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

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  • Imai, Kosuke
  • Park, Bethany
  • Greene, Kenneth F.

Abstract

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.

Suggested Citation

  • Imai, Kosuke & Park, Bethany & Greene, Kenneth F., 2015. "Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models," Political Analysis, Cambridge University Press, vol. 23(2), pages 180-196, April.
    • Handle: RePEc:cup:polals:v:23:y:2015:i:02:p:180-196_01
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    Citations

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    Cited by:

    1. María del Mar García Rueda & Pier Francesco Perri & Beatriz Rodríguez Cobo, 2018. "Advances in estimation by the item sum technique using auxiliary information in complex surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 455-478, July.
    2. Roe-Sepowitz, Dominique & Bontrager, Stephanie & Pickett, Justin T. & Kosloski, Anna E., 2019. "Estimating the sex buying behavior of adult males in the United States: List experiment and direct question estimates," Journal of Criminal Justice, Elsevier, vol. 63(C), pages 41-48.
    3. Cullen,Claire Alexis, 2020. "Method Matters : Underreporting of Intimate Partner Violence in Nigeria and Rwanda," Policy Research Working Paper Series 9274, The World Bank.
    4. Mahesh Karra & David Canning & Ryoko Sato, 2020. "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1251-1268, November.
    5. Alina Greiner & Maximilian Filsinger, 2022. "(Dis)Trust in the Aftermath of Sexual Violence: Evidence from Sri Lanka," HiCN Working Papers 377, Households in Conflict Network.
    6. Carole Treibich & Aurélia Lépine, 2019. "Estimating misreporting in condom use and its determinants among sex workers: Evidence from the list randomisation method," Health Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 144-160, January.
    7. Marco Gregori & Martijn G. Jong & Rik Pieters, 2024. "The Crosswise Model for Surveys on Sensitive Topics: A General Framework for Item Selection and Statistical Analysis," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 1007-1033, September.
    8. Yonghong An & Pengfei Liu, 2020. "Eliciting Information from Sensitive Survey Questions," Papers 2009.01430, arXiv.org.
    9. Carlo Koos & Richard Traunmüller, 2022. "The social and political consequences of wartime sexual violence: New evidence from list experiments in three conflict-affected populations," WIDER Working Paper Series wp-2022-11, World Institute for Development Economic Research (UNU-WIDER).
    10. David Boto‐García & Federico Perali, 2024. "The association between marital locus of control and break‐up intentions," American Journal of Economics and Sociology, Wiley Blackwell, vol. 83(1), pages 35-57, January.

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