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The item count method for sensitive survey questions: modelling criminal behaviour

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  • Kuha, Jouni
  • Jackson, Jonathan

Abstract

The item count method is a way of asking sensitive survey questions which protects the anonymity of the respondents by randomization before the interview. It can be used to estimate the probability of sensitive behaviour and to model how it depends on explanatory variables. We analyse item count survey data on the illegal behaviour of buying stolen goods. The analysis of an item count question is best formulated as an instance of modelling incomplete categorical data. We propose an efficient implementation of the estimation which also provides explicit variance estimates for the parameters. We then suggest pecifications for the model for the control items, which is an auxiliary but unavoidable part of the analysis of item count data. These considerations and the results of our analysis of criminal behaviour highlight the fact that careful design of the questions is crucial for the success of the item count method.

Suggested Citation

  • Kuha, Jouni & Jackson, Jonathan, 2014. "The item count method for sensitive survey questions: modelling criminal behaviour," LSE Research Online Documents on Economics 48069, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:48069
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    References listed on IDEAS

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    1. Corstange, Daniel, 2009. "Sensitive Questions, Truthful Answers? Modeling the List Experiment with LISTIT," Political Analysis, Cambridge University Press, vol. 17(1), pages 45-63, January.
    2. D. Oakes, 1999. "Direct calculation of the information matrix via the EM," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 479-482, April.
    3. 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.
    4. Blair, Graeme & Imai, Kosuke, 2012. "Statistical Analysis of List Experiments," Political Analysis, Cambridge University Press, vol. 20(1), pages 47-77, January.
    5. Imai, Kosuke, 2011. "Multivariate Regression Analysis for the Item Count Technique," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 407-416.
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    Cited by:

    1. Andreas Lagerås & Mathias Lindholm, 2020. "How to ask sensitive multiple‐choice questions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 397-424, June.
    2. Jiayuan Li & Wim Van den Noortgate, 2022. "A Meta-analysis of the Relative Effectiveness of the Item Count Technique Compared to Direct Questioning," Sociological Methods & Research, , vol. 51(2), pages 760-799, May.
    3. 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.
    4. Thorben C. Kundt & Florian Misch & Birger Nerré, 2017. "Re-assessing the merits of measuring tax evasion through business surveys: an application of the crosswise model," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(1), pages 112-133, February.
    5. Jackson, Jonathan & Bradford, Ben & Hough, Mike & Carrillo, Stephany, 2014. "Extending procedural justice theory: a Fiducia report on the design of new survey indicators," LSE Research Online Documents on Economics 62237, London School of Economics and Political Science, LSE Library.
    6. Yonghong An & Pengfei Liu, 2020. "Eliciting Information from Sensitive Survey Questions," Papers 2009.01430, arXiv.org.
    7. Groenitz, Heiko, 2016. "A covariate nonrandomized response model for multicategorical sensitive variables," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 124-138.
    8. Yin Liu & Guo-Liang Tian & Qin Wu & Man-Lai Tang, 2019. "Poisson–Poisson item count techniques for surveys with sensitive discrete quantitative data," Statistical Papers, Springer, vol. 60(5), pages 1763-1791, October.
    9. Alwyn Lim & Shawn Pope, 2022. "What drives companies to do good? A “universal” ordering of corporate social responsibility motivations," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 29(1), pages 233-255, January.

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    More about this item

    Keywords

    categorical data analysis; EM algorithm; list experiment; missing information; Newton-Raphson algorithm; randomized response;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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