2002
DOI: 10.1177/109442802237116
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Growth Modeling Using Random Coefficient Models: Model Building, Testing, and Illustrations

Abstract: In this article, the authors illustrate how random coefficient modeling can be used to develop growth 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 v… Show more

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Cited by 690 publications
(851 citation statements)
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References 26 publications
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“…To test Hypotheses 3-5, we conducted growth models using random coefficient modeling (Bliese, 2006;Bliese & Ployhart, 2002) in R using Pinheiro and Bates' (2000) This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.…”
Section: Analysesmentioning
confidence: 99%
“…To test Hypotheses 3-5, we conducted growth models using random coefficient modeling (Bliese, 2006;Bliese & Ployhart, 2002) in R using Pinheiro and Bates' (2000) This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.…”
Section: Analysesmentioning
confidence: 99%
“…Investigating systematic change patterns in the amount of tips received over the 10-day study period and the extent to which these change patterns differ between intervention and control group may provide further evidence that the effect of the intervention on tip resulted from a training process. We therefore investigated differences between the three groups in change of tips per client across the 10 days in a growth modeling analysis (Bliese & Ployhart, 2002). Training processes often follow a learning curve which can be characterized by a positive linear (sharp increase in the beginning), a negative quadratic, and a positive cubic trend (continued increase but at a slower rate) at the origin of time (Thoresen, Bradley, Bliese, & Thoresen, 2004).…”
Section: Supplementary Analysesmentioning
confidence: 99%
“…(6) We used LME for time series data because they account for temporal correlation among observations on the same experimental unit (Piepho et al 2004) and we used LME to test spatial correlations to account for the hierarchical study design by including different error variances of the different spatial scales (Crawley 2009). LME were tested for autocorrelation effects by including a first order autoregressive structure and for heteroscedasticity of residual variance by including variance functions (Bliese and Ployhart 2002). Model residuals were tested for normal distribution and data was transformed if necessary.…”
Section: Statistical Analysesmentioning
confidence: 99%