Mixed Effects Models are Sometimes Terrible

Mixed-effects models have emerged as the gold standard of statistical analysis in different sub-fields of linguistics (Baayen, Davidson u0026 Bates, 2008; Johnson, 2009; Barr, et al, 2013; Gries, 2015). One problematic feature of these models is their failure to converge under maximal (or even near-maximal) random effects structures. The lack of convergence is relatively unaddressed in linguistics and when it is addressed has resulted in statistical practices (e.g. Jaeger, 2009; Gries, 2015; Bates, et al, 2015b) that are premised on the idea that non-convergence is an indication that a random effects structure is over-specified (or not parsimonious), the parsimonious convergence hypothesis (PCH). We test the PCH by running simulations in lme4 under two sets of assumptions for both a linear dependent variable and a binary dependent variable in order to assess the rate of non-convergence for both types of mixed effects models when a known maximal effect structure is used to generate the data (i.e. when non-convergence cannot be explained by random effects with zero variance). Under the PCH, lack of convergence is treated as evidence against a more maximal random effects structure, but that result is not upheld with our simulations. We provide an alternative model, fully specified Bayesian models implemented in rstan (Stan Development Team, 2016; Carpenter, et al, in press) that removed the convergence problems almost entirely in simulations of the same conditions. These results indicate that when there is known non-zero variance for all slopes and intercepts, under realistic distributions of data and with moderate to severe imbalance, mixed effects models in lme4 have moderate to high non-convergence rates which can cause linguistic researchers to wrongfully exclude random effect terms.