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.

Concern over the effects of public policies based on misspecified econometric models motivates interest in a procedure to test, diagnose, and improve the specification of models that have been estimated with three-stage least squares. A test of system-wide specification based on Hausman's specification test is employed in a test of the a priori restrictions placed on the parameters of a structural model of the U.S. shrimp market. The null hypothesis of proper specification is rejected. After diagnosis via a comparison of unrestricted and restricted reduced forms and respecification, the null hypothesis cannot be rejected.