Modeling incomplete longitudinal and cross-sectional data using latent growth structural models

Exp Aging Res. Autumn-Winter 1992;18(3-4):145-66. doi: 10.1080/03610739208253917.

Abstract

In this paper we describe some mathematical and statistical models for identifying and dealing with changes over age. We concentrate specifically on the use of a latent growth structural equation model approach to deal with issues of: (1) latent growth models of change, (2) differences in longitudinal and cross-sectional results, and (3) differences due to longitudinal attrition. This is a methodological paper using simulated data, but we base our models on practical and conceptual principles of modeling change in developmental psychology. Our results illustrate both benefits and limitations using structural models to analyze incomplete longitudinal data.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Aging / psychology*
  • Cross-Sectional Studies
  • Humans
  • Longitudinal Studies
  • Models, Psychological*
  • Software