en
Scientific article
Open access
English

Latent Curve Models and Latent Change Score Models Estimated in R

Published inStructural equation modeling, vol. 19, no. 4, p. 651-682
Publication date2012
Abstract

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.

Keywords
  • Latent change score models
  • Latent curve models
  • R environment
Citation (ISO format)
GHISLETTA, Paolo, MCARDLE, John J. Latent Curve Models and Latent Change Score Models Estimated in R. In: Structural equation modeling, 2012, vol. 19, n° 4, p. 651–682. doi: 10.1080/10705511.2012.713275
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Identifiers
ISSN of the journal1070-5511
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Technical informations

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