Multivariate analysis of covariance for heterogeneous and incomplete data

This article discusses the robustness of the multivariate analysis of covariance (MANCOVA) test for an emergent variable system and proposes a modification of this test to obtain adequate information from heterogeneous normal observations. The proposed approach for testing potential effects in heter...

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Bibliographic Details
Published inPsychological methods Vol. 29; no. 4; p. 731
Main Authors Vallejo, Guillermo, Fernández, María Paula, Livacic-Rojas, Pablo Esteban
Format Journal Article
LanguageEnglish
Published United States 01.08.2024
Subjects
Data Interpretation, Statistical
Humans
Models, Statistical
Multivariate Analysis
Psychology - methods
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Summary:This article discusses the robustness of the multivariate analysis of covariance (MANCOVA) test for an emergent variable system and proposes a modification of this test to obtain adequate information from heterogeneous normal observations. The proposed approach for testing potential effects in heterogeneous MANCOVA models can be adopted effectively, regardless of the degree of heterogeneity and sample size imbalance. As our method was not designed to handle missing values, we also show how to derive the formulas for pooling the results of multiple-imputation-based analyses into a single final estimate. Results of simulated studies and analysis of real-data show that the proposed combining rules provide adequate coverage and power. Based on the current evidence, the two solutions suggested could be effectively used by researchers for testing hypotheses, provided that the data conform to normality. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
ISSN:1939-1463
DOI:10.1037/met0000558