GSS Human

What is the General Social Survey (GSS) and why is it valuable for research?

The General Social Survey is a principal data collection activity of the National Data Program for the Social Sciences (NDPSS). It assembles high-quality, nationally representative survey data on societal trends in the United States, develops databases permitting international comparisons, and makes these data easily accessible to scholars, students, and the public with minimal delay . First fielded in 1972, the GSS is an especially important source of longitudinal data for social scientists, allowing researchers to attribute changes in demographic, attitudinal, and behavioral responses to real changes over time rather than to changes in question wording . As of 2016, more than 27,000 books, articles, chapters, and other research publications had drawn on GSS data, demonstrating its significant impact on social science research .

How has the GSS sampling and administration changed over time?

From 1972 until 1993, the GSS was conducted almost annually, with exceptions in 1979, 1981, and 1992 due to funding shortages. Beginning in 1994, the GSS shifted to biennial administration with larger samples as a cost-saving measure . The survey is primarily designed as a repeated cross-sectional study, drawing a new random sample of respondents each time it is conducted. Between 2006 and 2014, the GSS added a panel component to its basic design, reinterviewing each year's respondents in each of the two subsequent GSSs, producing three 3-wave, 2-year-interval panels (covering 2006-2010, 2008-2012, and 2010-2014) . This addition allowed researchers to distinguish between true change and unreliability at the individual level, enhancing the analytical value of the dataset.

What unique data does the GSS provide on gender and identity?

For the first time in its nearly 50-year history, the GSS's 2018 data release includes information on respondents' self-identified sex and gender . This significant methodological change represents an evolution from previous surveys, where interviewers selected "male" or "female" on behalf of—and without directly asking—respondents . The new data allows researchers to measure the size of transgender and gender non-binary populations and identify the challenges they face, information that can in turn shape public policy. This change acknowledges that since the GSS's first iteration, social scientists' understanding of sex has changed markedly in ways that conflict with previous measurement approaches .

How should researchers approach the GSS panel component in longitudinal analyses?

The GSS panel component (2006-2014) offers unique opportunities for analyzing individual-level change. When utilizing these panel data, researchers should:

• Apply appropriate panel weights to account for differential attrition across waves

• Employ statistical techniques designed for repeated measures (e.g., fixed-effects models, growth curve modeling)

• Consider the implications of the relatively short intervals (2 years) between waves for processes that may develop over longer periods

• Leverage the panel structure to distinguish between true change and measurement error

• Compare within-person changes to the aggregate trends captured in the cross-sectional components

This panel design is particularly valuable for studying attitude stability, life transitions, and how external events affect individuals over time, offering insights not available in the standard cross-sectional design.

What considerations are necessary when accessing and using GSS geographic data?

GSS Geographic Data files are available to researchers under special permissions and include state (1973-2022), state at age 16 (1978-2022), primary sampling unit (1973-1993), county (1993-2022), and census tract (1993-present) . When working with these sensitive geographic identifiers:

• Researchers must follow the protocols outlined in "Obtaining GSS Sensitive Data Files" to gain access

• Geographic data allows linking GSS responses to contextual variables (e.g., unemployment rates, policy environments, community characteristics)

• Researchers should ensure temporal alignment between GSS data collection and contextual variables

• Multi-level modeling approaches should be employed to properly account for the nested structure of the data

• Privacy and confidentiality protections must be maintained when reporting results using geographic identifiers

Access to geographic data significantly expands research possibilities, enabling analyses of how social contexts shape individual attitudes and behaviors.

How can researchers effectively utilize the international comparative dimension of the GSS?

The GSS pursues the development of data bases permitting comparisons of the U.S. to other societies through its participation in the International Social Survey Program (ISSP) . To effectively leverage this international dimension:

• Assess conceptual equivalence across countries to ensure constructs have similar meanings in different cultural contexts

• Test for measurement invariance before making direct comparisons to ensure observed differences reflect genuine variation rather than measurement artifacts

• Incorporate country-level variables to explain cross-national differences

• Employ multi-level modeling techniques to properly account for the nested structure of the data

• Consider timing differences in data collection across countries when interpreting results

• Account for cultural differences in response styles through appropriate statistical adjustments

These considerations help researchers avoid misattributing cultural artifacts as substantive findings when conducting comparative analyses.

How should researchers address weighting in GSS analyses?

The GSS sampling design has several features that require careful consideration of weighting:

• At the household level, the sample is "self-weighting" since the basic GSS sampling design assigns equal probabilities of selection to all eligible U.S. households .

• For individual-level analyses, several weighting considerations apply:

  • Variable ADULTS adjusts for the underrepresentation of adults living in larger households

  • Variable PHASE distinguishes first- and second-phase respondents in the two-stage sampling design (post-2002)

  • Variable OVERSAMP adjusts for oversampling of black adults in 1982 and 1987

• For 2004 and later GSS data:

  • WTSS adjusts for both number of adults and phase

  • WTSSNR includes an additional adjustment for differential nonresponse across areas

  • WTSSALL adjusts for household size until 2002, and for both household size and phase thereafter

Proper application of weights is essential for generating representative estimates from GSS data, particularly when combining multiple years or focusing on specific subpopulations.

What methodological approaches should be used when analyzing GSS questions on sensitive topics?

The GSS includes questions on sensitive topics (e.g., sexual behavior, drug use, controversial attitudes) that require methodological care:

• Assess potential social desirability bias by:

  • Comparing GSS estimates to those from studies using more anonymous collection methods

  • Examining internal consistency across related items

  • Analyzing patterns of item non-response

• Address higher rates of missing data by:

  • Implementing multiple imputation techniques appropriate for sensitive items

  • Using selection models that account for potential relationships between non-response and the substantive variable

  • Examining whether missingness varies systematically across demographic groups or contexts

• Consider interviewer effects:

  • Respondents may answer differently depending on interviewer characteristics

  • Include interviewer fixed effects in models when interviewer IDs are available

  • Examine potential interactions between interviewer and respondent characteristics

• Analyze contextual factors:

  • Social acceptability of certain attitudes or behaviors varies across regions and time

  • Examine geographic variation in response patterns

  • Analyze temporal trends in both response distributions and item non-response

These approaches help researchers generate more valid inferences when analyzing sensitive topics.

How can researchers address changes in question wording and variable continuity across GSS waves?

• Verify question consistency:

  • Check for changes in question wording, even minor ones

  • Note changes in question order or context that might affect responses

  • Review documentation for variables that have been conceptually modified

• Account for structural changes:

  • The shift from annual to biennial administration in 1994

  • The introduction of a panel component between 2006-2014

  • Changes in sampling methodology over time

• Handle methodological transitions:

  • The 2018 change to self-identified gender measures requires careful documentation when conducting trend analyses spanning pre-2018 and post-2018 periods

  • Frame updates (like the 2014 comprehensive frame evaluation that added 151 newly eligible institutions)

• Document decisions:

  • Clearly report how variables were harmonized across waves

  • Note limitations imposed by question changes

  • Consider sensitivity analyses with different harmonization approaches

These considerations are essential for valid trend analyses using GSS data.

What statistical techniques are most appropriate for analyzing attitudinal trends in GSS data?

When analyzing attitudinal trends using GSS data, researchers should consider:

These analytical approaches provide rigorous frameworks for understanding social change as captured in the GSS.

How can researchers effectively combine multiple years of GSS data?

When pooling multiple years of GSS data, researchers should:

• Address sampling design changes:

  • Account for the shift from annual to biennial administration in 1994

  • Consider changes in sample size across years

  • Apply appropriate weights based on the years included

• Ensure variable comparability:

  • Verify that question wording remained consistent

  • Check for changes in response categories or coding schemes

  • Create harmonized variables when necessary

• Consider temporal structure:

  • Decide whether to analyze by individual year, pool years into meaningful periods, or treat time as continuous

  • Test for period effects before assuming stability across pooled years

  • Consider whether to control for year fixed effects

• Apply proper variance estimation:

  • Account for the complex sampling design

  • Use appropriate software procedures for analyzing complex survey data

  • Consider year-level clustering when pooling

• Document decisions transparently:

  • Clearly specify which years were included and why

  • Note any harmonization procedures applied

  • Acknowledge limitations imposed by pooling decisions

These considerations ensure valid inferences when leveraging the longitudinal nature of the GSS.

What approaches should researchers use for analyzing the new gender and sexuality measures in the GSS?

With the 2018 GSS addition of self-identified sex and gender questions, researchers should:

These approaches maximize the analytical value of the new gender and sexuality measures while respecting their methodological complexities.

How can researchers effectively combine GSS data with other data sources for contextual analyses?

Researchers seeking to enhance GSS analyses with contextual information should:

• Utilize geographic identifiers:

  • Access restricted GSS geographic data files that include state, county, and census tract identifiers

  • Link these identifiers to contextual data sources (e.g., Census data, policy databases, economic indicators)

  • Ensure proper temporal alignment between GSS data collection and contextual variables

• Implement appropriate statistical approaches:

  • Use multi-level modeling to account for the nested structure of the data

  • Consider cross-level interactions between individual characteristics and contextual factors

  • Address potential endogeneity through instrumental variables or fixed effects approaches

• Select appropriate geographic scales:

  • Match the geographic level to the theoretical mechanism being studied

  • Consider multiple geographic levels simultaneously when appropriate

  • Test sensitivity of findings to alternative geographic specifications

• Maintain respondent confidentiality:

  • Follow data use agreements when working with restricted geographic identifiers

  • Avoid reporting results that could potentially identify individual respondents

  • Consider aggregation approaches that protect privacy while preserving analytical value

Contextual analyses significantly expand the GSS's research potential, enabling examination of how social environments shape individual attitudes and behaviors.

What are the best practices for replicating and extending previous GSS-based research?

When replicating or extending previous GSS-based research, scholars should:

• Obtain exact variable specifications:

  • Identify the precise GSS variables used in the original study

  • Note any recoding or transformation procedures applied

  • Verify whether weights were applied and which specific weight variables were used

• Account for temporal context:

  • Consider whether the original findings might be period-specific

  • Test whether patterns remain consistent in more recent GSS waves

  • Examine potential moderating effects of historical events or social changes

• Apply methodological advances:

  • Consider whether newer statistical approaches might enhance the analysis

  • Address limitations acknowledged in the original study

  • Implement more robust approaches to missing data or selection issues

• Extend substantively:

  • Identify unstudied moderators or mediators of established relationships

  • Consider additional outcomes that might be affected by the same processes

  • Examine whether relationships vary across subgroups not considered in the original research

• Document reproducibility:

  • Provide complete code for data preparation and analysis

  • Clearly report any departures from the original methodology

  • Note GSS version numbers and date of data retrieval

These practices advance cumulative science while respecting the contributions of previous scholars.

How should researchers approach comparative analyses using both GSS and survey data from other countries?

When conducting comparative analyses using GSS and international survey data, researchers should:

• Evaluate conceptual equivalence:

  • Assess whether constructs have similar meanings across cultural contexts

  • Compare question wording and response categories for functional equivalence

  • Consider whether concepts might be understood differently across populations

• Address methodological differences:

  • Compare sampling approaches across surveys

  • Note differences in mode of administration

  • Account for different response rates and non-response patterns

• Implement appropriate statistical techniques:

  • Test for measurement invariance before making direct comparisons

  • Use multiple-group structural equation modeling to assess equivalence

  • Consider alignment techniques for comparing latent constructs

• Consider contextual factors:

  • Incorporate country-level variables to explain cross-national differences

  • Account for historical, political, and cultural factors that shape responses

  • Examine policy environments that might influence attitudes or behaviors

• Interpret differences cautiously:

  • Avoid simplistic cultural attributions for observed differences

  • Consider alternative explanations, including methodological artifacts

  • Acknowledge limitations imposed by cross-national measurement challenges

What resources are available to help researchers work with GSS data?

Researchers working with GSS data can access:

• Documentation and codebooks:

  • Comprehensive documentation available at the project website (http://gss.norc.org/)

  • Detailed question wording and variable descriptions

  • Technical reports on sampling, weighting, and methodological changes

• Data access options:

  • Publicly available datasets through the GSS website

  • Special access protocols for sensitive data files containing geographic identifiers

  • Various file formats suitable for major statistical software packages

• Analysis tools:

  • Online data analysis tools that allow preliminary analyses without downloading data

  • Syntax files for common data management tasks

  • Resources for implementing complex survey designs in statistical software

• Training materials:

  • Workshops and webinars on GSS data use

  • Teaching modules for incorporating GSS data in courses

  • Methodological guides for specific analytical approaches

These resources help researchers navigate the complexities of GSS data and maximize its research potential.

What are the key considerations for managing and preparing GSS data for analysis?

When preparing GSS data for analysis, researchers should:

• Create analysis-ready datasets:

  • Select relevant variables and cases

  • Recode variables as needed for analysis

  • Create derived variables (e.g., scales, indices)

• Apply appropriate weights:

  • Select the correct weight variable based on the research question and survey years

  • Implement weights correctly in statistical software

  • Document weighting decisions in research reports

• Handle missing data:

  • Examine patterns of missingness

  • Consider multiple imputation for missing values

  • Document the approach used for handling missing data

• Document data management decisions:

  • Maintain detailed records of all data preparation steps

  • Create replication files that others can use

  • Note GSS release version and data access date

• Verify data integrity:

  • Check for coding errors or inconsistencies

  • Validate derived variables against original items

  • Compare descriptive statistics with published GSS reports

These practices ensure reproducible research and facilitate cumulative knowledge building using GSS data.