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Activity Number: 187
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #313222
Title: Models for Multivariate Longitudinal Data: A Comparative Study
Author(s): Gemechis Djira*+ and Budhinath Padhy
Companies: South Dakota State University and South Dakota State University
Keywords: Structural equation modeling ; linear mixed models ; latent variables ; multivariate data ; longitudinal data ; simulation
Abstract:

In the past few years, there has been a surge of research interest in modeling longitudinal data in a variety of fields including medicine, marketing research, psychology, social and behavioral sciences. As such, number of studies in multivariate longitudinal data is also growing. In this talk, among others, attention is placed on structural equation models (SEM) and linear mixed effect models (LME) because they are popular, flexible, and widely applicable. These models assume that measurements from a single subject share a set of latent or random effects which are used to generate an association structure between repeated measurements. The fact that latent structures generate associations implies that SEM and LME are very convenient for the joint or multivariate analysis of longitudinal data. The main research objective is to compare and contrast the evolution of associations and the association of evolutions of multivariate longitudinal responses using SEM and LME methodologies. In some situations, SEM provides enormous flexibility and power compared to LME. Real datasets and simulated multivariate longitudinal data are used for the analysis.


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