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Growth modeling using random coefficient models: Model building, testing, and illustrations
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DOI：10.1177/109442802237116.png)
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Abstract
En 中文
In this article, the authors illustrate how random coefficient modeling can be used to develop models for the analysis of longitudinal data. In contrast to previous discussions of random coefficient models, this article provides step-by-step guidance using a model comparison framework. By approaching the modeling this way, the authors are able to build off a regression foundation and progressively estimate and evaluate more complex models. In the model comparison framework, the article illustrates the value of using likelihood tests to contrast alternative models (rather than the typical reliance oil tests of significance involving individual parameters), and it provides code in the open-source language R to allow, readers to replicate the results. The article concludes with practical guidelines for estimating growth models.
Keywords:
HIERARCHICAL LINEAR-MODELS
INTERINDIVIDUAL DIFFERENCES
INTRAINDIVIDUAL CHANGES
INDIVIDUAL CHANGE
PERFORMANCE
CRITERIA
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