Model Specification in Mixed-Effects Models: A Focus on Random Effects

TL;DR

This paper clarifies how to appropriately specify random effects in mixed-effects models across different study designs, aiming to improve model accuracy and inference reliability for researchers and reviewers.

Contribution

It introduces a practical framework for selecting random effects in mixed-effects models tailored to various research scenarios, with illustrative examples and open-access resources.

Findings

Guidelines for random effects specification in longitudinal studies

Framework for factorial repeated measures designs

Advice on handling multiple sources of variance

Abstract

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.