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Estimating Induced Abortion and Foreign Irregular Presence Using the Randomized Response Crossed Model

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  • Pier Francesco Perri

    (University of Calabria)

  • Elvira Pelle

    (University of Calabria)

  • Manuela Stranges

    (University of Calabria)

Abstract

We present the theoretical framework and the results of a pilot survey conducted in Calabria, a region in the south of Italy, to investigate the prevalence of two sensitive characteristics, namely induced abortion among foreign women residing in this region, and irregular immigrant status. Collecting data on these two attributes by means of traditional survey modes typically produces underestimates of the diffusion of the phenomena due to the stigmatizing nature of the investigated topics. In order to overcome this problem, we employ an alternative data collection method known as the Randomized Response Technique. In particular, we focus on the implementation of the crossed model recently proposed by Lee et al. (Stat Probab Lett 83:399–409, 2013) to estimate two sensitive characteristics and some related measures of association.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:soinre:v:129:y:2016:i:2:d:10.1007_s11205-015-1136-x
    DOI: 10.1007/s11205-015-1136-x
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    References listed on IDEAS

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    1. 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.
    2. Lucio Barabesi & Giancarlo Diana & Pier Perri, 2015. "Gini index estimation in randomized response surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 45-62, January.
    3. Lucio Barabesi & Giancarlo Diana & Pier Perri, 2013. "Design-based distribution function estimation for stigmatized populations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 919-935, October.
    4. PETER G. M. van der HEIJDEN & GER van GILS & JAN BOUTS & JOOP J. HOX, 2000. "A Comparison of Randomized Response, Computer-Assisted Self-Interview, and Face-to-Face Direct Questioning," Sociological Methods & Research, , vol. 28(4), pages 505-537, May.
    5. Lee, Cheon-Sig & Sedory, Stephen A. & Singh, Sarjinder, 2013. "Estimating at least seven measures of qualitative variables from a single sample using randomized response technique," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 399-409.
    6. 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.
    7. 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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    Cited by:

    1. 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.
    2. 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.

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