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

Koen Vanbrabant*, Yannick Boddez, Philippe Verduyn, Merijn Mestdagh, Dirk Hermans, Filip Raes

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Web of Science)

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.
Original languageEnglish
Article number652
Number of pages10
JournalFrontiers in Psychology
Volume6
DOIs
Publication statusPublished - 28 May 2015
Externally publishedYes

Keywords

  • stimulus generalization
  • repeated measures ANOVA
  • hierarchical (linear) models
  • individual differences
  • R
  • Ime4
  • CONDITIONED FEAR
  • INFERENCE

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