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Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic

Psychometrika volume 82, pages 1052–1077 (2017)Cite this article

Abstract

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

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Authors and Affiliations

  1. University of Notre Dame, 118 Haggar Hall, Notre Dame, IN, 46556 , USA

    Daniel B. McArtor, Gitta H. Lubke & C. S. Bergeman

  2. VU University Amsterdam, Amsterdam, Netherlands

    Gitta H. Lubke

Authors
  1. Daniel B. McArtor
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  2. Gitta H. Lubke
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  3. C. S. Bergeman
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Correspondence to Daniel B. McArtor.

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McArtor, D.B., Lubke, G.H. & Bergeman, C.S. Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic. Psychometrika 82, 1052–1077 (2017). https://doi.org/10.1007/s11336-016-9527-8

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  • DOI: https://doi.org/10.1007/s11336-016-9527-8

Keywords

  • effect size
  • distances
  • MDMR
  • MDS
  • multivariate outcome
  • null distribution
  • person-centered
  • permutation