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Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models

  • Kosuke Imai (a1), Bethany Park (a2) and Kenneth F. Greene (a3)


The list experiment, also known as the item count technique, is becoming increasingly popular as a survey methodology for eliciting truthful responses to sensitive questions. Recently, multivariate regression techniques have been developed to predict the unobserved response to sensitive questions using respondent characteristics. Nevertheless, no method exists for using this predicted response as an explanatory variable in another regression model. We address this gap by first improving the performance of a naive two-step estimator. Despite its simplicity, this improved two-step estimator can only be applied to linear models and is statistically inefficient. We therefore develop a maximum likelihood estimator that is fully efficient and applicable to a wide range of models. We use a simulation study to evaluate the empirical performance of the proposed methods. We also apply them to the Mexico 2012 Panel Study and examine whether vote-buying is associated with increased turnout and candidate approval. The proposed methods are implemented in open-source software.


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Authors' note: The proposed methods are implemented via the open-source software list: Statistical Methods for the Item Count Technique and List Experiments, which is available for download at the Comprehensive R Archive Network ( Supplementary materials for this article are available on the Political Analysis Web site. The replication archive is available as Imai, Park, and Greene (2014). We thank Adam Glynn and anonymous reviewers for helpful comments.



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Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models

  • Kosuke Imai (a1), Bethany Park (a2) and Kenneth F. Greene (a3)


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