language: en

LATENT VARIABLES, MEASUREMENT ERROR AND METHODS FOR ANALYZING LONGITUDINAL ORDINAL DATA

Mari Palta, Chin-Yu Lin,
University of Wisconsin-Madison
Mari Palta,
504 N Walnut Street, Madison, WI 53705
Key Words: Latent variable, Longitudinal ordinal data, Structural equation model

Abstract We examine the effects of latent variables, between individual variability and measurement error in the outcome on commonly used methods for the analysis of longitudinal ordinal data, such as marginal and cluster-specific models. The impact of such variability becomes very clear when viewed in the context of structural equation modeling (e.g. Muthen 1979, 1983, 1984, 1988). The structural equation formulation also provides insight into the assumptions and differences in interpretation of regression coefficients of these various methods of analysis. We explore the potential for using structural equations to model random effects and to adjust for measurement error, and in the process compare results from marginal modeling using a SAS GEE routine (Karim and Zeger, 1988), Qu's GAUSS program (Qu, 1992) for generalized mixed models using GEE, the MIXOR package for cluster-specific mixed effects models (Hedeker and Gibbons, 1994), and LIS-COMP (Muthen, 1988) for structural equation models. These approaches are illustrated in a longitudinal data set of sleep disorders.

Introduction