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Advances in estimation by the item sum technique using auxiliary information in complex surveys

Author

Listed:
  • María del Mar García Rueda

    (University of Granada)

  • Pier Francesco Perri

    (University of Calabria)

  • Beatriz Rodríguez Cobo

    (University of Granada)

Abstract

To collect sensitive data, survey statisticians have designed many strategies to reduce nonresponse rates and social desirability response bias. In recent years, the item count technique has gained considerable popularity and credibility as an alternative mode of indirect questioning survey, and several variants of this technique have been proposed as new needs and challenges arise. The item sum technique (IST), which was introduced by Chaudhuri and Christofides (Indirect questioning in sample surveys, Springer-Verlag, Berlin, 2013) and Trappmann et al. (J Surv Stat Methodol 2:58–77, 2014), is one such variant, used to estimate the mean of a sensitive quantitative variable. In this approach, sampled units are asked to respond to a two-list of items containing a sensitive question related to the study variable and various innocuous, nonsensitive, questions. To the best of our knowledge, very few theoretical and applied papers have addressed the IST. In this article, therefore, we present certain methodological advances as a contribution to appraising the use of the IST in real-world surveys. In particular, we employ a generic sampling design to examine the problem of how to improve the estimates of the sensitive mean when auxiliary information on the population under study is available and is used at the design and estimation stages. A Horvitz–Thompson-type estimator and a calibration-type estimator are proposed and their efficiency is evaluated by means of an extensive simulation study. Using simulation experiments, we show that estimates obtained by the IST are nearly equivalent to those obtained using “true data” and that in general they outperform the estimates provided by a competitive randomized response method. Moreover, variance estimation may be considered satisfactory. These results open up new perspectives for academics, researchers and survey practitioners and could justify the use of the IST as a valid alternative to traditional direct questioning survey modes.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:alstar:v:102:y:2018:i:3:d:10.1007_s10182-017-0315-2
    DOI: 10.1007/s10182-017-0315-2
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    References listed on IDEAS

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    1. Kott, Phillip S. & Chang, Ted, 2010. "Using Calibration Weighting to Adjust for Nonignorable Unit Nonresponse," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1265-1275.
    2. Diana Lara & Sandra G. García & Charlotte Ellertson & Carol Camlin & Javier Suárez, 2006. "The Measure of Induced Abortion Levels in Mexico Using Random Response Technique," Sociological Methods & Research, , vol. 35(2), pages 279-301, November.
    3. 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.
    4. Blair, Graeme & Imai, Kosuke, 2012. "Statistical Analysis of List Experiments," Political Analysis, Cambridge University Press, vol. 20(1), pages 47-77, January.
    5. Giancarlo Diana & Pier Francesco Perri, 2012. "A calibration-based approach to sensitive data: a simulation study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 53-65, March.
    6. Antonio Arcos & María del Rueda & Sarjinder Singh, 2015. "A generalized approach to randomised response for quantitative variables," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1239-1256, May.
    7. Giancarlo Diana & Pier Perri, 2009. "Estimating a sensitive proportion through randomized response procedures based on auxiliary information," Statistical Papers, Springer, vol. 50(3), pages 661-672, June.
    8. 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.
    9. Giancarlo Diana & Pier Perri, 2011. "A class of estimators for quantitative sensitive data," Statistical Papers, Springer, vol. 52(3), pages 633-650, August.
    10. 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.
    11. Graeme Blair & Kosuke Imai & Jason Lyall, 2014. "Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1043-1063, October.
    12. 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.
    13. Giancarlo Diana & Pier Francesco Perri, 2010. "New scrambled response models for estimating the mean of a sensitive quantitative character," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1875-1890.
    14. 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.
    15. Shaul K. Bar-Lev & Elizabeta Bobovitch & Benzion Boukai, 2004. "A note on randomized response models for quantitative data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 60(3), pages 255-260, November.
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