Modeling Longitudinal and Multilevel Data: Practical Issues, by Todd D. Little, Kai U. Schnabel, Jrgen Baumert

By Todd D. Little, Kai U. Schnabel, Jrgen Baumert

This booklet makes a speciality of the sensible matters and methods to dealing with longitudinal and multilevel info. All info units and the corresponding command records can be found through the net. The operating examples come in the 4 significant SEM packages--LISREL, EQS, MX, and AMOS--and Multi-level packages--HLM and MLn. All equations and figural conventions are standardized throughout every one contribution. the cloth is out there to working towards researchers and scholars. clients can evaluate and distinction a variety of analytic techniques to longitudinal and multiple-group information together with SEM, Multi-level, LTA, and traditional GLM concepts. excellent for graduate scholars and practising researchers in social and behavioral sciences.

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For example, Hox (chap. 2, this volume) shows that SEM can be used for nested data. Chou, Bentler, and Pentz (chap. 3, this volume) demonstrate their similarity in the context of latent growth modeling, showing that a less complexalthough less efficienttwo-step approach produces approximately the same point estimates. As MacCallum and Kim (chap. 4, this volume) demonstrate, it is possible to analyze simultaneously more than one dependent variable in the HLM framework, thus enabling the researcher, when the variables are combined in a latent growth model, to test hypotheses about correlations in change components.

Most software offers a choice between the two methods. Because RML does not include the fixed parameters in the likelihood function, a deviance test based on RML can only be used to test for differences in the random part. Example of Multilevel Regression Analysis of Grouped Data The multilevel regression model is most appropriate for data structures that have many groups, because it is more flexible and more parsimonious than analysis-of-variance-type models. For instance, assume a study of school careers in 50 schools.

This is the domain of HLM, which, in turn, does not allow structuring error components according to a complex measurement structure (or it needs a fairly cumbersome setup). HLM is also more flexible when the repeated measurement occasions vary between individuals. From a practical standpoint, another important difference between the SEM and HLM approaches is related to the handling of missing dataa feature where HLM was thought to be the more appropriate tool. However, as Wothke (chap. 12, this volume) demonstrates, SEM procedures have narrowed the gap by using full information maximum likelihood estimation of the covariance matrix (as implemented in the latest versions of Amos and Mx).

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