Classification Methods—Part 3. Inferential Considerations in the MANOVA

Ira H. Bernstein, Calvin P. Garbin and Gary K. Teng
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Ira H. Bernstein: University of Texas at Arlington, Department of Psychology
Calvin P. Garbin: University of Nebraska at Lincoln, Department of Psychology
Gary K. Teng: Technical Evaluation and Management Systems, Inc.(TEAMS®)

Chapter 10 in Applied Multivariate Analysis, 1988, pp 315-344 from Springer

Abstract: Abstract I am going to conclude the general topic of classification and discrimination with a consideration of null hypothesis testing. Much of this chapter deals with the multivariate analysis of variance (MANOVA) and related themes. I have mentioned earlier at several points of the text that testing a multivariate hypothesis of centroid (vector, profile) differences is more complex than testing a univariate hypothesis of mean (location) difference. The basic point to remember is that an inferential test that is the most powerful for detecting a difference when centroids are concentrated is not necessarily the most powerful test for detecting a difference when centroids were diffuse. The general strategy is to treat all unknown differences in structure as if they are diffuse.

Keywords: Discriminant Function; Multiple Group; Omnibus Test; Group Centroid; Diffuse Structure (search for similar items in EconPapers)
Date: 1988
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DOI: 10.1007/978-1-4613-8740-4_10

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