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List Experiments with Measurement Error

  • Graeme Blair (a1), Winston Chou (a2) and Kosuke Imai (a3)


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.


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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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Political Analysis
  • ISSN: 1047-1987
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