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Sex, Lies and Measurement: Consistency tests for Indirect Response Survey Methods

Author

Listed:
  • Erica Chuang
  • Pascaline Dupas

    (Stanford University - Department of Economics - Stanford University)

  • Élise Huillery

    (LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Juliette Seban

    (Sciences Po - Sciences Po)

Abstract

Social scientists seeking to analyze socially sanctioned behaviors or attitudes increasingly rely on indirect response survey methods, meant to veil the answers of individual respondents. We propose simple internal consistency tests for two such methods, the list experiment and the randomized response technique (its Warner and Crosswise variants). We implement these tests in two studies on sexual and reproductive health behavior in Cameroon and Côte d'Ivoire. Non-compliance with instructions among surveyed individuals appears high and not easily char-acterizable. The tests we propose can be easily and cheaply embedded in measurement tools, allowing researchers to at least know whether their data is reliable before using it.

Suggested Citation

  • Erica Chuang & Pascaline Dupas & Élise Huillery & Juliette Seban, 2021. "Sex, Lies and Measurement: Consistency tests for Indirect Response Survey Methods," SciencePo Working papers Main hal-03119861, HAL.
    • Handle: RePEc:hal:spmain:hal-03119861
    DOI: 10.1016/j.jdeveco.2020.102582
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    References listed on IDEAS

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    1. McKenzie D. & Siegel M., 2013. "Eliciting illegal migration rates through list randomization," MERIT Working Papers 2013-023, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
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    3. Dupas, Pascaline & Huillery, Elise & Seban, Juliette, 2018. "Risk information, risk salience, and adolescent sexual behavior: Experimental evidence from Cameroon," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 151-175.
    4. Marc Höglinger & Ben Jann, 2018. "More is not always better: An experimental individual-level validation of the randomized response technique and the crosswise model," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-22, August.
    5. Eady, Gregory, 2017. "The Statistical Analysis of Misreporting on Sensitive Survey Questions," Political Analysis, Cambridge University Press, vol. 25(2), pages 241-259, April.
    6. Blair, Graeme & Imai, Kosuke, 2012. "Statistical Analysis of List Experiments," Political Analysis, Cambridge University Press, vol. 20(1), pages 47-77, January.
    7. Graeme Blair & Kosuke Imai & Yang-Yang Zhou, 2015. "Design and Analysis of the Randomized Response Technique," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1304-1319, September.
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    9. Ulf Böckenholt & Sema Barlas & Peter G. M. van der Heijden, 2009. "Do randomized‐response designs eliminate response biases? An empirical study of non‐compliance behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 377-392, April.
    10. Pascaline Dupas & Élise Huillery & Juliette Seban, 2018. "Risk Information, Risk Salience, and Teenagers Sexual Behavior: Experimental Evidence from Cameroon," Post-Print hal-01645677, HAL.
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