Multivariate Analysis of Variance (MANOVA)

By Leonard Onyiah. The SAGE Encyclopedia of Research Design, 2022

199 citations recorded by the source corpus.

This module calculates power for multivariate analysis of variance (MANOVA) designs having up to three factors. It computes power for three MANOVA test statistics: Wilks’ lambda, Pillai-Bartlett trace, and Hotelling-Lawley trace. MANOVA is an extension of common analysis of variance (ANOVA). In ANOVA, differences among various group means on a single-response variable are studied. In MANOVA, the number of response variables is increased to two or more. The hypothesis concerns a comparison of vectors of group means. The multivariate extension of the F-test is not completely direct. Instead, several test statistics are available. The actual distributions of these statistics are difficult to calculate, so we rely on approximations based on the Fdistribution.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/216a7a5355e747193a2bab9811817ef5c1ae95aa

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In Computational Theory and Mathematics

1. Age K. Smilde
2. Johan A. Westerhuis

In Statistics and Probability

1. Age K. Smilde
2. Johan A. Westerhuis
3. David J. Biagioni

Citations in the corpus

• ANOVA–principal component analysis and ANOVA–simultaneous component analysis: a comparison (2011, background): “Multivariate ANOVA (MANOVA) [3], the natural multivariate extension of ANOVA, breaks down when the number of measurements is smaller than the number of variables, which is commonplace for functional genomics data sets.”