Analysis of multivariate longitudinal data subject to nonrandom dropout

Mai Sherif Hafez, Irini Moustaki and Jouni Kuha

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: structural equation modeling; weighted least squares; response propensity (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

Published in Structural Equation Modeling, 2015, 22(2), pp. 193-201. ISSN: 1070-5511

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:60519

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