Testing Mean Differences among Groups

Multivariate and Repeated Measures Analysis with Minimal Assumptions

Bibliographic Data

ID19290232
AuthorsArne C Bathke (0000-0002-6260-3726, Department of Mathematics, University of Salzburg; Department of Statistics, University of Kentucky, corresponding author), Sarah Friedrich (0000-0003-0291-4378, Universität Ulm), Markus Pauly (0000-0002-0976-7190, Universität Ulm), Frank Konietschke (0000-0002-5674-2076, Department of Mathematical Sciences, University of Texas at Dallas), Wolfgang Staffen (Paracelsus Medical University), Nicolas Strobl (Paracelsus Medical University), Yvonne Höller (0000-0002-1727-8557, Paracelsus Medical University)
Year2018
Volume53
Issue3
Pages348-359
Publication date2018-05-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2018.1446320
PMID29565679
OpenAlexW2793036949
LanguageEN
Citations received8
References cited51

To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved

Covariance · Econometrics · Inference · Multivariate analysis · Multivariate analysis of variance · Multivariate normal distribution · Multivariate statistics · Normality · Parametric statistics · Repeated measures design · Statistics · Univariate · Artificial Intelligence · Computer Science · Mathematics · Optimal Experimental Design Methods · Sensory Analysis and Statistical Methods · Statistical Methods in Clinical Trials