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List Experiments with Measurement Error

Published online by Cambridge University Press:  20 May 2019

Graeme Blair
Assistant Professor of Political Science, UCLA, USA. Email:, URL:
Winston Chou
Ph.D. Candidate, Department of Politics, Princeton University, Princeton NJ 08544, USA. Email:, URL:∼wchou
Kosuke Imai*
Professor of Government and of Statistics, Harvard University, 1737 Cambridge Street, Institute for Quantitative Social Science, Cambridge MA 02138, USA. Email: Imai@Harvard.Edu, URL:


Measurement error threatens the validity of survey research, especially when studying sensitive questions. Although list experiments can help discourage deliberate misreporting, they may also suffer from nonstrategic measurement error due to flawed implementation and respondents’ inattention. Such error runs against the assumptions of the standard maximum likelihood regression (MLreg) estimator for list experiments and can result in misleading inferences, especially when the underlying sensitive trait is rare. We address this problem by providing new tools for diagnosing and mitigating measurement error in list experiments. First, we demonstrate that the nonlinear least squares regression (NLSreg) estimator proposed in Imai (2011) is robust to nonstrategic measurement error. Second, we offer a general model misspecification test to gauge the divergence of the MLreg and NLSreg estimates. Third, we show how to model measurement error directly, proposing new estimators that preserve the statistical efficiency of MLreg while improving robustness. Last, we revisit empirical studies shown to exhibit nonstrategic measurement error, and demonstrate that our tools readily diagnose and mitigate the bias. We conclude this article with a number of practical recommendations for applied researchers. The proposed methods are implemented through an open-source software package.

Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Contributing Editor: Jeff Gill

Authors’ note: All the proposed methods presented in this paper are implemented as part of the R package, list: Statistical Methods for the Item Count Technique and List Experiment, which is freely available for download at (Blair, Chou, and Imai 2017). The replication materials are available as Blair, Chou, and Imai (2019).


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