FAQ/Jaeger - CBU statistics Wiki

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When should I use a logit analysis as opposed to an arcsine transformed ANOVA?

Jaeger (2008) advocates and illustrates using a logistic regression approach when the proportions are near 0 and 1 over the traditional method of an arcsine transform in an ANOVA. The latter can give spuriously statistically significant results in these cases. He suggests using separate logistic regressions for each subject and item (Lorch and Myers, 1990) and also use of the lmer procedure in the freeware R for fitting random effects to binomial data using generalized linear mixed models. The mixed refers to allowing the fitting of both fixed and random factors. The logistic regression he says has the advantage of giving directional comparisons via its regression coefficients (Odds Ratios) whereas post-hoc contrasts are needed to obtain this information using the ANOVA approach.

A beta regression (Smithson and Verkuilen) models both the response and its variance when the response is within the interval [0-1]. The response is assumed to follow a beta distribution which allows for skewness. The authors have fitted this model using SPSS and SAS GLIMMIX although, contrary to the paper, syntax is no longer available on Michael Smithson's website although it may be available upon request to the authors. A simpler form of beta regression which models just the response may be implemented using the betareg procedure in R treating its variance as constant. Smithson claims such an approach is suboptimal

References

Jaeger TF (2008). Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. J. Mem Lang 59(4) 434-446.

Lorch RF and Myers JL (1990). Regression analyses of repeated measures data in cognitive research. Journal of Experimental Psychology: Learning, Memory and Cognition 16(1) 149-157.

Smithson M and Verkuilen J (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods 11(1) 54-71.