<codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>Unpacking the Role of In-Group Bias in US Public Opinion on Human Rights Violations</titl><IDNo agency="DOI">doi:10.7910/DVN/TGAL7M</IDNo></titlStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><distDate>2025-05-07</distDate></distStmt><verStmt source="archive"><version date="2025-05-07" type="RELEASED">1</version></verStmt><biblCit>Cordell, Rebecca, 2025, "Unpacking the Role of In-Group Bias in US Public Opinion on Human Rights Violations", https://doi.org/10.7910/DVN/TGAL7M, Harvard Dataverse, V1, UNF:6:A09mHJF6/pdZnmG7j8YdYA== [fileUNF]</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>Unpacking the Role of In-Group Bias in US Public Opinion on Human Rights Violations</titl><IDNo agency="DOI">doi:10.7910/DVN/TGAL7M</IDNo></titlStmt><rspStmt><AuthEnty affiliation="University of Pittsburgh">Cordell, Rebecca</AuthEnty></rspStmt><prodStmt><producer>Rebecca Cordell</producer></prodStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><contact email="rebecca.cordell@pitt.edu">Rebecca Cordell</contact><depositr>Cordell, Rebecca</depositr><depDate>2025-02-13</depDate></distStmt><holdings URI="https://doi.org/10.7910/DVN/TGAL7M"/></citation><stdyInfo><subject><keyword xml:lang="en">Social Sciences</keyword><keyword>Human Rights, Group Identity, Public Opinion, Survey Experiment</keyword></subject><abstract>Which actor identities and social and political cleavages drive public opinion on human rights violations? While in-group bias is known to influence public responses to government abuses, the relative impact of different identity characteristics has not been directly tested. Building on social identity theory and moral typecasting theory, I use a conjoint survey experiment in the United States of 3,200 respondents to examine the causal effects of in-group bias across multiple actor identities (perpetrator, target, and elite cue giver) and social and political divides (partisanship, race, religion, and citizenship). Party loyalty to the perpetrator dominates other group identities; simply changing the perpetrator’s political identity can be an important determinant for whether respondents oppose violations. Surprisingly, the target’s race, religion, citizenship has mixed impact, and partisan cues have little effect. These findings highlight when group loyalty outweighs human rights concerns and where public demand for government accountability may be reduced.</abstract><sumDscr/><notes>This dataset underwent an independent verification process, complying with the AJPS Verification Policy updated June 2023, which replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of the code. The verification process was carried out by the Cornell Center for Social Sciences at Cornell University.  
&lt;br>&lt;/br>
  
The associated article has been awarded the Open Materials Badge. Learn more about the Open Practice Badges from the &lt;a href="https://www.cos.io/">Center for Open Science&lt;/a>.  
&lt;br>&lt;/br>

&lt;img src="https://socialsciences.cornell.edu/sites/default/files/2024-04/materials_large_color.png" alt="Open Materials Badge " width="60" height="60">
&lt;br>&lt;/br>
Open Materials Badge</notes></stdyInfo><method><dataColl><sources><dataSrc>Data source: YouGov human rights survey, 3200 online interviews, fielded June 14, 2021 - June 28, 2021
&lt;br>&lt;/br>

Cordell, Rebecca. 2025. Data from: Unpacking the Role of In-Group Bias in US Public Opinion on Human Rights Violations. American Journal of Political Science Dataverse. https://doi.org/10.7910/DVN/TGAL7M</dataSrc></sources></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt><disclaimer>The &lt;i>American Journal of Political Science&lt;/i> and the Cornell Center for Social Sciences are not responsible for the accuracy or quality of data uploaded within the &lt;i>AJPS&lt;/i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.
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