NDLI: A new approach for modeling generalization gradients: a case for hierarchical models

Content Provider	PubMed Central
Author	Koen, Vanbrabant Yannick, Boddez Philippe, Verduyn Mestdagh, Merijn Hermans, Dirk Raes, Filip
Copyright Year	2015
Abstract	A case is made for the use of hierarchical models in the analysis of generalization gradients. Hierarchical models overcome several restrictions that are imposed by repeated measures analysis-of-variance (rANOVA), the default statistical method in current generalization research. More specifically, hierarchical models allow to include continuous independent variables and overcomes problematic assumptions such as sphericity. We focus on how generalization research can benefit from this added flexibility. In a simulation study we demonstrate the dominance of hierarchical models over rANOVA. In addition, we show the lack of efficiency of the Mauchly's sphericity test in sample sizes typical for generalization research, and confirm how violations of sphericity increase the probability of type I errors. A worked example of a hierarchical model is provided, with a specific emphasis on the interpretation of parameters relevant for generalization research.
Related Links	http://dx.doi.org/10.3389/fpsyg.2015.00652
Starting Page	652
File Format	PDF
ISSN	16641078
e-ISSN	16641078
Journal	Frontiers in Psychology
Volume Number	6
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2015-05-01
Access Restriction	Open
Rights Holder	Frontiers Media S.A.
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Psychology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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A new approach for modeling generalization gradients: a case for hierarchical models