title: A two-level structural equation model approach for analyzing multivariate longitudinal responses.
abstract: 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.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. xysong@sta.cuhk.edu.hk
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Song
authors.forename: Sik-Yum
authors.initials: SY
authors.lastname: Lee
authors.forename: Yih-Ing
authors.initials: YI
authors.lastname: Hser
mesh_terms: D000465:Algorithms; D019970:Cocaine-Related Disorders / Q000628:therapy*; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis