Model comparison in ANOVA

Jeffrey N. Rouder, Christopher R. Engelhardt, Simon McCabe, Richard D. Morey

Research output: Contribution to journalArticlepeer-review


Analysis of variance (ANOVA), the workhorse analysis of experimental designs, consists of F-tests of main effects and interactions. Yet, testing, including traditional ANOVA, has been recently critiqued on a number of theoretical and practical grounds. In light of these critiques, model comparison and model selection serve as an attractive alternative. Model comparison differs from testing in that one can support a null or nested model vis-a-vis a more general alternative by penalizing more flexible models. We argue this ability to support more simple models allows for more nuanced theoretical conclusions than provided by traditional ANOVA F-tests. We provide a model comparison strategy and show how ANOVA models may be reparameterized to better address substantive questions in data analysis.
Original languageEnglish
Pages (from-to)1779-1786
Number of pages8
JournalPsychon Bull Rev
Publication statusPublished - 11 Apr 2016


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