Mixed-effect regression models are powerful tools for researchers in a myriad of fields. Part of the appeal of mixed-effect models is their great flexibility, 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. As others have argued, we think there is a great deal of 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. 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. Ultimately, it is difficult to specify "the" appropriate random-effects structure for a mixed model, but by giving users tools to think more deeply about their random effects, we can improve the validity of statistical conclusions in many areas of research.
翻译:混合效应回归模型是众多领域研究人员的强大工具。 混合效应模型的部分吸引力是其巨大的灵活性,但灵活性是以复杂程度为代价的,如果用户在如何指定模型时没有小心谨慎,它们可能从数据中得出错误的推论。 正如其他人所认为的那样,我们认为,在适当随机效应上存在着很大的混乱,这些随机效应将包括在模型中,研究设计中,研究人员通常会更好地说明模型的固定效果,该模型的地图将附在他们的研究假设中。为此,我们提出了一个指导性框架,用以评估模型在三种不同情况下的随机效应:(1) 纵向设计;(2) 因素重复措施;(3) 处理多种差异源时。我们在一个在线储存库中提供使用开放代码和数据的实例。这个框架将帮助新的混合效应模型的学生和研究人员,以及可能不得不评价新模型作为其审查的一部分的审评员。最终,很难为混合模型指定“适当的随机效应”结构,但让用户更深刻地思考其随机研究领域。