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

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

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Department of Psychology, University of Virginia, Charlottesville 22903.

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.

PMID:: 1459161; [PubMed - indexed for MEDLINE]

Publication Types, MeSH Terms, Grant Support

Publication Types

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

MeSH Terms

Grant Support

AG07137/AG/NIA NIH HHS/United States

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Seniors' Health - MedlinePlus Health Information

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