CMPS 2016

Published Jun 2016

Collaborative Multiracial Post-election Survey (CMPS) 2016

National Toplines – by racial group

Full methodology statement

Codebook

List of contributors

Principal Investigators

Matt Barreto, UCLA

Edward Vargas, ASU

Lorrie Frasure-Yokley, UCLA

Janelle Wong, Maryland

Methodology: A total of 10,145 completed interviews were collected online in a respondent self-administered format from December 3, 2016 to February 15, 2017. The survey (and invitation) was available to respondents in English, Spanish, Chinese (simplified), Chinese (traditional), Korean, and Vietnamese. Because of the primary interest in the 2016 election, the project started with a large sample of registered voters, to provide large sample size for analyses, however non-voters are also included.

In spring 2016, scholars were invited to collaborate on the 2016 Collaborative Multi-Racial Post-Election Survey (CMPS). The goal of the project was to create the first cooperative, 100% user content driven, multi-racial, multi-ethnic, multi-lingual, post-election online survey in race, ethnicity and politics (REP) in the United States. In full, 86 social scientists across 55 different universities are collaborators. The survey’s main focus is on attitudes about the 2016 Election and candidates, debates over immigration, policing, and racial equality, and experiences with racial discrimination across many facets of American life. The full survey contains 394 questions and median completion time of 43.2 minutes.

The data are weighted within each racial group to match the adult population in the 2015 Census ACS 1-year data file for age, gender, education, nativity, ancestry, and voter registration status. A post-stratification raking algorithm was used to balance each category within +/- 1 percent of the ACS estimates. Data are not weighted to their national combined racial average. That is, Whites account for 10 percent of all cases, and each racial group roughly 30 percent.

UCLA Press Announcement of CMPS

Survey results video presentation

Blog post relying on CMPS-2016 data

Academic article relying on CMPS-2016 data