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.

翻译：混合效应模型是众多领域研究者灵活的分析工具，但这种灵活性伴随复杂性而来：若用户未能谨慎设定模型，其数据推论可能产生偏差。我们认为，基于研究设计如何选择恰当的随机效应仍存在显著混淆——研究者通常更擅长设定与研究假设对应的固定效应。为此，我们提出一个教学框架，指导评估三种不同情境下的随机效应设定：（1）纵向设计；（2）因子重复测量；（3）多变异来源处理。我们在在线存储库中提供了含开源代码与数据的实操案例。该框架对初次接触混合效应模型的学生、研究者以及需评审新颖模型的审稿人均具参考价值。