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Studying Identities with Experiments: Weighing the Risk of Posttreatment Bias Against Priming Effects

Published online by Cambridge University Press:  11 October 2019

Samara Klar ,
Thomas Leeper  and
Joshua Robison
Samara Klar
Affiliation:
School of Government and Public Policy, University of Arizona, Tucson, AZ 85721, USA, e-mail: Klar@email.arizona.edu, Twitter: @SamaraKlar
Thomas Leeper
Affiliation:
Department of Methodology, London School of Economics and Political Science, London WC2A 2AE, UK, e-mail: thosjleeper@gmail.com, Twitter: @ThosJLeeper
Joshua Robison
Affiliation:
Institute of Political Science, Leiden University, Leiden, Netherlands, e-mail: j.a.robison@fsw.leidenuniv.nl, Twitter: @JRob617
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Abstract

Scholars from across the social sciences argue that identities – such as race, ethnicity, and gender – are highly influential over individuals’ attitudes, actions, and evaluations. Experiments are becoming particularly integral for allowing identity scholars to explain how these social attachments shape our political behavior. In this letter, we draw attention to how identity scholars should approach the common practice of assessing moderators, measuring control variables, and detecting effect heterogeneity using covariates. Special care must be taken when deciding where to place measures of demographic covariates in identity-related experiments, as these cases pose unique challenges from how scholars traditionally approach experimental design. We argue in this letter that identity scholars, particularly those whose subjects identify as women or minorities, are often right to measure covariates of interest posttreatment.

Keywords

Identitiesexperimentsprimingexperimental designposttreatment bias
Type
Short Report
Information
Journal of Experimental Political Science , Volume 7 , Issue 1 , Spring 2020 , pp. 56 - 60
Copyright
© The Experimental Research Section of the American Political Science Association 2019 

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Footnotes

Citation of data: The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: doi:10.1017/XPS.2019.26.

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