A Structural Modeling Approach to a Multilevel Random Coefficients Model

Multivariate Behavioral Research
Michael J Rovine, Peter C M Molenaar

Abstract

A method for estimating the random coefficients model using covariance structure modeling is presented. This method allows one to estimate both fixed and random effects. A way of translating the general linear mixed model into a structural equation modeling (SEM) format is presented. In particular, a LISREL setup for the multiple group linear latent growth curve model is illustrated with suggestions on ways to parameterize more complex models. To illustrate the procedure, we apply the method to both simulated and real data. The method is shown to recover the simulated parameter values. Results and interpretation for the Belsky and Rovine (1990) marriage data are presented. Other applications of the more general model are suggested.

References

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Citations

Jul 25, 2013·Development and Psychopathology·Phillip Karl Wood, Kristina M Jackson
Dec 25, 2003·Psychology and Aging·Christopher Hertzog, John R Nesselroade
Apr 7, 2005·Psychological Methods·Mike W-L Cheung, Wai Chan
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May 6, 2017·Monographs of the Society for Research in Child Development·Michael J Rovine, Lawrence L Lo
Jul 17, 2021·Biometrical Journal. Biometrische Zeitschrift·Rob KesselsPeter G M van der Heijden

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