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Analysis of multivariate longitudinal data subject to nonrandom dropout

Hafez, Mai Sherif, Moustaki, Irini and Kuha, Jouni (2015) Analysis of multivariate longitudinal data subject to nonrandom dropout. Structural Equation Modeling, 22 (2). pp. 193-201. ISSN 1070-5511

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Identification Number: 10.1080/10705511.2014.936086


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.

Item Type: Article
Official URL:
Additional Information: © 2014 Taylor & Francis Group
Divisions: Methodology
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
Date Deposited: 15 Dec 2014 12:10
Last Modified: 20 Aug 2021 00:57

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