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Title: Effects of Unbalancedness and Heteroscedasticity on Two-Way MANOVA Tests

Citation Type: Dissertation/Thesis

Publication Year: 2013

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Abstract: Multivariate analysis of variance is a widely used multivariate method that is generally robust to minor deviations from normality and homoscedasticity. When data is balanced, standard multivariate tests for factor eff ects are exact. However, these tests can be biased when data is unbalanced and covariance matrices are heteroscedastic which emphasizes the need for proper methods. This master thesis aims to investigate how some newly proposed modi ed tests, which takes unbalancedness and heteroscedaticity into account, perform in relation to standard tests for two-way multivariate analysis of variance models with interactions. Two numerical examples are set up in order to compare performances of the modifi ed and standard tests. The obtained results show that diff erences between these tests are marginal when data is balanced. The modifi ed tests are overall less prone than standard tests to yield signifi cant results when data is unbalanced. Main implications from the results are that further studies of the testing procedure are needed but that modified tests are useful as a statistical tool in the presence of unbalancedness and heteroscedasticity.

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Authors: Zetterberg, Patrik

Institution: Stockholm University

Department: Statistics

Advisor: Tatjana von Rosen

Degree: Master's

Publisher Location: Stockholm, Sweden

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Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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