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Title: Data for Multiple social identities cloud norm perception: What we can learn from responses to COVID-19 among university aged Republicans and Democrats Open Access Deposited

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Methodology
  • The project builds on samples of students from Rice University, Prairie View A&M University (PVAMU) and Texas A&M University (TAMU) that were recruited to participate in two prior studies which began in 2016. Rice University is a private research university in Houston, Texas; Texas A&M i...  [more]
Description
  • The survey data used in this project is from two larger overarching projects titled the Rice Preferences Study and the Black Student Success Study. The Rice Preferences Study began with a sample of 661 entering undergraduates matriculating in August of 2016. This was 66.7% of the entering class, ran...  [more]
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  • ekrupka@umich.edu
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Funding agency
  • National Science Foundation (NSF)
Keyword
Date coverage
  • 2020-04-01 to 2020-10-01
Related items in Deep Blue Documents
  • Multiple social identities cloud norm perception: Responses to COVID-19 among university aged Republicans and Democrats Krupka, Erin; Hoover, Hanna; Ojumu, Oluwagbemiga; Rosenblat, Tanya; Sinha, Nishita; Wilson, Rick K. 2022. DOI: 10.7302/6621
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Last modified
  • 11/29/2022
Published
  • 11/29/2022
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DOI
  • https://doi.org/10.7302/ghvd-xw58
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To Cite this Work:
Krupka, E. (2022). Data for Multiple social identities cloud norm perception: What we can learn from responses to COVID-19 among university aged Republicans and Democrats [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/ghvd-xw58

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Title:
Multiple Social Identities Cloud Norm Perception: Responses to COVID-19 Among University Aged Republicans and Democrats

Authors and affiliations:
Erin L. Krupka; School of Information, University of Michigan. 105 S State St, Ann Arbor, MI 48109. email: ekrupka@umich.edu
Hanna Hoover; School of Information, University of Michigan. 105 S State St, Ann Arbor, MI 48109. email: hooverha@umich.edu
Catherine Eckel; Department of Economics, Texas A&M University. 2935 Research Parkway Suite 200 College Station, TX 77843. email: ceckel@tamu.edu
Oluwagbemiga Ojumu; Department of Management and Marketing, Prairie View A&M University. 700 University Drive, Prairie View, Texas 77446. email: oaojumu@pvamu.edu
Tanya Rosenblat; School of Information, University of Michigan. 105 S State St, Ann Arbor, MI 48109. email: trosenbl@umich.edu
Nishita Sinha; Department of Agricultural Economics, Texas A&M University. 2124 TAMU College Station, TX 77843-2124. email: nishita.sinha@tamu.edu
Rick K. Wilson; Department of Political Science, Rice University. 6100 Main MS-24 Houston, Texas 77005-1827. email: rkw@rice.edu

Method:
Data was generated using surveys sent out in three waves as described in the manuscript (can be found on E. Krupka's web page)

Description:
The main dataset, all_waves_and_demographics.dta, contains all of the survey responses which has been collected, coded, and cleaned by the research team at Rice University.

Copies of the experimental survey are included in an appendix which is available on request from E. Krupka.

The accompanying codebook includes descriptions of each variable. Note that the letter prefix denotes which wave the data was collected.
For example, the prefix "g_" denotes that the variable was collected during the 2020G wave.
Wave 1 of the study (April-May of 2020) the prefix "d_" is associated with this wave.
Wave 2 of the study (July-August of 2020) the prefix "e_" is associated with this wave.
Wave 3 of the study (October-November) the prefix "g_" is associated with this wave.

The variables id and college remain constant across waves. The variables sex is equal 1 if the respondent is male and is equal to 0 if the respondent is female.

The variable race_all is coded as the following: 1 for African-American, 2 for Asian, 3 for White/Caucasian, 4 for Hispanic, and 5 for Other Race

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