A new non-randomized response model: The parallel model

Guo-Liang Tian

Statistica Neerlandica, 2014, vol. 68, issue 4, 293-323

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.

Date: 2014
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