​​The GSS gathers data on contemporary American society in order to monitor and explain trends and constants in attitudes, behaviors, and attributes. Hundreds of trends have been tracked since 1972. In addition, since the GSS adopted questions from earlier surveys, trends can be followed for up to 70 years.

The GSS contains a standard core of demographic, behavioral, and attitudinal questions, plus topics of special interest. Among the topics covered are civil liberties, crime and violence, intergroup tolerance, morality, national spending priorities, psychological well-being, social mobility, and stress and traumatic events.

Altogether the GSS is the single best source for sociological and attitudinal trend data covering the United States. It allows researchers to examine the structure and functioning of society in general as well as the role played by relevant subgroups and to compare the United States to other nations. (Source)

This dataset is a csv version of the Cumulative Data File, a cross-sectional sample of the GSS from 1972-current.

The General Social Surveys (GSS) have been conducted by the "https://www.norc.org/Pages/default.aspx" Target="_blank">National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. The 2016-2020 GSS consisted of re-interviews of respondents from the 2016 and 2018 Cross-Sectional GSS rounds. All respondents from 2018 were fielded, but a random subsample of the respondents from 2016 were released for the 2020 panel. Cross-sectional responses from 2016 and 2018 are labelled Waves 1A and 1B, respectively, while responses from the 2020 re-interviews are labelled Wave 2.

The 2016-2020 GSS Wave 2 Panel also includes a collaboration between the General Social Survey (GSS) and the "https://electionstudies.org/" Target="_blank">American National Election Studies (ANES). The 2016-2020 GSS Panel Wave 2 contained a module of items proposed by the ANES team, including attitudinal questions, feelings thermometers for presidential candidates, and plans for voting in the 2020 presidential election. These respondents appear in both the ANES post-election study and the 2016-2020 GSS panel, with their 2020 GSS responses serving as their equivalent pre-election data. Researchers can link the relevant GSS Panel Wave 2 data with ANES post-election data using either ANESID (in the GSS Panel Wave 2 datafile) or V200001 in the ANES 2020 post-election datafile.

The China GSS is an annual or biannual questionnaire survey of China's urban and rural households aiming to monitor systematically the changing relationship between social structure and quality of life in urban and rural China. The objectives of the China GSS are: (1) to gather longitudinal data on social trends; (2) to address issues of theoretical and practical significance; and (3) to serve as a global resource for the international scholarly community. Includes: labour force activity, demographic variables, household size and composition, ethnicity of R and parents, mobility, dwelling, income, expenditures and facilities, education, military service, etc. 1 data file (1,000 logical records) & accompanying documentation (5 pdf files) in both English and Chinese characters.

Some surveys contain multiple units of observation, while others come in many parts. This workshop will give participants hands-on experience using both types of files. The General Social Survey, Cycle 8 and the Canadian Travel Surveys will be used as examples. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-216.)

Understanding Society, the UK Household Longitudinal Study, is a longitudinal survey of the members of approximately 40,000 households (at Wave 1) in the United Kingdom. The overall purpose of Understanding Society is to provide high quality longitudinal data about subjects such as health, work, education, income, family, and social life to help understand the long term effects of social and economic change, as well as policy interventions designed to impact upon the general well-being of the UK population. The Understanding Society main survey sample consists of a large General Population Sample plus three other components: the Ethnic Minority Boost Sample, the former British Household Panel Survey sample and the Immigrant and Ethnic Minority Boost Sample.

Longitudinal or panel surveys offer unique benefits for social science research, but they typically suffer from attrition, which reduces sample size and can result in biased inferences. Previous research tends to focus on the demographic predictors of attrition, conceptualizing attrition propensity as a stable, individual- level characteristic—some individuals (e.g., young, poor, residentially mobile) are more likely to drop out of a study than others. We argue that panel attrition reflects both the characteristics of the individual respondent as well as her survey experience, a factor shaped by the design and implementation features of the study. In this paper, we examine and compare the predictors of panel attrition in the 2008-2009 American National Election Study, an on- line panel, and the 2006-2010 General Social Survey, a face-to-face panel. In both cases, survey experience variables are predictive of panel attrition above and beyond the standard demographic predictors, but the particular measures of relevance differ across the two surveys. The findings inform statistical corrections for panel attrition bias and provide study design insights for future panel data collections.

"The Taiwan Social Change Survey (TSCS) tracks the long-term trend of social changes through national representative survey data. Since the first nation-wide survey completed in 1985, this cross-sectional survey project has followed 5-year cycles that rotate selective modules. These modules cover various topics including family, religion, stratification, mass communication, and political participation. As of 2006, the TSCS had accumulated 37 surveys. Many of these surveys carry repetitive modules that have run through up to four cycles of survey operations, which enable researchers to understand social change from longitudinal perspectives. With more than 80,000 face-to-face interviews completed over the past 22 years, the TSCS has become the largest survey series among all of the general social surveys in the world...

"The TSCS team also initiates and participates in international comparative surveys. Since 2001, the TSCS has been an active member in both the "https://issp.org/" Target="_blank">International Social Survey Programme (ISSP) and the "https://www.eassda.org/" Target="_blank">East Asian Social Survey (EASS). In the wave of the globalization of social surveys, not only will the TSCS continue to cover its major national research agenda, but it also will aim to present and demonstrate the characteristics of Taiwanese social changes by incorporating both ISSP and EASS modules into the surveys. Such a combination of local, regional, and global research interests should preserve the tradition of the TSCS while it expands into the international community." (Source: Methodology notes provided by Academia Sinica.) The 1985 Taiwan Social Change Survey is the first phase and first wave of Questionnaire 2.

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

In Wave 5 adult respondents who finished higher education in 1995 or after at a UK institution were asked about the higher education establishment that they attended. This dataset contains the higher education institution identifiers for up to four higher education establishments per respondent.

In Wave 11 the data was re-gathered on the same basis. As well as the higher education institution identifiers the ukprn of the establishment has also been released. Users of the Wave 11 file should be aware that approximately 2,000 respondents that didn’t give consent to a linkage consent question were incorrectly not asked for their establishment identifiers.

In Wave 12 the respondents affected by the incorrect data collection in Wave 11 were asked the same questions again and data was also collected of newly eligible survey members.

For full details of this dataset including explanations of the issues at Wave 11 and how they were corrected at Wave 12 please refer to the High Education user guide.

The details in this dataset can be linked to the main Understanding Society datasets SN 6614 (end user licence), SN 6931 (special licence) and SN 6676 (secure access) using the crosswave personal identifier pidp. The institution identifiers in the data files can be used to link to publicly available datasets published by HESA and elsewhere.

Latest Edition Information

For the 5th edition (November 2024), Wave 14 data has been added and the User Guide updated.

The GESIS Panel provides a probability-based mixed-mode access panel infrastructure located at GESIS Leibniz Institute for the Social Sciences in Mannheim, Germany. The project offers the social science community an opportunity to collect survey data from a representative sample of the German population. Submitted study proposals are evaluated based on a scientific review process.

Panel members were initially recruited in 2013 in face-to-face interviews followed by a self-administered profile survey. The mode was chosen by the participants. All participants of the profile survey are considered as members of the panel and invited to the bi-monthly regular waves. The starting cohort encompassed 4900 panelists at the beginning of 2014.

In order to compensate for panel attrition, a refreshment sample was drawn in 2016, using the General Social Survey (ALLBUS) interview as vehicle. The initial cohort encompasses German speaking respondents aged between 18 and 70 years (at the time of recruitment) and permanently residing in Germany, whereas the second cohort includes respondents from the age of 18 without upper restriction.

In 2018 a third recruitment sample was drawn, which was integrated with the wave ge. The third cohort also includes respondents aged 18 and over without an upper limit. Retroactively, cases up to and including wave fc (third wave from 2018) were added to the data. The Data Manual (ZA5664-65_sd_data-manual) has been reissued and there is a corresponding recruitment report (ZA5664-65_mb_recruitment2018).

The ALLBUS Sample is based on a disproportional sampling of respondents from the western and eastern part of Germany. A design weight that enables integration of the two recruitment cohorts is included into the dataset. For more details, please see the methods reports of the recruitment processes and die GESIS Panel reference paper (Bosnjak et al., 2017).

In March 2020, a special GESIS panel survey was conducted on the SARS-CoV-2 resp. COVID-19 coronavirus outbreak in Germany.

In 2021, the fourth recruitment sample was drawn using the German International Social Survey Programme (ISSP), which was integrated with wave ja. The fourth cohort also includes respondents aged 18 and older with no upper limit. For more information, see the corresponding recruitment report (ZA5664-65_r_i12.pdf).

In 2023, the fifth recruitment sample was drawn using the German European Social Survey (ESS Round 11), which was integrated with the wave la. The fifth cohort includes respondents aged 18 and over with no upper limit. For more information, see the corresponding recruitment report (ZA5664-65_r_k12.pdf).

GESIS Panel Demographic Dataset Starting with version 43-0-0 the longitudinal demographic dataset became part of the dissemination package. The dataset is a longitudinal dataset (long format), with harmonized measurements on demographic variables: Respondent ID; timepoint of survey; corresponding wave; survey year; recruitment cohort; sex of respondent; year of birth; month of birth; highest level of education; personal net income; household net income; marital status; AAPOR disposition code; mode of invitation; mode of participation.

These data are not available through ICPSR. To apply for access to the data please visit the China Family Panel Studies Web site. The China Family Panel Studies (CFPS) is a nationally representative, annual longitudinal general social survey project designed to document changes in Chinese society, economy, population, education, and health. The CFPS was launched in 2010 by the the Institute of Social Science Survey (ISSS) of Peking University, China. The data were collected at the individual, family, and community levels and are targeted for use in academic research and public policy analysis. All members over age 9 in a sampled household are interviewed. These individuals constitute core members of the CFPS and follow-up of all core members of the CFPS is designed to take place on a yearly basis. CFPS focuses on the economic and non-economic well-being of the Chinese people, and covers topics such as economic activities, educational attainment, family relationships and dynamics, migration, and physical and mental health.

This study utilizes data from the General Social Survey (GSS), a nationally representative, repeated cross-sectional survey administered by NORC at the University of Chicago. The GSS is one of the most authoritative sources of longitudinal public opinion data in the United States, tracking American attitudes, beliefs, and behaviors across a wide range of social, political, and economic domains since 1972.

For the purposes of this analysis, the dataset was restricted to survey waves from 2000 to 2022, to capture contemporary patterns of polarization around economic redistribution and party identity, particularly during the post-9/11 and post-2016 political realignments. Data were accessed and downloaded through the GSS Data Explorer (https://gssdataexplorer.norc.org/), using the platform’s variable filtering and trend tools.

Key variables used in the analysis include:

• Dependent variable: Support for redistribution, measured by agreement with the statement “The government should reduce income differences between the rich and the poor.”

• Independent variables:

  • Party identification (Democrat, Republican, Independent/Other)

  • Racial resentment indicators, including agreement with items such as “Blacks should work their way up without special favors”

  • Year (centered for interaction and trend modeling)

  • Demographic controls: age, gender, income, education, and geographic region

The analytic sample includes respondents with valid responses to all core variables, totaling 5,483 observations after listwise deletion and multiple imputation for missing attitudinal items. All analyses were conducted using R and Python, with appropriate statistical methods for logistic regression, rolling OLS estimation, and interaction modeling. Attempts to estimate a Markov Switching model encountered convergence issues and are excluded from the final analysis.

The GSS sampling design includes multistage area probability sampling and post-stratification weights to ensure representativeness of the U.S. adult population. All interpretations in this study are based on weighted data unless otherwise noted.

How to Replicate the Analysis

To replicate the statistical models presented in this study, follow these steps:

1. Upload the Dataset to Google Colab

   • Open Google Colab

   • Create a new notebook

   • Click the folder icon on the left sidebar

   • Upload the Excel file (MAGADATA.xlsx) containing the cleaned General Social Survey (GSS) data used in this study

2. Load the Analysis Code

   • Open the accompanying Word document (MAGACode.docx)

   • Copy the code blocks written in R and Python from the document

   • Paste the code into the Colab notebook code cells

3. Run the Notebook

   • Click the “▶️” (Run) button at the top left of each code cell

   • Ensure all packages load successfully (Colab will install them if not preloaded)

   • Once the notebook runs, it will execute:

     • Logistic regression on redistribution preferences

     • Interaction models between racial resentment and party ID

     • Rolling OLS trend models by year

     • Summary statistics and plots

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Note: n’s were calculated at baseline (1999); Victimization score and biological parents weremeasured before the participant turned 18; income, and prestige were measured over the entire survey period and are time-varyingaProportions of each group in samplebCumulative years of two biological parents in the house before age 18cMaximum number of years of education of father or motherdProportion of participants in any post-secondary education over the 11 year (i.e., persons could be enrolled in more than one year)Demographic Information of Sample: The National Longitudinal Survey of Youth, 1999–2009 (N = 8,901).

The GESIS Panel provides a probability-based mixed-mode access panel infrastructure located at GESIS Leibniz Institute for the Social Sciences in Mannheim, Germany. The project offers the social science community an opportunity to collect survey data from a representative sample of the German population. Submitted study proposals are evaluated based on a scientific review process.

Panel members were initially recruited in 2013 in face-to-face interviews followed by a self-administered profile survey. The mode was chosen by the participants. All participants of the profile survey are considered as members of the panel and invited to the bi-monthly regular waves. The starting cohort encompassed 4900 panelists at the beginning of 2014.

In order to compensate for panel attrition, a refreshment sample was drawn in 2016, using the General Social Survey (ALLBUS) interview as vehicle. The initial cohort encompasses German speaking respondents aged between 18 and 70 years (at the time of recruitment) and permanently residing in Germany, whereas the second cohort includes respondents from the age of 18 without upper restriction.

In 2018 a third recruitment sample was drawn, which was integrated with the wave ge. The third cohort also includes respondents aged 18 and over without an upper limit. Retroactively, cases up to and including wave fc (third wave from 2018) were added to the data. The Data Manual (ZA5664-65_sd_data-manual) has been reissued and there is a corresponding recruitment report (ZA5664-65_mb_recruitment2018).

The ALLBUS Sample is based on a disproportional sampling of respondents from the western and eastern part of Germany. A design weight that enables integration of the two recruitment cohorts is included into the dataset. For more details, please see the methods reports of the recruitment processes and die GESIS Panel reference paper (Bosnjak et al., 2017).

In March 2020, a special GESIS panel survey was conducted on the SARS-CoV-2 resp. COVID-19 coronavirus outbreak in Germany.

In 2021, the fourth recruitment sample was drawn using the German International Social Survey Programme (ISSP), which was integrated with wave ja. The fourth cohort also includes respondents aged 18 and older with no upper limit. For more information, see the corresponding recruitment report (ZA5664-65_r_i12.pdf).

In 2023, the fifth recruitment sample was drawn using the German European Social Survey (ESS Round 11), which was integrated with the wave la. The fifth cohort includes respondents aged 18 and over with no upper limit. For more information, see the corresponding recruitment report (ZA5664-65_r_k12.pdf).

GESIS Panel Demographic Dataset Starting with version 43-0-0 the longitudinal demographic dataset became part of the dissemination package. The dataset is a longitudinal dataset (long format), with harmonized measurements on demographic variables: Respondent ID; timepoint of survey; corresponding wave; survey year; recruitment cohort; sex of respondent; year of birth; highest level of education; personal net income; household net income; marital status; AAPOR disposition code; mode of invitation; mode of participation.

Yearly Occupational Prestige Scores and Income of Sample: The National Longitudinal Survey of Youth, 1999–2009.

This study utilizes data from the General Social Survey (GSS), a nationally representative, repeated cross-sectional survey administered by NORC at the University of Chicago. The GSS is one of the most authoritative sources of longitudinal public opinion data in the United States, tracking American attitudes, beliefs, and behaviors across a wide range of social, political, and economic domains since 1972.

For the purposes of this analysis, the dataset was restricted to survey waves from 2000 to 2022, to capture contemporary patterns of polarization around economic redistribution and party identity, particularly during the post-9/11 and post-2016 political realignments. Data were accessed and downloaded through the GSS Data Explorer (https://gssdataexplorer.norc.org/), using the platform’s variable filtering and trend tools.

Key variables used in the analysis include:

• Dependent variable: Support for redistribution, measured by agreement with the statement “The government should reduce income differences between the rich and the poor.”

• Independent variables:

  • Party identification (Democrat, Republican, Independent/Other)

  • Racial resentment indicators, including agreement with items such as “Blacks should work their way up without special favors”

  • Year (centered for interaction and trend modeling)

  • Demographic controls: age, gender, income, education, and geographic region

The analytic sample includes respondents with valid responses to all core variables, totaling 5,483 observations after listwise deletion and multiple imputation for missing attitudinal items. All analyses were conducted using R and Python, with appropriate statistical methods for logistic regression, rolling OLS estimation, and interaction modeling. Attempts to estimate a Markov Switching model encountered convergence issues and are excluded from the final analysis.

The GSS sampling design includes multistage area probability sampling and post-stratification weights to ensure representativeness of the U.S. adult population. All interpretations in this study are based on weighted data unless otherwise noted.

aCumulative years of two biological parents in the house before age 18bMaximum number of years of education of father or mothercProportion of participants in any post-secondary educationNote: X indicates multiplication and is used to describe interaction effects. Variables interacted with themselves (e.g., year X year) represent non-linear effects of those predictors on outcomes.Multivariate Linear Regression of the Effect of Victimization on Changes in Prestige and Annual Income: The National Longitudinal Survey of Youth, 1999–2009 (N = 80,018 time points nested in 8,901 persons).

Multivariate Path Model of the Indirect and Total Effects of Parent Highest Education and Respondent Victimization on Prestige and Annual Income: The National Longitudinal Survey of Youth, 1999–2009 (N = 80,018 time points nested in 8,901 persons).

aCumulative years of two biological parents in the house before age 18bMaximum number of years of education of father or mothercProportion of participants in any post-secondary educationNote: X indicates multiplication and is used to describe interaction effects. Variables interacted withthemselves (e.g., sex X victimization) represent non-linear effects of those predictors on outcomes.Multivariate Linear Regression of the Effect of Victimization on Occupational Prestige and Income: The National Longitudinal Survey of Youth, 1999–2009 (N = 80,018 time points nested in 8,901 persons).

English:
The HaSpaD project harmonizes and pools longitudinal data for the analysis of partnership biographies from nine German survey programs. These are in detail:

• The German Family Panel (pairfam), Data file Version 12.0.0
• ALLBUS/GGSS 1980-2016 (Kumulierte Allgemeine Bevölkerungsumfrage der Sozialwissenschaften / Cumulated German General Social Survey 1980-2016)
• Family Surveys 1988-2000 (Change and Development of Forms of Family Life in West Germany (Survey of Families), Family and Partner Relations in Eastern Germany (Survey of Families), Change and Development of Ways of Family Life - 2nd Wave (Survey of Families), Change and Development of Families` Way of Life - 3rd Wave (Family Survey))
• Mannheim Divorce Study 1996
• German Fertility and Family Survey (FFS) 1992
• German Life History Studies (Courses of Life and Historical Change in East Germany (Life History Study LV DDR), Courses of Life and Social Change: Courses of Life and Welfare Development (Life History Study LV-West I), Courses of Life and Social Change: The Between-the-War Cohort in Transition to Retirement (Life History Study LV-West II A - Personal Interview), Courses of Life and Social Change: The Between-the-War Cohort in Transition to Retirement (Life History Study LV-West II T - Telephone Interview), Courses of Life and Social Change: Access to Occupation in Employment Crisis (Life History Study LV-West III), East German Life Courses After Unification (Life History Study LV-Ost Panel), East German Life Courses After Unification (Life History Study LV Ost 71), Education, Training, and Occupation: Life Courses of the 1964 and 1971 Birth Cohorts in West Germany (Life History Study LV-West 64/71), Early Careers and Starting a Family: Life Courses of the 1971 Birth Cohorts in East and West Germany (Life History Study LV-Panel 71))
• Generations & Gender Survey (German Subsample) GGS Waves 1 and 2
• The Survey of Health, Ageing and Retirement in Europe (SHARE), German Sample (Share Waves 1, 2, and 3) and
• Socio-Economic Panel (SOEP), data for the years 1984-2018.
  

The HaSpaD projects does not distribute own datasets. Instead, the HaSpaD syntax package allows to harmonize and pool all German surveys with partnership biographical data which are available for secondary use via a research data repository. Data access to these source data must be arranged autonomously by users of the HaSpaD syntax. The scripts harmonize and pool the partnership biographical data, as well as additional variables on respondents and their partnerships. These include, for example, gender, religious affiliation, and nationality of the respondents. The pooled data set provides the opportunity to analyse previously unanswered questions on marriage and partnership stability from a historical and life course theoretical perspective, in particular on the long-term increase in divorce rates and on social changes in risk factors for separation. In addition, methodological developments of research syntheses will be facilitated.


Deutsch:
Das HaSpaD-Projekt harmonisiert und kumuliert Längsschnittdaten zur Analyse von Partnerschaftsbiografien aus neun deutschen Umfrageprogrammen. Dies sind im Einzelnen:

• Beziehungs- und Familienpanels pairfam, Release 12.0
• Kumulierte Allgemeine Bevölkerungsumfrage der Sozialwissenschaften (ALLBUS / GGSS) 1980-2016
• Familiensurvey 1988 - 2000 (Wandel und Entwicklung familialer Lebensformen in Westdeutschland (Familiensurvey), Familie und Partnerbeziehungen in Ostdeutschland (Familiensurvey), Wandel und Entwicklung familialer Lebensformen - 2. Welle (Familiensurvey), Wandel und Entwicklung familialer Lebensformen - 3. Welle (Familiensurvey))
• Mannheimer Scheidungsstudie 1996
• Deutscher Fertility and Family Survey 1992
• Lebensverlaufsstudien (Lebensverläufe und historischer Wandel in Ostdeutschland (Lebensverlaufsstudie LV-DDR), Lebensverläufe und gesellschaftlicher Wandel: Lebensverläufe und Wohlfahrtsentwicklung (Lebensverlaufsstudie LV-West I), Lebensverläufe und gesellschaftlicher Wandel: Die Zwischenkriegskohorte im Übergang zum Ruhestand (Lebensverlaufsstudie LV-West II A - Persönliche Befragung), Lebensverläufe und gesellschaftlicher Wandel: Die Zwischenkriegskohorte im Übergang zum Ruhestand (Lebensverlaufsstudie LV-West II T - Telefonische Befragung), Lebensverläufe und gesellschaftlicher Wandel: Berufszugang in der Beschäftigungskr...