Replication Data for: List Experiments with Measurement Error

We provide new tools for diagnosing and mitigating measurement error in list experiments. First, we demonstrate that the nonlinear least squares regression (NLS) 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 ML and NLS estimates. Third, we show how to model measurement error directly, proposing new estimators that preserve the statistical efficiency of the ML estimator while improving robustness. Lastly, we revisit empirical studies shown to exhibit nonstrategic measurement error, and demonstrate that our tools readily diagnose and mitigate the resulting 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.