Bayesian Estimation of Latent Class Model for Survey Data Subject to Item Nonresponse

• Published in: Pakistan journal of statistics and operation research Vol. 15; no. 2; pp. 303 - 318
• Main Authors: Zakaria, Samah, Hafez, Mai Sherif, Gad, Ahmed Mahmoud
• Format: Journal Article
• Language: English
• Published: Lahore University of the Punjab, College of Statistical & Actuarial Science 01.01.2019
• Subjects: Bayesian analysis; Computer simulation; Demographics; Economic models; Latent class analysis; Markov chains; Monte Carlo simulation; Parameter estimation; Variables
• ISSN: 1816-2711; 2220-5810
• DOI: 10.18187/pjsor.v15i2.2612

Latent variable models are widely used in social sciences for measuring constructs (latent variables) such as ability, attitude, behavior, and wellbeing. Those unobserved constructs are measured through a number of observed items (variables). The observed variables are often subject to item nonresponse, that may be nonignorable. Incorporating a missingness mechanism within the model used to analyze data with nonresponse is crucial to obtain valid estimates for parameters, especially when the missingness is nonignorable.In this paper, we propose a latent class model (LCM) where a categorical latent variable is used to capture a latent phenomenon, and another categorical latent variable is used to summarize response propensity. The proposed model incorporates a missingness mechanism. Bayesian estimation using Markov Chain Monte Carlo (MCMC) methods are used for fitting this LCM. Real data with binary items from the 2014 Egyptian Demographic and Health Survey (EDHS14) are used. Different levels of missingness are artificially created in order to study results of the model under low, moderate and high levels of missingness.