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      The COVID‐19–Social Identity–Digital Media Nexus in India: Polarization and Blame


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          Drawing on social identity theory and research on digital media and polarization, this study uses a quasi‐experimental design with a random sample ( n = 3304) to provide causal evidence on perceptions of who is to blame for the initial spread of COVID‐19 in India. According blame to three different social and political entities—Tablighi Jamaat (a Muslim group), the Modi government, and migrant workers (a heterogeneous group)—are the dependent variables in three OLS regression models testing the effect of the no‐blame treatment, controlling for Facebook use, social identity (religion), vote in the 2019 national election, and other demographics. Results show respondents in the treatment group were more likely to allay blame, affective polarization (dislike for outgroup members) was social identity based, not partisan based, and Facebook/Instagram use was not significant. Congress and United Progressive Alliance voters in 2019 were less likely to blame the Modi government for the initial spread. Unlike extant research in western contexts, affective and political polarization appear to be distinct concepts in India where social identity complexity is important. This study of the first wave informs perceptions of blame in future waves, which are discussed in conclusion along with questions for future research.

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          Most cited references 71

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

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              Fear and Loathing across Party Lines: New Evidence on Group Polarization


                Author and article information

                Polit Psychol
                Polit Psychol
                Political Psychology
                John Wiley and Sons Inc. (Hoboken )
                18 July 2021
                18 July 2021
                [ 1 ] University of Exeter
                [ 2 ] Indian Institute of Technology Guwahati
                [ 3 ] Carleton University
                [ 4 ] Emory University
                [ 5 ] Cleveland State University
                Author notes
                [* ] Correspondence concerning this article should be addressed to Holli A. Semetko, Emory University, 1555 Dickey Drive, Atlanta, GA 30322‐1007, USA.

                E‐mail: holli.semetko@ 123456emory.edu

                © 2021 International Society of Political Psychology

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                Page count
                Figures: 1, Tables: 2, Pages: 18, Words: 21622
                Special Issue Article
                Special Issue Article
                Custom metadata
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.7 mode:remove_FC converted:17.09.2021


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