Bates, D., Kliegl, R., Vasishth, S., & Baayen, R.H. (2015, arXiv). Parsimonious mixed models.
The analysis of experimental data with mixed-effects models requires deci- sions about the specification of the appropriate random-effects structure. Recently, Barr, Levy, Scheepers, and Tily 2013 recommended fitting ‘maximal’ models with all possible random effect components included. Estimation of maximal models, however, may not converge. We show that failure to converge typically is not due to a suboptimal estima- tion algorithm, but is a consequence of attempting to fit a model that is too complex to be properly supported by the data, irrespective of whether estimation is based on maxi- mum likelihood or on Bayesian hierarchical modeling with uninformative or weakly infor- mative priors. Importantly, even under convergence, overparameterization may lead to uninterpretable models. We provide diagnostic tools for detecting overparameterization and guiding model simplification.
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