This is the main release of the 2018 CCES Common Content Dataset. The data includes a nationally representative sample of 60,000 American adults. This release includes the data from the survey, vote validation for the respondents, and a full guide and codebook. See the guide for a more detailed explanation of the data.

The Cooperative Congressional Election Study (CCES), now called the Cooperative Election Study (CES), is one of the largest political surveys in the United States. This dataset contains the respondents from the Common Content of the CCES (n = 701,955), combining all available Common Content datasets from 2006 - 2024. It includes select standardized variables including demographics, geography, vote choice, validated vote, representative approval, the economy, etc.. See the attached guide for a full list of variables, methodology, and ways to load the data.

This dataverse contains the data and supporting documents for the CCES 2016 Harvard University. This project was supported by the National Science Foundation, Grant Number SES-1559125.

Supplemental data files for the 2020 Cooperative Congressional Election Study. Datasets include the race and ethnicity of congressional candidates.

This dataverse contains the data and supporting documents for the CCES 2018 MIT team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

2013 Cooperative Congressional Election Study Common Content data and questionnaire.

This dataverse contains the data and supporting documents for the CCES 2018 University of Delaware team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

Supplemental datasets for the 2014 Cooperative Congressional Election Study.

This dataverse contains the data and supporting documents for the CCES 2016 Texas A&M University. This project was supported by the National Science Foundation, Grant Number SES-1559125.

The premise that constituents hold representatives accountable for their legislative decisions undergirds political theories of democracy and legal theories of statutory interpretation. But studies of this at the individual level are rare, examine only a handful of issues, and arrive at mixed results. We provide an extensive assessment of issue accountability at the individual level. We trace the congressional rollcall votes on 44 bills across seven Congresses (2006--2018), and link them to constituent's perceptions of their representative's votes and their evaluation of their representative. Correlational, instrumental variables, and experimental approaches all show that constituents hold representatives accountable. A one-standard deviation increase in a constituent's perceived issue agreement with their representative can improve net approval by 35 percentage points. Congressional districts, however, are heterogeneous. Consequently, the effect of issue agreement on vote is much smaller at the district-level, resolving an apparent discrepancy between micro and macro studies.

This dataverse contains the data and supporting documents for the CCES 2018 University of Missouri team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

This dataverse contains the data and supporting documents for the CCES 2018 Fordham University team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

Weights for a non-random subsample in the 2016 Cooperative Congressional Election Study. This subsample consists of early respondents to the survey invitation who answered a number of questions that were later cut due to time constraints. Dataset includes post-stratification weights and case identifiers for respondents in the subsample for merging.

This dataverse contains the data and supporting documents for the CCES 2018 University of Virginia team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

This dataverse contains the data and supporting documents for the CCES 2018 UCLA (1) team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

This dataverse contains the data and supporting documents for the CCES 2018 University of Notre Dame team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

This dataverse contains the data and supporting documents for the CCES 2018 University of Massachusetts Amherst team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

This is the final release of the 2020 CES Common Content Dataset. The data includes a nationally representative sample of 61,000 American adults. This release includes the data from the survey, a full guide to the data, and the questionnaires. The dataset includes vote validation performed by Catalist. Please consult the guide and the study website (https://cces.gov.harvard.edu/frequently-asked-questions) if you have questions about the study. Special thanks to Marissa Shih and Rebecca Phillips for their work in preparing this data for release.

This capsule contains the code and data needed to replicate all results reported in "A General Approach to Measuring Electoral Competitiveness for Parties and Governments" by Axel Cronert and Pär Nyman.

We construct measures of U.S. state partisan identification, self-reported ideology, and policy mood using data from the 2000 and 2004 National Annenberg Election Surveys (NAES) and the 2006 Cooperative Congressional Election Study (CCES). These measures improve on existing methods for estimating state-level preferences because the surveys provide larger state samples without pooling across years. After detailing our methods for constructing the measures, we assess their validity through comparisons with measures already in use by scholars of state politics. We find that our measures correlate strongly with those created by Erikson, Wright, and McIver (1993) and Berry et al. (1998) and with measures from state-level polls. We conclude that our measures can be useful to research in state politics.