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John J. McArdle & John R. Nesselroade (2014). Longitudinal Data Analysis Using Structural Equation Models American Psychological Association, 426 pp, $89.95

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John J. McArdle & John R. Nesselroade (2014). Longitudinal Data Analysis Using Structural Equation Models American Psychological Association, 426 pp, $89.95

Published online by Cambridge University Press:  01 January 2025

Kevin Grimm
Kevin Grimm*
Affiliation:
Arizona State University
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Abstract

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Type
Book Review
Information
Psychometrika , Volume 82 , Issue 2 , June 2017 , pp. 530 - 532
Copyright
Copyright © 2017 The Psychometric Society

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References

Baltes, P. B., & Nesselroade, J. R. Nesselroade, J. R., & Baltes, P. B. (1979). History and rationale of longitudinal research. Longitudinal research in the study of behavior and development, New York: Academic Press 1–39.Google Scholar
Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change, New York: Routledge.CrossRefGoogle Scholar
McArdle, J. J. Nesselroade, J. R., & Cattell, R. B. (1988). Dynamic but structural equation modeling of repeated measures data. The handbook of multivariate experimental psychology, New York: Plenum Press 561–614CrossRefGoogle Scholar
McArdle, J. J. Maydeu-Olivares, A., & McArdle, J. J. (2005). The development of the RAM rules for latent variable structural equation modeling. Contemporary psychometrics: A festschrift for Roderick P. McDonald, Mahwah, NJ: Lawrence Erlbaum Associates 225–274.Google Scholar
McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model for moment structures. British Journal of Mathematical and Statistical Psychology, 37, 234–251CrossRefGoogle ScholarPubMed