Abstract
For inferential purposes such as hypothesis testing or confidence interval calculations, analysis of repeated measures data needs to account for within-subject dependence of observations. Multivariate analysis of variance (MANOVA) is a suitable traditional technique for this purpose. It assumes an unconstrained within-subject covariance matrix and balanced data. However, the so-called mixed-model approach is a viable alternative to analyzing this type of data, because its underlying statistical assumptions are equivalent to the MANOVA model. While MANOVA is the classical approach, the mixed-model methodology, although by now implemented in all major statistical software packages, still is a relatively recent statistical development. The equivalence of both approaches to analyzing repeated measures data has frequently been noted in the literature. Nevertheless, in terms of test-statistics both approaches differ. While in large samples the test-statistics are essentially equivalent, their small sample behavior is not well known. In this article, we investigate by computer simulation the performance of several test-statistics calculated either from the MANOVA or the mixed-model approach for testing the interaction hypothesis with balanced data.
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Schuster, C., Lubbe, D. (2015). MANOVA Versus Mixed Models: Comparing Approaches to Modeling Within-Subject Dependence. In: Stemmler, M., von Eye, A., Wiedermann, W. (eds) Dependent Data in Social Sciences Research. Springer Proceedings in Mathematics & Statistics, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-319-20585-4_16
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DOI: https://doi.org/10.1007/978-3-319-20585-4_16
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