Five steps in latent curve and latent change score modeling with longitudinal data

John J. McArdle, Kevin J. Grimm

Research output: Chapter in Book/Report/Conference proceedingChapter

59 Scopus citations

Abstract

This paper describes a set of applications of one class of longitudinal growth analysis-latent curve (LCM) and latent change score (LCS) analysis using structural equation modeling (SEM) techniques. These techniques are organized in five sections based on Baltes & Nesselroade (1979). (1) Describing the observed and unobserved longitudinal data. (2) Characterizing the developmental shape of both individuals and groups. (3) Examining the predictors of individual and group differences in developmental shapes. (4) Studying dynamic determinants among variables over time. (5) Studying group differences in dynamic determinants among variables over time. To illustrate all steps, we present SEM analyses of a relatively large set of data from the National Longitudinal Survey of Youth (NLSY). The inclusion of all five aspects of latent curve modeling is not often used in longitudinal analyses, so we discuss why more efforts to include all five are needed in developmental research.

Original languageEnglish (US)
Title of host publicationLongitudinal Research with Latent Variables
PublisherSpringer Berlin Heidelberg
Pages245-273
Number of pages29
ISBN (Print)9783642117596
DOIs
StatePublished - Dec 1 2010
Externally publishedYes

ASJC Scopus subject areas

  • Mathematics(all)

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