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September 2018 Bayesian randomized response technique with multiple sensitive attributes: The case of information systems resource misuse
Ray S. W. Chung, Amanda M. Y. Chu, Mike K. P. So
Ann. Appl. Stat. 12(3): 1969-1992 (September 2018). DOI: 10.1214/18-AOAS1139

Abstract

The randomized response technique (RRT) is a classical and effective method used to mitigate the distortion arising from dishonest answers. The traditional RRT usually focuses on the case of a single sensitive attribute, and discussion of the case of multiple sensitive attributes is limited. Here, we study a business case to identify some individual and organizational determinants driving information systems (IS) resource misuse in the workplace. People who actually engage in IS resource misuse are probably not willing to provide honest answers, given the sensitivity of the topic. Yet, to develop the causal relationship between IS resource misuse and its determinants, a version of the RRT for multivariate analysis is required. To implement the RRT with multiple sensitive attributes, we propose a Bayesian approach for estimating covariance matrices with incomplete information (resulting from the randomization procedure in the RRT case). The proposed approach (i) accommodates the positive definite condition and other intrinsic parameter constraints in the posterior to improve statistical precision, (ii) incorporates Bayesian shrinkage estimation for covariance matrices despite incomplete information, and (iii) adopts a quasi-likelihood method to achieve Bayesian semiparametric inference for enhancing flexibility. We show the effectiveness of the proposed method in a simulation study. We also apply the Bayesian RRT method and structural equation modeling to identify the causal relationship between IS resource misuse and its determinants.

Citation

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Ray S. W. Chung. Amanda M. Y. Chu. Mike K. P. So. "Bayesian randomized response technique with multiple sensitive attributes: The case of information systems resource misuse." Ann. Appl. Stat. 12 (3) 1969 - 1992, September 2018. https://doi.org/10.1214/18-AOAS1139

Information

Received: 1 July 2016; Revised: 1 November 2017; Published: September 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06979659
MathSciNet: MR3852705
Digital Object Identifier: 10.1214/18-AOAS1139

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 3 • September 2018
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