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A two‐level structural equation model approach for analyzing multivariate longitudinal responses
Author(s) -
Song XinYuan,
Lee SikYum,
Hser YihIng
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3266
Subject(s) - categorical variable , multivariate statistics , structural equation modeling , covariate , latent variable , statistics , missing data , longitudinal data , longitudinal study , multivariate analysis , mathematics , generalized estimating equation , econometrics , maximum likelihood , computer science , data mining
The analysis of longitudinal data to study changes in variables measured repeatedly over time has received considerable attention in many fields. This paper proposes a two‐level structural equation model for analyzing multivariate longitudinal responses that are mixed continuous and ordered categorical variables. The first‐level model is defined for measures taken at each time point nested within individuals for investigating their characteristics that are changed with time. The second level is defined for individuals to assess their characteristics that are invariant with time. The proposed model accommodates fixed covariates, nonlinear terms of the latent variables, and missing data. A maximum likelihood (ML) approach is developed for the estimation of parameters and model comparison. Results of a simulation study indicate that the performance of the ML estimation is satisfactory. The proposed methodology is applied to a longitudinal study concerning cocaine use. Copyright © 2008 John Wiley & Sons, Ltd.