Blair, Graeme, Winston Chou, and Kosuke Imai. (2019). ``List Experiments with Measurement Error.'' Political Analysis, Vol. 27, No. 4 (October), pp. 455-480.



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 software package.

  Other Information

Please see this page for the information about the project on the elicitation of truthful answers to sensitive survey questions.
The software package that implements the proposed method is available here for download.

© Kosuke Imai
 Last modified: Wed Oct 9 20:02:30 EDT 2019