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Evaluation & the Health Professions
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Analyzing Longitudinal Data With the Linear Mixed Models Procedure in SPSS

Brady T. West, MA

University of Michigan-Ann, Arbor, bwest{at}umich.edu

Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.

Key Words: linear mixed models • multilevel models • hierarchical linear models • SPSS software

This version was published on September 1, 2009

Evaluation & the Health Professions, Vol. 32, No. 3, 207-228 (2009)
DOI: 10.1177/0163278709338554
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