论文标题
混合效应模型中的模型规范：关注随机效应
Model Specification in Mixed-Effects Models: A Focus on Random Effects
论文作者
论文摘要
混合效应模型是许多领域中研究人员的灵活工具，但是灵活性是以复杂性为代价的，如果用户不谨慎地指定模型，他们可能会从数据中提出错误的推断。我们认为，鉴于研究设计的模型，围绕适当的随机效应存在重大混乱，研究人员通常更好地指定模型的固定效应，这些效果将其映射到其研究假设。为此，我们提出了一个具有启发性的框架，用于评估在三种不同情况下模型的随机效应：（1）纵向设计； （2）阶乘重复措施； （3）处理多种差异时。我们在在线存储库中提供了带有开放访问代码和数据的工作示例。我们认为，该框架将对新手混合效果模型的学生和研究人员有所帮助，以及可能必须评估新颖模型作为评论的一部分的审稿人。
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.
