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A validation of a computer‐assisted randomized response survey to estimate the prevalence of fraud in social security

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  • Gerty J. L. M. Lensvelt‐Mulders
  • Peter G. M. Van Der Heijden
  • Olav Laudy
  • Ger Van Gils

Abstract

Summary. In the Netherlands, there is a research tradition that measures fraud against regulations by interviewing eligible individuals using a survey. In these studies the sensitive questions about fraud are posed by using a randomized response method. The paper describes the results of a Dutch study into the consequences of replacing home interviews by trained interviewers with Internet‐delivered interviews in a survey on fraud in the area of disability benefits. Both surveys used computer‐assisted self‐interviews with randomized response questions. This study has three goals: first to present the research tradition that makes use of randomized response, second to compare the results of home interviews and the Internet survey and finally to introduce an adapted weighted logistic regression method to test the relationship between the probability of fraud and explanatory variables. The results show that there are no systematic differences between modes of interview, either for estimates of the prevalence of fraud or for the identification of associated variables. These outcomes result in the conclusion that the Internet survey is a useful and cost‐effective instrument for measuring fraud in a population, and that it is unlikely that replacing home interviews with the Internet survey will result in a significant break with tradition.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:2:p:305-318
    DOI: 10.1111/j.1467-985X.2006.00404.x
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    1. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Sabrina Giordano & Pier Perri, 2012. "Efficiency comparison of unrelated question models based on same privacy protection degree," Statistical Papers, Springer, vol. 53(4), pages 987-999, November.
    2. Elisabeth Coutts & Ben Jann, 2011. "Sensitive Questions in Online Surveys: Experimental Results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT)," Sociological Methods & Research, , vol. 40(1), pages 169-193, February.
    3. Carel F. W. Peeters & Gerty J. L. M. Lensvelt-Mulders & Karin Lasthuizen, 2010. "A Note on a Simple and Practical Randomized Response Framework for Eliciting Sensitive Dichotomous and Quantitative Information," Sociological Methods & Research, , vol. 39(2), pages 283-296, November.
    4. Pier Francesco Perri & Elvira Pelle & Manuela Stranges, 2016. "Estimating Induced Abortion and Foreign Irregular Presence Using the Randomized Response Crossed Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(2), pages 601-618, November.
    5. Andreas Quatember, 2019. "A discussion of the two different aspects of privacy protection in indirect questioning designs," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 269-282, January.
    6. 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.
    7. 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.
    8. Ulf Böckenholt & Peter van der Heijden, 2007. "Item Randomized-Response Models for Measuring Noncompliance: Risk-Return Perceptions, Social Influences, and Self-Protective Responses," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 245-262, June.
    9. Leif Appelgren, 2019. "Optimal auditing of social benefit fraud: a case study," Empirical Economics, Springer, vol. 56(1), pages 203-231, January.
    10. Truong-Nhat Le & Shen-Ming Lee & Phuoc-Loc Tran & Chin-Shang Li, 2023. "Randomized Response Techniques: A Systematic Review from the Pioneering Work of Warner (1965) to the Present," Mathematics, MDPI, vol. 11(7), pages 1-26, April.
    11. van den Hout, Ardo & van der Heijden, Peter G.M. & Gilchrist, Robert, 2007. "The logistic regression model with response variables subject to randomized response," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6060-6069, August.
    12. Groenitz, Heiko, 2016. "A covariate nonrandomized response model for multicategorical sensitive variables," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 124-138.
    13. Heiko Groenitz, 2015. "Using prior information in privacy-protecting survey designs for categorical sensitive variables," Statistical Papers, Springer, vol. 56(1), pages 167-189, February.

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