期刊
FRONTIERS IN PSYCHOLOGY
卷 6, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fpsyg.2015.00652
关键词
stimulus generalization; repeated measures ANOVA; hierarchical (linear) models; individual differences; R; Ime4
资金
- KU Leuven Centre of Excellence on Generalization Research (GRIP*TT) [PF/10/005]
- Research Fund of KU Leuven [GOA/15/003]
- Interuniversity Attraction Poles programme - Belgian government [IAP/P7/06]
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.
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