Latent Curve Models and Latent Change Score Models Estimated in R

In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also...

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Bibliographic Details
Published inStructural equation modeling Vol. 19; no. 4; pp. 651 - 682
Main Authors Ghisletta, Paolo, McArdle, John J.
Format Journal Article
LanguageEnglish
Published Hove Taylor & Francis Group 01.10.2012
Psychology Press
Subjects
Computation
Computer Software
Elementary School Students
Internet
latent change score models
latent curve models
Longitudinal Studies
Mathematical models
Open Source Technology
Preschool Children
R environment
Social sciences
Software
Statistical Analysis
Statistics
Structural Equation Models
Systems development
Wechsler Intelligence Scale for Children
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Summary:In recent years the use of the latent curve model (LCM) among researchers in social sciences has increased noticeably, probably thanks to contemporary software developments and the availability of specialized literature. Extensions of the LCM, like the the latent change score model (LCSM), have also increased in popularity. At the same time, the R statistical language and environment, which is open source and runs on several operating systems, is becoming a leading software for applied statistics. We show how to estimate both the LCM and LCSM with the sem, lavaan, and OpenMx packages of the R software. We also illustrate how to read in, summarize, and plot data prior to analyses. Examples are provided on data previously illustrated by Ferrer, Hamagami, and McArdle (2004). The data and all scripts used here are available on the first author's Web site.
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ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2012.713275