Attachment C: GSS Sampling Design and Weighting

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Appendix A

APPENDIX A:

SAMPLING DESIGN & WEIGHTING

In the original National Science Foundation grant, support was given for a modified probability sample. Samples for the 1972
through 1974 surveys followed this design. This modified probability design, described below, introduces the quota element at the
block level. The NSF renewal grant, awarded for the 1975-1977 surveys, provided funds for a full probability sample design, a design
which is acknowledged to be superior.
Thus, having the wherewithal to shift to a full probability sample with predesignated respondents, the 1975 and 1976 studies
were conducted with a transitional sample design, viz., one-half full probability and one-half block quota. The sample was divided
into two parts for several reasons: 1) to provide data for possibly interesting methodological comparisons; and 2) on the chance that
there are some differences over time, that it would be possible to assign these differences to either shifts in sample designs, or changes
in response patterns. For example, if the percentage of respondents who indicated that they were "very happy" increased by 10 percent
between 1974 and 1976, it would be possible to determine whether it was due to changes in sample design, or an actual increase in
happiness.
There is considerable controversy and ambiguity about the merits of these two samples. Text book tests of significance
assume full rather than modified probability samples, and simple random rather than clustered random samples. In general, the
question of what to do with a mixture of samples is no easier solved than the question of what to do with the "pure" types.
Investigators who have applied statistical tests to previous General Social Survey data should continue to apply those tests.
Investigators who have refrained from applying such tests may now want to perform analyses on the probability subsample. This
would, of course, reduce the number of cases by one-half. Whatever choice investigators make, it should be remembered that the two
subsamples represent the same universe. 1
Having allowed for the appearance of all items in the transitional sample design, the General Social Survey then switched to
a full probability sample for the 1977+ surveys. The variable SAMPLE can be used to separate the block quota and full probability
samples on the 1975 and 1976 surveys.
A similar split sample transition design was used in the 1983 survey to measure the effect of switching from the 1970 sample
frame to the 1980 sample frame. Half of the sample was drawn from the 1970 frame and half was drawn from the 1980 frame. The
variable SAMPLE separates cases from these two sample frames. Again in 1993, a split sample transition design was employed on the
1993 survey to measure the effect of switching from the 1980 sample frame to the 1990 sample frame. Half of the sample was drawn
from each frame. In 2004 a new frame based on the 2000 census was adopted. In 2012, a new sample frame based on the 2010 census
was implemented. More details on the 1970, 1980, 1990, 2000, and 2010 sample frames as well as the block quota samples appear
below.
The adult, household population of the United States covered about 97.3% of the resident population of the United States in
1985. Coverage varies greatly by age group. For those 18-24, 9.4% of the population in 1980 lived outside of households (mostly in
college dorms and military quarters). Among age groups from 25 to 64 the only 0.8-1.4% of the population lived outside of
households. For those 75 and older 11.4% were in group quarters, mostly in nursing homes and long-term care facilities. For more
details on the non-household population see Living Arrangements of Children and Adults, Census of Population, PC80-2-413, May,
1985.
Until 2006 the GSS only sampled the English speaking population. As defined for the GSS in 1983-1987, 98% of the adult,
household population is English speaking. The number of non-English speakers excluded is indicated in Table A.8. Spanish speakers
typically make up 60-65% of the language exclusions. About a dozen languages make up the remaining exclusions. Starting in 2006
the GSS samples Spanish speakers in addition to English speakers.
BLOCK QUOTA
The sample is a multi-stage area probability sample to the block or segment level. At the block level, however, quota
sampling is used with quotas based on sex, age, and employment status. The cost of the quota samples is substantially less than the
cost of a full probability sample of the same size, but there is, of course, the chance of sample biases mainly due to not-at-homes
which are not controlled by the quotas. However, in order to reduce this bias, the interviewers are given instructions to canvass and
1
C. Bruce Stephenson, "Probability Sampling with Quotas: An Experiment," GSS Methodological Report No.7, April, 1979.
Published in Public Opinion Quarterly, 43 (Winter, 1979), 477-496.

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Appendix A

interview only after 3:00 p.m. on weekdays or during the weekend or holidays. This type of sample design is most appropriate when
the past experience and judgment of a project director suggest that sample biases are likely to be small relative to the precision of the
measuring instrument and the decisions that are to be made.
Selection of PSUs
The Primary Sampling Units (PSUs) employed are Standard Metropolitan Statistical Areas (SMSAs) or non-metropolitan
counties selected in NORC's Master Sample. These SMSAs and counties were stratified by region, age, and race before selection. 2
Selection of Sample within PSUs
The units of selection of the second stage were block groups (BGs) and enumeration districts (EDs). These EDs and BGs
were stratified according to race and income before selection. 3 The third stage of selection was that of blocks. The blocks were
selected with probabilities proportional to size. In places without block statistics, measures of size for the blocks were obtained by
field counting. The average cluster size is five respondents per cluster. This provides a suitable balance of precision and economy.
Interviewer Instructions
At the block or segment level, the interviewer begins a travel pattern at the first DU (dwelling unit) from the northwest corner
of the block and proceeds in a specified direction until the quotas have been filled.
The quotas call for approximately equal numbers of men and women with the exact proportion in each segment determined
by the 1970 Census tract data. For women, the additional requirement is imposed that there be the proper proportion of employed and
unemployed women in the location. Again, these quotas are based on the 1970 Census tract data. For men, the added requirement is
that there be the proper proportion of men over and under 35 in the location.
These particular quotas have been established because past experience has shown that employed women and young men
under 35 are the most difficult to find at home for interviewing.
Sampling Error
Although the mean squared error cannot be estimated directly from a quota sample, one can make estimates of sampling
variability using procedures such as those outlined by Stephan and McCarthy. 4 Past experience would suggest that, for most purposes,
this sample of 1,500 could be considered as having about the same efficiency as a simple random sample of 1,000 cases. In making
this judgment concerning the design effect, we are concerned with the "average" effect upon a large set of different variables of the
clustering of households at the last stage of selection. 5 Any statement of sampling error assumes that the bias in quota sampling due to
the lack of control over respondent availability is slight for the study under consideration.
For those persons interested in investigating the within-sample variability of these data, we have included SAMPCODE
("sampling error code"), VSTRATA (variance stratum), and VPSU (variance primary sampling unit). Information about the use of this
code is available from the GSS project staff at NORC.
Probability, 1970 Frame
The NORC national probability sample is a stratified, multistage area probability sample of clusters of households in the
continental United States. 6 The selection of geographic areas at successive stages is in accordance with the method of probabilities
proportional to size (p.p.s.). Furthermore, the clusters of households are divided into replicated subsamples in order to facilitate
estimation of the variance of sample estimators of population characteristics.

2

For selection procedures, see Benjamin King and Carol Richards, "The 1972 NORC National Probability Sample." Chicago:
NORC, August, 1972.
3

Ibid.

4

Frederick Stephan and Philip McCarthy, Sampling Opinions. (New York: John Wiley and Sons, 1958, Chapter 10.)

5
For variable specific design effects from the 1993 GSS, see Tom W. Smith, Hee-Choon Shin, and Xiaoxi Tong, "A Report on the
Sample Frame Comparisons and Design Effects of the 1993 General Social Survey," GSS Methodological Report No. 87, 1994.
6

Alaska and Hawaii are not included in this sample.

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At the first stage of selection, Standard Metropolitan Statistical Areas (SMSAs) and nonmetropolitan counties covering the
total continental United States were grouped according to size strata within the nine Census regions. All population figures and other
demographic information were obtained from 1970 Census reports. Within each size stratum grouping based upon geographic location,
or racial characteristics (or both), was accomplished before selection. The final frame was further separated into zones or "paper
strata" of equal population size in order to facilitate the selection of replicated subsamples of primary sampling units (PSUs). 7 The
selection of PSUs was designed to produce four independent subsamples of equal size. The four subsamples were randomly combined
to form two larger subsamples of 101 PSUs each. 8 The large subsamples are thus internally separable into two replicated subsamples
for variance estimation purposes.
NORC has selected one of the two large subsamples described above to serve as its principal frame of households for the
remainder of the decade. The PSUs fall into 89 distinct SMSAs and nonmetropolitan counties. (New York, a very large SMSA,
represents five PSUs, whereas the smaller counties represent only one PSU.)
The second-stage procedure involved the direct selection of Census block groups or enumeration districts (E.D.s) within
SMSAs or counties, eliminating the traditional intermediate stage of clustering selections within urban places or county division. The
increase in geographic dispersion within the primary areas has a negligible effect on field costs. Before selection, the Census tracts,
minor civil divisions, and Census county divisions containing the block groups and E.D.s were carefully stratified by geographic
location, income, and race, in order to maximize the precision of sample estimation within a PSU. Block groups and E.D.s were then
selected with probabilities proportional to size in numbers sufficient to satisfy survey demands for households expected throughout the
decade. Lists of the separate households contained in the second stage blocks or E.D.s were constructed by field personnel or obtained
from directories. Thus, the principal NORC national probability sample is, in effect, an inventory of identifiable households, each with
a known probability of selection. In a typical sample survey with equal probability of selection for individual households (i.e., a
self-weighting sample), households at which interviews will take place are probabilistically selected from the available lists of
addresses for blocks and E.D.s. The method of probabilities proportional to size results in the assignment of approximately equal
numbers of interviews in each final stage cluster, which in turn leads to increased precision in the estimation of overall population
characteristics.
The NORC national probability frame, with its broad geographic dispersion, its reserves of additional SMSAs and counties,
and its built-in replication, provides sufficient flexibility for application to a wide range of survey tasks. Its design is based on the
consideration of sampling problems that NORC and other organizations have encountered in past surveys, and we believe that it
substantially eliminates many of these difficulties.
Probability, 1980 Frame
1980 National Sampling Frame
The 1980 frame was designed, selected, and listed jointly by NORC and the Survey Research Center. This frame and
subsequent frames cover the 50 states, not just the continental United States. The new frame was selected, in most cases, in two stages;
about one-fifth of the second stage units were subsampled, producing a third stage.
Eighty-four PSUs were selected at the first stage. The PSUs consist of counties, SMSAs, independent cities and, in New
England, parts of counties. Prior to selection, the United States was divided into PSUs; the PSUs were then grouped into 84 strata. The
strata were formed by grouping metropolitan and non-metropolitan PSUs within each of the four Census regions. Within each region,
additional variables were used to define strata. The stratifying variables included within-region geography and size; size was measured
by the 1980 Census count of occupied housing units. One PSU was selected from each stratum using a controlled selection procedure.
This procedure ensured proportionate representation along certain control dimensions (such as percentage Hispanic in the West). The
exact control variables (like the stratification variables) differed somewhat from region to region. Sixteen strata contained only one
PSU, which was selected with certainty. The remaining 68 PSUs were selected with probability proportional to size (measured in
housing units).
The unit for second stage selection was the block or enumeration district (ED). The number of secondary selections within a
PSU depended in part on the stratum size. The number of second stage selections listed for NORC's national frame in the 16 PSUs
selected with certainty ranged from 24 to six selections for PSU. In the remaining 68 sample PSUs, six second stage selections were
listed. (The same number of second stage selections were listed for SRC's national frame; further, both organizations retained a similar
7

The selection methods used are similar to those described in standard textbooks, e.g., W. E. Deming, Sample Design in Business
Research (New York: Wiley & Sons, 1960), and L. Kish, Survey Sampling (New York: Wiley & Sons, 1965).
8
In the actual implementation of the selection method, subsamples 1 and 4 resulted in 51 PSUs, whereas subsamples 2 and 3
produced only 50 PSUs. The result was not unexpected and is due to a technical reason, details of which will be provided on request.
The inequality of subsample sizes does not affect the equal probability characteristics of the sample.

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Appendix A

number of second stage selections as a reserve for future use.) All total, the new frame includes 562 secondary selections.
Prior to selection, the second stage units within each sample PSU were sorted by county, by minor civil division (in some
areas), by Census Tract or ED number, and by block number. Counties were ordered within PSUs according to size and geography
(e.g., in SMSAs, the county containing the central city came first, then counties containing nearby suburbs, and so on). In twenty
states, information was available on the size and median family income of minor civil divisions (MCDs), which are governmental
units below the county level (such as cities or towns). Where this information was available, we sorted the block and EDs by MCD
and ordered the MCDs by size and income.
Next, we sorted all blocks and EDs by Census Tract number and then by block or ED number; these sorts establish a
geographic ordering. The secondary selections were made using systematic zone selection; the probabilities of selection were
proportional to size (measured in housing units). Each secondary selection included at least 50 housing units.
In enumeration districts and blocks with a large number of dwelling units, a third stage of selection was carried out. The
block or ED was subdivided into pieces which were "field counted" by field staff from NORC or SRC. In a field count, an area is
scouted and a rough count of the number of housing units is made. Based on the field count we selected a piece of the sample block or
ED with probability proportional to its size. 9
Comparison of the 1970 and 1980 Frames
Table A.1 summarizes the main differences between the old and the new frames. Aside from the obvious differences in the
number of selections at each stage and in the measures of size (people vs housing), the two frames differ in three main respects.

Table A.1: Main Differences Between Frames
First Stage
Number of Selections
Measure of Size
Methods of Selection
Stratification

Second Stage
Number of Selections
Unit
Measure of Size
Method of Selection
Stratification

Third Stage
Unit
Measure of Size
Method of Selection

1970 Frame
100
Persons--at least 10,000
Systematic Zone Selection
(Implicit) Census
division, urbanization
% Black

1980 Frame
84
Housing--at least 4,000 units
Controlled selection
84 explicit strata
plus control variables

1800 (18 per PSU)
BG or ED
Person
Systematic Zone Selection
(Implicit) income,
% Black

562
Block or ED
Housing units--at least 50
Systematic Zone Selection
(Implicit) County: size,
geography; MCD (20 states):
income, size; CT, ED block: geography

"Segment"

Part of a block or ED (in most areas,
no 3rd stage)
Housing units--at least 50
One selection with
probability proportional
to size

Housing units--at least 100
One selection with
probability proportional
to size

First, the stratification variables differ. In particular, percentage Black, which was used as an implicit stratifier at the first and
second stages in the old frame is no longer used in the new sample (except in rural areas of the Deep South). Instead, the new frame
uses a variety of geographic control variables at both the first and second stages. Second, the new frame was, for the most part,
selected in two rather than three stages. A third stage was used only to subsample within blocks and EDs too large to be listed in a
single trip to the field.

9
Steven G. Heeringa and Judith H. Connor, The 1980 SRC/NORC National Sample Design and Development. Ann Arbor:
Institute for Social Research, 1984.

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The most important change involves the method of selection at the first stage. The new frame used a controlled selection
procedure that assures proportionate representation along the control dimensions as well as the dimensions used to define strata. This
procedure should yield additional statistical precision (Leslie Kish estimates the gains at up to 20%). An added benefit of controlled
selection from explicit strata is that it allows us to use the Keyfitz procedure to draw a new national sample. The Keyfitz procedure is
a method for updating national samples using new Census data; it maximizes the retention of PSUs from an existing national sample.
It could not be easily applied to a sample drawn using systematic selection procedures (such as the old frame). The controlled
selection procedure used to select the PSUs has one drawback--it will be more difficult to subsample PSUs. The zone selection
procedure used to select the old frame is easy to select a half-sample; in fact, the old frame was selected in two replicates.
Subsampling is not such an automatic procedure with the new frame.
1983 GSS Sample: Fifty PSUs from the new frame were selected for the 1983 GSS. All 16 certainty PSUs were selected for
the GSS subsample. The remaining 68 PSUs were paired, according to size and region; one PSU was selected at random from each
pair. Within each of the 50 subsample PSUs, half of the segments (a total of 141) were selected systematically for the 1983 GSS. Fifty
PSUs were also selected from the old frame with three segments per PSU (total of 150).

1982 Black Oversamples
In 1982 the National Science Foundation funded as a separate project an oversample of Blacks on the GSS. Two different
sample frames were used. First, an additional sample of lines was drawn from the 1970 sample frame (as the regular 1982 GSS cross
section was). These households were screened for race and eventually yielded 107 extra Black respondents. Second, a special sample
frame designed to sample Blacks was drawn from 1980 Census data. In this sample frame localities were selected according to their
Black population rather than their total population. This procedure increases the take-rate among selected lines yielding a more
efficient sampling of Blacks. This oversample proportionate to the Black population resulted in 247 extra Black respondents. In all,
the Black samples added 354 extra Black respondents. When added to the 156 Blacks who were part of the regular cross section, that
made a total of 510 Black respondents in 1982.
The three groups of Blacks can be added together to form a national probability sample of Black Americans. The Blacks and
Non-Blacks in the regular 1982 cross-section can be used as a national sample of all races without using the OVERSAMP weight. If
one wants to use all 1982 cases to form a national sample (the 1,506 from the cross section and the black oversamples of 354) then the
OVERSAMP weight must be used to establish the proper racial balance. The OVERSAMP variable is designed to match the racial
distribution found in the regular cross section and to reproduce the total number of unweighted cases (i.e. 1,506 + 354 = 1,860).
For a report on the black oversamples including a comparison of the two methods of oversampling blacks, see Roger
Tourangeau and A. Wade Smith, "Finding Subgroups for Surveys," Public Opinion Quarterly, 49 (Fall, 1985), 351-365.
Information on response rates is given in Table A.8.
1987 Black Oversample
In 1987 the National Science Foundation funded as a separate project an oversample of Blacks on the GSS. An additional
sample of lines was drawn from the 1980 sample frame (as the regular 1987 GSS cross section was). These households were screened
for race and eventually yielded 353 extra Black respondents. When added to the 191 Blacks who were part of the regular cross section
that made a total of 544 Black respondents in 1987.
1990 National Sample
Like its predecessor, the 1990 National Sample was selected in two major stages, with PSUs consisting of one or more
counties selected at the first stage and segments consisting of one or more blocks selected at the second. In a few cases, segments were
subsampled, a procedure that constituted a third stage of sample selection.
The 1990 sample included 100 first stage selections. The PSUs consisted of metropolitan areas or nonmetropolitan counties.
The metropolitan PSUs include metropolitan areas of all three types distinguished in the 1990 Census--Metropolitan Statistical Areas
(or MSAs, which correspond to the SMSAs used in 1980), Consolidated Metropolitan Statistical Areas (or CMSAs, which join
metropolitan area of a very large city with the adjacent metropolitan area of one of its suburbs), and New England County
Metropolitan Areas (or NECMAs, which are the whole county counterparts of the New England MSAs). Prior to selection, the United
States was divided into 2,489 PSUs; the PSUs were then sorted into strata. The major strata again grouped metropolitan and
nonmetropolitan PSUs within each of the four Census regions. The nonmetropolitan PSUs were further sorted by state; then, within
state, by percent minority; and, finally, within percent minority groupings, by per capita income. Each PSU was classified according to
the percent of its population who are minority group members; this encompassed everyone but non-Hispanic Whites. Percent minority
groupings were formed by classifying each PSU according to percent minority quartiles within its major strata. The metropolitan
PSUs were sorted by Census division, minority quartile, and per capita income. The sample PSUs were selected using systematic
selection, with the selection probability for a PSU proportional to the number of housing units. This selection procedure ensured

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Appendix A

proportionate representation along each of the sort variables. Nineteen PSUs were so large that they had to be included in the sample
with certainty.
The second stage sampling unit in the 1990 National Sample was again the segment, consisting of one or more adjoining
blocks. The number of segments selected within a PSU again depended on the whether the PSU was a certainty selection. From three
to 26 segments were selected in the 19 certainty PSUs; in each of the remaining 81 sample PSUs, three segments were selected. All
told, the 1990 National Sample includes 384 second stage selections. Prior to selection, the segments within each sample PSU were
sorted successively by a) whether they were within the central city of a metropolitan area or outside of it (in metropolitan PSUs), b)
state (in those PSUs that crossed state lines), c) county, d) place, e) percent minority quartile within the PSU, and f) census tract (CT)
or block numbering area (BNA). The sample segments were selected using systematic sampling with probability proportional to size
(in housing units). Undersized blocks were linked to adjacent ones to assure that each segment included at least 50 housing units.
Similarly, census tracts with fewer than 50 housing units were linked with adjoining CTs.
In the smallest PSUs, it was possible for a segment to be selected more than once. In such cases and when segments included
unexpectedly large numbers of dwellings, a third stage of sampling was carried out. The segment was subdivided into pieces by a field
count; based on the field count, one piece of the segment was selected with probability proportional to its estimated size. 10
Comparison of the 1980 and 1990 Sample Frames
The two national samples are quite similar. Table A.2 summarizes the main features of each sample design.
Table A.2. Main Features of Two National Samples

First Stage
Number of Selections
Measure of Size/Minimum
Method of Selection
Stratification

Second Stage
Number of Selections
Unit
Measure of Size/Minimum
Method of Selection
Stratification

Third Stage
Unit
Measure of Size/Minimum
Method of Selection

1980 National Sample

1990 National Sample

84
Housing units/4000
Controlled selection
Region, metropolitan status; additional
control variables

100
Housing units/2000
Systematic selection
Region, metropolitan status,
division/state, % minority,
per capita income

562
Blocks or EDs
Housing units/50
Systematic
County (ordered by size), MCD
(ordered by size and income), census
tract/ED number

384
Blocks
Housing units/50
Systematic
County (ordered by size and state),
place, % minority,
census tract/BNA

Part of block or ED
Housing units/50
One selection per segment with
probability proportional to size
Note: In both samples, the third stage was generally not needed in most areas.

Part of block or ED
Housing units/50
One selection per segment with
probability proportional to size

However, there are a number of differences between the two samples, most of them minor. At the first stage of sampling, the
1990 National Sample included more selections than the 1980 Sample (100 vs. 84), and it used a systematic rather than a controlled
procedure to make the selections. The two sampling methods do not differ markedly; they have similar theoretical properties (e.g.,
both allow additional control beyond that afforded by ordinary stratified sampling) and yield similar results in practice. The systematic
procedure makes it somewhat easier to select subsamples from the 1990 National Sample.
At the second stage of selection, the two samples again differ in their sample sizes (384 selections in 1990 vs. 562 in 1980)
but the same method of selection was used. There was a small change in how the second stage units were defined. By 1990, the
Census Bureau had divided the entire nation into blocks and no longer used Enumeration Districts; segments in the new sample could
10
For further details on the 1990 National Sample, see Roger Tourangeau, Robert A. Johnson, Jiahe Qian, and Hee-Choon Shin,
Selection of NORC's 1990 National Sample. Chicago: NORC, 1993.

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thus be defined exclusively in terms of blocks. At both of the first two stages of sample selection, the 1990 National Sample classified
the sampling units by their minority population; the earlier sample had not made such extensive use of this variable
1993 GSS Sample
1980 National Sample. For the 1993 GSS, approximately 1,100 housing units were selected from the 1980 National Sample.
These dwelling units were clustered within 141 segments in 50 of the sample PSUs. Within each of the 16 certainty PSUs,
approximately one-fourth of the sample segments were selected for the 1993 GSS, yielding a total of 39 segments. Within the
remaining 68 sample PSUs, a subsample of 34 PSUs was first selected; then, within each of these 34 PSUs, three segments (out of the
six available) were included in the 1993 GSS. Overall, then, the 1993 GSS sample included approximately one segment in four from
the 1980 National Sample─one-fourth within the 16 certainty PSUs plus one-half of the segments within a randomly selected half of
the 68 remaining sample PSUs. The subsampling of the noncertainty PSUs was carried out by pairing PSUs from similar strata; one
PSU was selected randomly from each pair. The subsampling of segments was done using a simple systematic selection procedure.
1990 National Sample. The new National Sample also contributed approximately 1,100 housing units to the 1993 GSS
sample. These were drawn from 191 segments in 68 of the sample PSUs. Within each of the 19 certainty PSUs, approximately onehalf of the sample segments─a total of 71─were selected for the 1993 GSS. Within the remaining 81 sample PSUs, a subsample of 40
PSUs was first selected; within each of these 40 PSUs, all three available segments were sampled for the 1993 GSS. About half of the
segments from the 1990 National Sample─one-half of the segments within the 19 certainty PSUs and all of the segments within a
random half of the 81 other PSUs─were included in the 1993 GSS sample. The subsampling of the noncertainty PSUs was carried out
by pairing PSUs that were nearest neighbors in the final sorted file from which the first stage selections were made; one PSU was
selected at random from each pair. The last PSU of the 81 noncertainty PSUs could not be paired with another sample PSU; a random
procedure was used to determine whether it would be included in the 1993 GSS sample. Segments in the certainty PSUs were
subsampled using a simple systematic selection procedure.
The 1994-2004 surveys are sub-divided into two: samples A and B. Sample A consists of versions 1-6 in 1994 and versions
4-6 in 1996, 1998, and 2000. Sample B consists of version 6-9 in 1994 and versions 4-6 in 1996, 1998, and 2000. Both samples
contain approximately half of the cases.
For a comparison of results from the two sample frames, see Tom W. Smith, Hee-Choon Shin, and Xiaoxi Tong, "A Report
on the Sample Frame Comparisons and Design Effects of the 1993 General Social Survey," GSS Methodological Report No. 87, 1994.
2004 GSS National Sample Design
NORC has introduced an innovative approach to demographic sampling frame construction and sample design for NORC’s
program of face-to-face surveys from 2004 onwards. 11 The important changes from previous GSS designs are: (i) the construction of a
new list-assisted sampling frame for 72% of the population; (ii) an increase in the size of the certainty stratum (the proportion of the
population covered by certainty area selections); (iii) designation of new primary sampling units (PSUs) for the certainty stratum; (iv)
designation of new secondary sampling units (SSUs) for the remaining “urban” areas; and (v) designation of larger SSUs for the
remaining areas.
Considerations of cost and feasibility have determined that essentially all national sample designs worldwide are multi-stage
samples with administrative/political areas as the primary sampling units. We denote these areas as national frame areas (NFAs). The
skewness of the distribution of the sizes of administrative area units typically leads to the definition of the largest of these area units as
certainty selections; these areas appear in the sample with certainty. These selections are sometimes called “self-representing PSUs”
whereas they are in fact separate sub-strata; the nomenclature has led to some confusion in the literature and in the perception of the
characteristics of national samples. The traditional area-probability design also includes stratification of the area units at the various
stages of selection, incorporating our prior knowledge of the population structure into the design.
The absence of any satisfactory population register in the USA has led to the use of one or more stages of area sampling
followed by listing of addresses/housing units in the selected ultimate area units [UAUs] 12. This approach characterizes both the
sample designs from the Census Bureau and those from the major social research centers such as the University of Chicago’s NORC
and the University of Michigan’s Institute for Social Research (ISR).
The confluence of three developments in recent years in the US has made it possible to re-assess this traditional approach to
sample design.

11

A more complete presentation of the approach may be found in O’Muircheartaigh, Colm, (2003) “There and Back Again:
Demographic Survey Sampling in the 21st Century” Keynote Address, Federal Committee on Statistical Methodology 2003
Conference. http://www.fcsm.gov/events/papers2003.html
12
The UAU is the unit used at the final stage of selection involving areas for each part of the population. Beyond this stage, the
sampling unit is the housing unit (HU).

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•
•

•

Appendix A

First, it has become possible to obtain access (for sampling purposes) to the frame of addresses constructed and maintained by
the United States Postal Service (USPS). Research at NORC has established that this frame is generally superior to the listings
obtained from traditional field listing methods. 13
Second, it is possible to obtain data that allow for census geographies (based on blocks or combinations of blocks) to be
classified into two categories – those blocks that have street-style addresses, and those that do not. The classification is based on
the U.S. Census Bureau Type of Enumeration (TEA) code that was used to classify blocks as suitable for mail-out/mail-back
data collection in Census 2000.
Third, the quality of the maps based on TIGER has improved dramatically, and mapping software and databases have been
developed that permit the accurate geocoding of almost all those street-style addresses.

The MSA/county is the basic frame area in Table A.3. Frame areas were first categorized according to population and list quality as
follows:
• the largest MSAs/counties (certainty areas) with high density population dominated by tracts with street-style addresses
(category 1);
• small counties with either less than 30,000 population or less than 15,000 population in tracts with street-style addresses
(category 3);
• all other counties/MSAs (category 2).

Table A.3 describes the population.
Category

Units

Description

Extent

1

MSAs

2

MSAs/counties

45% of housing units (HUs) in
4.5% of the area
40%of HUs in 25% of area

3

Counties

The largest MSAs, likely
certainty selections
Intermediate MSAs not included
in categories 1 and 2
Counties with less than 30% of
housing units or with fewer than
15,000 people living in type A
tracts

15% of HUs in 70% of area

The shortcoming of this classification is that within all three categories of areas, urban areas (i.e. those with street-style
addresses) are interspersed with rural areas (those with non-street-style addresses). Tracts with predominantly street-style addresses
are designated type A tracts 14; all other tracts are designated type B. Figures 1 through 4 show the partitioning of areas into type A and
type B tracts. Figures 1 and 2 show the Los Angeles MSA and the Chicago MSA with type A tracts (dark) and type B tracts (pale);
these are category 1 MSAs. Figures 3 and 4 show two category 2 MSAs: the Champaign/Urbana category 2 MSA in Illinois (Figure 3),
and the Worcester category 2 MSA in Massachusetts (Figure 4).
Cost and timeliness are two major concerns in sample design. Consider the two extreme situations. In case I, a high quality
list of addresses/HUs exists for every area unit in the population; in case II, there are no address lists. In case II, the sample of areas
must be designed and selected well in advance of the survey to leave time for field listing of HUs in the selected areas. In case I, the
sample of areas can be selected very close to the time of the survey fieldwork. The cost of listing in case II will be very large, making
it impossible for many projects to support it; as a result the cost of listing will need to be amortized over a number of projects,
implying that the design of the sample of areas must be sufficiently general to be appropriate for a wide range of surveys. The design
can thus not be tailored to the particular survey. In those terms the US population frame is a mixture. For part of the population there
is a list frame; for the rest, there is not. And the two parts are intermingled in a complex way.
The design solution is to partition the frame into two parts, in one of which HUs/addresses can be selected directly from the
list; in the second part field listing must be carried out in the selected sample areas. The distinctive feature of the design is that the two
parts are not constructed from spatially connected areas, thereby giving the frame a somewhat mottled appearance.
For category 1 type MSAs, type A tracts dominate. The design solution for category 1 is to remove the type B tracts from the
category 1 MSAs. Stratum 1 is defined as those parts of category 1 MSAs that consist of type A tracts. Stratum 1 includes more than
90% of the population of category 1, but less than 50% of the area. The residual areas are treated separately (see discussion of stratum
3.2 below).
13

See O’Muircheartaigh, Colm, Stephanie Eckman, Ned English, and Catherine Haggerty,(2004)“Sampling for Inner-City Faceto-Face Surveys” 2003Proceedings of the Section on Survey Research Methods of the American Statistical Association and
O’Muircheartaigh, Colm, Stephanie Eckman, and Charlene Weiss (2003) “Traditional and Enhanced Field Listing for Probability
Sampling” 2002 Proceedings of the Section on Survey Research Methods of the American Statistical Association.
14
Type A tracts are tracts in which at least 95% of the housing units (HUs) are in blocks designated with TEA code 1 – suitable
for mail-out/mail-back data collection in Census 2000.

Page 3179

Appendix A

The composition of MSAs in category 2 is less extreme, in that there is a more even distribution of type A and type B tracts.
Consider again figure 3, Urbana/Champaign. This MSA is divided into two NFAs. The first consists of the areas centered on Urban
and Champaign, shaded dark in the figure. These are the type A tracts in the MSA. The pale tracts constitute a separate NFA. Stratum
2 is defined as the set of type A NFAs from category 2 MSAs; the dark areas in Figures 3 and 4 are examples. These stratum 2 NFAs
include 75% of the population of category 2, but only 20% of the area.
In category 3, the problem arises in reverse; though the dominant type of tract is type B, there are type A tracts interspersed
among them. However, though category 3 NFAs also contain both type A and type B tracts, the size of these MSA/counties is
insufficient to warrant subdivision.
Stratum 3 comprises those parts of the population where in general the USPS address list is inadequate for use as a sampling
frame. This stratum has two substrata. Stratum 3.1 contains, as NFAs: (i) the type B parts of category 2 MSAs – thus, for example, the
type B tracts in Champagin/Urbana constitute an NFA; and (ii) the category 3 NFAs. These are the primary sampling units for stratum
3.1. Once the PSUs have been selected, segments are constructed within the selected NFAs as they have been for previous national
samples, and a field listing is carried out in the selected segments.
Stratum 3.2 comprises the type B tracts in category 1 NFAs. The pale areas in figures 1 and 2 are examples of stratum 3.2
areas. All of these NFAs appear with certainty in the sample, and fieldwork will be conducted throughout these NFAs. Consequently it
is not necessary to introduce an extra stage of sampling for this part of the population. In stratum 3.2, segments are selected directly
into the sample, and field listing is carried out as with the stratum 3.1 segments. Thus, the PSU in stratum 3.2 is the segment. See
Table A.4.
The important changes from previous GSS designs are:
•

•
•

•
•

A new list-assisted sampling frame has been constructed for 72% of the population; this frame will permit re-design and
retargeting of the sample for each successive GSS. While the same sample design, and the same selected area sampling units,
can be kept for 2006 and beyond, the design and selection could be revisited for each successive GSS without major cost
implications. Stratification and measures of size, for instance, could be adjusted based on information from the American
Community Survey.
The size of the certainty stratum (the proportion of the population covered by certainty area selections) has been increased.
Almost half (45%) of the HUs in the population are now included in this stratum.
Within the certainty stratum, new primary sampling units (PSUs) are being used. The PSU is now the tract (for the listassisted part of the population). Tracts contain about 1000-2000 HUs and therefore can be expected to have considerably
lower intracluster correlation coefficients (ρ) than the blocks/block groups (minimum size 75 HUs) that were used for
previous designs.
In the second “urban” stratum, the new secondary sampling units (SSUs) are tracts rather than blocks/block groups; this
should lead to similar efficiency gains to those indicated above for the certainty stratum.
In the “rural” stratum, the minimum size of SSU has been increased from 75 to 300 HUs; this should lead to smaller
intracluster correlation coefficients, ρ.

Table A.4: Sample design for the GSS 2006 sample
Stratum

% of
popn.

Description

Primary (area)
sampling unit
(PSU)
Tract

Secondary (area)
sampling
unit
(SSU)
No 2nd area stage

1

42%

All type A tracts in
category 1 areas

2

30%

All type A tracts in
category 2 areas

MSA/county
[part]

Tract

3.1

25%

All counties not in
category 1 or 2; all
remaining tracts in
category 2 areas

County
[all or part]

Segment

3.2

3%

Type B tracts in
category 1 areas

Segment

No 2nd area stage

Final stage

Housing units
(HUs) from list
frame within
tract.
HUs from list
frame within
tract.
HUs from
NORC-listed
master sample
within selected
segments
HUs from
NORC-listed
master sample
within selected
segments

Page 3180

Appendix A

Table A.5 gives the numbers of PSUs, SSUs, and UAUs selected within each major stratum.

Table A.5: Numbers of area units by stratum
Stratum

No. of NFAs

No. of PSUs

UAUs

12

No.of
SSUs
n.a.
120
(tracts)
112
(segment)
n.a

1
2

24 15
30 16

168 (tracts)
30 (part MSAs/ counties)

3.1

25 17

25 (part counties/MSAs)

3.2

24 18

Total

79 19

Tracts
Tracts

No. of
UAUs
168
120

Segments

100

Segments

12

235

n.a.

--

400

Figure 1: The Los Angeles MSA

15

90% of the population of these 24 NFAs is in stratum 1

16

These NFAs consist of the type A tracts in 30 MSAs

17

These NFAs are either whole counties/MSAs with few street-style addresses or the type B tracts from MSAs/counties
comprising stratum 2
18

This stratum contains the non-type A tracts in stratum 1 NFAs; they make up 6% of the population in those NFAs.

19

The 24 NFAs in strata 1 and 3.2 are the same areas and thus the total number of NFAs is 79.

Page 3181

Figure 2: The Chicago MSA

Figure 3: The Urbana/Champaign MSA

Appendix A

Page 3182

Appendix A

Figure 4: The Worcester MSA

2010 National Sample Design
The 2010 NORC National Sample Design is an update and expansion to the 2000 NORC National Sample Design. Whereas
the 2000 NORC National Sample Design has 79 first-stage selections (called NFAs or PSUs in previous designs), including 24 selfrepresenting areas large enough to be selected with certainty, the 2010 NORC National Sample Design has 126 first-stage selections,
including 38 self-representing areas. In the 2000 NORC National Sample Design, each non-certainty first-stage selection represents
1% of the U.S. population, while in the 2010 NORC National Sample Design, each non-certainty first-stage selection represents 0.5%
of the U.S. population. However, this is too many first-stage selections for GSS. Therefore, GSS uses a subset of 76 first-stage
selections comparable to the 2000 National Sample Design’s 79 first-stage selections.
The 2010 NORC National Sample Design also contains 1,516 second-stage selections (segments) compared to 899 for the
2000 NORC National Sample Design. The GSS will continue to generally use a subset of 400 second-stage selections. Just as in the
2000 National Sample Design, the GSS second-stage units will be a subsample of the larger set of National Sample Design secondstage units, which will allow segments to be rotated in and out throughout the decade just as was done for the 2000 National Sample
Design.
Some slight changes to the 2010 National Sample Design were made, though the general outline was the same. The basic
building blocks to make first-stage units in the sampling frame are again counties and metropolitan areas defined by OMB; the
metropolitan areas are now called Consolidated Statistical Areas (CSAs) or Core Based Statistical Areas (CBSAs). First, counties,
CBSAs, and CSAs that had a population less than 10,000 were combined with a neighboring non-certainty county, CBSA, or CSA
(the closest geographically, regardless of size) to make first-stage units in the frame.
First-stage units in the frame were then categorized according to population and list quality as follows:
•

the largest CSAs (certainty areas) with a population of at least 1,543,728 (0.5 percent of the 2010 Census U.S. population)
were placed in category 1; these areas have a high population density, and are dominated by tracts with street-style addresses;

•

small counties and CBSAs with less than 8 tracts that are predominantly street-style addresses (category 3);

•

all other counties, CBSAs, and CSAs with at least 8 tracts that are predominantly street-style addresses (category 2).

Rural Alaska Boroughs and Census Areas with less than 0.180 housing units per square miles and at least 5,000 square miles
in area were excluded. This dropped 0.03 percent of the U.S. population (12.9 percent of Alaska’s population)
As was done in the 2000 National Sample Design, the Category 2 first-stage units are partitioned into two separate first-stage
units; a first-stage unit consisting of the tracts where HUs/addresses will be selected directly from a postal delivery sequence file

Page 3183

Appendix A

(DSF) is placed in Category 2A while another first-stage unit where listing will be needed is placed in Category 2B. Once again, the
Category 2B and 3 first-stage selections will be selected together.
GSS uses only 76 out of the 126 2010 National Sample Design first-stage units. Only half of the non-certainty (Categories
2A, 2B, and 3) were selected. Similarly, the probabilities of selection for the certainty Category 1 first-stage units are cut in half. For
17, they are still selected with certainty, but selecting the remaining 21 with probabilities proportional to size results in 15 out of the
21 being selected (for a total of 32 Category 1 selections).
Table A.6 Summary of the First-Stage Units for the 2010 National Sample Design
Description
Share of U.S.
National
NFA Category
population
Frame
1
The largest CSAs, certainty selections
56%
38
2A
DSF portions (8 or more tracts)
30%
60
2B
Tracts where listing is needed
5%
28
Counties/CBSAs with less than 8 urban
3
9%
tracts
TOTAL
100%
126

GSS
32
30
14
76

Just as in the 2000 National Sample Design, the frame was partitioned into two parts for the selection of the second-stage
units (segments). If DSF use was expected to be possible, tracts were selected. Where listing was expected to be required, block
groups were selected. In both cases, the minimum size for a sample unit (whether tract or block group) was 300. Units not meeting this
minimum size were merged with a neighbor.
In Category 1 first-stage selections, type A tracts were selected separately from block groups in type B tracts. Within
category 1, 16 segments were selected per 1 percent of the population covered = 16 * 56% = 896 segments. The proper proportion of
type B segments is 32, so 864 type A tracts and 32 block-groups within type B tracts were selected. GSS uses a standard of 4 segments
per 1 percent of the population covered, so GSS will use approximately 216 type A tracts and 8 block-groups within type B tracts.
For category 2A first-stage selections, 8 segments per first-stage selection results in 480 segments in the 2010 National
Sample Design, but GSS uses 120 (half of the selections in half of the first-stage selections) of them.
For category 2B/3 first-stage selections, the 2010 National Sample Design only selected 5 segments per first-stage selection,
but GSS uses 4 in half of them for a total of 56 segments.

Table A7 Summary of the Second-Stage Units for the 2010 National Sample Design
Expected
First-stage
Sample
GSS First-Stage GSS Sample
Type of
selections
Segments
Selections
Segments
Category
Address
Listing
City Style
-864
-216
Address
1
Rural
-32
-8
City Style
30
60
480
120
2a
Address
Rural
28
140
14
56
2b/3
1,516
400
Total
The 2000 NORC National Sample Design used the DSF or listing strictly based on Census enumeration methods, which
corresponded to whether the DSF was usable. However, we have strengthened the criteria. We have compared the number of units in
the DSF to the number of occupied housing units in the Census. If the ratio of DSF to occupied housing units was less than 0.9 or
greater than 1.25, we examined whether DSF geocoding errors could be corrected or not. If not, we selected one block group with
probability proportional to the number of housing units for listing. Before adding this segment to the listing work, we checked to see if
the DSF was adequate for this block group. Only if the DSF was inadequate for this block groups was the block group listed. Out of
1,344 tracts, 37 (2.8 percent) were listed.
Similarly, we checked the DSF for block groups that were expected to be listed. While the DSF was inadequate for the tracts
these block groups were in, we determined the DSF to be adequate for 84 out of the 172 (48.8 percent) block groups selected.
Therefore, the 2010 NORC National Sample Design listed 125 out of the 1,516 (8.2 percent) second-stage units selected.

Page 3184

Appendix A

NON-RESPONSIVE SUB-SAMPLING
The basic concept is to subsample the nonrespondents, adjusting the weights to keep the design unbiased. The subsample is
weighted up to represent all nonrespondents as of the cutoff date. Subsampling allows the focusing of resources on a smaller set of the
difficult cases for further attempts, thereby potentially reducing both response error and nonresponse bias.
The subsampling of nonrespondents constitutes a two-phase design, or a double-sampling scheme, that was first introduced
by Hansen and Hurwitz in 1946. 20 The subsampling of nonrespondents has been used in many other surveys, such as the Census
Bureau’s American Community Survey and the Urban Institute’s 1999 and 2002 National Survey of America’s Families. At NORC,
the double-sampling scheme has been used for the Chicago Health and Social Life Survey.
The typical pattern for area probability studies, such as GSS, is for a small percentage of the difficult cases to absorb much of
the resources, especially near the end of the data collection period. Increasing the initial sample size boosts the number of less difficult
cases available from the start. After the first pass, the remaining cases – those that are so much more difficult to complete, are
subsampled. Considerable time and effort is spent on the subsampled cases, but since there are fewer of them, the overall field effort is
reduced.
For the 2004 GSS at the end of the preliminary field period for release 1 after about ten weeks, there were 1440 out-of-scope
cases (not housing units, vacant, etc.), 2162 completed cases, 143 partial cases and appointments, 144 final nonrespondents, and 2171
temporary nonrespondents. Temporary nonrespondents are originally sampled cases + missed dwelling units whose status is
“pending” (i.e. they do not have a final status or outcome determined). They may ultimately be coded as out-of-scope,
nonrespondents, or completed cases. The temporary nonrespondents were sampled at 50% and 1086 were retained in the study and
1085 were eliminated. The retained sub-sample cases and the partial/appointment cases were then pursued for approximately another
10 weeks. Ultimately 2812 cases were obtained.
For the 2006 GSS at the end of the preliminary field period for release 1 after about eleven weeks, there were 1490 out-ofscope cases (not housing units, vacant, etc.), 3418 completed cases, 283 partial cases and appointments, 259 final nonrespondents, and
4209 temporary nonrespondents. The temporary nonrespondents were sampled at 45% and 2068 were retained in the study and 2141
were eliminated. The retained sub-sample cases and the partial/appointment cases were then pursued for approximately another 10
weeks. Ultimately 4510 cases were obtained.
For the 2008 GSS at the end of the preliminary field period for release 1 after about ten weeks, there were 681 out-of-scope
cases (not housing units, vacant, etc.), 1610 completed cases, 134 partial cases and appointments, 96 final nonrespondents, and 1738
temporary nonrespondents. The temporary nonrespondents were sampled at 41% and 713 were retained in the study and 1025 were
eliminated. The retained sub-sample cases and the partial/appointment cases were then pursued for approximately another 10 weeks.
Ultimately 2023 cases were obtained.
For the 2010 GSS at the end of the preliminary field period for release 1 after about ten weeks, there were 657 out-of-scope
cases (not housing units, vacant, etc.), 1575 completed cases, 137 partial interviews/appointments/Spanish-language cases, 72 final
nonrespondents, and 1695 temporary nonrespondents. The temporary nonrespondents were sampled at 47% and 800 were retained in
the study and 895 were eliminated. The retained sub-sample cases and the partial/appointment/Spanish-language cases were then
pursued. After approximately seven weeks, 137 of the 895 cases that were removed during sub-sampling were released back into the
sample to be re-contacted for interviews. These 137 re-released cases, along with all other pending cases, were then pursued for about
four more weeks. Ultimately 2044 cases were obtained.
For the 2012 GSS at the end of the preliminary field period for release 1 after about ten weeks, there were 492 out-of-scope
cases (not housing units, vacant, etc.), 1395 completed cases, 167 partial interviews/appointments/Spanish-language/special situation
cases, 56 final nonrespondents, and 2040 temporary nonrespondents. The temporary nonrespondents were sampled at 47% and 965
were retained in the study and 1075 were eliminated. The retained sub-sample cases and the partial/appointment/Spanish-language
cases were then pursued. Ultimately 1974 cases were obtained.
For the 2014 GSS at the end of the preliminary field period for release 1 after about ten weeks, there were 764 out-of-scope
cases (not housing units, vacant, etc.), 1523 completed cases, 269 partial interviews/appointments/Spanish-language/special situation
cases, 110 final nonrespondents, and 2532 temporary nonrespondents. The temporary nonrespondents were sampled at 65% and 1653
were retained in the study and 879 were eliminated. The retained sub-sample cases and the partial/appointment/Spanish-language
cases were then pursued. Ultimately 2538 cases were obtained.
For the 2016 GSS at the end of the preliminary field period for release 1 after about eleven weeks, there were 692 out-ofscope cases (not housing units, vacant, etc.), 1757 completed cases, 146 partial interviews/appointments/Spanish-language/special
20
Marcus Hansen and W. Hurwitz, "The Problem of Non-response in Sample Surveys," Journal of the American Statistical
Association, 41 (Dec., 1946), 517-529.

Page 3185

Appendix A

situation cases, 90 final nonrespondents, and 3685 temporary nonrespondents. The temporary nonrespondents were sampled at 72.2%
and 2660 were retained in the study, eliminating 1025. The retained sub-sample cases and the partial/appointment/Spanish-language
cases were then pursued. Ultimately, 2867 cases were obtained.
For the 2018 GSS at the end of the preliminary field period for release 1 after about twelve weeks, there were 668 out-ofscope cases (not housing units, vacant, etc.), 1785 completed cases, 335 partial interviews/appointments/Spanish-language/special
situation cases, 463 final nonrespondents, and 1949 temporary nonrespondents. The temporary nonrespondents were sampled at
54.8% and 1069 were retained in the study, eliminating 880. The retained sub-sample cases and the partial/appointment/Spanishlanguage cases were then pursued. Ultimately, 2348 cases were obtained.
Since temporary nonrespondents were subsampled at about 50%, they must essentially be given a weight of about 2 to make
the sample representative. The weights that must be used for the 2004-2014 GSS are discussed below in the section on Weighting. In
addition, the subsampling of nonrespondents also means that weighted figures must be used in calculating the response and other
outcome rates. The procedure utilized is discussed in Standard Definitions: Final Disposition of Case Codes and Outcome Rates for
Surveys. Lenexa, KS: American Association for Public Opinion Research, 2011. Also available at www.aapor.org
WEIGHTING
The GSS contains several weight variables (ADULTS, OVERSAMP, FORMWT, WTSSNR, WTSSALL) that users should
use as needed as well as weight-related variables (ISSP+PHASE). This section briefly discusses these variables.
ADULTS
The full-probability GSS samples used since 1975 are designed to give each household an equal probability of inclusion in
the sample. (Call this probability Ph.) Thus for household-level variables, the GSS sample is self- weighting. In those households
which are selected, selection procedures within the household give each eligible individual equal probability of being interviewed. In a
household with n eligible respondents, each has probability Ph of being in a selected household, and 1/n * Ph of actually being
interviewed. Persons living in large households are less likely to be interviewed, because one and only one interview is completed at
each preselected household. The simplest way to compensate would be to weight each interview proportionally to n, the number of
eligible respondents in the household where the interview was conducted. N is the number of persons over 18 (ADULTS) in the
household. A discussion of the weight as well and a post-stratification variant of weighting by ADULTS appears in GSS
Methodological Report No. 3. 21
OVERSAMP
As described in the previous section, the 1982 survey included an oversample of blacks. To make the 1982 survey a
representative cross-section, the user can either exclude the black oversample cases by excluding codes 4 and 5 on SAMPLE or
weight the file by OVERSAMP. To make the 1987 survey a representative cross-section the user can either exclude the black
oversample by excluding code 7 on SAMPLE or weight the file by OVERSAMP. Users should adopt one of these procedures in all
cases except when analyzing only blacks from the 1982 and/or 1987 cross-sections and oversamples.
FORMWT
Problems with form randomization procedures on the 1978, 1980, 1982-1985 surveys necessitate the use of FORMWT when
variables appearing on only one form are analyzed. A complete list of form-related variables appears in Appendix P. Full details on
the form randomization problem and of the weight created to correct for it appear in GSS Methodological Report No. 36. 22
ISSP
The International Social Survey Program supplement was administered to Form 1 cases in 1985 and as such must be
weighted for FORMWT as discussed above. In addition because this was a self-administered supplement completed after the main
GSS questionnaire there is supplement non-response. Users may wish to use the variable ISSP to study supplement non-response bias
and perhaps develop a weight to compensate for same. 23
21

C. Bruce Stephenson, "Weighting the General Social Surveys for Bias Related to Household Size," GSS Technical Report No. 3,
Chicago: NORC, February, 1978.
22

Tom W. Smith and Bruce L. Peterson, "Problems in Form Randomization on the General Social Surveys," July, 1986.

23
See Tom W. Smith, "Attrition and Bias on the International Social Survey Program Supplement," GSS Methodological Report No.
42, February, 1986.

Page 3186

Appendix A

POST-STRATIFICATION
In general, the GSS samples closely resemble distributions reported in the Census and other authoritative sources. Because of
survey non-response, sampling variation, and various other factors the GSS sample does deviate from known population figures for
some variables. The GSS does not calculate any post-stratification weights to adjust for such differences. For relevant discussion of
distributional variation caused by non-response and other factors see GSS Methodological Reports No. 3, 5, 9, 16, 21, 25, 79. 24
Differences from the Census and other changes in distributions due to alterations in sampling include the following:
1.

In 1972 blacks were over-represented. The 1972 survey was the last to utilize the 1960 NORC sample frame and it is
believed to have under covered rapidly growing suburban areas.

2.

All full-probability samples under-represent males. This is discussed in GSS Methodological Report No. 9.

3.

Block quota samples under-represented men in full-time employment, see GSS Methodological Report No. 7.

4.

Coverage of Mormons increased significantly when the 1980 sample frame was adopted. This was due to the
addition of a primary sampling unit in Utah. For more details see GSS Methodological Report No. 43.

5.

People eighteen years old appear to be under-sampled although this is actually not the case. Age is assigned based
on year of birth and the assumption that one's birthday has already occurred. However, to be in the sample one must
have actually reached his/her eighteenth birthday and since the GSS is fielded in March every year only about
one-quarter of those born eighteen years prior to the current year have reached majority by the interview dates. Thus
nineteen year olds as classified on the GSS consist of approximately one-quarter who have turned nineteen since the
first of the year and three-quarters who will turn nineteen by the end of the calendar year. The same is true for ages
20 and up. For eighteen year olds on the GSS only those who have turned eighteen since the first of the year are
included. Thus the number of eighteen year olds in the GSS is approximately one-quarter the number of nineteen
year olds (See Appendix E). The "missing" eighteen year olds are not under-represented in the sample, but are
merely counted as nineteen year olds.

Weights for 2004+ GSS 25
Due to the adoption of the non-respondent, sub-sampling design described above, a weight must be employed when using the
2004+ GSSs. One possibility is to use the variable PHASE and weight by it so that the sub-sampled cases were properly represented.
If one wanted to maintain the original sample size, one would weight by PHASE*0.86258 in 2004 and PHASE*.80853 in 2006, etc.
This weight would only apply to 2004+ and would not take into account the number of adults weight discussed above. As such, it
would be appropriate for generalizing to households and not to adults. A second possibility is to use the variable WTSS. This variable
takes into consideration a) the sub-sampling of non-respondents, and b) the number of adults in the household. It also essentially
maintains the original sample size. In years prior to 2004+ a one is assigned to all cases so they are effectively unweighted. To adjust
for number of adults in years prior to 2004, a number of adults weight would need to be utilized as described above. WTSSALL takes
WTSS and applies an adult weight to years before 2004. A third possibility is to use the variable WTSSNR. It is similar to WTSS, but
adds in an area non-response adjustment. Thus, this variable takes into consideration a) the sub-sampling of non-respondents, b) the
number of adults in the household, and c) differential non-response across areas. It also essentially maintains the original sample size.
As with WTSS, WTSSNR has a value of one assigned to all pre-2004 cases and as such they are effectively unweighted. Number of
adults can be utilized to make this adjustment for years prior to 2004, but no area non-response adjustment is possible prior to 2004.
Details on the construction of WTSS and WTSSNR follow:
WTSS and WTSSNR calculation:
24
C. Bruce Stephenson, "Probability with Quotas: An Experiment," GSS Methodological Report No. 3, April, 1979; Tom W. Smith,
"Response Rates on the 1975-1978 General Social Surveys with Comparisons to the Omnibus Surveys of the Survey Research Center,
1972-1976," GSS Methodological Report No. 5, June, 1968; Tom W. Smith, "Sex and the GSS: Nonresponse Differences," GSS
Methodological Report No. 9, August, 1979; Tom W. Smith, "The Hidden 25%: An Analysis of Nonresponse on the 1980 General
Social Survey," GSS Methodological Report No. 16, May, 1981; Tom W. Smith, "Using Temporary Refusers to Estimate
Nonresponse Bias," GSS Methodological Report No. 21, February, 1983; Tom W. Smith, "Discrepancies in Past Presidential Vote,"
GSS Methodological Report No. 25, July, 1982; and Tom W. Smith, "Notes on John Brehm, The Phantom Respondent: Opinion
Surveys and Political Representation." GSS Methodological Report No. 79, 1993.
25
Harter, Rachel, Stephanie Eckman, Ned English, and Colm O’Muircheartaigh (2010). “Applied Sampling for Large-Scale MultiStage Area Probability Designs.” In Handbook of Survey Research, Second Edition, P. Marsden and J. Wright, eds. Elsevier.

Page 3187

Appendix A

W0: Within each NFA, we calculate a probability of selection, n/N. W0 is the reciprocal of this probability of selection (N/n).
At this point, each observation stands in for a given number of cases in the frame. Because the secondary sample release was
only in the urban NFAs, cases in urban NFAs have a slightly higher probability of selection, and thus a slightly lower
baseweight, than cases in the urban NFAs.
∑W0 = frame size
W1: At the end of Phase I of data collection, we subsampled the non-responding cases with a sampling fraction f=.5 in 2004.
W1 for the selected non-responding cases is, for example, then WO*(1/.5) in 2004 or for 2006 is WO*(1/.45), etc. W1 is
missing for the unselected non-responding cases. W1=W0 for cases which were not subsampled.
∑W1 = frame size
W2: Next, we adjust the baseweight for eligibility. Not all cases in the frame are truly eligible for the survey: some addresses
in our frame are businesses, do not exist or are unoccupied. We use the eligibility rate of the sampled cases to estimate the
eligibility rate for the frame. We calculate the eligibility rate at the NFA level.
This adjustment sets the weights of the ineligible cases to missing. Cases whose eligibility could not be determined are given
fractional eligibility equal to be eligibility rate for their NFA.
Now the sum of the weights is the estimated number of eligible cases (or occupied housing units) in the frame.
∑W2 = estimated eligible cases in the frame < ∑W1
W2NR: For the non-response version of the weight only (WTSSNR), we adjust for non-response at this point. Weights for
responding cases increase by the reciprocal of the response rate, calculated at the NFA level. The responding cases take on
the additional weight of the non-responding cases. W2NR is missing for the non-response cases. The sum of the weights is
the same as the previous step: the estimated number of eligible cases in the frame.
∑ W2NR = ∑W2 = estimated eligible cases in the frame
W3 (WTSS): To account for the random selection of an adult respondent, this weight is the household-level weight (W2)
multiplied by the number of adults in the household. The sum of the weights in this step is the total number of adults in all
eligible households in the frame.
∑ W3 = estimated adults in eligible cases in the frame > ∑W2
W3NR (WTSSNR): To account for the random selection of an adult respondent, this weight is the non-response adjusted
household-level weight (W2NR) multiplied by the number of adults in the household. The sum of the weights in this step is
the total number of adults in all eligible households in the frame.
∑ W3NR = estimated adults in eligible cases in the frame > ∑W2NR
WTSS: We then rescale W3 so that the sum is the total number of completed interviews. This adjustment helps prevent errors
that arise in SPSS and in some procedures in SAS where the sum of the weights is assumed to be equal to the sample size.
The relative weights are unchanged by this adjustment.
WTSSNR: We also rescale W3NR so that the sum is the total number of completed interviews. This adjustment helps prevent
errors that can arise in SPSS and in some procedures in SAS where the sum of the weights is assumed to be equal to the
sample size. The relative weights are unchanged by this adjustment.
∑ WTSS = ∑ WTSSNR = number of completed interviews, but the WTSSNR adjusts for non-response at the NFA level
while WTSS does not.
WTSSALL:
As mentioned above, WTSSALL takes WTSS and applies an adult weight to years before 2004. The weight value of
WTSSALL is the same as WTSS for 2004 and after. Thus, researchers who use the GSS data before or after 2004 may
consider using the WTSSALL weight variable.
Calculating Design-Corrected Standard Errors for the General Social Survey, 1975-2016
The General Social Survey (GSS) is an area-probability sample that uses the NORC National Sampling Frame for an equalprobability multi-stage cluster sample of housing units for the entire United States. Since the sample for the GSS is a cluster sample,
standard errors are larger for the GSS than simple random sample calculations (calculated without correction for the design). To
correctly calculate standard errors, design variables must be used in statistical software (such as SVY in STATA). Without these
design variables, statistical software will assume a simple random sample and underestimate standard errors.
We then rescale W3 so that the sum is the total number of completed interviews. This adjustment helps prevent errors that
can arise in SPSS and in some procedures in SAS where the sum of the weights in assumed to be equal to the sample size. The relative

Page 3188

Appendix A

weights are unchanged by this adjustment.
∑ WEIGHT = number of completed interviews
We provide two design variables for every GSS interview from 1975-2016: VSTRAT and VPSU. VSTRAT is the
Variance STRATum while VPSU is the Variance Primary Sampling Unit. The stratum and PSU reflect the first-stage and secondstage units selected as part of the NORC National Sampling Frame, and are unique to a particular round.
First stage units in the NORC National Sampling Frame are called National Frame Areas, (NFAs), each of which is
composed of one or more counties (previous to the 2010 National Frame, NFAs were called PSUs). The largest urban areas are
selected with certainty to guarantee their representation in NORC’s National Sampling Frame (there were no certainty NFAs prior to
1983). Second-stage stage units in the NORC National Sampling Frame are called segments, each of which is either a block, a group
of blocks, or an entire census tract. The first-stage and second-stage units are selected with probabilities proportional to size (in
housing units), and the sample housing units (third-stage units) are then selected to be an equal-probability sample, which results in
roughly the same number of housing units selected per second-stage sampling unit.
To create the variables VSTRAT and VPSU, we recode the NFAs and segments, depending on whether the NFA was
selected with certainty. In certainty NFAs, segments are paired into strata with one segment assigned to VPSU = 1 while the other
segment is assigned to VPSU = 2. Often, small segments are combined into one VPSU. Non-certainty NFAs are paired into strata with
one NFA assigned to VPSU = 1 while the other NFA is assigned to VPSU = 2. It is rare, but possible, for NFAs to be combined in one
VPSU. This strategy has been adapted from the National Longitudinal Survey of Youth, 1997 cohort strategy designed by Kirk Wolter.
Here is sample Stata code to analyze the variable ANALYSISVAR within a GSSDATAFILE with the weight variable WTVAR
(either WTSSALL or WTSSNR):
use GSSDATAFILE.dta, clear
svyset vpsu [weight=WTVAR], strata (vstrat)
svy: proportion ANALYSISVAR // point estimates and design adjusted s.e.'s
svy: tabulate ANALYSISVAR, deff //deff
tab ANALYSISVAR [weight=round(WTVAR,1.0)] // Weighted frequency
Note that it is possible to combine multiple years of GSS data into one GSSDATAFILE. SPSS is menu-driven, so no code is given
here, but you can create design-corrected standard errors within SPSS using the Complex Samples add-on.
TIME
If the merged GSS is thought of as designed to equally sample time, there are numerous deviations due to such factors as 1)
sample size variation across surveys, 2) the absence of surveys in 1979, 1981, 1992, and in odd years after 1993, 3) experiments (See
Appendix O), 4) switching of items from permanent to rotating status, 5) switching from across-survey rotation to sub-sample rotation,
6) late starting and terminated time series, or 7) some combination of these. For more information on these issues and possible
adjustments see GSS Methodological Report No. 52. 26

26

Tom W. Smith, "Rotation Designs of the GSS," Chicago: NORC, February, 1988.

Page 3189

Appendix A

Table A.8
NON‑RESPONSE RATES ON THE 1975‑2016 GENERAL SOCIAL SURVEYS
(Full Probability Samples Only)
Dispostion of Cases

A. Original Sample
B. -Out of Sample

Surveys
1975

1976

1977

1978

1980

1982

1982B

1983

1984

1985

1986

1987

1987B

1988

1989

1990

1991

1993

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

1102

1113

2317

2344

2210

2221

2900

2222

2157

2201

2192

2250

4750

2250

2250

2165

2312

2296

4559

4559

4567

4883

4890

6260

9535

4220

4093

4100

5125

6200

5200

11

16

0

20

1

0

2258

a

3

0

0

0

0

3916

a

0

2

0

0

0

0

1

0

0

0

1085

2141

1025

758

1075

879

e

1025

877

43

126

93

130

117

84

77

45

73

77

106

78

57

70

85

65

103

158

158

242

152

638

392

230

178

175

261

283

274

172

197

227

176

206

328

261

212

232

256

246

524

493

573

531

622

608

1058

424

467

351

479

504

413

C. -Not a Dwelling

116

Unit

219

D. -Vacant

74

217

190

197

245

E. -Language Problem

27

33

54

59

46

46

6

31

52

28

49

43

0

52

72

47

67

66

143

136

146

178

209

301

139

57

60

43

65

85

53

F. +New Dwelling Unit

24

44

79

102

97

129

77

82

42

47

50

21

42

57

74

41

46

31

57

43

55

94

36

0

41

39

30

50

23

24

0

G. Net Sample

972

991

1999

2084

1933

1942

494

2014

1873

1948

1944

1945

442

1916

1981

1857

1950

1950

3846

3814

3745

4026

3943

4713

7987

3548

3418

3581

3464

4327

4323

H. Completed Cases

735

744

1530

1532

1468

1506

354

1599

1473

1534

1470

1466

353

1481

1537

1372

1517

1606

2992

2904

2832

2817

2765

2812

4510

2023

2044

1974

2538

2867

2348

I. Refusals

162

417

309

297

66

320

320

344

365

358

57

359

346

355

323

285

708

757

755

1044

1031

621

987

410

527

390

793

1232

1092

48

30

41

23

22

46

5

19

26

61
54

18

18

60

66

97

59

65

48

20

38

44

36

76

50

339
206

J. Break-offs
K. No one Home to

2

7

22

54

56

Complete Screener
L. R Unavailable Entire

17

13

26

22

38

23

49
18

8

13

20

3

7

41

Field Period

12

M. Ill

21

26
b

15

37

43
N. Other

8

44

51

75

18

60

31

39

74

55

24

50

59

54

56

41

128

93

92

68-

88-

130

185

73

73

98

97

152

93

972

991

1999

2084

1933

1942

494

2014

1873

1948

1944

1945

442

1916

1981

1857

1950

1950

3846

3814

3745

4026

3943

3628

5730

2526

2682

2506

3464

4327

3585

Eligibility Rate (G/A)

0.882

0.890

0.863

0.889

0.875

0.874

0.170

0.906

0.868

0.885

0.887

0.864

0.093

0.852

0.884

0.858

0.843

0.849

0.844

0.837

0.820

0.824

0.806

0.701

0.771

0.781

0.797

0.815

0.811

0.852

0.829

Response Rate (H/G)c

0.756

0.751

0.765

0.735

0.759

0.775

0.717

0.794

0.786

0.787

0.756

0.754

0.799

0.773

0.776

0.739

0.778

0.824

0.778

0.761

0.756

0.700

0.701

0.704

0.712

0.704

0.703

0.714

0.692

0.613

0.595

Refusal Rate (I+J/G)d

0.169

0.208

0.173

0.200

0.160

0.153

0.134

0.159

0.171

0.177

0.188

0.184

0.129

0.187

0.175

0.191

0.166

0.146

0.184

0.198

0.202

0.259

0.261

0.225

0.233

0.241

0.245

0.210

0.264

0.327

0.358

Unavailable Rate
(K+L/G)

0.036

--

0.040

0.034

0.035

0.033

0.113

0.017

0.026

0.016

0.018

0.034

0.018

0.014

0.017

0.041

0.028

0.009

0.005

0.016

0.018

0.024

0.015

0.024

0.011

0.012

0.018

0.023

0.012

0.020

0.016

Other Rate (M+N/G)

0.039

--

0.022

0.031

0.046

0.039

0.036

0.030

0.017

0.02

0.038

0.028

0.054

0.026

0.030

0.029

0.029

0.021

0.033

0.024

0.025

0.017

0.022

0.047

0.044

0.043

0.034

0.053

0.032

0.040

0.031

O. Net Sample

a

Includes screened households with no Blacks.

b

The net sample size in 2004+ here is after sub-sampling. On the top line G in 2004+ it is before sub-sampling.

c

This corresponds to RR5 (response rate 5) in the American Association for Public Opinion Research's
Standard Definitions of the Final Dispositions of Case Codes and Outcome Rates for RDD Telephone Surveys and In-Person Household Surveys (2006).

d
e

Refusal rate 3 in AAPOR's Standards.

The cases sub-sampled out are reported here.
See the discussion of sub-sampling earlier in Appendix A and in Tom W. Smith "The Subsampling of Nonrespondents
on the 2004 General Social Survey" GSS Methodological Report No. 106, Chicago: NORC, 2006.

REMARKS: In 2004+ the rate is a weighted response rate as provided in AAPOR (2006). The case figures in the 2004+ columns do not yield the calculated rates because they are unweighted.
Also, see Appendix A, "Non-response sub-sampling" on the sub-sampling on non-respondents in 2004+.