Prediction of a Sensitive Feature under Indirect Questioning via Warner’s Randomized Response Technique and Latent Class Model

Shen-Ming Lee, Phuoc-Loc Tran, Truong-Nhat Le and Chin-Shang Li ()
Additional contact information
Shen-Ming Lee: Department of Statistics, Feng Chia University, Taichung 40724, Taiwan
Phuoc-Loc Tran: Department of Mathematics, College of Natural Science, Can Tho University, Can Tho, Vietnam
Truong-Nhat Le: Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Chin-Shang Li: School of Nursing, The State University of New York, University at Buffalo, Buffalo, NY 14214, USA

Mathematics, 2023, vol. 11, issue 2, 1-21

Abstract: We investigate the association of a sensitive characteristic or latent variable with observed binary random variables by the randomized response (RR) technique of Warner in his publication (Warner, S.L. J. Am. Stat. Assoc. 1965 , 60 , 63–69) and a latent class model. First, an expectation-maximization (EM) algorithm is provided to easily estimate the parameters of the null and alternative/full models for the association between a sensitive characteristic and an observed categorical random variable under the RR design of Warner’s paper above. The likelihood ratio test (LRT) is utilized to identify observed categorical random variables that are significantly related to the sensitive trait. Another EM algorithm is then presented to estimate the parameters of a latent class model constructed through the sensitive attribute and the observed binary random variables that are obtained from dichotomizing observed categorical random variables selected from the above LRT. Finally, two classification criteria are conducted to predict an individual in the sensitive or non-sensitive group. The practicality of the proposed methodology is illustrated with an actual data set from a survey study of the sexuality of first-year students, except international students, at Feng Chia University in Taiwan in 2016.

Keywords: bootstrap; expectation-maximization (EM) algorithm; latent class; likelihood ratio test; maximum likelihood; randomized response; sensitive attribute (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/2/345/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/2/345/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:2:p:345-:d:1029960

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().