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Classification Methods—Part 3. Inferential Considerations in the MANOVA

  • Ira H. Bernstein
  • Calvin P. Garbin
  • Gary K. Teng

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • Ira H. Bernstein
    • 1
  • Calvin P. Garbin
    • 2
  • Gary K. Teng
    • 3
  1. 1.Department of PsychologyUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of PsychologyUniversity of Nebraska at LincolnLincolnUSA
  3. 3.Technical Evaluation and Management Systems, Inc.(TEAMS®)DallasUSA

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