample within each stratum for a
stratified simple random sample design.
1.

Establish the Monthly Sample Frame
As mentioned in Section 1421 (page 27), a listing of all TANF cases that
received assistance for the sample month by the end of the month (including
initial assistance cases and cases that are reinstated) comprises the frame
from which the sample is selected.

2.

Determine the Number of Families on the Sample Frame
Many automated simple random sampling routines need to know the
number of sampling units on the sample frame and the number of units to

be selected prior to execution of the sample selection routine. For the
TANF active sample, the sampling units are the families receiving TANF
assistance. If a stratified simple random sample is used, the State must
determine the number of families in each stratum for the sample month.
3.

Determine Sample Size
The minimum required annual sample sizes of completed cases are shown
in Section 1410 (page 21). In our illustration, the minimum sample size is
used.

An estimate of the percent of cases that may possibly be reported as "listedin-error" during the TANF data collection process will need to be made in
order to arrive at the required completed sample size. For example,
assuming that 5 percent of the selected cases will be reported as "listed-inerror", the number of cases to be selected can be computed as follows:

4.

Establish Average Monthly Sample Size
The average monthly sample size is obtained by dividing the sample size
for the sample period by the number of months in the period. In our
illustration, the average monthly sample size is 3,158 ÷ 12, which is
263.167 cases or 263 cases.

5.

Select Monthly Sample
The most practical way of selecting a sample of TANF cases using a simple
random sample is with the use of automated routines. These routines use a
random number generator to select n (the number of units to be selected)
out of N (the number of units on the sample frame). The n sample cases
should be selected without replacement. To illustrate using a monthly
sample frame with 42,600 families and a monthly sample size of 263
sample cases, the automated sampling routine would select 263 numbers
between 1 and 42,600 inclusive. If the random numbers generated include
20, 175, 183. 500, etc., then the 20th, 175th, 183rd, and 500th case on the
sample frame would be drawn into the sample.

6.

Submission of Caseload Size, Sample Interval, and Sample Cases Selected
If a State or Tribe uses a stratified sample design, it must submit the
monthly caseload sizes by stratum for each month of the quarter within 45
days after the end of the quarter. These data are needed for weighting
purposes. States and Tribes that use non-stratified sample designs report
their total monthly caseload numbers on the TANF Data Report - Section
Three. These figures are used to weight the State data.
Regardless of the method used to select the sample cases, each State and
Tribe that opts to collect data for and report on a sample of cases must
submit the monthly list of selected sample cases (including reserve pool
cases, if applicable) within 10 days of the date of selection specified in the
State or Tribe sampling plan.

1450

Retention of Sampling Records

The regulations at 45 CFR 92.42 set forth record retention and access requirements
applicable to all financial and programmatic records, supporting documents, statistical
records, and other records of grantees or subgrantees. Regarding record retention, 45
CFR 94.42(b) requires a 3-year period – or longer, “if any litigation, claim, negotiation, ,
audit, or other action involving the records has been started before the expiration of the 3year period. When one of the enumerated events occurs, the retention period extends
“until completion of the action and resolution of all issues which arise from it, or until the
end of the regular 3-year period, whichever is later.”
Each State and Tribe shall retain all sampling records for an annual sample period in
accordance with the policy stated in the preceding paragraph. These materials shall
include the
1.

original monthly sample frames from which the sample was selected;

2.

computer programs used to construct the sample frames and select the
sample cases;

3.

caseload estimate worksheets;

4.

sample intervals and random start numbers;

5.

sample size;

6.

lists of selected cases, including supplemental and reserve pool cases if

applicable;
7.

audit trail tracking logs;

8.

the quarterly TANF Data Reports amd . Of a[[;ocab;e. Tje SS{-MOE
Data Reports; and

9.

the annual report containing information on the TANF progeram and, if
applicable, the State’s MOE program(s).

In addition, the State and Tribe shall retain the approved sampling plan until a revised
plan is approved and implemented. When the revised approved sampling plan is
implemented, the previously approved sampling plan should be retained for three years.
These materials are to be made available to the Regional staff upon request.

1500

CORRECTION FOR UNDERSAMPLING AND EXCESSIVE
OVERSAMPLING

When using systematic random sampling, imprecise caseload projections or an
unexpected drop rate will result in the State or Tribe not obtaining its target sample size.
If the actual universe is larger than the estimated size, oversampling may occur. If the
actual universe is smaller than the estimated size, undersampling may occur. A State
agency and Tribal grantee must correct for undersampling to the extent necessary to meet
sample size requirements for TANF reporting and a State agency must correct for
undersampling to the extent necessary to meet sample size requirements for reporting of
separate State programs. A State agency or Tribal grantee has the option as to whether or
not to correct for excessive oversampling. However, we encourage States and Tribes to
select larger than the minimum required annual sample size in order to increase the
precision of statistics that are estimated from the sample data.
In correcting the TANF or SSP sample size, care must be taken to assure that the
statistical principles of "randomness" and measurability are not violated. The selection of
additional families for the TANF and SSP samples or deletion of units from the samples
must be done in a manner that assures all cases in the population have a known, non-zero
probability of selection into the final sample. In addition, techniques of stratification
should not be employed in such a way that small additional strata are created for which
computed estimates may be unreliable, resulting in a loss of precision in population
estimates.
The procedures that a State (Tribe) uses to correct for excessive oversampling or correct
for undersampling will depend partly on the procedures the State (Tribe) used to select its
original sample cases. States and Tribes may choose from a wide variety of sampling
methods. State agencies or Tribal grantees that select their TANF samples or State
agencies that select their SSP samples using the systematic sampling method can use the
procedures in Sections 1510, 1520, or 1530 of this manual to adjust sample sizes. State
agencies or Tribal grantees that select their TANF samples or State agencies that select
their SSP samples using the simple random sampling method can use the procedures in
Sections 1540 to adjust sample sizes. For State agencies or Tribal grantees that use
another method to select their TANF samples, ACF Regional Office staff will be happy to
provide technical guidance on procedures to correct for excessive oversampling or
undersampling to ensure that the principles of probability sampling are retained.
Monthly sample sizes should be monitored throughout the reporting period and correction
should be made only when it becomes clear that target samples will not be met. It is good
practice to re-estimate caseloads at the end of each quarterly reporting period. Waiting to
the end of the annual period to make necessary corrections could create difficulties in

collecting the information and adversely affect the State's (Tribe's) ability to submit data
in a timely manner.
The following procedures allow State agencies and Tribal grantees to make corrections in
all months starting with the first month of the reporting period. A consideration for a
State in selecting this method is that, in certain circumstances, it may be difficult to obtain
accurate information for past months. This method does not involve the creation of
additional strata.

1510

Standard Method to Correction for Undersampling or Oversampling when
Sample Selected Using Systematic Random Sampling

1510.1

Correction for Oversampling
1.

Using the procedure described in Section 1430, Step 1 (page 32),
re-estimate the caseload size, adding on the expected number of cases to be
dropped as listed-in-error, and compute a revised sample interval.
For each month in which the sample cases have already been selected:

2.

Divide the size of the monthly sample frame by the revised sample interval
(Step 1) to obtain the revised estimate of the number of sample cases that
should have been selected.

3.

Subtract the number of cases obtained in Step 2 from the number of sample
cases that have been selected. This is the number of sample cases to be
eliminated.

4.

Divide the number of sample cases that have been selected by the number
of cases to be eliminated (Step 3) to obtain the secondary sample interval to
be used in identifying the cases to be eliminated.

5.

Use a random start and apply the secondary sample interval obtained in
Step 4 to select cases from the list of sample cases already selected. The
cases so identified are to be eliminated regardless of whether or not data
had already been collected.
For months in the annual period for which sample cases have not yet been
selected:

6.

Use the corrected sample interval for the period obtained in Step 1 to select

sample cases from the monthly frames.

1510.2

Correcting for Undersampling
1.

Using the procedure described in Section 1430, Step 1, (page 32)
re-estimate the caseload size, adding on the expected number of cases to be
dropped as listed-in-error, and compute a revised sample interval.
For each month in which the sample cases have already been selected:

2.

Divide the size of the monthly sample frame by the revised sample interval
(Step 1) to obtain the revised estimate of the number of sample cases that
should have been selected.

3.

Subtract the number of sample cases already selected from the number
obtained in Step 2. This is the number of additional sample cases to be
selected from the monthly frame.

4.

Divide the total monthly sample frame size by the number identified in Step
3 to obtain the secondary sample interval to be used in selecting additional
cases from the monthly sample frame.

5.

Use a random start and apply the secondary sample interval obtained in
Step 4 to the monthly sample frame from which cases have already been
selected. (If correction for undersampling is required only for the third
and/or fourth quarters of the annual period, the State has the option of
applying the secondary interval either to the first month of the sample
period (October) or the first month of the applicable quarter (April or July)).
Add the specific cases identified to the cases already selected for the same
month as the month of the sample frame from which they were selected. If
a case previously selected in the sample is again selected and identified for
the same month as previously selected, an alternate case is to be selected by
using a table of random numbers.
For months in the annual period for which sample cases have not yet been
selected:

6.

Use the corrected sample interval for the period obtained in Step 1 to select
sample cases from the monthly frames.

1520

Alternate Method of Correcting for Undersampling or Oversampling When
Sample Selected Using Systematic Random Sampling

An alternate method involves no adjustment for the months for which cases were already
selected, however it does result in stratification of the sample by time. The alternative
method entails the computation of a new sample interval that will either (1) undersample
the remaining months of the 12-month sample period to meet sample size requirements if
the earlier months had been oversampled, or (2) oversample the remaining months of the
annual period to meet sample size requirements if the earlier months had been
undersampled.
Because two different sample intervals will have been used, results of cases selected by
each sample interval cannot be directly added to obtain State-wide (Tribal-wide)
estimates as the proportions of the monthly frames sampled are different, i.e., the total
sample is not a self-weighting sample. 1/ The alternate method will require all data to be
weighted at the end of the 12-month period. The procedure involves inflating the various
frequencies (e.g., number of families with an adult working, the number of families with a
minor parent head of household, cases with earned income, etc.) in cases obtained using
each sample interval, to their representation in the caseload and dividing the result by the
caseload. 2/ This gives the weighted rate for the State (Tribe). In order to make each of
the frequencies (number of families with an adults working, the number of families with a
minor parent head of household, etc.) comparable with those of other States (Tribes), it is
necessary to multiply the weighted rate by the total sample size. The equation for this
procedure is as follows:

1/

It should be noted that a self-weighting sample, except for rounding, must
possess the following characteristic:
Sample cases selected in a specific
month

Cases in sample frame for same
month
=

Total sample cases selected in
sample period

Total cases in all sample frames in
sample period

2/ "Caseload", for the purpose, is defined as the completed sample size multiplied
by the sample interval.

where:
=

the sum of . . . all strata ("stratum" is defined as part of the annual
period using the same sample interval);

=

the

=

"characteristic of interest" in the

=

completed sample size in the

=

sample interval used in the

stratum (m is the stratum index);
stratum;
stratum; and
stratum.

For example, assume that a State originally had estimated that its caseload would average
80,000 cases for the annual sample period. Assuming a 5 percent drop rate, the State
used a sample interval of 303. Actual experience after 10 months resulted in the State
revising its average caseload to 75,000, making no change in its drop rate. If the State
made no corrections, the final completed sample size for the period would be short
approximately 188 cases.
Assume that the State decides to obtain the additional 188 cases by using a revised
sample interval of 219 for the last 2 months of the sample period. Also assume for the
first 10 months of the sample period (
) that the -Number of cases completed (

) = 2,350

Number of cases with "characteristic of interest" (
and for the last 2 months of the sample period (
Number of cases completed (

) = 112

) that the --

) = 650

Number of cases with "characteristic of interest" (

) = 37

Using the definition of "caseload" as defined earlier, i.e., sample cases completed
multiplied by the sample interval, the weighted proportion of the case with the
characteristic of interest would be computed as follows:

=
=
The State case proportion for the "characteristic of interest" would be .0492. The
reported number of cases with the characteristic of interest for the 12-month period, for
comparability with other States, would be 148, i.e., .0492 x 3,000.
Note that each frequency of occurrence or proportion of the total sample must be
calculated in the same way, e.g., number of families with an adults working, the number
of families with a minor parent head of household, the number of child only cases, the
number of cases with earned income, etc. Caseload weights are to be used in computing
State-wide (Tribal-wide) characteristics.
Note, it is important that the appropriate code be entered on the coding schedule to
identify the stratum from which the case was selected.

1530

Correcting for Undersampling Using a Reserve Sample Pool

Correcting for undersampling using the sample interval (see Section 1520, page 44)
involves resampling the original frame using a new sample interval. A State (Tribe) may
find this to be difficult and/or costly. The same result can be achieved by selecting a
reserve sample pool at the time of original sample selection. The designated reserve
sample cases are to be used only if correction for undersampling is required. Properly
selected reserve pool cases retain the self-weighting property of the final sample.
However, careful attention to the controls is necessary to ensure that cases are properly
selected. Any number of cases may be designated as a reserve pool -- a good number
could be 10 or 15 percent of the required sample size.
The State (Tribal) sampling plan must describe in detail the procedures for setting up a
reserve sample pool. If a random number generator is used, the type of generator and
seed number is to be specified.

1531

Procedure for Setting Up a Reserve Sample Pool

Procedures for setting up a reserve sample pool are similar to those outlined in
Section 1430 (page 32). To illustrate the procedures, the example in Section 1430, is
used, i.e., a State (Tribe) uses the systematic random sampling method, elects the standard
sample size, has an estimated average monthly caseload of 42,600 and estimates a 5

percent drop rate for the 12-month sample period. In addition, the State (Tribe) specifies
15 percent of its selected sample as reserve pool cases each month.
1.

Determine Average Monthly Sample Size

Divide the number of sample cases for which data is to be collected in the
annual sample period by (1 - 0.15) to obtain the estimated total number of
sample cases to be selected. In our example, according to Section 1430, Step
2, (page 33) the number of sample cases (completed and dropped cases) is
3,158. The number of cases to be selected would be 3158 ÷ (0.85), or 3,715,
or an average of 309 cases per month. The average number to be placed in a
reserve pool each month is 15% of 309 cases, or 46 cases (rounded down).
Note that the reserve pool is only to be used to correct for undersampling; it is
not to be used to replace dropped cases.
2.

Select Monthly Sample
Using the monthly sample size from Step 1, 309 cases, and the procedures
outlined in Section 1430, Steps 5, 6 and 7, (page 34) compute the sample
interval, determine a random start and select monthly sample cases from the
sample frame.

3.

Compute Secondary Interval for Selection of Reserve Pool Cases
Compute a secondary sample interval to be applied to the list of sample cases
selected each month. This is obtained by dividing the estimated average
monthly sample size by the average estimated number of cases designated for
the reserve pool. In our illustration, the sample interval is 309 ÷ 46, or 6.72.

4.

Select and Identify Monthly Reserve Pool Cases
Since the interval obtained in Step 3 above is not a whole number, the
acceptable method is to round up to the next higher number. (Note that
rounding up is recommended to ensure that the basic sample will have a
sufficient number of cases.) In our example, 1 out of every 7 cases on the
monthly list of selected sample cases would be identified for the reserve pool,
using a random start number between 1 and 7 inclusive. It is important in
selecting monthly reserve pool cases to apply the same sample interval to the
entire list of selected cases each month. This is an important part of the
sample design and should not be violated in order to obtain a specific number
of reserve pool cases each month.

5.

Submission of Sample Cases Selected
The estimated average caseload, the specified percentage of monthly selected
sample cases for the reserve pool, the computed sample intervals, manually
generated random start and seed numbers to be used in the 12-month sample
period for selection of total sample cases and reserve pool cases should be sent
to the ACF Regional Administrator thirty (30) calendar days before the
October sample selection. The monthly list of selected sample cases, with
reserve pool cases identified, and computer generated random start and seed
numbers should be submitted within 10 days of the date of selection specified
in the State sampling plan.

If random numbers are used to identify cases for the reserve pool, it is absolutely essential
that the total number of sample cases selected each month is known.
The following procedures are to be used and repeated each month if random numbers are
used.
1.

Determine the total number of sample cases selected. In our example,
assume that the number selected for October was 309.

2.

Multiply the number obtained in Step 1 by the percentage of selected
sample cases specified for the reserve pool. In our illustration, the number
of cases to be placed in a reserve pool for October is 309 x .15, or 46 cases
(rounded down). Note that the same percentage must be applied each
month.

3.

Randomly select and identify reserve pool cases. In our example, 46
random numbers between 1 and 309, inclusive, would be selected.
If a Table of Random Numbers is used (see Appendix A, page 103), a
photocopy of the page(s) used, showing start number, direction, and all
selected numbers circled, is to be submitted each month along with the total
list of selected sample cases. Sample cases corresponding to the circled
random numbers are to be identified on the total list.

1532

Procedure for Obtaining Cases from a Reserve Sample Pool

States (Tribes) with reserve pools must use the same procedures in correcting for
undersampling as outlined in Section 1520 (page 44) and 1530 (page 47). A revised
estimate of the number of sample cases that should have been selected (excluding reserve
pool cases) is to be computed. The difference between the number that should have been

selected and the number that was selected is the number of additional sample cases that
will need to be selected from the reserve pool.
If a State (Tribe) uses a disproportionate stratified sample design, the State must maintain
a separate reserve sample pool for each stratum. If a State with a disproportionate
stratified sample design undersamples, the State must use the allocation procedures
specified in its sampling plan to determine in which stratum (or strata) the State has
undersampled. The State must correct for undersampling in each stratum in which
undersampling occurred.
The same primary sample interval as determined in Section 1430, Step 5 (page 34), Step
2, must be used to select sample cases for months in the annual period for which such
cases have not yet been selected. However, a new secondary sample interval to be
applied to the monthly lists of selected sample cases must be used in identifying cases for
the reserve pool.
The revised secondary sample interval is the product of the original secondary sample
interval and the number of cases in the reserve sample pool prior to selection of cases
from the reserve sample pool divided by the number of cases remaining in the reserve
sample pool after selection of cases from the reserve sample pool.

1540

Correction for Undersampling or Oversampling When Sample Was Selected
Using Simple Random Sampling

As described in Sections 1510 and 1520 (pages 42 and 44), there are two basic
approaches to correcting the annual samples. The first approach is to correct the sample
for both the months for which the sample has already been selected and the months for
which the sample has not been selected. This approach provides an annual sample with
approximately one-twelfth of the sample selected each month. The second approach is to
make the entire adjustment in the months for which the sample has not yet been selected.
Monthly samples selected using simple random sampling are less likely to need large
adjustments for undersampling than for samples selected using systematic random
sampling. This is true because under simple random sampling a fix number of sampling
units is selected each month regardless of the monthly caseload. Under systematic
random sampling, a fixed proportion of the caseload is selected each month. However,
caseloads can vary from month to month. This variation of the monthly caseload results
in variation in the monthly sample size.

1540.1

Correcting for Undersampling

If a small correction (e.g., less than 50 cases) is needed to ensure the State agency or

Tribal grantee will meet its minimum required annual sample size and no month is
substantially short of the approximate one-twelfth of the annual sample, then the State or
Tribe should correct for undersamping by adjusting the sample size in months for which
the sample has not yet been selected. If the sample for all months have been selected,
then the adjustments should be made for the months in the last quarter of the fiscal year.
On the other hand, if a large correction is needed for months in which the sample have
already been selected, the State or Tribe should consider making adjustments to all
monthly samples. To correct a monthly sample for undersampling , use the following
procedures:
1.

Retrieve the Original Monthly Sample Frame
As required under Section 1460, States and Tribes must to retain their
original monthly sample frames. The State or Tribe should locate the
original month sample frame for use in selecting the additional sample
cases.

2.

Review Original Determinations of Total Monthly Caseload and Average
Monthly Sample Size
Review the original application of the sample selection procedures from
Section 1440 Steps 2, 3, and 4 (Page 38) to identify the reason for
undersampling (e.g., under estimated the number of listed-in-error cases).

3.

Determine the Number of Additional Sample Cases Needed
Subtract the number of completed cases for the sample month from the
required number of sample cases for the month to determine the short fall.
Allowing for some additional listed-in-error cases (using the procedures in
Section 1440, Step 4 (Page 38)), determine the number of additional sample
cases to be selected from the original sample frame.

4.

Select the Additional Sample Cases
Using the same procedures as in Section 1440, Step 5 (page 38), select the
addition sample cases from the sample frame and forward the sample
selection list to the ACF Region Office.

1540.2

Correcting for Oversampling

States are not required to correct for excessive oversampling. If correction is desired, the
procedures to correct for excessive oversampling are similar to correcting for

undersampling. However, instead of using the original monthly sample frame, the State
or Tribe would use its monthly sample selection list and apply the following procedures:
1.

Determine the Number of Cases on the Original Monthly Sample Selection
List
This is the total number of sample cases, including cases that were listed-inerror.

2.

Determine the Number of Excess Cases
In determining the number of excess cases, make allowances for the number
of listed-in-error cases in the original sample. A proportion of these cases
will be selected as cases to be removed. For example, if the original
monthly sample had 325 cases of which 25 were listed-in-error, then the
listed-in-error cases represent about 7.7% of the total sample and the total
number of case of the sample frame. In reducing the sample so that there
are at least 250 completed cases, the State or Tribe could expect about 20
listed-in-error cases. Therefore, the number of excess cases is 325 - 270 or
55 cases.

3.

Select the Excess Cases to be Removed from the Sample
Using the same process as in Section 1440, Step 5 (page 38), select the
number of excess cases from the original sample and forward the sample
selection list of excess cases to the ACF Regional Office.

1600

WORK PARTICIPATION RATES

The purpose of the chapter is to provide States and Tribes with the methodology for
calculating the monthly and annual work participation rates. In applying the
methodology it is necessary to understand the statutory and regulatory provisions on the
work participation requirements and the TANF Data Report - Section One data elements
used to capture the information needed for these calculations. It is for this reason that we
have included, as background, certain mandatory work requirements from the law and the
final regulation, which States must adhere to in administrating their TANF programs.
These provisions include:

1610

1.

Establishing the minimum all families and two-parent work participation
rate requirements;

2.

Defining the monthly and annual work participation rate calculations,
including families that are to be included in or excluded from the
calculation;

3.

Identifying countable work activities, including limitation on certain
activities;

4.

Specifying the hourly requirements for engaged in work for the all families
and two-parent family rates and related special circumstances (e.g., deemed
engaged in work); and

Work Participation Rate Standards and Caseload Reduction Credit

For each fiscal year, the statute specifies the all families and two-parent families
minimum work participation rate standards that States must meet in administering their
TANF and SSP-MOE Programs. These standards are as follows: For the overall work
participation, States must achieve a minimum required work participation rate of 50
percent. For the two-parent work participation rate, States must achieve a minimum
required work participation rate of 90 percent.
States that are successful in moving welfare recipients from welfare to self-sufficiency or
otherwise reducing their welfare rolls are given credit for their efforts with respect to
these standards. If the average number of cases receiving assistance, including assistance
under a separate State program, for the State in the preceding fiscal year was lower than
the average number of cases receiving assistance in FY '2005, then the minimum work
participation rate standard that the State must meet for the fiscal year will decrease by the

amount of percentage points the caseload has fallen in comparison to the FY '2005
caseload. This reduction in the minimum work participation rate standard is referred to as
the caseload reduction credit. The caseload reduction credit will not include changes that
are required by Federal law or that are as a result of changes in State eligibility criteria.
The minimum two-parent families participation rate the State must meet for the fiscal
year decreases, at State option, by either:
1.

The number of percentage points the prior-year two-parent caseload,
including assistance under a separate State program (as provided in
§261.42(b)), fell in comparison to the FY 2005 two-parent caseload; or

2.

The number of percentage points the prior-year overall caseload, including
assistance under a separate State program (as provided in §261.42(b)), fell
in comparison to the FY 2005 overall caseload.

These calculations must disregard the net caseload reduction (i.e., caseload decreases
offset by increases) due either to requirements of Federal law or to changes that a State
has made in its eligibility criteria in comparison to its criteria in effect in FY 1995.
We will determine the total and two-parent caseload reduction credits that apply to each
State based on the information and estimates reported to us by the State on eligibility
policy changes, application denials, and case closures. In order to receive a caseload
reduction credit, a State must submit a Caseload Reduction Report to us containing the
following information:
1.

A listing of, and implementation dates for, all
State and Federal eligibility changes, as defined
at §261.42, made by the State since the beginning
of FY 2005;

2.

A numerical estimate of the positive or negative
impact on the applicable caseload of each
eligibility change (based, as appropriate, on
application denials, case closures or other
analyses);

3.

An overall estimate of the total net positive or
negative impact on the applicable caseload as a
result of all such eligibility changes;

4.

An estimate of the State's caseload reduction
credit;

5.

Total prior year caseload;

6.

The number of application denials and case
closures for fiscal year 2005 and the prior fiscal
year;

7.

The distribution of such denials and case
closures, by reason, for fiscal year 1995 and the
prior fiscal year;

8.

A description of the methodology and the
supporting data that the State used to calculate
its caseload reduction estimates;

9.

A certification that it has provided the public an
appropriate opportunity to comment on the
estimates and methodology, considered their
comments, and incorporated all net reductions
resulting from Federal and State eligibility
changes; and

10.

A summary of all public comments.

We will calculate the caseload reduction credit that applies
to the work participation rate(s). However, we will not
calculate a caseload reduction credit unless the State
reports case-record data on individuals and families served
by any separate State program, as required under §265.3(d).
A State may only apply to its participation rate a caseload
reduction credit that we have calculated. If a State
disagrees with the caseload reduction credit, it may appeal
the decision as an adverse action in accordance with §262.7.
A State must report the necessary documentation on caseload
reductions for the preceding fiscal year by December 31.
1620

Definitions of Annual and Monthly Work Participation
Rates

The statute defines the overall annual participation rate
and the overall monthly participation rate as follow:
Overall Annual Participation Rate is the average of the
State's overall participation rates for each month in
the fiscal year.
Overall Monthly Participation Rate is: (1) the number
of families receiving TANF and/or SSP-MOE assistance
that include a work-eligible individual who is engaged
in work for the month (the numerator), divided by (2)
the number of families receiving TANF and/or SSP-MOE

assistance during the month that include a workeligible individual minus the number of families that
are subject to a penalty for refusing to work in that
month (the denominator). However, if a family has been
sanctioned for more than three of the last 12 months,
we will not exclude it from the participation rate
calculation.
Other circumstances for which a family may be disregarded
from the overall monthly work participation rate calculation
are:
1.

A State has the option of not requiring a single
custodial parent caring for a child under age one
to engage in work. If the State adopts this
option, we will disregard such a family in the
participation rate calculation for a maximum of 12
months;

2.

At State option, a family that is participating in
a Tribal Work Program may be included or excluded
from the work participation rate calculation. If
the State has opted to exclude all Tribal Work
Program participants from its work participation
rate, such families will be excluded from the
calculation;

The statute defines the two-parent annual participation rate
and the two-parent monthly participation rate as follow:
Two-parent Family Annual Participation Rate is the
average of the State's two-parent participation rates
for each month in the fiscal year.
Two-parent Family Monthly Participation Rate is: (1)
the number of two-parent families receiving TANF and/or
SSP-MOE assistance in which the work-eligible parents
meet the requirements set forth in §261.32 for the
month (the numerator), divided by (2) the number of
two-parent families receiving TANF amd/or SSP-MOE
assistance during the month minus the number of twoparent families that are subject to a penalty for
refusing to work in that month (the denominator).
However, if a family has been sanctioned for more than
three of the last 12 months, we will not exclude it
from the participation rate calculation.
Other circumstances for which a family may be disregarded
from the two-parent monthly work participation rate

calculation are:
1.

At State option, a family that is participating in
a Tribal Work Program may be included or excluded
from the work participation rate calculation. If
the State has opted to exclude all Tribal Work
Program participants from its work participation
rate, such two-parent families will be excluded
from the two-parent participation rate
calculation; and

2.

If a two-parent family includes a disabled parent,
we will not consider the family as a two-parent
family for the purpose of calculating the twoparent work participation rate.

For the purpose of calculating the two-parent work
participation rate, the two-parent families must include,
but is not limited to, any family with two natural or
adoptive parents (of the same minor child) who are workeligible individuals and living in the home, unless both are
minor and neither are head-of-household. This is a minimal
definition. At State option, a broader definition of twoparent families may be used. For example, a State may want
to include step-parents and/or non-custodial parents.
A State may opt to include a noncustodial parent as part of
the eligible family receiving assistance. If the State does
so, the noncustodial parent may receive assistance or other
services and may participate in work activities. The
included noncustodial parent must live in the State, but may
not live with his/her child(ren). A noncustodial parent may
participate in work activities funded under the State TANF
Program. In addition, the State must report the
noncustodial parent as part of the TANF family. However,
the State may choose whether a two-parent family with a
noncustodial parent as one of the two parents is a twoparent family for the purposes of calculating the two-parent
work participation rate. If a State chooses to exclude a
two-parent family with a noncustodial parent as one of the
parents from the two-parent work participation rate, the
State must code the TANF Data Report data element "Type of
Family for Work Participation" with a "1" and code the data
element "Work Participation Status" for the noncustodial
parent with a "99."

1630

Countable Work Activities

The statute requires that adults and minor child heads-ofhousehold participate in certain work activities. Countable
work activities include the following:
1.

Unsubsidized employment;

2.

Subsidized private sector employment;

3.

Subsidized public sector employment;

4.

Work experience;

5.

On-the-job training (OJT);

6.

Job search and job readiness assistance;

7.

Community service programs;

8.

Vocational educational training;

9.

Job skills training directly related to
employment;

10.

Education directly related to employment, in the
case of a recipient who has not received a high
school diploma or a certificate of high school
equivalency;

11.

Satisfactory attendance at secondary school or in
a course of study leading to a certificate of
general equivalence, if a recipient has not
completed secondary school or received such a
certificate; and

12.

Providing child care services to an individual who
is participating in a community service program.

Each adult (or minor child head-of-household) has a lifetime limit on countable hours of participation for
vocational educational training. Vocational educational
training may only count as a work activity for a total of 12
months.
There are four limitations on job search and job readiness
training. These are:
1.

Job search and job readiness assistance only count
for 6 weeks in any fiscal year;

2.

An individual's participation in job search and
job readiness assistance counts for no more than 4
consecutive weeks;

3.

If the State's (Tribe's) total unemployment rate
for a fiscal year is at least 50 percent greater
than the United States' total unemployment rate
for that fiscal year or the State is a "needy"
State (within the meaning of Section 403 (b)(6)),
then an individual's participation in job search
or job readiness assistance counts for up to 12
weeks in that fiscal year; and

4.

A State may count 3 or 4 days of job search and
job readiness assistance during a week as a full
week of participation, but only once for any
individual.

1640

Required Hours of Work to be "Engaged in Work"

A family counts as participating in work for the overall
work participation rate for a month in which a work-eligible
individual is engaged in work. That is, the work-eligible
individual participates in countable work activities during
the month for at least the minimum average number of 30
hours per week. At least 20 of the 30 hours per week must
come from work activities (1) through (8) and (12), as shown
in Section 1630. (Hereafter, we will refer to these work
activities as "core" work activities.) Hours above the 20
hours per week may also come from work activities (9), (10),
and (11) as shown in Section 1630.
A two-parent family counts as engaged in work for the month
in determining the two-parent rate, if one of the following
is applicable:
1.

If the family does not receive federally-funded
child care and the work-eligible parents in the
family are participating in work activities for an
average of at least 35 hours per week during the
month, and, at least 30 of the 35 hours per week
come from participation in the core work
activities, (1) through (8) and (12) listed in
Section 1630. The family counts as engaged in
work. Above the 30 hours per week, countable
hours may also come from work activities (9),
(10), and (11) from those work activities listed
in Section 1630.

2.

1641

If the family receives federally-funded child
care, an adult in the family is not disabled or
caring for a severely disabled child, and the
work-eligible parents in the family are
participating in work activities for an average of
at least 55 hours per week during the month, and,
at least 50 of the 55 hours per week come from
participation in the core work activities, (1)
through (8) and (12) listed in Section 1630. The
family counts as engaged in work. Above the 50
hours per week, countable hours may also come from
work activities (9), (10), and (11) from those
work activities listed in Section 1630.

Deeming Core Hours

Under sections 261.31 and 261.32 of the TANF interim final rule, if a work-eligible
individual participates in work experience or a community services program for the
maximum number of hours per week that a State may require under the applicable
Federal or State minimum wage law but falls short of the hours needed to meet the “core”
hours requirement, we will “deem” the individual to have participated in the remaining
core hours needed. We refer to these remaining hours as “deemed core hours.” This
policy is limited to States that combine the value of TANF and food stamp benefit
amounts when calculating the maximum hours of participation permitted based on the
applicable minimum wage. A State can include the amount of food stamp allotment by
adopting the mini-simplified Food Stamp Program option.

1642

Deemed Engaged In Work

For purposes of the overall work participation and two-parent work participation rate, a
family with single minor child head-of-household or married teen parent is deemed
engaged in work in a month if (s)he maintains satisfactory attendance at a secondary
school or the equivalent during the month or participates in education directly related to
employment for an average of at least 20 hours per week during the month.
A single custodial parent or caretaker relative with a child under age six will count as
engaged in work if (s)he participates for at least an average of 20 hours per week in core
work activities.

1642

The Thirty (30) Percent Limit

In counting families for each monthly participation rate, not more than 30 percent of
families with individuals engaged in work in a month may be included in the numerator
because the individuals are: (1) participating in vocational educational training; or (2)
individuals deemed to be engaged in work by participating in work activities (10) and
(11) as listed in Section 1630. For each month in which the State exceeds the 30% limit,
its overall and two parent work participation rates will be adjusted by decreasing the
number of participating families until the 30% limit is not exceeded.

1650

Methodology Used in Calculating the Monthly Work Participation Rate

The monthly TANF and SSP-MOE population consists of all families who receive
assistance under the State TANF and/or SSP-MOE Programs for the reporting month.
For the all family (and two parent family) work participation rate, we are interested in a
portion of these families. This smaller grouping is referred to as a subpopulation or
subdomain. For the overall work participation rate, the subpopulation of interest is all
TANF and SSP-MOE families with a wprl-eligible individual, except those families that
are disregarded due to:
1.

Single custodial parent with child under 12 months;

2.

Sanctioned for the reporting month, but not sanctioned for more than 3
months within the preceding 12-month period;

3.

Participating in a Tribal Work Program, State has opted to exclude all
Tribal Work Program participants from its Work Participation rate;

Similarly, for the two parent work participation rate, the subpopulation of interest is all
two parent TANF and SSP-MOE families with work-eligible parents, except those that
are disregarded due to:
1.

Sanctioned for the reporting month, but not sanctioned for more than 3
months within the preceding 12-month period;

2.

Participating in a Tribal Work Program, State has opted to exclude all
Tribal Work Program participates from its Work Participation rate;

The standard statistical methodology for estimating means (proportions are special cases
of means) over subpopulations from universe data, non-stratified samples and stratified
samples are shown below.

1651

Calculation of the Monthly Work Participation Rate from Universe Data

For a State that reports the TANF Data Report (and/or SSP-MOE Data Report) for its
entire caseload, the monthly work participation rate (R) is the total number of families
participating from the subpopulation (Yj) divided by the total number of families in the
subpopulation (Nj) and is calculated as follows:

where i
Yi

NJ

=

1, 2, ..., N

=

1, if the ith family is participating in jth subpopulation

=

0, if the ith family is not participating in jth subpopulation

=

the number of cases in the jth subpopulation

For example, a State with a monthly caseload of 42,600 families reports its entire
caseload as follows:

The number of families that are:

All Families

Two-Parent
Families

1.

Reported (i.e., total caseload)

42600

3,000

2.

No WEI Families

13,500

-

3.

Listed-in-error

50

-

4.

Disregarded: single custodial parent with
child under 12 months

2,200

-

5.

Disregarded: sanctioned for the reporting
month, but not sanctioned for more than 3
months in the preceding 12-month period

1,775

540

6.

Disregarded: Participating in a Tribal
work program

25

0

The number of families that are:

All Families

Two-Parent
Families

7.

Required to Participate (item #1 minus
item 2 through item 6)

25,050

2,460

8.

Participating

8,338

1,225

9.

Counting toward the 30% limit

1,731

120

The ratio estimator for the all family work participation rate is:

The ratio estimator for the two-parent work participation rate is:

1652

Calculation of the Monthly Work Participation Rate from Sample Data

For a State that samples but does not stratify, the estimated monthly work participation
rate is calculated using the ratio estimator.
The ratio estimator is:

yi

xi

=

1, if the ith family is participating in jth subpopulation

=

0, if the ith family is not participating in jth subpopulation

=

1, if the ith family is in jth subpopulation

=

0, if the ith family is not in jth subpopulation

The estimated variance for the ratio estimator is:

where:

For example, a State with a monthly caseload of 42,600 families reports based on a nonstratified sample as follows:

The number of families that are:

All Families

Two-Parent
Families

42.600

3,000

1.

Total Caseload

2.

Reported (sample size)

255

51

3.

No WEI Families

60

-

4.

Listed-in-error

2

-

5.

Disregarded: single custodial parent
with child under 12 months

10

-

The number of families that are:

All Families

Two-Parent
Families

6.

Disregarded: sanctioned for the
reporting month, but not sanctioned
for more than 3 months in the
preceding 12-month period

29

9

7.

Disregarded: Participating in a Tribal
work program

0

0

8.

Required to Participate (item #2
minus item 3 through item 7) in the
sample

154

42

9.

Participating in the sample

56

21

12

3

10. Counting toward the 30% limit in the
sample
For the all family rate:

The estimated total number of families required to participate from the total
caseload (i.e., the denominator of the participation rate) is:

The estimated total number of families that are participating from the total
caseload (i.e., the numerator of the participation rate) is:

The estimated monthly all family work participation rate is:

The estimated number of participating families that count toward the 30% limit is:

The number of participating families due to vocational education (and after 1999 due to
deemed engaged in work based on work activities 10 and 11 from Section 1630) is less
than 30% of total participating families. Therefore, no adjustment is necessary.
For the two-parent work participation rate:
The estimated total number of two-parent families required to participate from the
total caseload (i.e., the denominator of the participation rate) is:

The estimated total number of two-parent families that are participating from the
total caseload (the numerator of the participation rate) is:
The estimated monthly two-parent work participation rate is:

the estimated number of participating two-parent families that count toward the
30% limit is:

The number of two-parent participating families due to vocational education (and after
1999 due to deemed engaged in work based on work activities 10 and 11 from Section
1630) is less than 30% of total number of two-parent families that are participating.
Therefore, no adjustment is necessary.

1653

Calculation of the Monthly Work Participation Rate from Stratified Sample
Data

For a State that selects a stratified sample the monthly work participation rate as
estimated with the ratio estimator is:

where k

=

1, 2, ... nhj

h

=

1, 2, ... H

nhj

=

the number of families in hth stratum and the jth
subpopulation

H

=

the number of strata

yhjk

=

1, if the ith family from stratum h is participating in the
jth subpopulation.

=

0, if the ith family from stratum h is not participating in
the jth subpopulation.

The estimated variance for the ratio estimator is :

where:

For example, a State with a monthly caseload of 42,600 families reports based on a
stratified sample, in which the two-parent families are in stratum 02 and all other families
are in stratum 01, as follows:
Two-Parent
Families

The number of families that are:

All Families

Strata

01

02

02

39.600

3,000

3,000

2. Reported (sample size)

204

51

51

3. No WEI Families

60

0

-

4. Listed-in-error

2

0

-

5. Disregarded: single custodial parent
with child under 12 months

10

0

-

6. Disregarded: sanctioned for the
reporting month, but not sanctioned
for more than 3 months in the
preceding 12-month period

30

9

9

7. Disregarded: Participating in a Tribal
work program

0

0

0

1. Total Caseload

8.

Required to Participate (item #2 minus
item 3 through item 9) in the sample

112

42

42

9.

Participating in the sample

35

21

21

9

3

3

10. Counting toward the 30% limit in the
sample

For the all family rate:
The estimated total number of families required to participate from the total caseload (i.e.,
the denominator of the participation rate) is:

The estimated total number of families that are participating from the total
caseload (i.e., the numerator of the participation rate) is:

The estimated monthly all family work participation rate is:

The estimated number of participating families that count toward the 30% limit is:

In this example, the two-parent work participation rate is based on the data in stratum 02
and the result are the same as in the previous example.

1654

Adjusting the Monthly Work Participation Rate for Exceeding the 30% Limit

If, in the example from Section 1652 for the all family work participation rate, the number
of participating families that count toward the 30% limit is 20 sample cases (instead of 12
sample cases), then the 30%limit is exceeded. In this instance, the estimated total number
of participating families that count toward the limit is:

The estimate number of participating families that counts toward the 30% limit (3,341.17)
exceeds the 30% limit (3,341.1765 / 9,355.2941 = .3571.) To make the adjustment, first
determine the number of participating families that do not count toward the 30% limit
(9,355.2941 - 3,341.175 = 6,014.1176). This group represents the 70% of the total
adjusted number of participating families. Thus, the total adjusted number of
participating families is calculated by dividing the number of participating families that
do not count toward the 30% limit by 0.7 (i.e., 6,014.1176 / 0.7 = 8,591.5966). The
adjusted all family work participation rate is

The adjustment from 0.3636 to 0.3340 is a decrease in the participation rate of 0.296 or
2.96%.

1670

TANF Data Reporting Elements Used in Calculating the Monthly Work
Participation Rate

The overall and two parent work participation rates are calculated based on data provided
on the TANF Data Report - Section One and, for States that do not use a stratified
sample, the TANF Data Report - Section Three, data element #8, the total number of
families. For States that use a stratified sample design, the State must submit for each
month the number of families in each stratum. The TANF Data Report - Section One
data elements used in the calculation are listed below:
Item Number

Data Element

1

State FIPS code

4

Reporting Month

5

Stratum

9

Disposition

11

Number of Family Members

12

Type of Family for Work Participation

17

Receives Subsidized Child Care

27

Waiver Evaluation Experimental and Control Group

60

Family Affiliation

Item Number

Data Element

31

Non-custodial Parent

32

Date-of-Birth

37

Marital Status

38

Relationship to Head-of-Household

39

Parent with a Minor Child

48

Work-Eligible Individual Indicator

49

Work Participation Status

50

Unsubsidized employment

51

Subsidized private sector employment

52

Subsidized public sector employment

53

Work experience

54

On-the-job training (OJT)

55

Job search and job readiness assistance

56

Community service programs

57

Vocational educational training

58

Job skills training directly related to employment

59

Education directly related to employment, in the case of a
recipient who has not received a high school diploma or a
certificate of high school equivalency

60

Satisfactory attendance at secondary school or in a course of
study leading to a certificate of general equivalence, if a
recipient has not completed secondary school or received such
a certificate

61

Providing child care services to an individual who is
participating in a community service program

62

Additional Work Activities Permitted Under Waiver

64

Required Hours of Work

68

Date-of-Birth (Child)

1700

STATISTICAL METHODS IN DATA ANALYSIS

A State or Tribal grantee may comply with the reporting requirements of TANF by
reporting on the entire TANF caseload or by using data collected through scientifically
acceptable sampling methods approved by the Secretary. In addition to information
necessary to compute participation rates, the sample will provide demographic and
financial characteristics of families, including age, race, sex, education, income, and type
and amount of assistance of family members. Together with a sample of closed cases,
States will be able to generate data on families applying for assistance, families receiving
assistance, and families that have become ineligible. By carefully analyzing the data,
States will be able to examine trends in employment and earnings of families with minor
children. (If the sample is sufficiently large enough, the State will be able to produce
accurate and reliable information on the number of hours of participation in different
activities such as, education, subsidized employment, unsubsidized employment, job
search, etc.)
The following subsections outline some of the more common statistical techniques that
can be used in the statistical analysis process. States are encouraged to do their own
research and develop statistical methodology to meet their own special needs in data
analysis.

1710

Statistical Tests of Significance

Because sample results will normally be in error by some amount simply because they are
based on a sample, inferences from sample results must take into account sampling error.
The means for doing this is known as testing statistical hypotheses and estimation
(including confidence interval construction) for statistically significant differences. The
"difference" may be between two or more samples or between a sample and the
population. The hypothesis used in testing differences (called the null hypothesis) is that
there is no "true" difference between the observed results, i.e., that the observed
difference is only due to random errors or chance. When the observed difference is
sufficiently larger than the sampling error, it can be stated that there is a statistically
significant difference, i.e., that a "true" difference most likely exists.
This section is concerned with various statistical procedures that test null hypotheses.
The tests that follow are appropriate for the systematic random or simple random
sampling methods.

1711

Testing the Representativeness of the Sample with the Caseload

There are several statistical techniques that can be used to ensure that the sample is
acceptably representative of the caseload from which it is drawn. These techniques
involve the comparison of sample case findings with known caseload information. The
two statistical methods that are discussed are: (1) the confidence interval estimate of
population parameters for averages and proportions, and (2) the one-sample chi-square
test for distribution of sample findings.
All States and Tribal grantees collect information on their entire caseload on an ongoing
basis -- monthly, quarterly, or annually. Caseload data closest to those of the sample
period should be used in making the comparisons. If the test reveals significant
differences in results, the method of sample selection and sample sizes should be reexamined to provide assurance that no errors have occurred in the sample selection
process.
Sections 1711.1 and 1711.2 below illustrate the methods using the confidence interval to
estimate representativeness of the sample when proportions are not used and when
proportions are used.

1711.1

Comparison of Sample and Total Caseload When Proportions Are Not Used

In order to determine whether the sample average dollar amount of assistance is
representative of the caseload, use the following procedure:
For our example, assume that the average dollar amount of assistance in the total caseload
is $90.20 and in the sample, $95.35 with a standard error of $5.48.
The equation for a 95 percent confidence interval in this calculation is approximately as
follows:

where:
=

number of sample cases for which a review was completed;

=

mean dollar amount of assistance per sample case for which data

was collected =

;

=

the sum operator;

=

estimated standard deviation =

=

actual dollar amount of assistance for a sample case; and

=

estimated standard error of

;

.

If, in our example, the estimated standard error of the sample average dollar amount of
assistance is $5.48, then 1.96 times the standard error is $10.74. Therefore, the 95
percent confidence limits are $95.35 ± $10.74, or $84.61 to $106.09. Since the confidence
interval in this case includes the "true" or total caseload average dollar amount of
assistance of $90.20, there is no evidence that the sample is not representative of the
caseload from which it is drawn.

1711.2

Comparison of Sample and Total Caseload When Proportions Are Used

If information on the proportion of the entire caseload having certain characteristics is
available, a similar test can be conducted. For example, if the proportion of 2-parent
families in the entire caseload is known, the sample proportion can be compared to this
figure. In this situation, a confidence interval is calculated around the total caseload, or
population value, to see if the sample value is included.
The sample proportion of 2-parent families should fall within the following interval:

where:
=

proportion of 2-parent families in the caseload; and

=

number of completed sample cases

It should be noted that the best estimate of a standard error uses the most complete data
readily available. Theoretically, total caseload data, if available, should be used to
calculate the standard error wherever findings are compared between sample cases and
the total caseload. Calculation of the standard error from total caseload data is a
relatively simple process where proportions are being compared. However, where
proportions are not used, as in comparing average dollar amount of assistance, calculating
the best estimate of the standard error from the total caseload is a very lengthy process. In
such circumstances, the standard error is calculated from the sample data.

1711.3

One Sample Chi-Square (

) Test

This method for testing the representativeness of samples compares the distribution of
sample cases by certain characteristics with that of the total caseload. The assumption is
that a certain amount of information is available based upon universe counts of the entire
caseload.
The most readily available characteristic that can be compared is the distribution of cases
by county, or other geographic areas. If cases in the sample have been drawn with each
case having an equal chance of selection, they would be distributed among the counties or
other geographic areas in the same proportions as cases in the total caseload. To
determine if the county (or other geographic area) variations in sample cases are large
enough to support a possible suspicion of bias, the chi-square test of significant
differences can be computed. In the chi-square test, theoretical, i.e., expected values are
computed. If the observed values differ greatly from these expected values, a significant
concentration is in evidence.
The equation for computing the chi-square statistic is as follows:

where:
=

the sum of all categories;

=

observed number of cases in each category (or case characteristic);
and

=

expected number of cases in each category (or case characteristic)

which when calculated is as follows:

The following example will illustrate the method.

Comparison of Distributions of Cases by County Groups

County
Groups

Total Caseload ( )

Observed Number
of Cases in
Sample ( )

#1

1,000

11

#2

3,000

33

#3

5,000

58

#4

4,000

57

#5

2,000

13

#6

2,000

13

#7

3,000

15

Expected Number of Cases
in Sample

Comparison of Distributions of Cases by County Groups

County
Groups

Total Caseload ( )

Observed Number
of Cases in
Sample ( )

20,000

Expected Number of Cases
in Sample

200

200

=

+

+

+

+

+

=
To show significance, the computed value must exceed the critical value in the following
table.
Critical Chi-Square (
Degrees of Freedom

) Values

Critical Value of

1

3.84

2

5.99

3

7.81

4

9.49

5

11.1

6

12.6

Statistic

Critical Chi-Square (
Degrees of Freedom

) Values

Critical Value of

7

14.1

8

15.5

9

16.9

10

18.3

11

19.7

12

21.0

13

22.4

14

23.7

15

25.0

16

26.3

17

27.6

18

28.9

19

30.1

20

31.4

21

32.7

22

33.9

23

35.2

24

36.4

25

37.7

26

38.9

27

40.1

Statistic

The critical value is dictated by the number of "degrees of freedom." Problems of this
type have degrees of freedom equal to the number of categories minus "1", in this
example,
. The critical value of 12.6 is clearly exceeded. Thus, a suspicion
of possible bias in the sample is given greater validity and observed variation in such

categories is more than can reasonably be attributed to chance. (The table of values is set
at 95 percent, i.e., when a computed value exceeds the table value, there is less than 5
chances out of 100 that the large observed differences are due to chance. This predefined
statistical probability, in this table, set at alpha = .05 is called a Type I error.)
Note that the chi-square test is inapplicable, i.e., serious distortions of results may appear,
when 20 percent or more of the groups have expected values of less than "5" or any group
has an expected frequency of less than "1." Under these circumstances, groups must be
combined until the requirements are satisfied. When practical, such combinations should
be made before obtaining or looking at the sample results, in order to avoid biases in the
test. The combinations should be meaningful, e.g., rural counties, northern counties, etc.
If there are only two groups, each expected value must be "5" or more. In such tables, the
preferred calculation of chi-square is as follows:
1

1712

Testing Differences of Proportions Between Samples

Repeated sampling from a given population should not differ from each other by more
than chance fluctuations.
The equations used to determine the statistical significance the of difference in
proportions, such as participation rates, between two reporting periods and using a
predefined probability (Type I error, or alpha = .05) are as follows:

where:
=

1

weighted participation rate for reporting periods A and B combined;

The parallel bars, **, indicate absolute value of the term, i.e., ignore the sign and
assume positive. The 0.5 figure is called the Yates Correction for Continuity.

=

participation rate for reporting period A;

=

participation rate for reporting period B;

=

number of sampled cases in reporting period A; and

=

number of cases reviewed in reporting period B.

The equation for the statistic is as follows:
2

If the computed value of "
between

" is greater than 1.96, a significant difference exists

and

For example, assume a participation rate of 33.2 percent based on 1,573 sample cases in
sample period A is compared with an participation rate of 25.7 percent based on 1,495
sample cases in sample period B. The test of significance would be computed as follows:

2

The " " test is satisfactory only if both

and

are large.

Since the computed value of
(4.55) is larger than 1.96, the difference between the
participation rates is statistically significant. If the computed value was less than 1.96,
the difference would not have been statistically significant. It is, therefore, reasonable to
deduce that the observed difference in the participation rate is not attributable to chance
fluctuations.

1713

Testing Differences Within the Same Sample -- Chi-Square (

)

A test of statistical significance can be used to determine if the characteristics of one
group vary significantly from the characteristics of another. For example, this test can be
used to compare the distribution of participants in one county versus another.
In testing this hypothesis, the chi-square test uses "column" and "row" groupings.
Although the expected values are computed differently than in Section 1711.3 (page 76),
the overall equation is computed the same way:

where:
=

observed number of cases in each grouping; and

=

proportional number of cases expected, if no sampling variation was
present, computed as follows:

To determine whether the distribution of participants by activity varies between two

counties, assume the following data were observed from the sample:

Number of Sample Cases
Type of Activity

County A

County B

280

160

120

Education

45

30

15

Subs. Employment

40

15

25

Unsubs. Employment

22

12

10

Public Sector Empl.

17

7

10

Job Search

55

35

20

Job Skills

54

34

20

Voc. Training

47

27

20

Total

Total

Expected Values (

County A
Education

Subs. Empl.

Unsubs. Empl

)

County B

Expected Values (

County A
Public Sector
Empl.
Job Search

Job Skills

Voc. Training

=

)

County B

=

.72 + .96 + 2.73 + 3.65 + .03 + .04 +
.75 + 1.00 + .41 + .55 + 0

+ 0

= 10.29

To determine if the computed chi-square value is significant, i.e., the concentrations of
error can not be reasonably regarded as due to sampling variation, the table of Critical
Chi-Square Values in Section 1713 (page 82), should be used. The appropriate number of
degrees of freedom (DF) for examples of this type (with any number of rows or columns)
is computed by the following equation:

Again, if the computed chi-square value exceeds the table value, the value is significant,
i.e., participation in different activities varies from County A to County B. In this
example, DF = 6. Since 10.67 does not exceed 12.6, the data is not significant at the .05
level.
Interpretation of significant data is a somewhat more complex task. Briefly, the analyst
must look to the source of the greatest variation, noting whether the observed value was
larger or smaller than expected. If this test had shown statistical significance, the analyst
would need to further examine the subsidized employment category, where County A had
a smaller than expected number of cases while County B had a greater than expected
number of cases.
The restrictions on the use of this table are the same as in Section 1711.3 (page 76) -- that
the test is inapplicable, i.e., serious distortions of results may appear, when 20 percent or
more of the cells have expected values of less than "5" or any cell has an expected value
of less than "1." Under these circumstances, rows and/or columns must be combined
until the requirements are satisfied.
As indicated in Section 1711.3 (page 76), in a 2 x 2 table, each expected value must be
"5" or more. (In such tables, the preferred method for computing the chi-square is by the
use of the equation given in Section 1711.3.)

1720

Trends

It may be important in a State for the TANF system to have feedback on apparent changes
over time for a variety of statistics (e.g., changes in caseload, in participation rates, in outof-wedlock births, in error rates). The general direction of change in data over time is
called the "trend" and can be used, for example, to assess the effectiveness of State
policies or of corrective actions in reducing error rates. Throughout this section, we are
using the error rate, however the methodology is applicable to other proportions. Trends
can be based on moving averages of error rates or on individual monthly error rates.

1721

Moving Averages

Trends based on a moving average involve taking the averages calculated over a fixed
number of months and progressively dropping data for the earliest month and adding data
for the latest month. In this way, the composition of each fixed time period average
remains approximately the same because any given average covers early, middle, and late
months of the fixed period. Monthly aberrations are smoothed because these fixed
groupings are not particularly sensitive to any given monthly rate. Thus, the long term
trend can be judged visually.
For TANF purposes, a six-month moving average is recommended. Six-month moving
averages can be computed on reviewed sample cases by either month of review or by
month of completion (see Figures 2. and 3. below). The advantage of computing sixmonth averages by month of review is that the effectiveness of corrective actions for
which results are expected at a given point in time can be observed more clearly than if
computed by month of completion. On the other hand, averages computed by month of
completion have the advantage of timeliness, i.e., there is no delay of several months for
cases to be completed before a trend can be observed. (It should be noted,
however, unless cases are completed on a more continuous flow basis than is generally
true at the present time, moving averages based on month of completion can lead to
spurious peaks and valleys in the data.)

Figure 2.
Six-Month Moving Averages of Completed Sample Cases
By the Month of Review

Figure 3.
Six-Month Moving Averages of Completed Sample Cases
Regardless of Month of Review

1722

Individual Monthly Rates

Individual rates are generally examined for short-term time periods. Because each
month's sample is small, the monthly error rates tend to fluctuate much more than sixmonth moving averages. The classic way of measuring this trend is to fit a mathematical

trend line, called a regression line, estimated by the method of "least squares." While a
trend line could be drawn by inspection, such a line probably would be inaccurate and
would be graphed differently by different people, depending on who was drawing the
line. The advantages to the regression line are: (1) the sum of squares of monthly error
rate deviations from the trend line is minimized; (2) all analysts fit the same line;
(3) different measures, e.g., degree of relationship, can be computed; and (4) future
estimates can readily be extrapolated from the line.

1723

Computation of a Regression Line by "Least Squares" Method

It is best to fit the line after all sample cases for the annual sample period have been
completed. In our example, we are using the error rate; however, the methodology is
applicable to other proportions. If a regression line is to be fitted for shorter or longer
periods, the overall error rate for the shorter or longer period must be used in the
computation. The form of the equation used is as follows:

where

is the estimated error rate for a given month. The equation for "b" is:

where:
=

error rate change (increase or decrease) per unit month advance;

=

number of sample cases completed for the

=

actual proportion of error cases in sample for the

=

actual proportion of error cases in sample for annual sample period;

month;

month;

=

"1" for first month; "2" for second month; etc.; and

=

.

The equation for "a" (the y intercept) is:

To illustrate the "least-squares" method of fitting a trend line, data for a six-month period
are used. Assume the number of sample cases completed and the case error rate for each
month to be as follows:
Number of Cases

Case Error

Month

m

April

1

203

.082

May

2

201

.088

June

3

197

.065

July

4

194

.049

August

5

202

.080

September

6

204

.063

Total

Step 1. Compute

Reviewed (

= 1,201

:

)

Proportions(

= .071

)

Step 2.

Compute b:

Month
(m)
1

203

(.082-.071)

(1-3.5)

-5.583

1,268.75

2

201

(.088-.071)

(2-3.5)

-5.126

452.25

3

197

(.065-.071)

(3-3.5)

.591

49.25

4

194

(.049-.071)

(4-3.5)

-2.134

48.50

5

202

(.080-.071)

(5-3.5)

2.727

454.50

6

204

(.063-.071)

(6-3.5)

-4.080

1275.00

3 = -13.605

3 = 3,548.25

=

Step 3.

Substitute and solve for "

" ("the y intercept"):

=

Step 4.

=

Substitute the equation for the line into the general form and solve for
values using m = 6 and m = 0:

When m

=

6, then
.

When m

=

0, then
.

Step 5.

Draw a trend line on a graph (Figure 4.) using the values of
from step 4.

and

Figure 4.
Trend Line
Error Rate

Month (m)

The graph shows an inverse relationship between month sequence and error rates, i.e., the
error rates decrease as the months progress. The trend line would be more accurate if
twelve months of data were used instead of six months.
Once the trend line is established, it is possible to compute from it what the estimated
error rate would be each month if only the factor of trend affected the rate; in other
words, what the error rate would have been if there were no unpredictable or cyclical
factors affecting it.

1723.1

Practical Uses of Trend Line and Trend Values

The differences between the actual and trend values of the error rates show whether the
actual values are above or below the values they would have been if only trend affected
the rates. These differences may reveal the combined effect of such factors as policy
changes and staff turnover on the eligibility and payment process in the TANF program.
The trend line also provides a basis for estimating probable error rates in future periods.
The accuracy of such estimates will depend on the number of points used in the time
series and the assumptions made regarding the future effects of unpredictable factors on
the error rates. (It should be noted, however, that the line of best fit is an average line,
and predicting error rates beyond the range of values used to compute the line assumes
the same scattergram beyond the range.)
The regression line
discussed above is restricted to linear regression
only, i.e., fitting a straight line to the data. If the scatter diagram from the data indicates
non-linearity (e.g., no pattern or curvature), the model given in Section 1723 (page 90) is
not applicable. Other appropriate methods or models should be considered.

1723.2

Testing Trend for Statistical Significance

Testing for a significant trend is actually a test of the null hypothesis, i.e., b = 0 in the
equation
. The test statistic used is again the chi-square test. The
following equation is not in the form shown earlier for chi-square but it can be shown that
this statistic is distributed as chi-square with one degree of freedom when the number of
months is large. Therefore, the critical value is 3.84 (see Section 1711.3 (page 76),
Critical Chi-Square (
) Values).

All of the terms in the equation have been previously computed for the regression line
itself. Thus, substituting in the equation using the data for the example in Section 1723
(page 90):

=

=

Since the computed
significant.

value (0.791) is less than 3.84, the trend is not statistically

Note that in this example, there are only 6 months available. This may not be large
enough to ensure the satisfactory use of the chi-square test. The example is used only to
illustrate the computation. Basing predictions on a linear fit that is not statistically
significant is highly questionable.

1723.3

Relationship Between Time Sequence and Error Rates

In comparing the error rates over the months of the sample period, it is frequently
desirable to measure the degree of relationship. One way of looking at this relationship is
to determine how similar or the closeness of the relationship between the error rates and
time.
The statistic usually used to determine the mutual relationship between two variables is
called the coefficient of correlation (
). It ranges from +1 to -1. If a perfect
relationship exists as rates rise over the period, the coefficient of correlation equals +1. If
a perfect relationship exists as rates decline over the period, the coefficient equals -1. If
no relationship exists, the computed value equals zero. Rarely are there situations where
.
The following equation is used to compute the coefficient of linear correlation:

The only term that has not been computed for our example in Section 1723 (page 90) is
. This computation is as follows:

Month (m)

1

203

(.082 - .071)

.025

2

201

(.088 - .071)

.058

3

197

(.065 - .071)

.007

4

194

(.049 - .071)

.094

5

202

(.080 - .071)

.016

6

204

(.063 - .071)

.013
= .213

Substituting all the computed terms in the above formula, the coefficient of correlation is:

Thus, the degree of relationship on the scale of -1 to +1 is -0.49.
It should be emphasized at this point, that there is no direct or proportional comparison
between different values of . For example, when the coefficient of correlation ( )
between two variables is +0.8, it does not mean that the association is twice as good as
that shown by a value of = +0.4.
Assume that in our example, the State wants to know how much of the variation in the
error rate is associated with or explained by the time sequence. A simple method of
measuring this explained variation in terms of a percentage of the total variation has been

developed through the use of the coefficient of determination (

):

Coefficient of determination = 100
(explained variation)
From this formula, the percentage of unexplained variation can also be calculated:
Unexplained variation = 100 (1 -

)

In our example, the coefficient of correlation was -0.49. Therefore, only 24 percent, or
, of the total variation in error rates is accounted for by the time
sequence. Conversely, it can be determined that the time sequence fails to account for 76
percent of the total variation in error rates, or
. Obviously, other
factors play a more important role in the decrease in error rates and must be brought into
the analysis.
As noted in Section 1723.2, (page 95), since in this example the regression line is not
statistically significant, neither the coefficient of correlation nor the coefficient of
determination is statistically significant. The example only serves to illustrate the
computations. It should be recognized that when is based on a sample, it is subject to
chance variation, just as is any other statistic based on a sample. Thus, before assuming a
strong or weak correlation, consideration should be given not only to the value of , but
also to the size of the sample. Furthermore, sample correlation analysis has some basic
limitations. A common-sense approach is needed to tell whether two variables (in this
example, error rates and time) are, in fact, casually related or the apparent relationship is
just a coincidence.

1730

Statistical Procedures for Developing Profiles of Error-Prone Cases

The purpose for developing profiles of error-prone or high risk cases or characteristics is
to facilitate the identification of those particular types of cases or characteristics that
should be singled out for special consideration, review, or treatment. For example, cases
with a particular combination of factors might be redetermined for eligibility more
frequently than other cases; particular elements might require more verification; or cases
more likely to be in error might be emphasized in training.
In determining the kind of statistical method to be used in developing error-prone
profiles, a State should consider sample size, whether the error rate is high or low, and
whether it wants the profile to have limited or broad error-prone groups. Resource
demands and statistical availability should also be considered. Demands upon State
resources will vary with the procedure selected.

Various statistical procedures are used in analyzing and predicting the risk and the
expected amount of error of cases possessing a specific type of error. One predictive
technique used with quantitative or numerical data is called multiple regression. Another
technique, known as discriminant analysis, uses multivariate quantitative information.
Multiple regression techniques can be used to predict the expected dollars in error in
cases possessing certain characteristics. Corrective action can then be focussed on cases
possessing characteristics associated with the highest average dollars in error.
Discriminant analysis can be used to determine the likelihood of a case being in error.
This predictive technique tries to define a functional relationship for assigning certain
types of cases to various groups.
Most of the procedures that have been used in the TANF program establish specific
characteristics from the sample by which a case is determined to belong to a certain
group. They are generally case-driven procedures that take one of two approaches. Either
a search is conducted for characteristic combinations that have a high concentration of
case errors or a procedure is developed to rank cases from most error-prone to least errorprone. (It should not be too difficult to make these procedures dollar-driven. In the
former procedure, the search criteria can become a high concentration of dollar errors. In
the latter, the definition of error can be modified so that most error-prone implies most
prone to high dollar error. This might be accomplished by defining an error case as one
in which (1) the amount in error exceeds a certain amount, such as the median amount of
error; or (2) the percent of the amount in error exceeds a certain percentage, perhaps of
the total payment. Techniques of regression analysis would be well suited for developing
a procedure that predicts the amount in error for a given case.)
All procedures used in the TANF program are based on a prior quality control sample. If
the conditions under which the sample was reviewed remain constant, the sample can be
used to predict cases most likely to contain errors. However, if these conditions change,
so must the procedures.

1731

Criteria for Setting Up Error-Prone Profile M odels

Cases selected and reviewed as error-prone should have the highest likelihood of being in
error and should produce the highest cost savings to a State. The error-prone model
should meet the following specifications:
1.

Cases are ranked by error proneness so that resources are expended more
efficiently;

2.

Screening models are easy to use so that extensive time is not required to

train supervisors;
3.

Criteria used are quick to apply so that extensive time is not needed to
identify error-prone characteristics in the case file;

4.

Models can be incorporated into the existing case processing system;

5.

Models include a monitoring component that informs the agency of success
rates;

6.

System is easily updated so that staff can adjust the model to reflect changes
in caseload; and

7.

System is cost-effective and feasible.

Appendix A
Table of Random Numbers

A table of random numbers is a compilation of numbers whose frequency and sequence
of occurrence have been determined by chance. Since the position that any digit occupies
is a result of chance, any number formed by a combination of these digits, in any
sequence, by any progression, systematic or random, in any direction from any starting
point, may be regarded as a random grouping or selection.
The only requirement is that all of the items from which a random selection is to be made
have, or were assigned, individual identifying numbers. The entire group of numbered
items may be regarded, for certain purposes, as a statistical population. A selection of any
part of that statistical population by means of a table of random numbers may be regarded
as a random sample of the population.
For example, if the population to be sampled consists of 84 cases, numbered from 1
through 84, random numbers of two digits are required. If the population to be sampled
consists of 796 cases, random numbers of three digits are required. To obtain a two-digit,
three-digit, seven digit or other size number from the table, combine adjacent digits as
needed. It makes no difference where in the table one begins or in which direction one
moves in selecting random numbers. However, each time the table is used, select a
different starting point.
Example: If the highest consecutively numbered case in the population is 7,543, assume
that a randomly selected location starts with the four digits in line 49, column 1. Assume
also that it is decided in advance that the numbers to be used in drawing the sample will
be consecutive numbers obtained by reading across the columns from left to right on each
consecutive line in the table until a sample of the desired size has been accumulated. If
the first four digits of each number in each five-digit column are used, the sample would
consist of cases identified as 6837, 7076, 1059, 0454, 5432, 0234, 1724, 2886, 1477,
6273, 1566, and so on until the desired sample size is obtained. The numbers 9501, 9352,
7646, 9227, as well as any other number larger than 7,543 that may later be encountered
are not usable for this universe and are, therefore, rejected.

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

1
2
3
4
5

10480
22368
24130
42167
37570

15011
46573
48360
93093
39975

01536
25595
22527
06243
81837

02011
85393
97265
61680
16656

81647
30995
76393
07856
06121

91646
89198
64809
16376
91782

69179
27982
15179
39440
60468

14194
53402
24830
53537
81305

62590
93965
49340
71341
49684

36207
34095
32081
57004
60672

20969
52666
30680
00849
14110

99570
19174
19655
74917
06927

91291
39615
63348
97758
01263

90700
99505
58629
16379
54613

6
7
8
9
10

77921
99562
96301
89579
85475

06907
72905
91977
14342
36857

11008
56420
05463
63661
43342

42751
69994
07972
10281
53988

27756
98872
18876
17453
53060

53498
31016
20922
18103
59533

18602
71194
94595
57740
38867

70659
18738
56869
84378
62300

90655
44013
69014
25331
08158

15053
48840
60045
12566
17983

21916
63213
18425
58678
16439

81825
21069
84903
44947
11458

44394
10634
42508
05585
18593

42880
12952
32307
56941
64952

11
12
13
14
15

28918
63553
09429
10365
07119

69578
40961
93969
61129
97336

88231
48235
52636
87529
71048

33276
03427
92737
85689
08178

70997
49626
88974
48237
77233

79936
69445
33488
52267
13916

56865
18663
36320
67689
47564

05859
72695
17617
93394
81056

90106
52180
30015
01511
97735

31595
20847
08272
26358
85977

01547
12234
84115
85104
29372

85590
90511
27156
20285
74461

91610
33703
30613
29975
28551

78188
90322
74952
89868
90707

16
17
18
19
20

51085
02368
01011
52162
07056

12765
21382
54092
53916
97628

51821
52404
33362
46369
33787

51259
60268
94904
58586
09998

77452
89368
31273
23216
42698

16308
19885
04146
14513
06691

60756
55322
18594
83149
76988

92144
44819
29852
98736
13602

49442
01188
71585
23495
51851

53900
65255
85030
64350
46104

70960
64835
51132
94738
88916

63990
44919
01915
17752
19509

75601
05944
92747
35156
25625

40719
55157
64951
35749
58104

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

21
22
23
24
25

48663
54164
32639
29334
02488

91245
58492
32363
27001
33062

85828
22421
05597
87637
28834

14346
74103
24200
87308
07351

09172
47070
13363
58731
19731

30168
25306
38005
00256
92420

90229
76468
94342
45834
60952

04734
26384
28728
15398
61280

59193
58151
35806
46557
50001

22178
06646
06912
41135
67658

30421
21524
17012
10367
32586

61666
15227
64161
07684
86679

99904
96909
18296
36188
50720

32812
44592
22851
18510
94953

26
27
28
29
30

81525
29676
00742
05366
91921

72295
20591
57392
04213
26418

04839
68086
39064
25669
64117

96423
26432
66432
26422
94305

24878
46901
84673
44407
26766

82651
20849
40027
44048
25940

66566
89768
32832
37937
39972

14778
81536
61362
63904
22209

76797
86645
98947
45766
71500

14780
12659
96067
66134
64568

13300
92259
64760
75470
91402

87074
57102
64584
66520
42416

79666
80428
96096
34693
07844

95725
25280
98253
90449
69618

31
32
33
34
35

00582
00725
69011
25976
09763

04711
69884
65797
57948
83473

87917
62797
95876
29888
73577

77341
56170
55293
88604
12908

42206
86324
18988
67917
30883

35126
88072
27354
48708
18317

74087
76222
26575
18912
28290

99547
36086
08625
82271
35797

81817
84637
40801
65424
05998

42607
93161
59920
69774
41688

43808
76038
29841
33611
34952

76655
65855
80150
54262
37888

62028
77919
12777
85963
38917

76630
88006
48501
03547
88050

36
37
38
39
40

91567
17955
46503
92157
14577

42595
56349
18584
89634
62765

27958
90999
18845
94824
35605

30134
49127
49618
78171
81263

04024
20044
02304
84610
39667

86385
59931
51038
82834
47358

29880
06115
20655
09922
56873

99730
20542
58727
25417
56307

55536
18059
28168
44137
61607

84855
02008
15475
48413
49518

29080
73708
56942
25555
89656

09250
83517
53389
21246
20103

79656
36103
20562
35509
77490

73211
42791
87338
20468
18062

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

41
42
43
44
45

98427
34914
70060
53976
76072

07523
63976
28277
54914
29515

33362
88720
39475
06990
40980

64270
82765
46473
67245
07391

01638
34476
23219
68350
58745

92477
17032
53416
82948
25774

66969
87589
94970
11398
22987

98420
40836
25832
42878
80059

04880
32427
69975
80287
39911

45585
70002
94884
88267
96189

46565
70663
19661
47363
41151

04102
88863
72828
46634
14222

46880
77775
00102
06541
60697

45709
69348
66794
97809
59583

46
47
48
49
50

90725
64364
08962
95012
15664

52210
67412
00358
68379
10493

83974
33339
31662
93526
20492

29992
31926
25388
70765
38391

65831
14883
61642
10593
91132

38857
24413
34072
04542
21999

50490
59744
81249
76463
59516

83765
92351
35648
54328
81652

55657
97473
56891
02349
27195

14361
89286
69352
17247
48223

31720
35931
48373
28865
46751

57375
04110
45578
14777
22923

56228
23726
78547
62730
32261

41546
51900
81788
92277
85653

51
52
53
54
55

16408
18629
73115
57491
30405

81899
81953
35101
16703
83946

04153
05520
47498
23167
23792

53381
91962
87637
49323
14422

79401
04739
99016
45021
15059

21438
13092
71060
33132
45799

83035
97662
88824
12544
22716

92350
24822
71013
41035
19792

36693
94730
18735
80780
09983

31238
06496
20286
45393
74353

59649
35090
23153
44812
68668

91754
04822
72924
12515
30429

72772
86772
35165
98931
70735

02338
98289
43040
91202
25499

56
57
58
59
60

16631
96773
38935
31624
78919

35006
20206
64202
76384
19474

85900
42559
14349
17403
23632

98275
78985
82674
53363
27889

32388
05300
66523
44167
47914

52390
22164
44133
64486
02584

16815
24369
00697
64758
37680

69298
54224
35552
75366
20801

82732
35083
35970
76554
72152

38480
19687
19124
31601
39339

73817
11052
63318
12614
34806

32523
91491
29686
33072
08930

41961
60383
03387
60332
85001

44437
19746
59846
92325
87820

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

61
62
63
64
65

03931
74426
09066
42238
16153

33309
33278
00903
12426
08002

57047
43972
20795
87025
26504

74211
10119
95452
14267
41744

63445
89917
92648
20979
81959

17361
15665
45454
04508
65642

62825
52872
09552
64535
74240

39908
73823
88815
31355
56302

05607
73144
16553
86064
00033

91284
88662
51125
29472
67107

68833
88970
79375
47689
77510

25570
74492
97596
05974
70625

38818
51805
16296
52468
28725

46920
99378
66092
16834
34191

66
67
68
69
70

21457
21581
55612
44657
91340

40742
57802
78095
66999
84979

29820
02050
83197
99324
46949

96783
89728
33732
51281
81973

29400
17937
05810
84463
37949

21840
37621
24813
60563
61023

15035
47075
86902
79312
43997

34537
42080
60397
93454
15263

33310
97403
16489
68876
80644

06116
48626
03264
25471
43942

95240
68995
88525
93911
89203

15957
43805
42786
25650
71795

16572
33386
05269
12682
99533

06004
21597
92532
73572
50501

71
72
73
74
75

91227
50001
65390
27504
37169

21199
38140
05224
96131
94851

31935
66321
72958
83944
39117

27022
19924
28609
41575
89632

84067
72163
81406
10573
00959

05462
09538
39147
08619
16487

35216
12151
25549
64482
65536

14486
06878
48542
73923
49071

29891
91903
42627
36152
39782

68607
18749
45233
05184
17095

41867
34405
57202
94142
02330

14951
56087
94617
25299
74301

91696
82790
23772
84387
00275

85065
70925
07896
34925
48280

76
77
78
79
80

11508
37449
46515
30986
63798

70225
30362
70331
81223
64995

51111
06694
85922
42416
46583

38351
54690
38329
58353
09765

19444
04052
57015
21532
44160

66499
53115
15765
30502
78128

71945
62757
97161
32305
83991

05422
95348
17869
86482
42865

13442
78662
45349
05174
92520

78675
11163
61796
07901
83u31

84081
81651
66345
54339
80377

66938
50245
81073
58861
35909

93654
34971
49106
74818
81250

59894
52924
79860
46942
54238

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

81
82
83
84
85

82486
21885
60336
43937
97656

84846
32906
98782
46891
63175

99254
92431
07408
24010
89303

67632
09060
53458
25560
16275

43218
64297
13564
86355
07100

50076
51674
59089
33941
92063

21361
64126
26445
25786
21942

64816
62570
29789
54990
18611

51202
26123
85205
71899
47348

88124
05155
41001
15475
20203

41870
59194
12535
95434
18534

52689
52799
12133
98227
03862

51275
28225
14645
21824
78095

83556
85762
23541
19585
50136

86
87
88
89
90

03299
79626
85636
18039
08362

01221
06486
68335
14367
15656

05418
03574
47539
61337
60627

38982
17668
03129
06177
36478

55758
07785
65651
12143
65648

92237
76020
11977
46609
16764

26759
79924
02510
32989
53412

86367
25651
26113
74014
09013

21216
83325
99447
64708
07832

98442
88428
68645
00533
41574

08303
85076
34327
35398
17639

56613
72811
15152
58408
82163

91511
22717
55230
13261
60859

75928
50585
93448
47908
75567

91
92
93
94
95

79556
92608
23982
09915
50937

29068
82674
25835
96306
33300

04142
27072
40055
05908
26695

16268
32534
67006
97901
62247

15387
17075
12293
28395
69927

12856
27698
02753
14186
76123

66227
98204
14827
00821
50842

38358
63863
22235
80703
43834

22478
11951
35071
70426
86654

73373
34648
99704
75647
70959

88732
88022
37543
76310
79725

09443
56148
11601
88717
93872

82558
34925
35503
37890
28117

05250
57031
85171
40129
19233

96
97
98
99
100

42488
46764
03237
86591
38534

78077
86273
45430
81482
01715

69882
63003
55417
52667
94964

61657
93017
63282
61583
87288

34136
31204
90816
14972
65680

79180
36692
17349
90053
43772

97526
40202
88298
89534
39560

43092
35275
90183
76036
12918

04098
57306
36600
49199
86537

73571
55543
78406
43716
62738

80799
53203
06216
97548
19636

76536
18098
95787
04379
51132

71255
47625
42579
46370
25739

64239
88684
90730
28672
56947

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

101
102
103
104
105

13284
21224
99052
00199
60578

16834
00370
47887
50993
06483

74151
30420
81085
98603
28733

92027
03883
64933
38452
37867

24670
96648
66279
87890
07936

36665
89428
86432
94624
98710

00770
41583
65793
69721
98539

22878
17564
83287
57484
27186

02179
27395
34142
67501
31237

51602
63904
13241
77638
80612

07270
41548
30590
44331
44488

76517
49197
97760
11257
97819

97275
82277
35848
71131
70401

45960
24120
91983
11059
95419

106
107
108
109
110

91240
97458
35249
38980
10750

18312
14229
38646
46600
52745

17441
12063
34475
11759
38749

01929
59611
72417
11900
87365

18163
32249
60514
46743
58959

69201
90466
69257
27860
53731

31211
33216
12489
77940
89295

54288
19358
51924
39298
59062

39296
02591
86871
97838
39404

37318
54263
92446
95145
13198

65724
88449
36607
32378
59960

90401
01912
11458
68038
70408

79017
07436
30440
89351
29812

62077
50813
52639
37005
83126

111
112
113
114
115

36247
70994
99638
72055
24038

27850
66986
94702
15774
65541

73958
99744
11463
43857
85788

20673
72438
18148
99805
55835

37800
01174
81386
10419
38835

63835
42159
80431
76939
59399

71051
11392
90628
25993
13790

84724
20724
52506
03544
35112

52492
54322
02016
21560
01324

22342
36923
85151
83471
39520

78071
70009
88598
43989
76210

17456
23233
47821
90770
22467

96104
65438
00265
22965
83275

18327
59685
82525
44247
32286

116
117
118
119
120

74976
35553
35676
74815
45246

14631
71628
12797
67523
88048

35908
70189
51434
72985
65173

28221
26436
82976
23183
50989

39470
63407
42010
02446
91060

91548
91178
26344
63594
89894

12854
90348
92920
98924
36063

30166
55359
92155
20633
32819

09073
80392
58807
58842
68559

75887
41012
54644
85961
99221

36782
36270
58581
07648
49475

00268
77786
95331
70164
50558

97121
89578
78629
34994
34698

57676
21059
73344
67662
71800

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

121
122
123
124
125

76509
19689
42751
11946
96518

47069
90332
35318
22681
48688

86378
04315
97513
45045
20996

41797
21358
61537
13964
11090

11910
97248
54955
57517
48396

49672
11188
08159
59419
57177

88575
39062
00337
58045
83867

97966
63312
80778
44067
86464

32466
52496
27507
58716
14342

10083
07349
95478
58840
21545

54728
79178
21252
45557
46717

81972
33692
12746
96345
72364

58975
57352
37554
33271
86954

30761
72862
97775
53464
55580

126
127
128
129
130

35726
39737
97025
62814
25578

58643
42750
66492
08075
22950

76869
48968
56177
09788
15227

84622
70536
04049
56350
83291

39098
84864
80312
76787
41737

36083
64952
48028
51591
79599

72505
38404
26408
54509
96191

92265
94317
43591
49295
71845

23107
65402
75528
85830
86899

60278
13589
65341
59860
70694

05822
01055
49044
30883
24290

46760
79044
95495
89660
01551

44294
19308
81256
96142
80092

07672
83623
53214
18354
82118

131
132
133
134
135

68763
17900
71944
54684
25946

69576
00813
60227
93691
27623

88991
64361
63551
85132
11258

49662
60725
71109
64399
65204

46704
88974
05624
29182
52832

63362
61005
43836
44324
50880

56625
99709
58254
14491
22273

00481
30666
26160
55226
05554

73323
26451
32116
78793
99521

91427
11528
63403
34107
73791

15264
44323
35404
30374
85744

06969
34778
57146
48429
29276

57048
60342
10909
51376
70326

54149
60388
07346
09559
60251

136
137
138
139
140

01353
99083
52021
78755
25282

39318
88191
45406
47744
69106

44961
27662
37945
43776
59180

44972
99113
75234
83098
16257

91766
57174
24327
03225
22810

90262
35571
86978
14281
43609

56073
99884
22644
83637
12224

06606
13951
87779
55984
25643

51826
71057
23753
13300
89884

18893
53961
99926
52212
31149

83448
61448
63898
58781
85423

31915
74909
54886
14905
32581

97764
07322
18051
46502
34374

75091
80960
96314
04472
70873

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

141
142
143
144
145

11959
11644
06307
76285
55322

94202
13792
97912
75714
07589

02743
98190
68110
89585
39600

86847
01424
59812
99296
60866

79725
30078
95448
52640
63007

51811
28197
43244
46518
20007

12998
55583
31262
55486
66819

76844
05197
88880
90754
84164

05320
47714
13040
88932
61131

54236
68440
16458
19937
81429

53891
22016
43813
57119
60676

70226
79204
89416
23251
42807

38632
06862
42482
55619
78286

84776
94451
33939
23679
29015

146
147
148
149
150

78017
44768
25100
83612
41347

90928
43342
19336
46623
81666

90220
20696
14605
62876
82961

92503
26331
86603
85197
60413

83375
43140
51680
07824
71020

26986
69744
97678
91392
83658

74399
82928
24261
58317
02415

30885
24988
02464
37726
33322

88567
94237
86563
84628
66036

29169
46138
74812
42221
98712

72816
77426
60069
10268
46795

53357
39039
71674
20692
16308

15428
55596
15478
15699
28413

86932
12655
47642
29167
05417

151
152
153
154
155

38128
60950
90524
49897
18494

51178
00455
17320
18278
99209

75096
73254
29832
67160
81060

13609
96067
96118
39408
19488

16110
50717
75792
97056
65596

73533
13878
25326
43517
59787

42564
03216
22940
84426
47939

59870
78274
24904
59650
91225

29399
65863
80523
20247
98768

67834
37011
38928
19293
43688

91055
91283
91374
02019
00438

89917
33914
55597
14790
05548

51096
91303
97567
02852
09443

89011
49326
38914
05819
82897

156
157
158
159
160

65373
40653
51638
69742
58012

72984
12843
22238
99303
74072

30171
04213
56344
62578
67488

37741
70925
44587
83575
74580

70203
95360
83231
30337
47992

94094
55774
50317
07488
69482

87261
76439
74541
51941
58624

30056
61768
07719
84316
17106

58124
52817
25472
42067
47538

70133
81151
41602
49692
13452

18936
52188
77318
28616
22620

02138
31940
15145
29101
24260

59372
54273
57515
03013
40155

09075
49032
07633
73449
74716

Table of Random Sampling Numbers
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Line/Col. (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

161
162
163
164
165

18348
59614
75688
13941
96656

19855
09193
28630
77802
86420

42887
58064
39210
69101
96475

08279
29086
52897
70061
86458

43206
44385
62748
35460
54463

47077
45740
72658
34576
96419

42637
70752
98059
15412
55417

45606
05663
67202
81304
41375

00011
49081
72789
58757
76886

20662
26960
01869
35498
19008

14642
57454
13496
94830
66877

49984
99264
14663
75521
35934

94509
24142
87645
00603
59801

56380
74648
89713
97701
00497

166
167
168
169
170

03363
70366
47870
79504
46967

82042
08390
36605
77606
74841

15942
69155
12927
22761
50923

14549
25496
16043
30518
15339

38324
13240
53257
28373
37755

87094
57407
93796
73898
98995

19069
91407
52721
30550
40162

67590
49160
73120
76684
89561

11087
07379
48025
77366
69199

68570
34444
76074
32276
42257

22591
94567
95605
04690
11647

65232
66035
67422
61667
47603

85915
38918
41646
64798
48779

91499
65708
14557
66276
97907

171
172
173
174
175

14558
12440
32293
10640
47615

50769
25057
29938
21875
23169

35444
01132
68653
72462
39571

59030
38611
10497
77981
56972

87516
28135
98919
56550
20628

48193
68089
46587
55999
21788

02945
10954
77701
87310
51736

00922
10097
99119
69643
33133

48189
54243
93165
45124
72696

04724
06460
67788
00349
32605

21263
50856
17638
25748
41569

20892
65435
23097
00844
76148

92955
79377
21468
96831
91544

90251
53890
36992
30651
21121

176
177
178
179
180

16948
21258
15072
99154
08759

11128
61092
48853
57412
61089

71624
66634
15178
09858
23706

72754
70335
30730
65671
32994

49084
92448
47481
70655
35426

96303
17354
48490
71479
36666

27830
83432
41436
63520
63988

45817
49608
25015
31357
98844

67867
66520
49932
56968
37533

18062
06442
20474
06729
08269

87453
59664
53821
34465
27021

17226
20420
51015
70685
45886

72904
39201
79841
04184
22835

71474
69549
32405
25250
78451

181
182
183
184
185

67323
09255
36304
15884
18745

57839
13986
74712
67429
32031

61114
84834
00374
86612
35303

62192
20764
10107
47367
08134

47547
72206
85061
10242
33925

58023
89393
69228
44880
03044

64630
34548
81969
12060
59929

34886
93438
92216
44309
95418

98777
88730
03568
46629
04917

75442
61805
39630
55105
57596

95592
78955
81869
66793
24878

06141
18952
52824
93173
61733

45096
46436
50937
00480
92834

73117
58740
27954
13311
64454

186
187
188
189
190

72934
17626
27117
93995
67392

40086
02944
61399
18678
89421

88292
20910
50967
90012
09623

65728
57662
41399
63645
80725

38300
80181
81636
85701
62620

42323
38579
16663
85269
84162

64068
24580
15634
62263
87368

98373
90529
79717
68331
29560

48971
52303
94696
00389
00519

09049
50436
59240
72571
84545

59943
29401
25543
15210
08004

36538
57824
97989
20769
24526

05976
86039
63306
44686
41252

82118
81062
90946
96176
14521

191
192
193
194
195

04910
81453
19480
21456
89406

12261
20283
75790
13162
20912

37566
79929
48539
74608
46189

80016
59839
23703
81011
76376

21245
23875
15537
55512
25538

69377
13245
48885
07481
87212

50420
46808
02861
93551
20748

85658
74124
86587
72189
12831

55263
74703
74539
76261
57166

68667
35769
65227
91206
35026

78770
95588
90799
89941
16817

04533
21014
58789
15132
79121

14513
37078
96257
37738
18929

18099
39170
02708
59284
40628

196
197
198
199
200

09866
86541
10414
49942
23995

07414
24681
96941
06683
68882

55977
23421
06205
41479
42291

16419
13521
72222
58982
23374

01101
28000
57167
56288
24299

69343
94917
83902
42853
27024

13305
07423
07460
92196
67460

94302
57523
69507
20632
94783

80703
97234
10600
62045
40937

57910
63951
08858
78812
16961

36933
42876
07685
35895
26053

57771
46829
44472
51851
78749

42546
09781
64220
83534
46704

03003
58160
27040
10689
21983

Appendix B
Definitions

Note: The definitions listed below pertain to this manual only.

1. Absolute Value - disregards the
sign of a number; considers all
numbers positive.

7. Adult - An individual who is not
a minor child (See Section 419
of Act.)

2. ACF - the Administration for
Children and Families.

8. AFDC - Aid to Families with
Dependent Children.

3. Act - Social Security Act

9. Aid to Families with Dependent
Children - the welfare program
in effect under title IV-A of prior
law.

4. Adequate Sample - pertains most
commonly to the size of a
sample; a sample is adequate if
its size is large enough to give
the degree of precision or
reliability required in a given
sample estimate.
5. Adjusted State Family
Assistance Grant, or Adjusted
SFAG, - the SFAG amount,
minus any reductions for Tribal
Family Assistance Grants paid to
Tribal grantees on behalf of
Indian families residing in the
State and any transfers to the
Social Services Block Grant or
the Child Care and Development
Block Grant.
6. Administrative Costs has the
meaning specified at §263.01(b)
of the final rule.

10. Alpha - the allowable probability
associated with observed
differences attributed to chance.
If the probability associated with
sample differences is less than
alpha, we can reasonably
conclude that a real difference
between samples exists (see
Risk).
11. Annual Sample Period - (also
called Fiscal Year) - The 12month period, October 1 through
September 30.
12. Annual W ork Participation Rate
- the overall (or two-parent)
work participation rate for a
fiscal year is the average of the
State's overall (or two-parent)
work participation rates for each

month in the fiscal year.
13. Application - The action by
which an individual indicates in
writing to the agency
administering the State TANF
program (or separate State
program) his/her desire to
receive assistance.
14. Assistance - The term
"assistance" includes cash,
payments, vouchers, and other
forms of benefits designed to
meet a family's ongoing basic
needs (i.e., for food, clothing,
shelter, utilities, household
goods, personal care items, and
general incidental expenses). It
includes such benefits even
when they are provided in the
form of payments by a TANF
agency, or other agency on its
behalf, to individual recipients
and conditioned on their
participation in work experience,
community service, or other
work activities (See §261.30 of
final rule).
The term "assistance" excludes:
a.
Nonrecurrent, short-term
benefits (such as payments
for rent deposits or
appliance repairs) that:
1.
Are designed to
deal with a specific
crisis situation or
episode of need;
2.
Are not intended to
meet recurrent or
ongoing needs; and

3.

Will not extend
beyond four
months.
b.
Work subsidies (i.e.,
payments to employers or
third parties to help cover
the costs of employee
wages, benefits,
supervision, and training);
c.
Supportive services such as
child care and
transportation provided to
families who are employed;
d.
Refundable earned income
tax credits;
e.
Contributions to, and
distributions from,
Individual Development
Accounts;
f.
Services such as
counseling, case
management, peer support,
child care information and
referral, transitional
services, job retention, job
advancement, and other
employment-related
services that do not provide
basic income support; and
g.
Transportation benefits
provided under an Access
to Jobs or Reverse
Commute project, pursuant
to section 404(k) of the
Act, to an individual who is
not otherwise receiving
assistance.
The exclusion of nonrecurrent,
short-term benefits under (1) of
this paragraph also covers
supportive services for recently
employed families, for

temporary periods of
unemployment, in order to
enable continuity in their
service arrangements.
15. Basic MOE means the
expenditure of State funds that
must be made in order to meet
the MOE requirement at section
409(a)(7) of the Act
16. Bias - systematic error, leading
to distortion in one direction of a
statistical result; distinct from
random error, where distortion in
both directions may be largely
self-canceling.
17. Caseload - is comprised of the
members of the "target"
population. For example, the
State's TANF caseload is the
families (cases) receiving
assistance under the State TANF
Program. The caseload size is
the number of such families.
18. Cash assistance - when provided
to participants in the Welfare-toWork program (WtW), has the
meaning specified at §260.32.
19. CCDBG - means the Child Care
and Development Block Grant
Act of 1990, as amended, 42
U.S.C. 9858 et. seq.
20. CCDF - means the Child Care
and Development Fund, or those
child care programs and services
funded either under section
418(a) of the Act or CCDBG.

21. Child - (also called Minor Child)
An individual who has not
attained 18 years of age; or has
not attained 19 years of age and
is a full time student in a
secondary school (or in the
equivalent level of vocational or
technical training).
22. Child Care/ Family Care
Services - Services that assist an
individual in meeting his/her
family care needs during
participation. Family care
ranges from day care inside or
outside the home to after school
programs inside or outside the
home. It usually includes
supervision and shelter and may
include meals and transportation.
23. Closed Case (TANF) - A case
(family) whose assistance under
the State TANF Program was
terminated for the reporting
month (does not include families
whose assistance was
temporarily suspended), but
received assistance under the
State's TANF Program in the
prior month. Thus, TANF
eligible families that are
transferred to a separate State
program for the reporting month
are considered closed cases for
reporting purposes in the State
TANF Program.
24. Closed Case (State MOE) - A
case (family) whose assistance
under the separate State program
was terminated for the reporting

month (does not include
families whose assistance
was temporarily
suspended), but received
assistance under the
separate State program in
the prior month. Thus,
TANF eligible families that
are transferred to a State
TANF Program from a
separate State programs for
the reporting month are
considered closed cases for
reporting purposes in the
separate State program.
25. Commingled State TANF
Expenditures - means
expenditures of State funds that
are made within the TANF
program and commingled with
Federal TANF funds.
26. Completed Case - A case for
which the State (or Tribe)
collects all required
disaggregated data and reports
the information to DHHS.
27. Complete and Accurate Report
for Disaggregated Data - a report
that -a.
The reported data
accurately reflect
information available to the
State in its case records,
financial records, and
automated data systems;
b.
The data are free from
computational errors and
are internally consistent
(e.g., items that should add

c.

d.

e.

to totals do so);
The data are reported for
all elements (i.e., no data
are missing);
1. The data are provided
for all families; or
2. If the State opts to use
sampling, for all families
selected in a sample that
meets the specifications
and procedures in the
TANF Sampling Manual
for minimum sample size
requirements (except for
families listed-in-error);
and
Where estimates are
required (e.g., some types
of assistance may require
cost estimates), the State
uses reasonable methods to
develop these estimates.

28. Complete and Accurate Report
for Aggregated Data - a report
that -a.
The reported data
accurately reflect
information available to the
State in its case records,
financial records, and
automated data systems;
b.
The data are free from
computational errors and
are internally consistent
(e.g., items that should add
to totals do so);
c.
The data are reported for
all applicable elements;
and
d.
Monthly totals are
unduplicated counts for all

families (e.g., the
number of families
and the number of
out-of-wedlock
births are
unduplicated
counts).
29. Complete and Accurate Report
for the TANF Financial Data - a
report that -a.
The reported data
accurately reflect
information available to the
State in its case records,
financial records, and
automated data systems;
b.
The data are free from
computational errors and
are internally consistent
(e.g., items that should add
to totals do so);
c.
The data are reported for
all applicable elements;
and;
d.
All expenditures have been
made in accordance with
§92.20(a) of the Code of
Federal Regulations.
30. Confidence Interval - the
interval between two sample
values, known as confidence
limits, within which it may be
asserted with a specified degree
of confidence that the true
population value lies.
31. Confidence Limits - the values
that form the upper and lower
limits of the confidence interval.

32. Contingency Fund - means
Federal TANF funds available
under section 403(b) of the Act,
and Contingency Funds means
the Federal monies made
available to States under that
section. Neither term includes
any State funds expended
pursuant to section 403(b).
33. Contingency Fund MOE - means
the MOE expenditures that a
State must make in order to meet
the MOE requirements at
sections 403(b)(6) and
409(a)(10) of the Act and
subpart B of part 264 of the
regulations and to retain the
contingency funds made
available to the State. The only
expenditures that qualify for
Contingency Fund MOE are
State TANF expenditures.
34. Control group is a term relevant
to continuation of a "waiver" and
has the meaning specified at
§260.71.
35. Countable State expenditures has
the meaning specified at §264.0.
36. DHHS - U.S. Department of
Health and Human Services
37. Discretionary Fund of the CCDF
refers to child care funds
appropriated under the CCDBG.
38. Disposed of Case - A case
(family) for which the data was
collected and reported to DHHS

or the case was reported as
dropped, listed-in-error.
39. Disabled Individual - An
individual who has a physical or
mental impairment that
substantially limits one or more
of the major life activities of
such an individual, who has a
record of such impairment, or
who is regarded as having such
an impairment.
40. DV W aiver (or Federally
recognized good cause domestic
violence waiver) has the
meaning specified in §260.51.
41. EA - Emergency Assistance.
42. Earned Income - Income in cash
or in-kind earned by an
individual through the receipt of
wages, salary, commissions or
profit from activities in which
he/she is engaged as a selfemployed individual or as an
employee.
43. Earned Income Credit (EIC) - A
refundable tax credit for families
with dependent children. EIC
payments are received either
monthly (as advance payment
through the employer), annually
(as a refund from IRS), or both.
44. Eligible State - means a State
that, during the 27-month period
ending with the close of the first
quarter of the fiscal year, has
submitted a TANF plan that we

have determined is complete.
45. Emergency Assistance - the
program option available to
States under sections 403(a)(5)
and 406(e) of prior law to
provide short-term assistance to
needy families with children.
46. Employed - An individual who is
currently a paid employee; works
in his/her own business,
profession, or farm; works 15
hours or more per week as an
unpaid worker in an enterprise
operated by a member of the
family; or is one who is not
working, but has a job or
business from which he/she is
temporarily absent because of
illness, bad weather, vacation,
labor-management dispute, or
personal reasons, whether or not
paid by the employer for time off
and whether or not seeking
another job. Employed also
includes active duty military.
47. Equal Probability of Selection selection of a sample where
every case has an independent
and equal chance of inclusion in
the sample (also called selfweighted sample).
48. Expenditure means any amount
of Federal TANF or State MOE
funds that a State expends,
spends, pays out, or disburses
consistent with the requirements
of parts 260 through 265 of the
regulations. It may include

expenditures on the
refundable portions of
State or local tax credits, if
they are consistent with the
provisions at §260.33. It
does not include any
amounts that merely
represent avoided costs or
foregone revenue.
Avoided costs include such
items as contractor penalty
payments for poor
performance and purchase
price discounts, rebates,
and credits that a State
receives. Foregone
revenue includes State tax
provisions -- such as
waivers, deductions,
exemptions, or
nonrefundable tax credits -that reduce a State's tax
revenue.
49. Experimental group is a term
relevant to continuation of a
"waiver" and has the meaning
specified at §260.71.
50. Family Violence Option (or
FVO) has the meaning specified
at §260.51.
51. FAMIS - Family Assistance
Management Information
System - the automated
statewide management
information system under
sections 402(a)(30), 402(e), and
403 of prior law.
52. Federal Expenditures -

expenditures by a State of
Federal TANF funds.
53. Federal TANF Funds - means all
funds provided to the State under
section 403 of the Act, including
WtW funds awarded under
section 403(a)(5). The term
includes the SFAG, any bonuses,
supplemental grants, or
contingency funds.
54. Federally recognized good cause
domestic violence waiver has the
meaning specified at §260.51.
55. Fiscal Year - (also called Annual
Sample Period) - The 12-month
period, October 1 through
September 30.
56. Frame - the list of cases from
which the sample is actually
selected; also known as the
sample selection list.
57. FY - fiscal year.
58. Good cause domestic violence
waiver has the meaning specified
at §260.51.
59. Governor - the Chief Executive
Officer of the State. It thus
includes the Governor of each of
the 50 States and the Territories
and the Mayor of the District of
Columbia.
60. Housing Assistance - Services
that assist individuals in
maintaining or obtaining

adequate shelter for
themselves and their
families while they are
receiving employment,
training or other supportive
services.

i.
j.
k.
l.

61. IEVS - the Income and
Eligibility Verification System
operated pursuant to the
provisions in section 1137 of the
Act.
62. Inconsistent is a term relevant to
continuation of a "waiver" and
has the meaning specified at
§260.71.
63. Indian Tribe - has the meaning
given such terms by section 4 of
the Indian Self-Determination
and Education Assistance Act
(25 U.S.C. 450b), except that the
term "Indian tribe" means, with
respect to the State of Alaska,
only the Metlakatla Indian
Community of the Annette
Islands Reserve and the
following Alaska Native
regional nonprofit corporations:
a. Arctic Slope Native
Association;
b. Kawerak, Inc.;
c. Maniilaq Association;
d. Association of Village
Council Presidents;
e. Tanana Chiefs Council;
f. Cook Inlet Tribal
Council;
g. Bristol Bay Native
Association;
h. Aleutian and Pribilof

Island Association;
Chugachmuit;
Tlingit Haida Central
Council;
Kodiak Area Native
Association; and
Copper River Native
Association.

64. Individual Development
Accounts has the meaning
specified at §263.20 of the Act.
65. Job Opportunities and Basic
Skills Training Program - the
program under title IV-F of prior
law to provide education,
training and employment
services to welfare recipients.
66. JOBS - the Job Opportunities
and Basic Skills Training
Program.
67. Listed-in-error - cases included
in the sample selection list that
are not included in the
population of interest.
68. Mean - a measure of the central
tendency of data. The sum of
the values divided by the number
of values.
69. Medical Assistance - Medical
assistance services received by
an individual under the State
plan approved under title XIX of
the Social Security Act.
70. Minor Child - An individual who
has not attained 18 years of age;

or has not attained 19 years
of age and is a full time
student in a secondary
school or in the equivalent
level of vocational or
technical training.
71. MOE - maintenance-of-effort.
72. Needy State - is a term that
pertains to the provisions
regarding the Contingency Fund
and the penalty for failure to
meet participation rates. It
means, for a month, a State
where:
a.
1. The average rate of total
unemployment (seasonally
adjusted) for the most
recent 3-month period for
which data are published
for all States equals or
exceeds 6.5 percent; and
2. The average rate of total
unemployment (seasonally
adjusted) for such 3-month
period equals or exceeds
110 percent of the average
rate for either (or both) of
the corresponding 3-month
periods in the two
preceding calendar years;
or
b.
The Secretary of
Agriculture has determined
that the average number of
individuals participating in
the Food Stamp program in
the State has grown at least
10 percent in the most
recent 3-month period for
which data are available.

73. Noncustodial Parent - as used
here, means a parent of a minor
child who: (1) lives in the State
and (2) does not live in the same
household as the minor child.
74. Non-Sampling Error - the error
or deviation from the true
population value in sample
estimates that cannot be
attributed to chance sampling
variations. Examples are errors
resulting from imperfections in
the selection of sample units,
bias in the estimating procedure
used, mistakes in arithmetical
calculations, inconsistent review
procedures, etc.
75. Normal Distribution - a
symmetrical, bell shaped curve
that describes the sampling
distribution of many common
sample statistics. While the
sampling distributions of
proportions and ratios as used in
TANF are more correctly
described by the binomial
distribution, they are often
closely approximated by the
normal distribution, and it is
common practice to use the
normal distribution for this
purpose. The normal
distribution provides the
theoretical basis for the
determination of confidence
limits, for the specification of
particular levels or degrees of
confidence involved in making
sample estimates, and in
evaluating sampling error.

76. Not in Labor Force - An
individual who is classified as
neither employed nor
unemployed.

disregard such a family in
the participation rate
calculation for a maximum
of 12 months.

77. Oversampling - selecting more
sample cases than required.

79. Parameter - a value, property, or
characteristic of a population,
which can normally be estimated
from a sample. Examples are a
mean, proportion or percentage,
total, range, or standard
deviation of a population.

78. Overall Monthly Work
Participation Rate - (also known
as All Families Work
Participation Rate) - The State's
overall participation rate for a
month is defined as follows:
a.
The number of families
receiving TANF and/or
SSP-MOE assistance that
include a work-eligible
individual who is engaged
in work for the month (the
numerator), divided by
b.
The number of families
receiving TANF and/or
SSP-MOE assistance
during the month that
include a work-eligible
individual minus the
number of families that are
subject to a penalty for
refusing to work in that
month (the denominator).
However, if a family has
been sanctioned for more
than three of the last 12
months, we will not deduct
it from the denominator. A
State has the option of not
requiring a single custodial
parent caring for a child
under age one to engage in
work. If the State adopts
this option, it may

80. Population of Interest - those
units about which we wish to
form conclusions from which a
sample is selected and estimates
made.
81. Precision - see definition for
Reliability. The degree to which
a sample estimate approximates
the value obtained from a
complete count of all units using
the same methods.
82. Prior law - means the provisions
of title IV-A and IV-F of the
Social Security Act in effect as
of August 21, 1996. They
include provisions related to Aid
to Families with Dependent
Children (or AFDC), Emergency
Assistance (or EA), Job
Opportunities and Basic Skills
Training (or JOBS), and Family
Assistance Management
Information System (FAMIS).
83. Probability - relative frequency
of occurrence; the probability of
an event is the relative frequency

of occurrence of the event
in an indefinitely large
number of observations.
84. Probability Sampling - any
method of sample selection that
is based on the theory of
probability. Probability
sampling, which requires that at
any stage of selection the
probability of any unit or set of
units being selected must be
known, is the only general
method of sampling that makes
it possible to obtain a
mathematical measure of the
precision of the sample estimate.
The term "random sampling" is
used in the sense of probability
sampling.
85. PRWORA - the Personal
Responsibility and Work
Opportunity Reconciliation Act
of 1996, or Public Law 104-193.
86. Qualified Aliens has the
meaning prescribed under
section 431 of PRWORA, as
amended, 8 U.S.C. 1641.
87. Qualified State Expenditures means the total amount of State
funds expended during the fiscal
year that count for basic MOE
purposes. It includes
expenditures, under any State
program, for any of the
following with respect to eligible
families:
a.
Cash assistance;
b.
Child care assistance;

c.

d.

e.

Educational activities
designed to increase selfsufficiency, job training,
and work, excluding any
expenditure for public
education in the State
except expenditures
involving the provision of
services or assistance of an
eligible family that is not
generally available to
persons who are not
members of an eligible
family;
Any other use of funds
allowable under subpart A
of part 263 of the
regulations; and
Administrative costs in
connection with the matters
described in paragraphs
(1), (2), (3) and (4) of this
definition, but only to the
extent that such costs do
not exceed 15 percent of
the total amount of
qualified State
expenditures for the fiscal
year.

88. Random Numbers - series of
digits, each occurring
independently of each other.
Each digit tends to appear as
many times as any other, in any
progression, if the series selected
is large.
89. Random Sampling - the process
of selecting a sample from a
population so that every unit in
the population has a known

chance of being included in
the sample.
90. Random Start - in selecting a
systematic random sample at
intervals of some specified
number of items in an ordered
frame, it is mandatory to select
the first item completely without
bias. Such selection is then said
to have given the sample "a
random start."
91. Range - the largest minus the
smallest of a group of values.
92. Reliability See definition of
Precision - the uniformity of
sample results when obtained
from repeated samples of the
same size and type from the
sample population; the degree to
which a sample estimate
approximates the value obtained
from a complete count of all
units using the same methods.
93. Reporting M onth - the specific
calendar or fiscal month for
which data is being collected.
The reporting month and the
sample month are always the
same month.
94. Risk - as used here, refers to the
degree of risk associated with
given degrees of confidence.
For example, if a statement is
made "with 95 percent
confidence" that the true
population parameter lies within
a specified interval, there is a "5

percent risk" that the parameter
actually lies outside that interval
(also called alpha).
95. Sample - part of a population; a
limited or finite number of items
selected from a population, by a
prescribed procedure, with the
objective of estimating certain
values (mean, total proportion,
etc.) of the parent population, or
of testing the validity of certain
assumptions or hypotheses with
respect to particular properties of
the population.
96. Sample Interval - in systematic
sampling, the number of cases
between two consecutive
selections on the sampling
frame.
97. Sample Month - the specific
calendar or fiscal month for
which the sample is selected.
The sample month and the
reporting month are always the
same month.
98. Sample Period - the 12 month
period October 1 through
September 30.
99. Sample Selection List - the list
of cases from which the sample
is actually selected; also known
as the sample frame.
100. Sample Size - the number of
items in the sample.
101. Sampling Distribution - the

distribution of a (sample)
statistic, such as a sample
mean or a sample
proportion or percentage,
that would be formed by
obtaining such statistics
from all possible samples
of a given fixed size
selected by some specified
sampling procedure; a
population of all possible
sample values of the
statistic under
consideration.
102. Sampling Error - that part of the
difference between a population
value, and an estimate of that
value obtained from a random
sample, which is due solely to
the fact that only a sample of
values is observed; to be
distinguished from non-sampling
error which is due to biased or
imperfect sample selection, or
real differences due to changes
over time, error of observation,
recording, calculation, etc.
103. Scientifically Acceptable
Sampling Method - a probability
sampling method in which every
sampling unit from the
population has a known, nonzero chance to be included in the
sample, and the sample size
requirements are met.
104. Secretary - Secretary of the
Department of Health and
Human Services or any other
Department official duly

authorized to act on the
Secretary's behalf.
105. Segregated State TANF
Expenditures - means
expenditures of State funds
within the TANF program that
are not commingled with Federal
TANF funds.
106. Separate State Program - means
a program operated outside of
TANF in which the expenditures
of State funds may count for
basic MOE purposes.
107. SFAG - State Family Assistance
Grant.
108. SFAG Payable - means the
SFAG amount, reduced, as
appropriate, for any Tribal
Family Assistance Grants made
on behalf of Indian families
residing in the State and any
penalties imposed on a State.
109. Significant Difference - a
difference is statistically
significant if it can be concluded
from a sample, with a given
degree of risk, that the difference
actually exists in the universe. A
difference observed in a sample
is judged not statistically
significant if it could easily have
occurred purely as a result of
random sampling variations.
110. Simple Random Sample - a
probability sample selected in
such a way that each unit of the

frame has an equal and independent
chance of being included in the sample;
for samples of any given size, all
possible combinations of units that could
form samples of that size must have the
same probability of selection (usually
uses random digits for item selection).
111. Single audit - means an audit or
supplementary review conducted
under the authority of the Single
Audit Act at 31 U.S.C. chapter
75.
112. Social Services Block Grant
means the social services
program operated under title XX
of the Act, pursuant to 42 U.S.C.
1397.
113. SSBG means the Social Services
Block Grant.
114. Standard Deviation - the most
widely used measure of the
dispersion (scatter or variability)
of frequency distributions from
their arithmetic means. The
standard deviation of the
sampling distribution of any
given statistic is also known as
the "standard error" of that
statistic.
115. Standard Error - the standard
deviation of the sampling
distribution of a given statistic;
used in measuring precision of
an estimate.
116. State - the 50 States of the
United States, the District of

Columbia, the Commonwealth
of Puerto Rico, the United States
Virgin Islands, Guam, and
American Samoa, unless
otherwise specified.
117. State agency - means the agency
that the Governor certifies as the
administering and supervising
agency for the TANF program,
pursuant to section 402(a)(4) of
the Act.
118. State Family Assistance Grant means the amount of the basic
block grant allocated to each
eligible State under the formula
at section 403(a)(1) of the Act.
119. State MOE Expenditures means the expenditure of State
funds that may count for
purposes of the basic MOE
requirements at section 409(a)(7)
of the Act and the Contingency
Fund MOE requirements at
sections 403(b)(4) and
409(a)(10) of the Act.
120. State M OE Family - For
reporting purposes only, the
State M OE family is the eligible
family receiving assistance plus
the following persons living in
the household if they are not
already in the eligible family
receiving assistance:
a.
a parent or caretaker
relative of any minor child
in the eligible family
receiving assistance,
b.
a minor sibling of any child

in the eligible family receiving
assistance, and
c.
any person whose income
or resources are counted in
determining the eligibility
for or the amount of the
assistance for the eligible
family.
121. State TANF Expenditures means the expenditure of State
funds within the TANF program.
122. Stratified Random Sampling random sampling of a population
that has been divided in a
number of sub-populations
according to some
predetermined criterion
(geographic location,
characteristic, etc.). The
percentage size of each sample
must be equal or have individual
weighting factors taken into
account before the subpopulation sample results can be
combined.
123. Stratum - a segment of the
population for which separate
estimates are computed for some
special reason. All strata must
be combined if an estimate of the
total population is to be made.
124. Subsidized Child Care - A
benefit provided by the
government to a parent to
support, in part or whole, the
cost of child care services
provided by an eligible provider
to an eligible child.

125. Subsidized Housing - Money
paid by the government or
through a private social service
agency to the family or to the
owner of the housing to assist
the family in paying rent.
126. Supplemental Case - a case
added to the caseload for the
review month after the regular
sample frame, (i.e., the payroll
listing or master file listing) has
been compiled for the monthly
sample selection.
127. Suspended Case - a formalized
agency action that results in no
assistance provided to the family
for one or more months without
removing the family from the
eligible rolls.
128. Systematic Random Sample - a
sample attained by selecting
from a file, list or computer tape,
individual items at equally
spaced intervals (as every 10th,
140th, 850th, etc. item, as
required to obtain a total sample
of a given size), with the starting
point within the first such
interval being determined by
random selection.
129. TANF - Temporary Assistance
for Needy Families.
130. TANF Family - For reporting
purposes only, the TANF family
is the eligible family receiving
assistance plus the following
persons living in the household

a.

b.

c.

if they are not already in
the eligible family
receiving assistance:
a parent or caretaker
relative of any minor child
in the eligible family
receiving assistance,
a minor sibling of any child
in the eligible family
receiving assistance, and
any person whose income
or resources are counted in
determining the eligibility
for or the amount of
assistance of the eligible
family.

131. TANF Funds - all funds
provided to the State under
section 403 of the Act, including
the SFAG, any bonuses,
supplemental grants, or
contingency funds, except
Welfare to Work funds.
132. TANF MOE - the expenditure of
State funds that must be made in
order to meet the MOE
requirement at section 409(a)(7)
of the Act.
133. TANF Program - a State
program of family assistance
operated by an "eligible State"
under its State TANF plan.
134. Teen Parent - A teen parent is a
person who is under 20 years of
age and whose child is also a
member of the TANF family.
135. Territories - the Commonwealth

of Puerto Rico, the United States
Virgin Islands, Guam, and
American Samoa.
136. Title IV-A - refers to the title
and part of the Act that now
includes TANF, but previously
included AFDC and EA. For the
purpose of the TANF program
regulations, this term does not
include child care programs
authorized and funded under
section 418 of the Act, or their
predecessors, unless we specify
otherwise.
137. Tolerance - the proportion of
error that has been determined to
be acceptable.
138. Transportation - Services that
ensure mobility between home
and the location of employment,
training, or other supportive
services.
139. Tribal Family Assistance Grant means a grant paid to a Tribe
that has an approved Tribal
family assistance plan under
section 412(a)(1) of the Act.
140. Tribal grantee means a Tribe that
receives Federal TANF funds to
operate a Tribal TANF program
under section 412(a) of the Act.
141. Tribal TANF program - means a
TANF program developed by an
eligible Tribe, Tribal
organization, or consortium and
approved by us under section

412 of the Act.
142. Tribe - means Indian Tribe or
Tribal organization, as defined
elsewhere in this section. The
definition may include Tribal
consortia (i.e., groups of
federally recognized Tribes or
Alaska Native entities that have
banded together in a formal
arrangement to develop and
administer a Tribal TANF
program).
143. Two-Parent M onthly Work
Participation Rate - The twoparent participation rate for a
fiscal year is the average of the
State's two-parent participation
rate for each month in the fiscal
year. We determine a State's
two-parent participation rate for
a month as follows:
a.
The number of two-parent
families receiving TANF
and/or SSP-MOE
assistance in which the
work-eligible parents meet
the requirements set forth
in §261.32 for the month
(the numerator), divided by
b.
The number of two-parent
families receiving TANF
and/or SSP-MOE
assistance during the
month minus the number of
two-parent families`that are
subject to a penalty for
refusing to work in that
month (the denominator).
However, if a family has
been sanctioned for more

than three of the last 12
months, we will not deduct
it from the denominator.
144. Unemployed - An individual
who is not employed, who is
available for work, and who has
made specific efforts to find a
job within the prior four weeks.
Included as unemployed are
those who are not working, are
available for work, and are
waiting to be called back to a job
from which they were laid off.
145. Unearned Income - Cash
payment or in-kind contributions
or benefits from government
agencies, private organizations
or individuals.
146. Victim of domestic violence has
the meaning specified at
§260.51.
147. Waiver - has the meaning
specified at §260.71. (It is
distinguished from the domestic
violence waiver.)
148. Welfare-to-Work - means the
program for funding work
activities at section 403(a)(5) of
the Act.
149. WtW - Welfare-to-Work.
150. WtW cash assistance has the
meaning specified at §260.32.

Appendix C
Standard Error of Percentages Based on Selected Sample Sizes

The following table provides a quick reference of the standard error of percentages of
case error rates obtained from a specified sample size. For example, if a simple random
sample of 800 cases is found to have 80 errors, the case error rate would be 10 percent
and the standard error would be 1.1 percent.
The entries in the table are estimated by the following equation for a normal distribution:

where:
p

=

estimated proportion of error cases; and

n

=

sample size.

The 95 percent confidence interval can be obtained by multiplying the standard error by
1.96. In the above example, the 95 percent confidence interval would be 1.96 x 1.1% or
approximately ± 2.2%.
Note that the table provides only approximate standard errors. The approximation is good
when the sample fraction

is small. When the fraction is large, the standard error

given in the table is overestimated by a factor of

.

STANDARD ERROR OF PERCENTAGES BASED ON SELECTED SAMPLE SIZES
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))
Sample Size or Base of Estimated Percentage
Est.
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))
Percent
50
80
100 150 200 250 300 350 400 500 600 700 800 900 1000 1200 1500
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))
1
2
3
4
5

1.4
2.0
2.4
2.8
3.1

1.1
1.6
1.9
2.2
2.4

1.0
1.4
1.7
2.0
2.2

0.8
1.1
1.4
1.6
1.8

0.7
1.0
1.2
1.4
1.5

0.6
0.9
1.1
1.2
1.4

0.6
0.8
1.0
1.1
1.3

0.5
0.7
0.9
1.0
1.2

0.5
0.7
0.9
1.0
1.1

0.4
0.6
0.8
0.9
1.0

0.4
0.6
0.7
0.8
0.9

0.4
0.5
0.6
0.7
0.8

0.4
0.5
0.6
0.7
0.8

0.3
0.5
0.6
0.7
0.7

0.3
0.4
0.5
0.6
0.7

0.3
0.4
0.5
0.6
0.6

0.3
0.4
0.4
0.5
0.6

6
7
8
9
10

3.4
3.6
3.8
4.0
4.2

2.7
2.9
3.0
3.2
3.4

2.4
2.6
2.7
2.9
3.0

1.9
2.1
2.2
2.3
2.4

1.7
1.8
1.9
2.0
2.1

1.5
1.6
1.7
1.8
1.9

1.4
1.5
1.6
1.7
1.7

1.3
1.4
1.5
1.5
1.6

1.2
1.3
1.4
1.4
1.5

1.1
1.1
1.2
1.3
1.3

1.0
1.0
1.1
1.2
1.2

0.9
1.0
1.0
1.1
1.1

0.8
0.9
1.0
1.0
1.1

0.8
0.9
0.9
1.0
1.0

0.8
0.8
0.9
0.9
0.9

0.7
0.7
0.8
0.8
0.9

0.6
0.7
0.7
0.7
0.8

11
12
13
14
15

4.4
4.6
4.8
4.9
5.0

3.5
3.6
3.8
3.9
4.0

3.1
3.2
3.4
3.5
3.6

2.6
2.7
2.7
2.8
2.9

2.2
2.3
2.4
2.5
2.5

2.0
2.1
2.1
2.2
2.3

1.8
1.9
1.9
2.0
2.1

1.7
1.7
1.8
1.9
1.9

1.6
1.6
1.7
1.7
1.8

1.4
1.5
1.5
1.6
1.6

1.3
1.3
1.4
1.4
1.5

1.2
1.2
1.3
1.3
1.3

1.1
1.1
1.2
1.2
1.3

1.0
1.1
1.1
1.2
1.2

1.0
1.0
1.1
1.1
1.1

0.9
0.9
1.0
1.0
1.0

0.8
0.8
0.9
0.9
0.9

20
25
30
35
40

5.7
6.1
6.5
6.7
6.9

4.5
4.8
5.1
5.3
5.5

4.0
4.3
4.6
4.8
4.9

3.3
3.5
3.7
3.9
4.0

2.8
3.1
3.2
3.4
3.5

2.5
2.7
2.9
3.0
3.1

2.3
2.5
2.6
2.8
2.8

2.1
2.3
2.4
2.5
2.6

2.0
2.2
2.3
2.4
2.4

1.8
1.9
2.0
2.1
2.2

1.6
1.8
1.9
1.9
2.0

1.5
1.6
1.7
1.8
1.9

1.4
1.5
1.6
1.7
1.7

1.3
1.4
1.5
1.6
1.6

1.3
1.4
1.4
1.5
1.5

1.2
1.3
1.3
1.4
1.4

1.0
1.1
1.2
1.2
1.3

45
7.0
5.6
5.0
4.1
3.5
3.1
2.9
2.7
2.5
2:2
2.0
1.9
1.8
1.7
1.6
1.4
1.3
50
7.1
5.6
5.0
4.1
3.5
3.2
2.9
2.7
2.5
2.2
2.0
1.9
1.8
1.7
1.6
1.4
1.3
)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))

Appendix D
TANF Sample Plan Guidance

TANF sampling plan requirements are detailed in the TANF Manual, Sampling and
Statistical Methods (Sections 1300, 1400, and 1500). Sampling plans for the active cases
(including newly approved applicants) and the closed cases are required. The plans must
conform to principles of probability sampling, i.e., each case in the population must have
a known, non-zero probability of selection and computational methods of estimation must
lead to a unique estimate. More specifically the plan must describe the following:

I.

Sample Frame - Documentation of methods for constructing and
maintaining the sample frame(s)., including assessment of frame
completeness and any potential problems associated with using the sample
frame(s). The plan must explicitly describe the following sample frame
characteristics:

A.

Date(s) when the sample cases (both regular and supplemental, if
applicable) for the sample month are selected, e.g., first workday of
the month following the sample month).

Comments:

B.

Source, components, accuracy and completeness of the sample frame
in relation to the total caseload; if not accurate or complete,
explanation of why not and how the State (Tribe) plans to correct for
the problems with the sample frame.

Comments:

C.

Procedures for ensuring that the sample frame contains complete
coverage of the applicable caseload (e.g., active TANF sample frame

includes all families receiving assistance under the State's or Tribe's
TANF Program, including all newly approved applicants for the
sample month and closed TANF sample frame includes all families
no longer receiving assistance under the State's TANF Program, i.e.,
assistance terminated effective for the sample month).

Comments:

D.

Whether or not the frame is constructed by combining more than one
list; if more than one list, an explanation of how the lists are
identified and how duplication of cases on lists are prevented.

Comments:

E.

How the frame is compiled, e.g., whether the frame is compiled
entirely in the State office, entirely in local offices, in the State office
based on information supplied by local offices, etc.

Comments:

F.

Form of the frame, e.g., a computer file, microfilm, or hard copy; if
parts of the frame are in different forms, specifications for each part.

Comments:

G.

Frequency and length of delays and method used in updating the
frame or its sources.

Comments:

H.

Procedures for estimating the proportion of sample cases for which
the State (Tribe) will not be able to collect and report case record
information (e.g., dropped as listed-in-error because the family did
not receive TANF assistance for the reporting month).

Comments:

I.

Methods of locating and deleting "listed-in-error" cases from the
frame.

Comments

J.

Structure of the frame, i.e., the order of cases within each list and the
data elements on the frame, including definitions of coded values.

Comments:

K.

Treatment of special populations under TANF (e.g., individuals
under a tribal family assistance plan, a non-custodial parent who
participates in work activities).

Comments:

L.

Criteria for stratifying sample (if applicable).

Comments:

II.

Sample Selection Procedures - The sampling plan must describe in detail
the procedures for selecting the sample cases. The plan must explicitly
describe the following characteristics:

A.

Procedures for estimation of caseload size.

Comments:

B.

Procedures for determination of an appropriate allowance for cases
that might be dropped from the sample for acceptable reasons.

Comments:

C.

Procedures for determining the required sample size and indication
of the sample size.

Comments:

D.

If stratified sample design, procedures for sample allocation.

Comments:

E.

Procedures for the computation of sample intervals and the
determination of random starts (systematic random sampling or
stratified systematic random sampling), if applicable.

Comments:

F.

Application of selection procedures to identify sample cases.

Comments:

G.

Procedures to compensate for excessive oversampling or
undersampling.

Comments:

H.

Time schedule for each step in the sampling procedure.

Comments:

I.

Relationship, if appropriate, to sampling frames for other programs
(e.g., Welfare-to-Work).

Comments:

III.

Additional Sampling Plan Information

A.

Treatment of any special cases or circumstances unique to the State
or Tribe.

Comments:

B.

Documentation of methods for estimating proportions and their
sampling errors, including the computation of weights where
appropriate.

Comments:

Appendix E
Tribal Codes for the TANF Program
This list of codes for tribes is based on the Federal Register: November 13, 1996 (Volume 61,
Number 220), Notices, Page 58211-58216. From the Federal Register Online via GPO Access
[wais.access.gpo.gov]
CODE
Alaska Non-Profit Association These non-profit associations are specified in
§417(4)(B) of P.L. 104-193 as the only Alaskan native entities eligible for block grants under the
Temporary Assistance for Needy Families (TANF) program.
801

Metlakatla Indian Community, Annette Island Reserve, Alaska

802

Arctic Slope Native Association

803

Kawerak, Inc.

804

Maniilag Association

805

Association of Village Council Presidents

806

Tanana Chiefs Conference

807

Cook Inlet Tribal Council

808

Bristol Bay Native Association

809

Aleutian and Pribolof Island Association

810

Chugachmuit

811

Tlingit Haida Central Council

812

Kodiak Area Native Association

813

Copper River Native Association

CODE

Alaska Corporations (as established by the ANCSA of December 18, 1971, as
amended by act of January 1, 1976)

814

Aleut Corporation

815

Artic Slope Native Corporation

816

Athna, Inc.

817

Bering Straits Native Corporation

818

Bristol Bay Native Corporation

819

Calista Corporation

820

Cook Inlet Region, Inc.

821

Chugach Native Corporation

822

Doyon, Ltd.

823

Kodiag, Incorporated

824

Nana Regional Corporation

825

Sealaska Corporation

826

Thirteenth Regional Corporation

CODE
826

Other Alaska Enities - Except villages
Maniilaq Manpower

Codes 827-899 are reserved for Alaskan entiities other than villages.
Questions about these ID codes may be addressed to either:
Gerald Joiremen
Statistician,
Division of Tribal TANF Management
Office of Family Assistance, Administration for Children and Families
370 L'Enfant Promenade, Washington, D.C. 20447-0001
Phone - Voice (202) 401-5097;
Fax1 (202) 205-5887;
Fax2 (202) 401-5554;
E-mail - [email protected]
OR
Ray Apocada
Tribal Specialist,
Division of Tribal TANF Management
Office of Family Assistance, Administration for Children and Families

370 L'Enfant Promenade, Washington, D.C. 20447-0001
Phone - Voice (202) 401-5020;
Fax1 (202) 205-5887;
Fax2 (202) 401-5554;
E-mail - [email protected]

CODES FOR TRIBAL TANF PROGRAMS (Cont.)

All Others:
Below are the codes for Indian entities in the contiguous 48 states which are Federally recognized
and eligible to establish a tribal TANF porgram or participate in a consortium of Tribes for a
Tribal TANF program. All three digits are to be used (for example, ‘001’ not ‘1’).

CODE

TRIBAL ENTITY

001

Absentee-Shawnee Tribe of Indians of Oklahoma

002

Agua Caliente Band of Cahuilla Indians of the Agua Caliente Indian
Reservation, California

003

Ak Chin Indian Community of Papago Indians of the Maricopa, Ak Chin
Reservation, Arizona

004

Alabama and Coushatta Tribes of Texas

005

Alabama-Quassarte Tribal Town of the Creek Nation of Oklahoma

006

Alturas Indian Rancheria of Pit River Indians of California

007

Apache Tribe of Oklahoma

008

Arapahoe Tribe of the Wind River Reservation, Wyoming

009

Aroostook Band of Micmac Indians of Maine

-----

Assiniboine & Gros Ventre is Fort Belknap Indian Community of the Fort
Belknap Reservation of Montana -- 086

010

Assiniboine and Sioux Tribes of the Fort Peck Indian Reservation, Montana

011

Augustine Band of Cahuilla Mission Indians of the Augustine Reservation,
California

CODE

TRIBAL ENTITY

012

Bad River Band of the Lake Superior Tribe of Chippewa Indians of the Bad
River Reservation, Wisconsin

013

Bay Mills Indian Community of the Sault Ste. Marie Band of Chippewa
Indians, Bay Mills Reservation, Michigan

014

Bear River Band of the Rohnerville Rancheria of California

015

Berry Creek Rancheria of Maidu Indians of California

016

Big Lagoon Rancheria of Smith River Indians of California

017

Big Pine Band of Owens Valley Paiute Shoshone Indians of the Big Pine
Reservation, California

018

Big Sandy Rancheria of Mono Indians of California

019

Big Valley Rancheria of Pomo & Pit River Indians of California

020

Blackfeet Tribe of the Blackfeet Indian Reservation of Montana

021

Blue Lake Rancheria of California

022

Bridgeport Paiute Indian Colony of California

023

Buena Vista Rancheria of Me-Wuk Indians of California

024

Burns Paiute Tribe of the Burns Paiute Indian Colony of Oregon

025

Cabazon Band of Cahuilla Mission Indians of the Cabazon
Reservation,California

026

Cachil DeHe Band of Wintun Indians of the Colusa Indian Community of the
Colusa Rancheria, California

027

Caddo Indian Tribe of Oklahoma

028

Cahuilla Band of Mission Indians of the Cahuilla Reservation, California

029

Cahto Indian Tribe of the Laytonville Rancheria, California

030

Campo Band of Diegueno Mission Indians of the Campo Indian Reservation,
California

031

Barona Group of Capitan Grande Band of Diegueno Mission Indians of the
Barona Reservation, California

032

Viejas (Baron Long) Group of Capitan Grande Band of Diegueno Mission
Indians of the Viejas Reservation, California

CODE

TRIBAL ENTITY

033

Catawba Tribe of South Carolina

034

Cayuga Nation of New York

035

Cedarville Rancheria of Northern Paiute Indians of California

036

Chemehuevi Indian Tribe of the Chemehuevi Reservation, California

037

Cher-Ae Heights Indian Community of the Trinidad Rancheria, California

038

Cherokee Nation of Oklahoma

039

Cheyenne-Arapaho Tribes of Oklahoma

040

Cheyenne River Sioux Tribe of the Cheyenne River Reservation, South Dakota

041

Chickasaw Nation of Oklahoma

042

Chicken Ranch Rancheria of Me-Wuk Indians of California

043

Chippewa-Cree Indians of the Rocky Boy's Reservation, Montana

044

Chitimacha Tribe of Louisiana

045

Choctaw Nation of Oklahoma

046

Citizen Potawatomi Nation, Oklahoma

047

Cloverdale Rancheria of Pomo Indians of California

048

Coast Indian Community of Yurok Indians of the Resighini Rancheria,
California

049

Cocopah Tribe of Arizona

050

Coeur D'Alene Tribe of the Coeur D'Alene Reservation, Idaho

051

Cold Springs Rancheria of Mono Indians of California

052

Colorado River Indian Tribes of the Colorado River Indian Reservation,
Arizona and California

053

Comanche Indian Tribe, Oklahoma

054

Confederated Salish & Kootenai Tribes of the Flathead Reservation, Montana

055

Confederated Tribes of the Chehalis Reservation, Washington

056

Confederated Tribes of the Colville Reservation, Washington

CODE

TRIBAL ENTITY

057

Confederated Tribes of the Coos, Lower Umpqua and Siuslaw Indians of
Oregon

058

Confederated Tribes of the Goshute Reservation, Nevada and Utah

059

Confederated Tribes of the Grand Ronde Community of Oregon

060

Confederated Tribes of the Siletz Reservation, Oregon

061

Confederated Tribes of the Umatilla Reservation, Oregon

062

Confederated Tribes of the Warm Springs Reservation of Oregon

063

Confederated Tribes and Bands of the Yakama Indian Nation of the Yakama
Reservation, Washington

064

Coquille Tribe of Oregon

065

Cortina Indian Rancheria of Wintun Indians of California

066

Coushatta Tribe of Louisiana

067

Cow Creek Band of Umpqua Indians of Oregon

068

Coyote Valley Band of Pomo Indians of California

069

Crow Tribe of Montana

070

Crow Creek Sioux Tribe of the Crow Creek Reservation, South Dakota

071

Cuyapaipe Community of Diegueno Mission Indians of the Cuyapaipe
Reservation, California

072

Death Valley Timbi-Sha Shoshone Band of California

073

Delaware Tribe of Indians, Oklahoma

074

Delaware Tribe of Western Oklahoma

075

Devils Lake Sioux Tribe of the Devils Lake Sioux Reservation, North Dakota

076

Dry Creek Rancheria of Pomo Indians of California

077

Duckwater Shoshone Tribe of the Duckwater Reservation, Nevada

078

Eastern Band of Cherokee Indians of North Carolina

079

Eastern Shawnee Tribe of Oklahoma

080

Elem Indian Colony of Pomo Indians of the Sulphur Bank Rancheria,
California

CODE

TRIBAL ENTITY

081

Elk Valley Rancheria of California

082

Ely Shoshone Tribe of Nevada

083

Enterprise Rancheria of Maidu Indians of California

084

Flandreau Santee Sioux Tribe of South Dakota

085

Forest County Potawatomi Community of Wisconsin Potawatomi Indians,
Wisconsin

086

Fort Belknap Indian Community of the Fort Belknap Reservation of Montana

087

Fort Bidwell Indian Community of Paiute Indians of the Fort Bidwell
Reservation, California

088

Fort Independence Indian Community of Paiute Indians of the Fort
Independence Reservation, California

089

Fort McDermitt Paiute and Shoshone Tribes of the Fort McDermitt Indian
Reservation, Nevada

090

Fort McDowell Mohave-Apache Indian Community of the Fort McDowell
Indian Reservation, Arizona

091

Fort Mojave Indian Tribe of Arizona, California & Nevada

092

Fort Sill Apache Tribe of Oklahoma

093

Gila River Pima-Maricopa Indian Community of the Gila River Indian
Reservation of Arizona

094

Grand Traverse Band of Ottawa & Chippewa Indians of Michigan

095

Greenville Rancheria of Maidu Indians of California

096

Grindstone Indian Rancheria of Wintun-Wailaki Indians of California

-----

Gros Ventre & Assinibone is Fort Belknap Indian Community of the Fort
Belknap Reservation of Montana -- 086

097

Guidiville Rancheria of California

098

Hannahville Indian Community of Wisconsin Potawatomie Indians of
Michigan

099

Havasupai Tribe of the Havasupai Reservation, Arizona

CODE

TRIBAL ENTITY

100

Ho-Chunk Nation of Wisconsin
(formerly known as the Wisconsin Winnebago Tribe)

101

Hoh Indian Tribe of the Hoh Indian Reservation, Washington

102

Hoopa Valley Tribe of the Hoopa Valley Reservation, California

103

Hopi Tribe of Arizona

104

Hopland Band of Pomo Indians of the Hopland Rancheria, California

105

Houlton Band of Maliseet Indians of Maine

106

Hualapai Indian Tribe of the Hualapai Indian Reservation, Arizona

107

Huron Potawatomi, Inc., Michigan

108

Inaja Band of Diegueno Mission Indians of the Inaja and Cosmit
Reservation, California

110

Ione Band of Miwok Indians of California

111

Iowa Tribe of Kansas and Nebraska

112

Iowa Tribe of Oklahoma

113

Jackson Rancheria of Me-Wuk Indians of California

114

Jamestown Klallam Tribe of Washington

115

Jamul Indian Village of California

116

Jena Band of Choctaw Indians, Louisiana

117

Jicarilla Apache Tribe of the Jicarilla Apache Indian Reservation, New Mexico

118

Kaibab Band of Paiute Indians of the Kaibab Indian Reservation, Arizona

119

Kalispel Indian Community of the Kalispel Reservation, Washington

120

Karuk Tribe of California

121

Kashia Band of Pomo Indians of the Stewarts Point Rancheria, California

122

Kaw Nation, Oklahoma

123

Keweenaw Bay Indian Community of L'Anse and Ontonagon Bands of
Chippewa Indians of the L'Anse Reservation, Michigan

124

Kialegee Tribal Town of the Creek Indian Nation of Oklahoma

CODE

TRIBAL ENTITY

125

Kickapoo Tribe of Indians of the Kickapoo Reservation in Kansas

126

Kickapoo Tribe of Oklahoma

127

Kickapoo Traditional Tribe of Texas

128

Kiowa Indian Tribe of Oklahoma

129

Klamath Indian Tribe of Oregon

130

Kootenai Tribe of Idaho

131

La Jolla Band of Luiseno Mission Indians of the La Jolla Reservation,
California

132

La Posta Band of Diegueno Mission Indians of the La Posta Indian Reservation,
California

133

La Courte Oreilles Band of Lake Superior Chippewa Indians of the Lac Courte
Oreilles Reservation of Wisconsin

134

Lac du Flambeau Band of Lake Superior Chippewa Indians of the Lac du
Flambeau Reservation of Wisconsin

135

Lac Vieux Desert Band of Lake Superior Chippewa Indians of Michigan

136

Las Vegas Tribe of Paiute Indians of the Las Vegas Indian Colony, Nevada

137

Little River Band of Ottawa Indians of Michigan

138

Little Traverse Bay Bands of Odawa Indians of Michigan

139

Los Coyotes Band of Cahuilla Mission Indians of the Los Coyotes Reservation,
California

140

Lovelock Paiute Tribe of the Lovelock Indian Colony, Nevada

141

Lower Brule Sioux Tribe of the Lower Brule Reservation, South Dakota

142

Lower Elwha Tribe of the Lower Elwha Reservation, Washington

143

Lower Sioux Indian Community of Minnesota Mdewakanton Sioux Indians of
the Lower Sioux Reservation in Minnesota

144

Lummi Tribe of the Lummi Reservation, Washington

145

Lytton Rancheria of California

146

Makah Indian Tribe of the Makah Indian Reservation, Washington

CODE

TRIBAL ENTITY

147

Manchester Band of Pomo Indians of the Manchester-Point Arena Rancheria,
California

148

Manzanita Band of Diegueno Mission Indians of the Manzanita Reservation,
California

149

Mashantucket Pequot Tribe of Connecticut

150

Mechoopda Indian Tribe of Chico Rancheria, California

151

Menominee Indian Tribe of Wisconsin

152

Mesa Grande Band of Diegueno Mission Indians of the Mesa Grande
Reservation, California

153

Mescalero Apache Tribe of the Mescalero Reservation, New Mexico

154

Miami Tribe of Oklahoma

155

Miccosukee Tribe of Indians of Florida

156

Middletown Rancheria of Pomo Indians of California

157

Minnesota Chippewa Tribe, Minnesota
(All six component reservations: Bois Forte Band (Nett Lake); Fond du Lac
Band; Grand Portage Band; Leech Lake Band;
Mille Lacs Band; White Earth Band)

158

Mississippi Band of Choctaw Indians, Mississippi

159

Moapa Band of Paiute Indians of the Moapa River Indian Reservation, Nevada

160

Modoc Tribe of Oklahoma

161

Mohegan Indian Tribe of Connecticut

162

Mooretown Rancheria of Maidu Indians of California

163

Morongo Band of Cahuilla Mission Indians of the Morongo Reservation,
California

164

Muckleshoot Indian Tribe of the Muckleshoot Reservation, Washington

165

Muscogee (Creek) Nation of Oklahoma

166

Narragansett Indian Tribe of Rhode Island

167

Navajo Nation of Arizona, New Mexico & Utah

168

Nez Perce Tribe of Idaho

CODE

TRIBAL ENTITY

169

Nisqually Indian Tribe of the Nisqually Reservation, Washington

170

Nooksack Indian Tribe of Washington

171

Northern Cheyenne Tribe of the Northern Cheyenne Indian Reservation,
Montana

172

North Fork Rancheria of Mono Indians of California

173

Northwestern Band of Shoshoni Nation of Utah (Washakie)

174

Oglala Sioux Tribe of the Pine Ridge Reservation, South Dakota

175

Omaha Tribe of Nebraska

176

Oneida Nation of New York

177

Oneida Tribe of Wisconsin

178

Onondaga Nation of New York

179

Osage Nation of Oklahoma

180

Ottawa Tribe of Oklahoma

181

Otoe-Missouria Tribe of Indians, Oklahoma

182

Paiute Indian Tribe of Utah

183

Paiute-Shoshone Indians of the Bishop Community of the Bishop Colony,
California

184

Paiute-Shoshone Tribe of the Fallon Reservation and Colony, Nevada

185

Paiute-Shoshone Indians of the Lone Pine Community of the Lone Pine
Reservation, California

187

Pala Band of Luiseno Mission Indians of the Pala Reservation,California

188

Pascua Yaqui Tribe of Arizona

189

Paskenta Band of Nomlaki Indians of California

190

Passamaquoddy Tribe of Maine

191

Pauma Band of Luiseno Mission Indians of the Pauma & Yuima Reservation,
California

192

Pawnee Indian Tribe of Oklahoma

CODE

TRIBAL ENTITY

193

Pechanga Band of Luiseno Mission Indians of the Pechanga Reservation,
California

194

Penobscot Tribe of Maine

195

Peoria Tribe of Oklahoma

196

Picayune Rancheria of Chukchansi Indians of California

197

Pinoleville Rancheria of Pomo Indians of California

198

Pit River Tribe of California
(includes Big Bend, Lookout, Montgomery Creek & Roaring Creek Rancherias
& XL Ranch)

199

Poarch Band of Creek Indians of Alabama

200

Pokagon Band of Potawatomi Indians of Michigan

201

Ponca Tribe of Indians of Oklahoma

202

Ponca Tribe of Nebraska

203

Port Gamble S'Klallam Indian Community of the Port Gamble Reservation,
Washington

204

Potter Valley Rancheria of Pomo Indians of California

205

Prairie Band of Potawatomi Indians, Kansas

206

Prairie Island Indian Community of Minnesota Mdewakanton Sioux Indians of
the Prairie Island Reservation, Minnesota

207

Pueblo of Acoma, New Mexico

208

Pueblo of Cochiti, New Mexico

209

Pueblo of Jemez, New Mexico

210

Pueblo of Isleta, New Mexico

211

Pueblo of Laguna, New Mexico

212

Pueblo of Nambe, New Mexico

213

Pueblo of Picuris, New Mexico

214

Pueblo of Pojoaque, New Mexico

215

Pueblo of San Felipe, New Mexico

CODE

TRIBAL ENTITY

216

Pueblo of San Juan, New Mexico

217

Pueblo of San Ildefonso, New Mexico

218

Pueblo of Sandia, New Mexico

219

Pueblo of Santa Ana, New Mexico

220

Pueblo of Santa Clara, New Mexico

221

Pueblo of Santo Domingo, New Mexico

222

Pueblo of Taos, New Mexico

223

Pueblo of Tesuque, New Mexico

224

Pueblo of Zia, New Mexico

225

Puyallup Tribe of the Puyallup Reservation, Washington

226

Pyramid Lake Paiute Tribe of the Pyramid Lake Reservation, Nevada

227

Quapaw Tribe of Oklahoma

228

Quartz Valley Indian Community of the Quartz Valley Reservation of
California

229

Quechan Tribe of the Fort Yuma Indian Reservation, California & Arizona

230

Quileute Tribe of the Quileute Reservation, Washington

231

Quinault Tribe of the Quinault Reservation, Washington

232

Ramona Band or Village of Cahuilla Mission Indians of California

233

Red Cliff Band of Lake Superior Chippewa Indians of Wisconsin

234

Red Lake Band of Chippewa Indians of the Red Lake Reservation, Minnesota

235

Redding Rancheria of California

236

Redwood Valley Rancheria of Pomo Indians of California

237

Reno-Sparks Indian Colony, Nevada

238

Rincon Band of Luiseno Mission Indians of the Rincon Reservation, California

239

Robinson Rancheria of Pomo Indians of California

240

Rosebud Sioux Tribe of the Rosebud Indian Reservation, South Dakota

CODE

TRIBAL ENTITY

241

Round Valley Indian Tribes of the Round Valley Reservation, California
(formerly known as the Covelo Indian Community)

243

Rumsey Indian Rancheria of Wintun Indians of California

244

Sac & Fox Tribe of the Mississippi in Iowa

245

Sac & Fox Nation of Missouri in Kansas and Nebraska

246

Sac & Fox Nation, Oklahoma

247

Saginaw Chippewa Indian Tribe of Michigan, Isabella Reservation

248

Salt River Pima-Maricopa Indian Community of the Salt River Reservation,
Arizona

249

Samish Indian Tribe

250

San Carlos Apache Tribe of the San Carlos Reservation, Arizona

251

San Juan Southern Paiute Tribe of Arizona

252

San Manual Band of Serrano Mission Indians of the San Manual Reservation,
California

253

San Pasqual Band of Diegueno Mission Indians of California

254

Santa Rosa Indian Community of the Santa Rosa Rancheria, California

255

Santa Rosa Band of Cahuilla Mission Indians of the Santa Rosa Reservation,
California

256

Santa Ynez Band of Chumash Mission Indians of the Santa Ynez Reservation,
California

258

Santa Ysabel Band of Diegueno Mission Indians of the Santa Ysabel
Reservation, California

259

Santee Sioux Tribe of the Santee Reservation of Nebraska

260

Sauk-Suiattle Indian Tribe of Washington

261

Sault Ste. Marie Tribe of Chippewa Indians of Michigan

262

Scotts Valley Band of Pomo Indians of California

263

Seminole Nation of Oklahoma

264

Seminole Tribe of Florida, Dania, Big Cypress & Brighton Reservations

CODE

TRIBAL ENTITY

265

Seneca Nation of New York

266

Seneca-Cayuga Tribe of Oklahoma

267

Shakopee Mdewakanton Sioux Community of Minnesota (Prior Lake)

268

Sheep Ranch Rancheria of Me-Wuk Indians of California

269

Sherwood Valley Rancheria of Pomo Indians of California

270

Shingle Springs Band of Miwok Indians, Shingle Springs Rancheria (Verona
Tract), California

271

Shoalwater Bay Tribe of the Shoalwater Bay Indian Reservation, Washington

272

Shoshone Tribe of the Wind River Reservation, Wyoming

273

Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho

274

Shoshone-Paiute Tribes of the Duck Valley Reservation, Nevada

275

Sisseton-Wahpeton Sioux Tribe of the Lake Traverse Reservation, South
Dakota

276

Skokomish Indian Tribe of the Skokomish Reservation, Washington

277

Skull Valley Band of Goshute Indians of Utah

278

Smith River Rancheria of California

279

Soboba Band of Luiseno Mission Indians of the Soboba Reservation, California

280

Sokaogon Chippewa Community of the Mole Lake Band of Chippewa Indians,
Wisconsin

281

Southern Ute Indian Tribe of the Southern Ute Reservation, Colorado

----

Spirit Lake Sioux Tribe (see Devils Lake Sioux Tribe of the Devils Lake Sioux
Reservation, North Dakota , 075)

282

Spokane Tribe of the Spokane Reservation, Washington

283

Squaxin Island Tribe of the Squaxin Island Reservation, Washington

284

St. Croix Chippewa Indians of Wisconsin, St. Croix Reservation

285

St. Regis Band of Mohawk Indians of New York

286

Standing Rock Sioux Tribe of North & South Dakota

287

Stockbridge-Munsee Community of Mohican Indians of Wisconsin

CODE

TRIBAL ENTITY

288

Stillaguamish Tribe of Washington

289

Summit Lake Paiute Tribe of Nevada

290

Suquamish Indian Tribe of the Port Madison Reservation, Washington

291

Susanville Indian Rancheria of Paiute, Maidu, Pit River & Washoe Indians of
California

292

Swinomish Indians of the Swinomish Reservation, Washington

293

Sycuan Band of Diegueno Mission Indians of California

294

Table Bluff Rancheria of Wiyot Indians of California

295

Table Mountain Rancheria of California

296

Te-Moak Tribes of Western Shoshone Indians of Nevada (all bands)

297

Thlopthlocco Tribal Town of the Creek Nation of Oklahoma

298

Three Affiliated Tribes of the Fort Berthold Reservation, North Dakota

299

Tohono O'odham Nation of Arizona
(formerly known as the Papago Tribe of the Sells, Gila Bend & San Xavier
Reservation, Arizona)

300

Tonawanda Band of Seneca Indians of New York

301

Tonkawa Tribe of Indians of Oklahoma

302

Tonto Apache Tribe of Arizona

303

Torres-Martinez Band of Cahuilla Mission Indians of California (Duplicate of
513. This code should not be used)

304

Tule River Indian Tribe of the Tule River Reservation, California

305

Tulalip Tribes of the Tulalip Reservation, Washington

306

Tunica-Biloxi Indian Tribe of Louisiana

307

Tuolumne Band of Me-Wuk Indians of the Tuolumne Rancheria of California

308

Turtle Mountain Band of Chippewa Indians of North Dakota

309

Tuscarora Nation of New York

310

Twenty-Nine Palms Band of Luiseno Mission Indians of California

311

United Auburn Indian Community of the Auburn Rancheria of California

CODE

TRIBAL ENTITY

312

United Keetoowah Band of Cherokee Indians of Oklahoma

313

Upper Lake Band of Pomo Indians of Upper Lake Rancheria of California

314

Upper Sioux Indian Community of the Upper Sioux Reservation, Minnesota

315

Upper Skagit Indian Tribe of Washington

316

Ute Indian Tribe of the Uintah & Ouray Reservation, Utah

317

Ute Mountain Tribe of the Ute Mountain Reservation, Colorado, New Mexico
& Utah

318

Utu Utu Gwaitu Paiute Tribe of the Benton Paiute Reservation, California

319

Walker River Paiute Tribe of the Walker River Reservation, Nevada

320

Wampanoag Tribe of Gay Head (Aquinnah) of Massachusetts

321

Washoe Tribe of Nevada & California (Carson Colony, Dresslerville & Washoe
Ranches)

322

White Mountain Apache Tribe of the Fort Apache Reservation, Arizona

323

Wichita and Affiliated Tribes (Wichita, Keechi, Waco & Tawakonie),
Oklahoma

324

Winnebago Tribe of Nebraska

325

Winnemucca Indian Colony of Nevada

326

Wyandotte Tribe of Oklahoma

327

Yankton Sioux Tribe of South Dakota

328

Yavapai-Apache Nation of the Camp Verde Indian Reservation, Arizona

329

Yavapai-Prescott Tribe of the Yavapai Reservation, Arizona

330

Yerington Paiute Tribe of the Yerington Colony & Campbell Ranch, Nevada

331

Yomba Shoshone Tribe of the Yomba Reservation, Nevada

332

Ysleta Del Sur Pueblo of Texas

333

Yurok Tribe of the Yurok Reservation, California

334

Zuni Tribe of the Zuni Reservation, New Mexico

CODE

TRIBAL ENTITY

NOTE: Codes 109 , 186, 242 AND 257 were assigned in error, thus there were 330 Federally
recognized entities in the contiquous 48 States as of 9/29/97.

Appendix F
FIPS County Codes - Alphabetical List
Note that FIPS county codes are unique within state. You will usually need to pre-append
the 2-digit FIPS state code to form a complete FIPS county code.

ALABAMA - 01
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =

Autauga
Baldwin
Barbour
Bibb
Blount
Bullock
Butler
Calhoun
Chambers
Cherokee
Chilton
Choctaw
Clarke
Clay
Cleburne
Coffee
Colbert
Conecuh
Coosa
Covington
Crenshaw
Cullman
Dale
Dalla
DeKalb
Elmore
Escambia
Etowah
Fayette
Franklin

061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =

Geneva
Greene
Hale
Henry
Houston
Jackson
Jefferson
Lamar
Lauderdale
Lawrence
Lee
Limestone
Lowndes
Macon
Madison
Marengo
Marion
Marshall
Mobile
Monroe
Montgomery
Morgan
Perry
Pickens
Pike
Randolph
Russell
St. Clair
Shelby
Sumter
Talladega
Tallapoosa

125 =
127 =
129 =
131 =
133 =

Tuscaloosa
Walker
Washington
Wilcox
Winston

ALASKA - 02
013 = Aleutians East
016 = Aleutians West
Census
020 = Anchorage
050 = Bethel Census
060 = Bristol Bay
070 = Dillingham Census
090 = Fairbanks North Star
100 = Haines
110 = Juneau
122 = Kenai Peninsula
130 = Ketchikan Gateway
150 = Kodiak Island
164 = Lake and Peninsula
170 = Matanuska-Susitna
180 = Nome Census
185 = North Slope
188 = Northwest Arctic
201 = Prince of
Wales-Outer
Ketchikan Census
220 = Sitka
231 = Skagway-Yakutat-A

ngoon Census
240 = Southeast Fairbanks
Census
261 = Valdez-Cordova
Census
270 = Wade Hampton
Census
280 = Wrangell-Petersburg
Census
290 = Yukon-Koyukuk
Census

ARIZONA - 04
001 =
003 =
005 =
007 =
009 =
011 =
012 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =

Apache
Cochise
Coconino
Gila
Graham
Greenlee
La Paz
Maricopa
Mohave
Navajo
Pima
Pinal
Santa Cruz
Yavapai
Yuma

ARKANSAS - 05
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =

Arkansas
Ashley
Baxter
Benton
Boone
Bradley
Calhoun
Carroll
Chicot

019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =

Clark
Clay
Cleburne
Cleveland
Columbia
Conway
Craighead
Crawford
Crittenden
Cross
Dallas
Desha
Drew
Faulkner
Franklin
Fulton
Garland
Grant
Greene
Hempstead
Hot Spring
Howard
Independence
Izard
Jackson
Jefferson
Johnson
Lafayette
Lawrence
Lee
Lincoln
Little River
Logan
Lonoke
Madison
Marion
Miller
Mississippi
Monroe
Montgomery
Nevada
Newton
Ouachita

105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =

Perry
Phillips
Pike
Poinsett
Polk
Pope
Prairie
Pulaski
Randolph
St. Francis
Saline
Scott
Searcy
Sebastian
Sevier
Sharp
Stone
Union
Van Buren
Washington
White
Woodruff
Yell

CALIFORNIA - 06
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =

Alameda
Alpine
Amador
Butte
Calaveras
Colusa
Contra Costa
Del Norte
El Dorado
Fresno
Glenn
Humboldt
Imperial
Inyo
Kern
Kings

033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =

Lake
Lassen
Los Angeles
Madera
Marin
Mariposa
Mendocino
Merced
Modoc
Mono
Monterey
Napa
Nevada
Orange
Placer
Plumas
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Sierra
Siskiyou
Solano
Sonoma
Stanislaus
Sutter
Tehama
Trinity
Tular
Tuolumne
Ventura
Yolo
Yuba

COLORADO - 08
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =

Adams
Alamosa
Arapahoe
Archuleta
Baca
Bent
Boulder
Chaffee
Cheyenne
Clear Creek
Conejos
Costilla
Crowley
Custer
Delta
Denver
Dolores
Douglas
Eagle
Elbert
El Paso
Fremont
Garfield
Gilpin
Grand
Gunnison
Hinsdale
Huerfano
Jackson
Jefferson
Kiowa
Kit Carson
Lake
La Plata
Larimer
Las Animas
Lincoln
Logan
Mesa
Mineral
Moffat

083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =

Montezuma
Montrose
Morgan
Otero
Ouray
Park
Phillips
Pitkin
Prowers
Pueblo
Rio Blanco
Rio Grande
Routt
Saguache
San Juan
San Miguel
Sedgwick
Summit
Teller
Washington
Weld
Yuma

CONNECTICUT - 09
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =

Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Windham

DELAWARE - 10
001 = Kent
003 = New Castle
005 = Sussex

073 = Leon
075 = Levy
001 = District of Columbia 077 = Liberty
079 = Madison
081 = Manatee
083 = Marion
FLORIDA - 12
085 = Martin
087 = Monroe
001 = Alachua
089 = Nassau
003 = Baker
091 = Okaloosa
005 = Bay
093 = Okeechobee
007 = Bradford
095 = Orange
009 = Brevard
097 = Osceola
011 = Broward
099 = Palm Beach
013 = Calhoun
101 = Pasco
015 = Charlotte
103 = Pinellas
017 = Citrus
105 = Polk
019 = Clay
107 = Putnam
021 = Collier
109 = St. Johns
023 = Columbia
111 = St. Lucie
025 = Dade
113 = Santa Rosa
027 = DeSoto
115 = Sarasota
029 = Dixie
117 = Seminole
031 = Duval
119 = Sumter
033 = Escambia
121 = Suwannee
035 = Flagler
123 = Taylor
037 = Franklin
125 = Union
039 = Gadsden
127 = Volusia
041 = Gilchrist
129 = Wakulla
043 = Glades
131 = Walton
045 = Gulf
133 = Washington
047 = Hamilton
049 = Hardee
051 = Hendry
GEORGIA - 13
053 = Hernando
055 = Highlands
001 = Appling
057 = Hillsborough
003 = Atkinson
059 = Holmes
005 = Bacon
061 = Indian River
007 = Baker
063 = Jackson
009 = Baldwin
065 = Jefferson
011 = Banks
067 = Lafayette
013 = Barrow
069 = Lake
015 = Bartow
071 = Lee
DIST. OF COL. - 11

017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =

Ben Hill
Berrien
Bibb
Bleckley
Brantley
Brooks
Bryan
Bulloch
Burke
Butts
Calhoun
Camden
Candler
Carroll
Catoosa
Charlton
Chatham
Chattahoochee
Chattooga
Cherokee
Clarke
Clay
Clayton
Clinch
Cobb
Coffee
Colquitt
Columbia
Cook
Coweta
Crawford
Crisp
Dade
Dawson
Decatur
DeKalb
Dodge
Dooly
Dougherty
Douglas
Early
Echols
Effingham

105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =

Elbert
Emanuel
Evans
Fannin
Fayette
Floyd
Forsyth
Franklin
Fulton
Gilmer
Glascock
Glynn
Gordon
Grady
Greene
Gwinnett
Habersham
Hall
Hancock
Haralson
Harris
Hart
Heard
Henry
Houston
Irwin
Jackson
Jasper
Jeff Davis
Jefferson
Jenkins
Johnson
Jones
Lamar
Lanier
Laurens
Lee
Liberty
Lincoln
Long
Lowndes
Lumpkin
McDuffie

191 =
193 =
195 =
197 =
199 =
201 =
205 =
207 =
209 =
211 =
213 =
215 =
217 =
219 =
221 =
223 =
225 =
227 =
229 =
231 =
233 =
235 =
237 =
239 =
241 =
243 =
245 =
247 =
249 =
251 =
253 =
255 =
257 =
259 =
261 =
263 =
265 =
267 =
269 =
271 =
273 =
275 =
277 =

McIntosh
Macon
Madison
Marion
Meriwether
Miller
Mitchell
Monroe
Montgomery
Morgan
Murray
Muscogee
Newton
Oconee
Oglethorpe
Paulding
Peach
Pickens
Pierce
Pike
Polk
Pulaski
Putnam
Quitman
Rabun
Randolph
Richmond
Rockdale
Schley
Screven
Seminole
Spalding
Stephens
Stewart
Sumter
Talbot
Taliaferro
Tattnall
Taylor
Telfair
Terrell
Thomas
Tift

279 =
281 =
283 =
285 =
287 =
289 =
291 =
293 =
295 =
297 =
299 =
301 =
303 =
305 =
307 =
309 =
311 =
313 =
315 =
317 =
319 =
321 =

Toombs
Towns
Treutlen
Troup
Turner
Twiggs
Union
Upson
Walker
Walton
Ware
Warren
Washington
Wayne
Webster
Wheeler
White
Whitfield
Wilcox
Wilkes
Wilkinson
Worth

HAWAII - 15
001 =
003 =
005 =
007 =
009 =

Hawaii
Honolulu
Kalawao
Kauai
Maui

IDAHO - 16
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =

Ada
Adams
Bannock
Bear Lake
Benewah
Bingham
Blaine
Boise

017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =

Bonner
Bonneville
Boundary
Butte
Camas
Canyon
Caribou
Cassia
Clark
Clearwater
Custer
Elmore
Franklin
Fremont
Gem
Gooding
Idaho
Jefferson
Jerome
Kootenai
Latah
Lemhi
Lewis
Lincoln
Madison
Minidoka
Nez Perce
Oneida
Owyhee
Payette
Power
Shoshone
Teton
Twin Falls
Valley
Washington

ILLINOIS - 17
001 = Adams
003 = Alexander
005 = Bond

007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =

Boone
Brown
Bureau
Calhoun
Carroll
Cass
Champaign
Christian
Clark
Clay
Clinton
Coles
Cook
Crawford
Cumberland
DeKalb
De Witt
Douglas
DuPage
Edgar
Edwards
Effingham
Fayette
Ford
Franklin
Fulton
Gallatin
Greene
Grundy
Hamilton
Hancock
Hardin
Henderson
Henry
Iroquois
Jackson
Jasper
Jefferson
Jersey
Jo Daviess
Johnson
Kane
Kankakee

093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =

Kendall
Knox
Lake
La Salle
Lawrence
Lee
Livingston
Logan
McDonough
McHenry
McLean
Macon
Macoupin
Madison
Marion
Marshall
Mason
Massac
Menard
Mercer
Monroe
Montgomery
Morgan
Moultrie
Ogle
Peoria
Perry
Piatt
Pike
Pope
Pulaski
Putnam
Randolph
Richland
Rock Island
St. Clair
Saline
Sangamon
Schuyler
Scott
Shelby
Stark
Stephenson

179 =
181 =
183 =
185 =
187 =
189 =
191 =
193 =
195 =
197 =
199 =
201 =
203 =

Tazewell
Union
Vermilion
Wabash
Warren
Washington
Wayne
White
Whiteside
Will
Williamson
Winnebago
Woodford

INDIANA - 18
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =

Adams
Allen
Bartholomew
Benton
Blackford
Boone
Brown
Carroll
Cass
Clark
Clay
Clinton
Crawford
Daviess
Dearborn
Decatur
De Kalb
Delaware
Dubois
Elkhart
Fayette
Floyd
Fountain
Franklin
Fulton
Gibson

053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =

Grant
Greene
Hamilton
Hancock
Harrison
Hendricks
Henry
Howard
Huntington
Jackson
Jasper
Jay
Jefferson
Jennings
Johnson
Knox
Kosciusko
Lagrange
Lake
La Porte
Lawrence
Madison
Marion
Marshall
Martin
Miami
Monroe
Montgomery
Morgan
Newton
Noble
Ohio
Orange
Owen
Parke
Perry
Pike
Porter
Posey
Pulaski
Putnam
Randolph
Ripley

139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =

Rush
St. Joseph
Scott
Shelby
Spencer
Starke
Steuben
Sullivan
Switzerland
Tippecanoe
Tipton
Union
Vanderburgh
Vermillion
Vigo
Wabash
Warren
Warrick
Washington
Wayne
Wells
White
Whitley

IOWA - 19
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =

Adair
Adams
Allamakee
Appanoose
Audubon
Benton
Black Hawk
Boone
Bremer
Buchanan
Buena Vista
Butler
Calhoun
Carroll
Cass
Cedar

033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =

Cerro Gordo
Cherokee
Chickasaw
Clarke
Clay
Clayton
Clinton
Crawford
Dallas
Davis
Decatur
Delaware
Des Moines
Dickinson
Dubuque
Emmet
Fayette
Floyd
Franklin
Fremont
Greene
Grundy
Guthrie
Hamilton
Hancock
Hardin
Harrison
Henry
Howard
Humboldt
Ida
Iowa
Jackson
Jasper
Jefferson
Johnson
Jones
Keokuk
Kossuth
Lee
Linn
Louisa
Lucas

119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =
191 =
193 =
195 =
197 =

Lyon
Madison
Mahaska
Marion
Marshall
Mills
Mitchell
Monona
Monroe
Montgomery
Muscatine
O'Brien
Osceola
Page
Palo Alto
Plymouth
Pocahontas
Polk
Pottawattamie
Poweshiek
Ringgold
Sac
Scott
Shelby
Sioux
Story
Tama
Taylor
Union
Van Buren
Wapello
Warren
Washington
Wayne
Webster
Winnebago
Winneshiek
Woodbury
Worth
Wright

KANSAS - 20
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =

Allen
Anderson
Atchison
Barber
Barton
Bourbon
Brown
Butler
Chase
Chautauqua
Cherokee
Cheyenne
Clark
Clay
Cloud
Coffey
Comanche
Cowley
Crawford
Decatur
Dickinson
Doniphan
Douglas
Edwards
Elk
Ellis
Ellsworth
Finney
Ford
Franklin
Geary
Gove
Graham
Grant
Gray
Greeley
Greenwood
Hamilton
Harper
Harvey
Haskell

083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =

Hodgeman
Jackson
Jefferson
Jewell
Johnson
Kearny
Kingman
Kiowa
Labette
Lane
Leavenworth
Lincoln
Linn
Logan
Lyon
McPherson
Marion
Marshall
Meade
Miami
Mitchell
Montgomery
Morris
Morton
Nemaha
Neosho
Ness
Norton
Osage
Osborne
Ottawa
Pawnee
Phillips
Pottawatomie
Pratt
Rawlins
Reno
Republic
Rice
Riley
Rooks
Rush
Russell

169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =
191 =
193 =
195 =
197 =
199 =
201 =
203 =
205 =
207 =
209 =

Saline
Scott
Sedgwick
Seward
Shawnee
Sheridan
Sherman
Smith
Stafford
Stanton
Stevens
Sumner
Thomas
Trego
Wabaunsee
Wallace
Washington
Wichita
Wilson
Woodson
Wyandotte

KENTUCKY - 21
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =

Adair
Allen
Anderson
Ballard
Barren
Bath
Bell
Boone
Bourbon
Boyd
Boyle
Bracken
Breathitt
Breckinridge
Bullitt
Butler
Caldwell
Calloway

037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =

Campbell
Carlisle
Carroll
Carter
Casey
Christian
Clark
Clay
Clinton
Crittenden
Cumberland
Daviess
Edmonson
Elliott
Estill
Fayette
Fleming
Floyd
Franklin
Fulton
Gallatin
Garrard
Grant
Graves
Grayson
Green
Greenup
Hancock
Hardin
Harlan
Harrison
Hart
Henderson
Henry
Hickman
Hopkins
Jackson
Jefferson
Jessamine
Johnson
Kenton
Knott
Knox

123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =
191 =
193 =
195 =
197 =
199 =
201 =
203 =
205 =
207 =

Larue
Laurel
Lawrence
Lee
Leslie
Letcher
Lewis
Lincoln
Livingston
Logan
Lyon
McCracken
McCreary
McLean
Madison
Magoffin
Marion
Marshall
Martin
Mason
Meade
Menifee
Mercer
Metcalfe
Monroe
Montgomery
Morgan
Muhlenberg
Nelson
Nicholas
Ohio
Oldham
Owen
Owsley
Pendleton
Perry
Pike
Powell
Pulaski
Robertson
Rockcastle
Rowan
Russell

209 =
211 =
213 =
215 =
217 =
219 =
221 =
223 =
225 =
227 =
229 =
231 =
233 =
235 =
237 =
239 =

Scott
Shelby
Simpson
Spencer
Taylor
Todd
Trigg
Trimble
Union
Warren
Washington
Wayne
Webster
Whitley
Wolfe
Woodford

LOUISIANA - 22
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =

Acadia
Allen
Ascension
Assumption
Avoyelles
Beauregard
Bienville
Bossier
Caddo
Calcasieu
Caldwell
Cameron
Catahoula
Claiborne
Concordia
De Soto
East Baton Rouge
East Carroll
East Feliciana
Evangeline
Franklin
Grant
Iberia

047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =

Iberville
Jackson
Jefferson
Jefferson Davis
Lafayette
Lafourche
La Salle
Lincoln
Livingston
Madison
Morehouse
Natchitoches
Orleans
Ouachita
Plaquemines
Pointe Coupee
Rapides
Red River
Richland
Sabine
St. Bernard
St. Charles
St. Helena
St. James
St. John the Baptist
St. Landry
St. Martin
St. Mary
St. Tammany
Tangipahoa
Tensas
Terrebonne
Union
Vermilion
Vernon
Washington
Webster
West Baton Rouge
West Carroll
West Feliciana
Winn

MAINE - 23
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =

Androscoggin
Aroostook
Cumberland
Franklin
Hancock
Kennebec
Knox
Lincoln
Oxford
Penobscot
Piscataquis
Sagadahoc
Somerset
Waldo
Washington
York

MARYLAND - 24
001 =
003 =
005 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =

Allegany
Anne Arundel
Baltimore
Calvert
Caroline
Carroll
Cecil
Charles
Dorchester
Frederick
Garrett
Harford
Howard
Kent
Montgomery
Prince George's
Queen Anne's
St. Mary's
Somerset
Talbot
Washington

045 = Wicomico
047 = Worcester
510 = Baltimore

MASSACHUSETTS - 25
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =

Barnstable
Berkshire
Bristol
Dukes
Essex
Franklin
Hampden
Hampshire
Middlesex
Nantucket
Norfolk
Plymouth
Suffolk
Worcester

MICHIGAN - 26
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =

Alcona
Alger
Allegan
Alpena
Antrim
Arenac
Baraga
Barry
Bay
Benzie
Berrien
Branch
Calhoun
Cass
Charlevoix
Cheboygan
Chippewa
Clare

037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =

Clinton
Crawford
Delta
Dickinson
Eaton
Emmet
Genesee
Gladwin
Gogebic
Grand Traverse
Gratiot
Hillsdale
Houghton
Huron
Ingham
Ionia
Iosco
Iron
Isabella
Jackson
Kalamazoo
Kalkaska
Kent
Keweenaw
Lake
Lapeer
Leelanau
Lenawee
Livingston
Luce
Mackinac
Macomb
Manistee
Marquette
Mason
Mecosta
Menominee
Midland
Missaukee
Monroe
Montcalm
Montmorency
Muskegon

123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =

Newaygo
Oakland
Oceana
Ogemaw
Ontonagon
Osceola
Oscoda
Otsego
Ottawa
Presque Isle
Roscommon
Saginaw
St. Clair
St. Joseph
Sanilac
Schoolcraft
Shiawassee
Tuscola
Van Buren
Washtenaw
Wayne
Wexford

MINNESOTA - 27
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =

Aitkin
Anoka
Becker
Beltrami
Benton
Big Stone
Blue Earth
Brown
Carlton
Carver
Cass
Chippewa
Chisago
Clay
Clearwater
Cook
Cottonwood

035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =

Crow Wing
Dakota
Dodge
Douglas
Faribault
Fillmore
Freeborn
Goodhue
Grant
Hennepin
Houston
Hubbard
Isanti
Itasca
Jackson
Kanabec
Kandiyohi
Kittson
Koochiching
Lac qui Parle
Lake
Lake of the Woods
Le Sueur
Lincoln
Lyon
McLeod
Mahnomen
Marshall
Martin
Meeker
Mille Lacs
Morrison
Mower
Murray
Nicollet
Nobles
Norman
Olmsted
Otter Tail
Pennington
Pine
Pipestone
Polk

121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =

Pope
Ramsey
Red Lake
Redwood
Renville
Rice
Rock
Roseau
St. Louis
Scott
Sherburne
Sibley
Stearns
Steele
Stevens
Swift
Todd
Traverse
Wabasha
Wadena
Waseca
Washington
Watonwan
Wilkin
Winona
Wright
Yellow Medicine

MISSISSIPPI - 28
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =

Adams
Alcorn
Amite
Attala
Benton
Bolivar
Calhoun
Carroll
Chickasaw
Choctaw
Claiborne
Clarke

025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =

Clay
Coahoma
Copiah
Covington
DeSoto
Forrest
Franklin
George
Greene
Grenada
Hancock
Harrison
Hinds
Holmes
Humphreys
Issaquena
Itawamba
Jackson
Jasper
Jefferson
Jefferson Davis
Jones
Kemper
Lafayette
Lamar
Lauderdale
Lawrence
Leake
Lee
Leflore
Lincoln
Lowndes
Madison
Marion
Marshall
Monroe
Montgomery
Neshoba
Newton
Noxubee
Oktibbeha
Panola
Pearl River

111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =

Perry
Pike
Pontotoc
Prentiss
Quitman
Rankin
Scott
Sharkey
Simpson
Smith
Stone
Sunflower
Tallahatchie
Tate
Tippah
Tishomingo
Tunica
Union
Walthall
Warren
Washington
Wayne
Webster
Wilkinson
Winston
Yalobusha
Yazoo

MISSOURI - 29
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =

Adair
Andrew
Atchison
Audrain
Barry
Barton
Bates
Benton
Bollinger
Boone
Buchanan
Butler

025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =

Caldwell
Callaway
Camden
Cape Girardeau
Carroll
Carter
Cass
Cedar
Chariton
Christian
Clark
Clay
Clinton
Cole
Cooper
Crawford
Dade
Dallas
Daviess
DeKalb
Dent
Douglas
Dunklin
Franklin
Gasconade
Gentry
Greene
Grundy
Harrison
Henry
Hickory
Holt
Howard
Howell
Iron
Jackson
Jasper
Jefferson
Johnson
Knox
Laclede
Lafayette
Lawrence

111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
186 =
187 =
189 =
195 =
197 =

Lewis
Lincoln
Linn
Livingston
McDonald
Macon
Madison
Maries
Marion
Mercer
Miller
Mississippi
Moniteau
Monroe
Montgomery
Morgan
New Madrid
Newton
Nodaway
Oregon
Osage
Ozark
Pemiscot
Perry
Pettis
Phelps
Pike
Platte
Polk
Pulaski
Putnam
Ralls
Randolph
Ray
Reynolds
Ripley
St. Charles
St. Clair
Ste. Genevieve
St. Francois
St. Louis
Saline
Schuyler

199 =
201 =
203 =
205 =
207 =
209 =
211 =
213 =
215 =
217 =
219 =
221 =
223 =
225 =
227 =
229 =
510 =

Scotland
Scott
Shannon
Shelby
Stoddard
Stone
Sullivan
Taney
Texas
Vernon
Warren
Washington
Wayne
Webster
Worth
Wright
St. Louis

MONTANA - 30
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =

Beaverhead
Big Horn
Blaine
Broadwater
Carbon
Carter
Cascade
Chouteau
Custer
Daniels
Dawson
Deer Lodge
Fallon
Fergus
Flathead
Gallatin
Garfield
Glacier
Golden Valley
Granite
Hill
Jefferson

045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =

Judith Basin
Lake
Lewis and Clark
Liberty
Lincoln
McCone
Madison
Meagher
Mineral
Missoula
Musselshell
Park
Petroleum
Phillips
Pondera
Powder River
Powell
Prairie
Ravalli
Richland
Roosevelt
Rosebud
Sanders
Sheridan
Silver Bow
Stillwater
Sweet Grass
Teton
Toole
Treasure
Valley
Wheatland
Wibaux
Yellowstone
Yellowstone
National

NEBRASKA - 31
001 = Adams
003 = Antelope
005 = Arthur

007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =

Banner
Blaine
Boone
Box Butte
Boyd
Brown
Buffalo
Burt
Butler
Cass
Cedar
Chase
Cherry
Cheyenne
Clay
Colfax
Cuming
Custer
Dakota
Dawes
Dawson
Deuel
Dixon
Dodge
Douglas
Dundy
Fillmore
Franklin
Frontier
Furnas
Gage
Garden
Garfield
Gosper
Grant
Greeley
Hall
Hamilton
Harlan
Hayes
Hitchcock
Holt
Hooker

093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =

Howard
Jefferson
Johnson
Kearney
Keith
Keya Paha
Kimball
Knox
Lancaster
Lincoln
Logan
Loup
McPherson
Madison
Merrick
Morrill
Nance
Nemaha
Nuckolls
Otoe
Pawnee
Perkins
Phelps
Pierce
Platte
Polk
Red Willow
Richardson
Rock
Saline
Sarpy
Saunders
Scotts Bluff
Seward
Sheridan
Sherman
Sioux
Stanton
Thayer
Thomas
Thurston
Valley
Washington

179 =
181 =
183 =
185 =

Wayne
Webster
Wheeler
York

NEVADA - 32
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
027 =
029 =
031 =
033 =
510 =

Churchill
Clark
Douglas
Elko
Esmeralda
Eureka
Humboldt
Lander
Lincoln
Lyon
Mineral
Nye
Pershing
Storey
Washoe
White Pine
Carson

NEW HAMPSHIRE - 33
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =

Belknap
Carroll
Cheshire
Coos
Grafton
Hillsborough
Merrimack
Rockingham
Strafford
Sullivan

NEW JERSEY - 34
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =

Atlantic
Bergen
Burlington
Camden
Cape May
Cumberland
Essex
Gloucester
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Salem
Somerset
Sussex
Union
Warren

NEW MEXICO - 35
001 =
003 =
005 =
006 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
028 =

Bernalillo
Catron
Chaves
Cibola
Colfax
Curry
DeBaca
Dona Ana
Eddy
Grant
Guadalupe
Harding
Hidalgo
Lea
Lincoln
Los Alamos

029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =

Luna
McKinley
Mora
Otero
Quay
Rio Arriba
Roosevelt
Sandoval
San Juan
San Miguel
Santa Fe
Sierra
Socorro
Taos
Torrance
Union
Valencia

NEW YORK - 36
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =

Albany
Allegany
Bronx
Broome
Cattaraugus
Cayuga
Chautauqua
Chemung
Chenango
Clinton
Columbia
Cortland
Delaware
Dutchess
Erie
Essex
Franklin
Fulton
Genesee
Greene
Hamilton
Herkimer

045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =

Jefferson
Kings
Lewis
Livingston
Madison
Monroe
Montgomery
Nassau
New York
Niagara
Oneida
Onondaga
Ontario
Orange
Orleans
Oswego
Otsego
Putnam
Queens
Rensselaer
Richmond
Rockland
St. Lawrence
Saratoga
Schenectady
Schoharie
Schuyler
Seneca
Steuben
Suffolk
Sullivan
Tioga
Tompkins
Ulster
Warren
Washington
Wayne
Westchester
Wyoming
Yates

NORTH CAROLINA - 37
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =

Alamance
Alexander
Alleghany
Anson
Ashe
Avery
Beaufort
Bertie
Bladen
Brunswick
Buncombe
Burke
Cabarrus
Caldwell
Camden
Carteret
Caswell
Catawba
Chatham
Cherokee
Chowan
Clay
Cleveland
Columbus
Craven
Cumberland
Currituck
Dare
Davidson
Davie
Duplin
Durham
Edgecombe
Forsyth
Franklin
Gaston
Gates
Graham
Granville
Greene
Guilford

083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =

Halifax
Harnett
Haywood
Henderson
Hertford
Hoke
Hyde
Iredell
Jackson
Johnston
Jones
Lee
Lenoir
Lincoln
McDowell
Macon
Madison
Martin
Mecklenburg
Mitchell
Montgomery
Moore
Nash
New Hanover
Northampton
Onslow
Orange
Pamlico
Pasquotank
Pender
Perquimans
Person
Pitt
Polk
Randolph
Richmond
Robeson
Rockingham
Rowan
Rutherford
Sampson
Scotland
Stanly

169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =
191 =
193 =
195 =
197 =
199 =

Stokes
Surry
Swain
Transylvania
Tyrrell
Union
Vance
Wake
Warren
Washington
Watauga
Wayne
Wilkes
Wilson
Yadkin
Yancey

NORTH DAKOTA - 38
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =

Adams
Barnes
Benson
Billings
Bottineau
Bowman
Burke
Burleigh
Cass
Cavalier
Dickey
Divide
Dunn
Eddy
Emmons
Foster
Golden Valley
Grand Forks
Grant
Griggs
Hettinger
Kidder
LaMoure

047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =

Logan
McHenry
McIntosh
McKenzie
McLean
Mercer
Morton
Mountrail
Nelson
Oliver
Pembina
Pierce
Ramsey
Ransom
Renville
Richland
Rolette
Sargent
Sheridan
Sioux
Slope
Stark
Steele
Stutsman
Towner
Traill
Walsh
Ward
Wells
Williams

OHIO - 39
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =

Adams
Allen
Ashland
Ashtabula
Athens
Auglaize
Belmont
Brown
Butler

019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =

Carroll
Champaign
Clark
Clermont
Clinton
Columbiana
Coshocton
Crawford
Cuyahoga
Darke
Defiance
Delaware
Erie
Fairfield
Fayette
Franklin
Fulton
Gallia
Geauga
Greene
Guernsey
Hamilton
Hancock
Hardin
Harrison
Henry
Highland
Hocking
Holmes
Huron
Jackson
Jefferson
Knox
Lake
Lawrence
Licking
Logan
Lorain
Lucas
Madison
Mahoning
Marion
Medina

105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =

Meigs
Mercer
Miami
Monroe
Montgomery
Morgan
Morrow
Muskingum
Noble
Ottawa
Paulding
Perry
Pickaway
Pike
Portage
Preble
Putnam
Richland
Ross
Sandusky
Scioto
Seneca
Shelby
Stark
Summit
Trumbull
Tuscarawas
Union
Van Wert
Vinton
Warren
Washington
Wayne
Williams
Wood
Wyandot

OKLAHOMA - 40
001 = Adair
003 = Alfalfa
005 = Atoka

007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =

Beaver
Beckham
Blaine
Bryan
Caddo
Canadian
Carter
Cherokee
Choctaw
Cimarron
Cleveland
Coal
Comanche
Cotton
Craig
Creek
Custer
Delaware
Dewey
Ellis
Garfield
Garvin
Grady
Grant
Greer
Harmon
Harper
Haskell
Hughes
Jackson
Jefferson
Johnston
Kay
Kingfisher
Kiowa
Latimer
Le Flore
Lincoln
Logan
Love
McClain
McCurtain
McIntosh

093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =

Major
Marshall
Mayes
Murray
Muskogee
Noble
Nowata
Okfuskee
Oklahoma
Okmulgee
Osage
Ottawa
Pawnee
Payne
Pittsburg
Pontotoc
Pottawatomie
Pushmataha
Roger Mills
Rogers
Seminole
Sequoyah
Stephens
Texas
Tillman
Tulsa
Wagoner
Washington
Washita
Woods
Woodward

OREGON - 41
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =

Baker
Benton
Clackamas
Clatsop
Columbia
Coos
Crook
Curry

017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =

Deschutes
Douglas
Gilliam
Grant
Harney
Hood River
Jackson
Jefferson
Josephine
Klamath
Lake
Lane
Lincoln
Linn
Malheur
Marion
Morrow
Multnomah
Polk
Sherman
Tillamook
Umatilla
Union
Wallowa
Wasco
Washington
Wheeler
Yamhill

PENNSYLVANIA - 42
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =

Adams
Allegheny
Armstrong
Beaver
Bedford
Berks
Blair
Bradford
Bucks
Butler
Cambria

023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =

Cameron
Carbon
Centre
Chester
Clarion
Clearfield
Clinton
Columbia
Crawford
Cumberland
Dauphin
Delaware
Elk
Erie
Fayette
Forest
Franklin
Fulton
Greene
Huntingdon
Indiana
Jefferson
Juniata
Lackawanna
Lancaster
Lawrence
Lebanon
Lehigh
Luzerne
Lycoming
Mc Kean
Mercer
Mifflin
Monroe
Montgomery
Montour
Northampton
Northumberland
Perry
Philadelphia
Pike
Potter
Schuylkill

109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =

Snyder
Somerset
Sullivan
Susquehanna
Tioga
Union
Venango
Warren
Washington
Wayne
Westmoreland
Wyoming
York

RHODE ISLAND - 44
001 =
003 =
005 =
007 =
009 =

Bristol
Kent
Newport
Providence
Washington

SOUTH CAROLINA - 45
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =

Abbeville
Aiken
Allendale
Anderson
Bamberg
Barnwell
Beaufort
Berkeley
Calhoun
Charleston
Cherokee
Chester
Chesterfield
Clarendon
Colleton
Darlington
Dillon

035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =

Dorchester
Edgefield
Fairfield
Florence
Georgetown
Greenville
Greenwood
Hampton
Horry
Jasper
Kershaw
Lancaster
Laurens
Lee
Lexington
McCormick
Marion
Marlboro
Newberry
Oconee
Orangeburg
Pickens
Richland
Saluda
Spartanburg
Sumter
Union
Williamsburg
York

SOUTH DAKOTA - 46
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =

Aurora
Beadle
Bennett
Bon Homme
Brookings
Brown
Brule
Buffalo
Butte
Campbell

023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =

Charles Mix
Clark
Clay
Codington
Corson
Custer
Davison
Day
Deuel
Dewey
Douglas
Edmunds
Fall River
Faulk
Grant
Gregory
Haakon
Hamlin
Hand
Hanson
Harding
Hughes
Hutchinson
Hyde
Jackson
Jerauld
Jones
Kingsbury
Lake
Lawrence
Lincoln
Lyman
McCook
McPherson
Marshall
Meade
Mellette
Miner
Minnehaha
Moody
Pennington
Perkins
Potter

109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
135 =
137 =

Roberts
Sanborn
Shannon
Spink
Stanley
Sully
Todd
Tripp
Turner
Union
Walworth
Yankton
Ziebach

TENNESSEE - 47
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =

Anderson
Bedford
Benton
Bledsoe
Blount
Bradley
Campbell
Cannon
Carroll
Carter
Cheatham
Chester
Claiborne
Clay
Cocke
Coffee
Crockett
Cumberland
Davidson
Decatur
DeKalb
Dickson
Dyer
Fayette
Fentress
Franklin
Gibson

055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =

Giles
Grainger
Greene
Grundy
Hamblen
Hamilton
Hancock
Hardeman
Hardin
Hawkins
Haywood
Henderson
Henry
Hickman
Houston
Humphreys
Jackson
Jefferson
Johnson
Knox
Lake
Lauderdale
Lawrence
Lewis
Lincoln
Loudon
McMinn
McNairy
Macon
Madison
Marion
Marshall
Maury
Meigs
Monroe
Montgomery
Moore
Morgan
Obion
Overton
Perry
Pickett
Polk

141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =

Putnam
Rhea
Roane
Robertson
Rutherford
Scott
Sequatchie
Sevier
Shelby
Smith
Stewart
Sullivan
Sumner
Tipton
Trousdale
Unicoi
Union
Van Buren
Warren
Washington
Wayne
Weakley
White
Williamson
Wilson

TEXAS - 48
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =

Anderson
Andrews
Angelina
Aransas
Archer
Armstrong
Atascosa
Austin
Bailey
Bandera
Bastrop
Baylor
Bee
Bell

029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =

Bexar
Blanco
Borden
Bosque
Bowie
Brazoria
Brazos
Brewster
Briscoe
Brooks
Brown
Burleson
Burnet
Caldwell
Calhoun
Callahan
Cameron
Camp
Carson
Cass
Castro
Chambers
Cherokee
Childress
Clay
Cochran
Coke
Coleman
Collin
Collingsworth
Colorado
Comal
Comanche
Concho
Cooke
Coryell
Cottle
Crane
Crockett
Crosby
Culberson
Dallam
Dallas

115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
189 =
191 =
193 =
195 =
197 =
199 =

Dawson
Deaf Smith
Delta
Denton
DeWitt
Dickens
Dimmit
Donley
Duval
Eastland
Ector
Edwards
Ellis
El Paso
Erath
Falls
Fannin
Fayette
Fisher
Floyd
Foard
Fort Bend
Franklin
Freestone
Frio
Gaines
Galveston
Garza
Gillespie
Glasscock
Goliad
Gonzales
Gray
Grayson
Gregg
Grimes
Guadalupe
Hale
Hall
Hamilton
Hansford
Hardeman
Hardin

201 =
203 =
205 =
207 =
209 =
211 =
213 =
215 =
217 =
219 =
221 =
223 =
225 =
227 =
229 =
231 =
233 =
235 =
237 =
239 =
241 =
243 =
245 =
247 =
249 =
251 =
253 =
255 =
257 =
259 =
261 =
263 =
265 =
267 =
269 =
271 =
273 =
275 =
277 =
279 =
281 =
283 =
285 =

Harris
Harrison
Hartley
Haskell
Hays
Hemphill
Henderson
Hidalgo
Hill
Hockley
Hood
Hopkins
Houston
Howard
Hudspeth
Hunt
Hutchinson
Irion
Jack
Jackson
Jasper
Jeff Davis
Jefferson
Jim Hogg
Jim Wells
Johnson
Jones
Karnes
Kaufman
Kendall
Kenedy
Kent
Kerr
Kimble
King
Kinney
Kleberg
Knox
Lamar
Lamb
Lampasas
La Salle
Lavaca

287 =
289 =
291 =
293 =
295 =
297 =
299 =
301 =
303 =
305 =
307 =
309 =
311 =
313 =
315 =
317 =
319 =
321 =
323 =
325 =
327 =
329 =
331 =
333 =
335 =
337 =
339 =
341 =
343 =
345 =
347 =
349 =
351 =
353 =
355 =
357 =
359 =
361 =
363 =
365 =
367 =
369 =
371 =

Lee
Leon
Liberty
Limestone
Lipscomb
Live Oak
Llano
Loving
Lubbock
Lynn
McCulloch
McLennan
McMullen
Madison
Marion
Martin
Mason
Matagorda
Maverick
Medina
Menard
Midland
Milam
Mills
Mitchell
Montague
Montgomery
Moore
Morris
Motley
Nacogdoches
Navarro
Newton
Nolan
Nueces
Ochiltree
Oldham
Orange
Palo Pinto
Panola
Parker
Parmer
Pecos

373 =
375 =
377 =
379 =
381 =
383 =
385 =
387 =
389 =
391 =
393 =
395 =
397 =
399 =
401 =
403 =
405 =
407 =
409 =
411 =
413 =
415 =
417 =
419 =
421 =
423 =
425 =
427 =
429 =
431 =
433 =
435 =
437 =
439 =
441 =
443 =
445 =
447 =
449 =
451 =
453 =
455 =
457 =

Polk
Potter
Presidio
Rains
Randall
Reagan
Real
Red River
Reeves
Refugio
Roberts
Robertson
Rockwall
Runnels
Rusk
Sabine
San Augustine
San Jacinto
San Patricio
San Saba
Schleicher
Scurry
Shackelford
Shelby
Sherman
Smith
Somervell
Starr
Stephens
Sterling
Stonewall
Sutton
Swisher
Tarrant
Taylor
Terrell
Terry
Throckmorton
Titus
Tom Green
Travis
Trinity
Tyler

459 =
461 =
463 =
465 =
467 =
469 =
471 =
473 =
475 =
477 =
479 =
481 =
483 =
485 =
487 =
489 =
491 =
493 =
495 =
497 =
499 =
501 =
503 =
505 =
507 =

Upshur
Upton
Uvalde
Val Verde
Van Zandt
Victoria
Walker
Waller
Ward
Washington
Webb
Wharton
Wheeler
Wichita
Wilbarger
Willacy
Williamson
Wilson
Winkler
Wise
Wood
Yoakum
Young
Zapata
Zavala

UTAH - 49
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =

Beaver
Box Elder
Cache
Carbon
Daggett
Davis
Duchesne
Emery
Garfield
Grand
Iron
Juab
Kane
Millard

029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =

Morgan
Piute
Rich
Salt Lake
San Juan
Sanpete
Sevier
Summit
Tooele
Uintah
Utah
Wasatch
Washington
Wayne
Weber

VERMONT - 50
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =

Addison
Bennington
Caledonia
Chittenden
Essex
Franklin
Grand Isle
Lamoille
Orange
Orleans
Rutland
Washington
Windham
Windsor

VIRGINIA - 51
001 =
003 =
005 =
007 =
009 =
011 =

Accomack
Albemarle
Alleghany
Amelia
Amherst
Appomattox

013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
036 =
037 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =

Arlington
Augusta
Bath
Bedford
Bland
Botetourt
Brunswick
Buchanan
Buckingham
Campbell
Caroline
Carroll
Charles City
Charlotte
Chesterfield
Clarke
Craig
Culpeper
Cumberland
Dickenson
Dinwiddie
Essex
Fairfax
Fauquier
Floyd
Fluvanna
Franklin
Frederick
Giles
Gloucester
Goochland
Grayson
Greene
Greensville
Halifax
Hanover
Henrico
Henry
Highland
Isle of Wight
James City
King and Queen
King George

101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
125 =
127 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
153 =
155 =
157 =
159 =
161 =
163 =
165 =
167 =
169 =
171 =
173 =
175 =
177 =
179 =
181 =
183 =
185 =
187 =
191 =
193 =

King William
Lancaster
Lee
Loudoun
Louisa
Lunenburg
Madison
Mathews
Mecklenburg
Middlesex
Montgomery
Nelson
New Kent
Northampton
Northumberland
Nottoway
Orange
Page
Patrick
Pittsylvania
Powhatan
Prince Edward
Prince George
Prince William
Pulaski
Rappahannock
Richmond
Roanoke
Rockbridge
Rockingham
Russell
Scott
Shenandoah
Smyth
Southampton
Spotsylvania
Stafford
Surry
Sussex
Tazewell
Warren
Washington
Westmoreland

195 =
197 =
199 =
510 =
515 =
520 =
530 =
540 =
550 =
560 =
570 =
580 =
590 =
595 =
600 =
610 =
620 =
630 =
640 =
650 =
660 =
670 =
678 =
680 =
683 =
685 =
690 =
700 =
710 =
720 =
730 =
735 =
740 =
750 =
760 =
770 =
775 =
780 =
790 =
800 =
810 =
820 =
830 =

Wise
Wythe
York
Alexandria
Bedford
Bristol
Buena Vista
Charlottesville
Chesapeake
Clifton Forge
Colonial Heights
Covington
Danville
Emporia
Fairfax
Falls Church
Franklin
Fredericksburg
Galax
Hampton
Harrisonburg
Hopewell
Lexington
Lynchburg
Manassas
Manassas Park
Martinsville
Newport News
Norfolk
Norton
Petersburg
Poquoson
Portsmouth
Radford
Richmond
Roanoke
Salem
South Boston
Staunton
Suffolk
Virginia Beach
Waynesboro
Williamsburg

840 = Winchester

WASHINGTON - 53
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =

Adams
Asotin
Benton
Chelan
Clallam
Clark
Columbia
Cowlitz
Douglas
Ferry
Franklin
Garfield
Grant
Grays Harbor
Island
Jefferson
King
Kitsap
Kittitas
Klickitat
Lewis
Lincoln
Mason
Okanogan
Pacific
Pend Oreille
Pierce
San Juan
Skagit
Skamania
Snohomish
Spokane
Stevens
Thurston
Wahkiakum
Walla Walla
Whatcom
Whitman

077 = Yakima

WEST VIRGINIA - 54
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =

Barbour
Berkeley
Boone
Braxton
Brooke
Cabell
Calhoun
Clay
Doddridge
Fayette
Gilmer
Grant
Greenbrier
Hampshire
Hancock
Hardy
Harrison
Jackson
Jefferson
Kanawha
Lewis
Lincoln
Logan
McDowell
Marion
Marshall
Mason
Mercer
Mineral
Mingo
Monongalia
Monroe
Morgan
Nicholas
Ohio
Pendleton
Pleasants
Pocahontas

077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =

Preston
Putnam
Raleigh
Randolph
Ritchie
Roane
Summers
Taylor
Tucker
Tyler
Upshur
Wayne
Webster
Wetzel
Wirt
Wood
Wyoming

WISCONSIN - 55
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =

Adams
Ashland
Barron
Bayfield
Brown
Buffalo
Burnett
Calumet
Chippewa
Clark
Columbia
Crawford
Dane
Dodge
Door
Douglas
Dunn
Eau Claire
Florence
Fond du Lac
Forest
Grant

045 =
047 =
049 =
051 =
053 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =
069 =
071 =
073 =
075 =
077 =
078 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =

Green
Green Lake
Iowa
Iron
Jackson
Jefferson
Juneau
Kenosha
Kewaunee
La Crosse
Lafayette
Langlade
Lincoln
Manitowoc
Marathon
Marinette
Marquette
Menominee
Milwaukee
Monroe
Oconto
Oneida
Outagamie
Ozaukee
Pepin
Pierce
Polk
Portage
Price
Racine
Richland
Rock
Rusk
St. Croix
Sauk
Sawyer
Shawano
Sheboygan
Taylor
Trempealeau
Vernon
Vilas
Walworth

129 =
131 =
133 =
135 =
137 =
139 =
141 =

Washburn
Washington
Waukesha
Waupaca
Waushara
Winnebago
Wood

WYOMING - 56
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =

Albany
Big Horn
Campbell
Carbon
Converse
Crook
Fremont
Goshen
Hot Springs
Johnson
Laramie
Lincoln
Natrona
Niobrara
Park
Platte
Sheridan
Sublette
Sweetwater
Teton
Uinta
Washakie
Weston

AMER. SAMOA - 60
010 =
020 =
030 =
040 =
050 =

Eastern
Manu'a
Rose
Swains
Western

GUAN - 66
010 = Guam

PUERTO RICO - 72
001 =
003 =
005 =
007 =
009 =
011 =
013 =
015 =
017 =
019 =
021 =
023 =
025 =
027 =
029 =
031 =
033 =
035 =
037 =
039 =
041 =
043 =
045 =
047 =
049 =
051 =
053 =
054 =
055 =
057 =
059 =
061 =
063 =
065 =
067 =

Adjuntas
Aguada
Aguadilla
Aguas Buenas
Aibonito
Añasco
Arecibo
Arroyo
Barceloneta
Barranquitas
Bayamon
Cabo Rojo
Caguas
Camuy
Canovanas
Carolina
Cataño
Cayey
Ceiba
Ciales
Cidra
Coamo
Comerio
Corozal
Culebra
Dorado
Fajardo
Florida
Guanica
Guayama
Guayanilla
Guaynabo
Gurabo
Hatillo
Hormigueros

069 =
071 =
073 =
075 =
077 =
079 =
081 =
083 =
085 =
087 =
089 =
091 =
093 =
095 =
097 =
099 =
101 =
103 =
105 =
107 =
109 =
111 =
113 =
115 =
117 =
119 =
121 =
123 =
125 =
127 =
129 =
131 =
133 =
135 =
137 =
139 =
141 =
143 =
145 =
147 =
149 =
151 =
153 =

Humacao
Isabela
Jayuya
Juana Diaz
Juncos
Lajas
Lares
Las Marias
Las Piedras
Loiza
Luquillo
Manati
Maricao
Maunabo
Mayagüez
Moca
Morovis
Naguabo
Naranjito
Orocovis
Patillas
Peñuelas
Ponce
Quebradillas
Rincon
Rio Grande
Sabana Grande
Salinas
San German
San Juan
San Lorenzo
San Sebastian
Santa Isabel
Toa Alta
Toa Baja
Trujillo Alto
Utuado
Vega Alta
Vega Baja
Vieques
Villalba
Yabucoa
Yauco

VIRGIN ISLANDS - 78
010 = St. Croix
020 = St. John
030 = St. Thomas

INDEX

30% Limit 60, 62, 65, 66, 68-70
Absolute Value 80, 121
ACF 3, 17, 28, 37, 41, 48, 51, 52, 121, 154,
155
Adequate Sample 121
Agency 17, 26, 27, 41, 50, 100, 122,
135-137
Alpha 80, 81, 121, 133
Alternate method 44
Annual Sample 17, 21-26, 32-34, 37-39, 41,
45, 47, 50, 90, 121, 128
Annual Sample Period 17, 21, 23, 24, 32-34,
37, 39, 45, 47, 90, 121, 128
Annual Samples 50
Audit Trail 40
Bias 5, 9, 10, 76, 80, 123, 130, 133
Caseload 2, 5, 6, 8-11, 14, 17, 18, 20, 22,
24, 25, 28, 29, 31-34, 37,
39-48, 50, 51, 53-55, 61, 62,
64-66, 68, 69, 73-77, 86, 100,
123, 137, 147, 150
Caseload Reduction Credit 53-55
Caseload Size 6, 8, 20, 32, 34, 37, 39, 42,
43, 123, 150
Chi-square 74, 76, 77, 79, 80, 82, 85, 86, 95,
96
Coefficient of Correlation 96-98
Coefficient of Determination 98
Computation of Sample Size 13
Confidence Interval 10-13, 16, 73-75, 125,
141
Confidence Limits 22, 75, 125, 130
Countable Work Activities 53, 57, 59
Custodial Parent 19, 29, 30, 56, 60-62, 64,
68, 70, 131, 149
Data Analysis 73
Deemed Core Hours 60
Degrees of Freedom 79, 80, 85
Discriminant Analysis 99
Disposed of Case 126
Disposition 70
Dropped Cases 47

Emergency Assistance 126, 127, 131
Equal Probability of Selection 127
Error-prone Cases 98
Error-prone Profile 99
Expected Value 80, 86
Expenditures 31, 124-128, 132, 134, 136
Fiscal Year 50, 53-56, 58, 59, 121, 122, 126,
128, 132, 138
Foster Care 31
Frame 2, 6, 17-19, 26-35, 37-39, 42-44, 47,
48, 51, 52, 128, 133-135,
137, 147-149
Guam 135, 138, 198
Hours of Participation 58, 60, 73
Ineligible 73
Initial Payment 29
Intercept 91, 93
Least Squares 90
Listed-in-Error 19, 20, 26, 28, 33, 38, 42,
43, 51, 52, 62, 64, 68, 125,
126, 129, 149
Lower Limit 13
Lower Limits 13, 125
Mean 11, 75, 97, 129, 131, 133, 134
Moving Average 86
Multiple Regression 99
Non-Sampling Error 130, 134
Normal Distribution 12, 130, 141
Null Hypothesis 73, 95
Official 134
Optimal Allocation 8, 9
Oversampling 10, 20, 33, 41, 42, 44, 50, 51,
130, 151
Parameter 10, 131, 133
Payroll Listing 137
Plan Approval 18
Plan Requirements 2, 3, 17, 147
Population 2, 5-7, 10, 12-15, 18, 22, 26, 41,
61, 73-75, 81, 103, 123, 125,
129-134, 136, 147
Population of Interest 129, 131
Precision 6-11, 13, 14, 21-24, 41, 121,

131-133, 135
Probability 5-7, 11-13, 18, 26, 41, 80, 81,
121, 127, 131, 132, 134, 135,
147
Probability Sampling 5, 18, 26, 41, 131,
132, 134, 147
Proportional Allocation 8
Puerto Rico 135, 138, 198
Random Number 6, 38, 47
Random Numbers 6, 39, 43, 48, 49, 103,
132
Random Sampling 2, 6, 7, 16, 20, 22, 26, 29,
32, 37, 38, 41, 42, 44, 47, 50,
74, 132, 135, 136, 150
Random Start 17, 34-36, 40, 42, 43, 48, 133
Random Starts 20, 34, 150
Range 10, 11, 28, 95, 131, 133
Regional Administrator 17, 48
Regional Office 17, 41, 52
Reliability 9-11, 21, 23, 121, 131, 133
Reserve Pool 37, 39, 40, 47-50
Reserve Sample Pool 47, 49, 50
Resources 14, 26, 99, 100, 136, 137
Retention of Sampling Records 39
Review Month 137
Risk 11, 98, 99, 121, 133, 135
Sample 2, 3, 5-52, 63-66, 68-70, 73-77, 80,
82, 83, 86, 88-91, 96, 98, 99,
103, 121, 124, 125, 127-137,
141, 143, 147-150
Sample Frame 2, 6, 18, 26-32, 35, 37-39,
42-44, 48, 51, 52, 134, 137,
147
Sample Frames 17, 39, 40, 44, 51
Sample Interval 6, 33-37, 39, 42-50, 133
Sample Intervals 17, 20, 40, 44, 48, 150
Sample Month 18-20, 26, 27, 29, 30, 33, 37,
38, 51, 133, 147, 148
Sample Period 6, 17, 21, 23, 24, 32-34,
37-39, 43-48, 74, 82, 90, 96,
121, 128, 133
Sample Selection 2, 17, 20, 31, 37, 38, 47,
48, 51, 52, 74, 128, 129, 131,
134, 137, 149
Sample Selection List 51, 52, 128, 129, 134
Sample Selection Process 74

Sample Size 2, 3, 6-9, 11, 13-15, 20-26, 33,
34, 38-41, 44, 45, 47, 48, 50,
51, 64, 68, 98, 103, 125, 134,
141, 143, 150
Sample Sizes 15, 21-23, 26, 33, 38, 41, 42,
74, 141, 143
Samples 2, 5-8, 10-12, 14, 17, 18, 21, 26,
31, 41, 42, 50, 61, 63, 73, 76,
81, 121, 133-135
Sampling Distribution 130, 134, 135
Sampling Error 5, 11, 73, 130, 134
Sampling Errors 5, 17, 18, 151
Sampling Plan 2, 3, 8, 17, 18, 20, 27, 28, 37,
39, 40, 47-49, 147, 149, 151
Sampling Plans 2, 3, 147
Sampling Techniques 5
Significant Difference 73, 81, 135
Significant Differences 73, 74, 76
Simple Random Sample 11, 14, 37, 38, 135,
141
Simple Random Sampling 2, 6, 7, 16, 29,
32, 37, 38, 41, 50, 74
Standard Deviation 75, 131, 135
Standard Error 8, 9, 11-13, 15, 16, 74-76,
135, 141, 143
Standard Sample Size 47
State Agencies 5, 21, 26, 28, 41, 42
State Agency 17, 26, 41, 50, 135
Strata 7-9, 15, 41, 42, 45, 49, 67, 68, 136
Stratified Random Sample 16
Stratified Random Sampling 6, 136
Stratified Sample 7, 8, 14, 15, 20, 22, 37, 39,
49, 64, 66, 68, 70, 150
Stratified Samples 7, 14, 61
Stratum 7-9, 14-16, 22-24, 27, 32, 37-39,
45, 46, 49, 67-70, 136
Supplemental Case 137
Supplemental Cases 28
Suspended Case 137
Systematic Random Sample 16, 32, 133,
137
Systematic Random Samples 11
Systematic Random Sampling 2, 6, 7, 20,
22, 26, 32, 37, 41, 42, 44, 47,
50, 150
Territories 128, 138

Thirty (30) Percent Limit 60
Tolerance 11, 138
Trend 86, 87, 89-91, 93-96
Trend Line 90, 91, 93-95
Trends 32, 73, 86
Tribal 1, 2, 14, 17, 19, 21, 26-28, 30, 32, 37,
41, 42, 44, 46, 47, 50, 56, 57,
61, 62, 64, 68, 73, 74, 121,
129, 134, 138, 149, 153-155,
161, 169
Tribes 1-3, 21-24, 26, 28-30, 33, 39, 41, 44,
49, 51, 53, 138, 153,
155-159, 166, 168-170
Undersampling 20, 33, 41-44, 47, 49-51,
151
Universe 2, 13, 28, 41, 61, 76, 103, 135
Upper Limit 13
Validity 9, 10, 80, 133
Variance 8, 11, 14-16, 63, 67
Virgin Islands 135, 138, 198
Waiver 70, 71, 126-128, 139
Weight 15, 39
Weighted Sample 127
Work Activities 19, 26, 27, 29, 53, 57, 59,
60, 65, 66, 71, 122, 139, 149
Work Activity 58
Work Participation Rate 22, 30, 53-57,
59-63, 65, 66, 69, 70, 122,
130, 138
Work Participation Rates 3, 21-23, 53, 55,
60, 70, 122
Work-Eligible Individual 56, 59, 60, 71, 131
Yates 80, 188