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IRT Models for Expert-Coded Panel Data

Published online by Cambridge University Press:  03 September 2018

Kyle L. Marquardt  and
Daniel Pemstein
Kyle L. Marquardt*
Affiliation:
V-Dem Institute, Department of Political Science, University of Gothenburg, Gothenburg, Sweden. Email: [email protected]
Daniel Pemstein
Affiliation:
Department of Criminial Justice and Political Science, North Dakota State University, Fargo, ND 58105, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.

Keywords

Bayesian methodsexpert opinionlatent variablesIRT modelscross-national data
Type
Articles
Information
Political Analysis , Volume 26 , Issue 4 , October 2018 , pp. 431 - 456
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Authors’ note: Earlier drafts presented at the 2016 MPSA Annual Convention, the 2016 IPSA World Convention and the 2016 V-Dem Latent Variable Modeling Week Conference. We thank Chris Fariss, Juraj Medzihorsky, Pippa Norris, Jon Polk, Shawn Treier, Carolien van Ham and Laron Williams for their comments on earlier drafts of this paper, as well as V-Dem Project members for their suggestions and assistance. We are also grateful to the editor and two anonymous reviewers for their detailed suggestions. This material is based upon work supported by the National Science Foundation under Grant No. SES-1423944 (PI: Daniel Pemstein); the Riksbankens Jubileumsfond, Grant M13-0559:1 (PI: Staffan I. Lindberg); the Swedish Research Council, 2013.0166 (PI: Staffan I. Lindberg and Jan Teorell); the Knut and Alice Wallenberg Foundation (PI: Staffan I. Lindberg); the University of Gothenburg, Grant E 2013/43; and internal grants from the Vice-Chancellor’s office, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg. We performed simulations and other computational tasks using resources provided by the Notre Dame Center for Research Computing (CRC) through the High Performance Computing section and the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre in Sweden (SNIC 2016/1- 382, 2017/1-407 and 2017/1-68). We specifically acknowledge the assistance of In-Saeng Suh at CRC and Johan Raber at SNIC in facilitating our use of their respective systems. Replication materials available in Marquardt and Pemstein (2018).

Contributing Editor: R. Michael Alvarez

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