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Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data

In: Longitudinal Research with Latent Variables

Author

Listed:
  • John J. McArdle

    (University of Southern California, Department of Psychology)

  • Kevin J. Grimm

    (University of California, Davis, Department of Psychology)

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.

Suggested Citation

  • John J. McArdle & Kevin J. Grimm, 2010. "Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data," Springer Books, in: Kees van Montfort & Johan H.L. Oud & Albert Satorra (ed.), Longitudinal Research with Latent Variables, chapter 0, pages 245-273, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-11760-2_8
    DOI: 10.1007/978-3-642-11760-2_8
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