  Mixed-effect models are flexible tools for researchers in a myriad of fields,
but that flexibility comes at the cost of complexity and if users are not
careful in how their model is specified, they could be making faulty inferences
from their data. We argue that there is significant confusion around
appropriate random effects to be included in a model given the study design,
with researchers generally being better at specifying the fixed effects of a
model, which map onto to their research hypotheses. To that end, we present an
instructive framework for evaluating the random effects of a model in three
different situations: (1) longitudinal designs; (2) factorial repeated
measures; and (3) when dealing with multiple sources of variance. We provide
worked examples with open-access code and data in an online repository. We
think this framework will be helpful for students and researchers who are new
to mixed effect models, and to reviewers who may have to evaluate a novel model
as part of their review.
