Abstract
The general linear model relies on the assumptions of independent and normally distributed residuals with constant variance. If these assumptions are not met, beta regression models represent a viable alternative to model variables that show skewness and heteroscedasticity. In this study, the beta regression model is extended to include random effects. Such a mixed beta regression model can account for the data dependency present in longitudinal data by allowing for participant-specific effects. The mixed beta regression model is illustrated using longitudinal data on complex choice reaction times. Results show that a model with fixed age and missingness effects as well as random intercept and age slope effects fitted the data best. Importantly, it fitted the data much better than a linear mixed model did. Alternatives to the mixed beta regression model and potential applications are described in the discussion.
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