Replication Data for: List Experiments with Measurement Error
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 sof' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["auxiliary information, indirect questioning, item count technique, misspecification test, sensitive survey questions, unmatched count technique"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 sof' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 sof
auxiliary information, indirect questioning, item count technique, misspecification test, sensitive survey questions, unmatched count technique