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Longitudinal Research with Latent Variables pp 245–273Cite as

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

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.

Keywords

  • Structural Equation Modeling
  • Reading Comprehension
  • Antisocial Behavior
  • Latent Curve
  • Latent Growth Curve

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

The work described here has been supported since 1980 by the National Institute on Aging (Grant#AG-07137). Kevin Grimm was supported by a National Science Foundation REECE Program Grant (DRL-0815787) and the National Center for Research on Early Childhood Education, Institute of Education Sciences, U.S. Department of Education (R305A06021). We are especially grateful to the assistance of our close friend and colleague, John R. Nesselroade. This research was also influenced by discussions with many others, including Paul Baltes, Steven Boker, Emilio Ferrer, Paolo Ghisletta, Fumiaki Hamagami, John Horn, Bill Meredith, and Carol Prescott.

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Authors and Affiliations

  1. Department of Psychology, University of Southern California, Los Angeles, USA

    John J. McArdle

  2. Department of Psychology, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA

    Kevin J. Grimm

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  1. John J. McArdle
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  2. Kevin J. Grimm
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Correspondence to John J. McArdle .

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Editors and Affiliations

  1. Dept. Econometrics & Operations, Research, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands

    Kees van Montfort

  2. Behavioural Science Institute, Radboud University Nijmegen, Montessorilaan 3, Nijmegen, 6525 HR, Netherlands

    Johan H.L. Oud

  3. , Department of Economics and Business, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona, 08005, Spain

    Albert Satorra

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McArdle, J.J., Grimm, K.J. (2010). Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data. In: van Montfort, K., Oud, J., Satorra, A. (eds) Longitudinal Research with Latent Variables. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11760-2_8

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