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Capturing Rationalization Bias and Differential Item Functioning: A Unified Bayesian Scaling Approach

Published online by Cambridge University Press:  07 January 2020

Jørgen Bølstad Open the ORCID record for Jørgen Bølstad [Opens in a new window]
Jørgen Bølstad*
Affiliation:
Associate Professor of Political Science, University of Stavanger, Norway. Email: jorgen.bolstad@uis.no
*
*Email: jorgen.bolstad@uis.no
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Abstract

Information about the ideological positions of different political actors is crucial in answering questions regarding political representation, polarization, and voting behavior. One way to obtain such information is to ask survey respondents to place actors on a common ideological scale, but, unfortunately, respondents typically display a set of biases when performing such placements. Key among these are rationalization bias and differential item functioning (DIF). While Aldrich–McKelvey (AM) scaling offers a useful solution to DIF, it ignores the issue of rationalization bias, and this study presents Monte Carlo simulations demonstrating that AM-type models thus can give inaccurate results. As a response to this challenge, this study develops an alternative Bayesian scaling approach, which simultaneously estimates DIF and rationalization bias, and therefore performs better when the latter bias is present.

Keywords

ideal point estimationBayesian estimationcognitive biasspatial voting modelsurvey research
Type
Articles
Information
Political Analysis , Volume 28 , Issue 3 , July 2020 , pp. 340 - 355
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Jeff Gill

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https://doi.org/10.7910/DVN/HXAKGJ
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