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A new non-randomized response model: The parallel model

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  • Guo-Liang Tian

Abstract

type="main" xml:id="stan12034-abs-0001"> Despite certain advances for non-randomized response (NRR) techniques in the past 6 years, the existing non-randomized crosswise and triangular models have several limitations in practice. In this paper, I propose a new NRR model, called the parallel model with a wider application range. Asymptotical properties of the maximum likelihood estimator (and its modified version) for the proportion of interest are explored. Theoretical comparisons with the crosswise and triangular models show that the parallel model is always more efficient than the two existing NRR models for most of the possible parameter ranges. Bayesian methods for analyzing survey data from the parallel model are developed. A case study on college students' premarital sexual behavior at Wuhan and a case study on plagiarism at the University of Hong Kong are conducted and are used to illustrate the proposed methods. © 2014 The Authors. Statistica Neerlandica © 2014 VVS.

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  • Guo-Liang Tian, 2014. "A new non-randomized response model: The parallel model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(4), pages 293-323, November.
    • Handle: RePEc:bla:stanee:v:68:y:2014:i:4:p:293-323
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    References listed on IDEAS

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    1. Tan, Ming T. & Tian, Guo-Liang & Tang, Man-Lai, 2009. "Sample Surveys With Sensitive Questions: A Nonrandomized Response Approach," The American Statistician, American Statistical Association, vol. 63(1), pages 9-16.
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    3. Guo‐Liang Tian & Ming Tan & Kai Wang Ng, 2007. "An exact non‐iterative sampling procedure for discrete missing data problems," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(2), pages 232-242, May.
    4. Shaul Bar-Lev & Elizabeta Bobovich & Benzion Boukai, 2003. "A common conjugate prior structure for several randomized response models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 101-113, June.
    5. Jun-Wu Yu & Guo-Liang Tian & Man-Lai Tang, 2008. "Two new models for survey sampling with sensitive characteristic: design and analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 67(3), pages 251-263, April.
    6. Heiko Groenitz, 2014. "A new privacy-protecting survey design for multichotomous sensitive variables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(2), pages 211-224, February.
    7. James Abernathy & Bernard Greenberg & Daniel Horvitz, 1970. "Estimates of induced abortion in urban North Carolina," Demography, Springer;Population Association of America (PAA), vol. 7(1), pages 19-29, February.
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    Cited by:

    1. Heiko Groenitz, 2018. "Analyzing efficiency for the multi-category parallel method," METRON, Springer;Sapienza Università di Roma, vol. 76(2), pages 231-250, August.
    2. Raghunath Arnab & Dahud Kehinde Shangodoyin & Antonio Arcos, 2019. "Nonrandomized Response Model For Complex Survey Designs," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 67-86, March.
    3. 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.
    4. Arnab Raghunath & Shangodoyin Dahud Kehinde & Arcos Antonio, 2019. "Nonrandomized Response Model For Complex Survey Designs," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 67-86, March.

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