Analysis of Multivariate Longitudinal Data Subject to Nonrandom Dropout

Abstract

Longitudinal data are collected for studying changes across time. We consider multivariate longitudinal data where multiple observed variables, measured at each time point, are used as indicators for theoretical constructs (latent variables) of interest. A common problem in longitudinal studies is dropout, where subjects exit the study prematurely. Ignoring the dropout mechanism can lead to biased estimates, especially when the dropout is nonrandom. Our proposed approach uses latent variable models to capture the evolution of the latent phenomenon over time while also accounting for possibly nonrandom dropout. The dropout mechanism is modeled with a hazard function that depends on the latent variables and observed covariates. Different relationships among these variables and the dropout mechanism are studied via 2 model specifications. The proposed models are used to study people’s perceptions of women’s work using 3 questions from 5 waves from the British Household Panel Survey.

Keywords:

• nonignorable dropout
• ordinal variables
• response propensity
• structural equation modeling
• weighted least squares