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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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Extending Multivariate Distance Matrix Regression with an Effect Size Measure and the Asymptotic Null Distribution of the Test Statistic

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Extending Multivariate Distance Matrix Regression with an Effect Size Measure and the Asymptotic Null Distribution of the Test Statistic