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Log-Linear Randomized-Response Models Taking Self-Protective Response Behavior Into Account

Author

Listed:
  • Maarten J. L. F. Cruyff

    (Utrecht University, Netherlands, m.cruyff@uu.nl)

  • Ardo van den Hout

    (MRC Biostatistic Unit, Cambridge, United Kingdom)

  • Peter G. M. van der Heijden

    (Utrecht University, Netherlands)

  • Ulf Böckenholt

    (McGill University, Montreal, Canada)

Abstract

Randomized response (RR) is an interview technique designed to eliminate response bias when sensitive questions are asked. In RR the answer depends partly on the true status of the respondent and partly on the outcome of a randomizing device. Although RR elicits more honest answers than direct questions do, it is susceptible to self-protective response behavior; that is, the respondent gives an evasive answer irrespective of the outcome of the randomizing device. The authors present a log-linear RR model that accounts for this kind of self-protection (SP). The main results of this SP model are estimates of (1) the probability of SP, (2) the log-linear parameters describing the associations between the sensitive characteristics, and (3) the prevalence of the sensitive characteristics that are corrected for SP. The model is illustrated with two examples from a Dutch survey measuring noncompliance with social welfare rules.

Suggested Citation

  • Maarten J. L. F. Cruyff & Ardo van den Hout & Peter G. M. van der Heijden & Ulf Böckenholt, 2007. "Log-Linear Randomized-Response Models Taking Self-Protective Response Behavior Into Account," Sociological Methods & Research, , vol. 36(2), pages 266-282, November.
    • Handle: RePEc:sae:somere:v:36:y:2007:i:2:p:266-282
    DOI: 10.1177/0049124107301944
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    References listed on IDEAS

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    1. Gerty J. L. M. Lensvelt‐Mulders & Peter G. M. Van Der Heijden & Olav Laudy & Ger Van Gils, 2006. "A validation of a computer‐assisted randomized response survey to estimate the prevalence of fraud in social security," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 305-318, March.
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    Citations

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    Cited by:

    1. Blume, Andreas & Lai, Ernest K. & Lim, Wooyoung, 2019. "Eliciting private information with noise: The case of randomized response," Games and Economic Behavior, Elsevier, vol. 113(C), pages 356-380.
    2. Korndörfer, Martin & Krumpal, Ivar & Schmukle, Stefan C., 2014. "Measuring and explaining tax evasion: Improving self-reports using the crosswise model," Journal of Economic Psychology, Elsevier, vol. 45(C), pages 18-32.
    3. John, Leslie K. & Loewenstein, George & Acquisti, Alessandro & Vosgerau, Joachim, 2018. "When and why randomized response techniques (fail to) elicit the truth," Organizational Behavior and Human Decision Processes, Elsevier, vol. 148(C), pages 101-123.

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