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Multivariate and multiple contrast testing in general covariate-adjusted factorial designs

Marléne Baumeister et al.

Journal of Multivariate Analysis2026https://doi.org/10.1016/j.jmva.2026.105617article
AJG 3ABDC A
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0.50

Abstract

Evaluating intervention effects on multiple outcomes is a central research goal in a wide range of quantitative sciences. It is thereby common to compare interventions among each other and with a control across several, potentially highly correlated, outcome variables. In this context, researchers are interested in identifying effects at both, the global level (across all outcome variables) and the local level (for specific variables). At the same time, potential confounding must be accounted for. This leads to the need for powerful multiple contrast testing procedures ( mctp s) capable of handling multivariate outcomes and covariates. Given this background, we propose an extension of mctp s within a semiparametric mancova framework that allows applicability beyond multivariate normality, homoscedasticity, or non-singular covariance structures. To realise this, we implement a generalised resampling-based method for the determination of critical values. We illustrate our approach by analysing multivariate psychological intervention data, evaluating joint physiological and psychological constructs such as heart rate variability.

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https://doi.org/https://doi.org/10.1016/j.jmva.2026.105617

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@article{marléne2026,
  title        = {{Multivariate and multiple contrast testing in general covariate-adjusted factorial designs}},
  author       = {Marléne Baumeister et al.},
  journal      = {Journal of Multivariate Analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jmva.2026.105617},
}

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Multivariate and multiple contrast testing in general covariate-adjusted factorial designs

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0.50

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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