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Abstract
OPINION article Front. Psychol., 05 June 2013Sec. Quantitative Psychology and Measurement https://doi.org/10.3389/fpsyg.2013.00328
Highlights
In a recent paper on mixed-effects models for confirmatory analysis, Barr et al (2013) offered the following guideline for testing interactions: “one should have byunit [subject or item] random slopes for any interactions where all factors comprising the interaction are within-unit; if any one factor involved in the interaction is between-unit, the random slope associated with that interaction cannot be estimated, and is not needed” (p. 275)
For the 2 × 2 design, mixed-effects models with two different random effects structures were fit to the data: (1) byunit random intercept but no random slope for B (“random intercept only (RI)”), and (2) a maximal model including a slope for B in addition to the random intercept (“Max”)
The Type I error rate for ANOVA and maximal models were very close to the stated α-level of 0.05
Summary
In a recent paper on mixed-effects models for confirmatory analysis, Barr et al (2013) offered the following guideline for testing interactions: “one should have byunit [subject or item] random slopes for any interactions where all factors comprising the interaction are within-unit; if any one factor involved in the interaction is between-unit, the random slope associated with that interaction cannot be estimated, and is not needed” (p. 275). The following new guideline is proposed: models testing interactions in designs with replications should include random slopes for the highest-order combination of within-unit factors subsumed by each interaction. This new guideline implies that a model testing AB in a 2 × 2 design where A is between and B within should include a random slope for B.
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