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Classification Methods—Part 3. Inferential Considerations in the MANOVA

In: Applied Multivariate Analysis

Author

Listed:
  • 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®))

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.

Suggested Citation

  • Ira H. Bernstein & Calvin P. Garbin & Gary K. Teng, 1988. "Classification Methods—Part 3. Inferential Considerations in the MANOVA," Springer Books, in: Applied Multivariate Analysis, chapter 10, pages 315-344, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-8740-4_10
    DOI: 10.1007/978-1-4613-8740-4_10
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