The Fixed Versus Random Effects Debate and How It Relates to Centering in Multilevel Modeling
The Fixed Versus Random Effects Debate and How It Relates to Centering in Multilevel Modeling
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DOI：
10.1037/met0000239
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发表时间：
2020-06-01
影响因子：
7
通讯作者：
Muthen, Bengt
中科院分区：
文献类型：
--
作者：
Hamaker, Ellen L.;Muthen, Bengt
在许多学科中，研究人员使用纵向面板数据来研究两个变量之间的潜在因果关系。然而，各学科的惯例和关注点差异很大。在这里，我们关注两个问题，即：（a）关注随机效应与固定效应，这是（微观）计量经济学/社会学文献的核心;（B）关注总均值与群体（或个人）均值中心化，这是与心理学和教育科学等学科相关的多层次文献的核心。我们表明，这两个问题实际上是解决同一个根本问题。我们讨论了不同的建模方法的基础上，无论是多层次回归模型的数据在长格式，或结构方程模型的数据在宽格式，并比较这些方法与模拟数据。我们扩展了随机斜率的多层次模型，并讨论了这一后果。随后，我们提供了如何在不同的建模选项之间进行选择的指导方针。我们说明了使用这些指导方针的实证例子的基础上密集的纵向数据，在其中，我们认为这两个时变和时不变的协variable.Translational Abstract当涉及到黄金标准建模特定的数据，可以有明显的差异跨学科。在这篇文章中，我们一方面讨论心理学与（微观）计量经济学，社会学和政治学等学科之间的差异。具体来说，我们的重点是如何处理纵向数据，其中包括重复测量相同的情况下（例如，个人、夫妇、家庭或公司），当兴趣在于一个变量如何预测--甚至是导致--另一个变量时。我们表明，在这些学科中提出的关注似乎相当不同，但实际上是相同的。我们通过分析和模拟来展示这一点，并为研究人员提供指导方针，帮助他们决定在实践中使用哪种建模方法。
In many disciplines researchers use longitudinal panel data to investigate the potentially causal relationship between 2 variables. However, the conventions and concerns vary widely across disciplines. Here we focus on 2 concerns, that is: (a) the concern about random effects versus fixed effects, which is central in the (micro)econometrics/sociology literature; and (b) the concern about grand mean versus group (or person) mean centering, which is central in the multilevel literature associated with disciplines like psychology and educational sciences. We show that these 2 concerns are actually addressing the same underlying issue. We discuss diverse modeling methods based on either multilevel regression modeling with the data in long format, or structural equation modeling with the data in wide format, and compare these approaches with simulated data. We extend the multilevel model with random slopes and discuss the consequences of this. Subsequently, we provide guidelines on how to choose between the diverse modeling options. We illustrate the use of these guidelines with an empirical example based on intensive longitudinal data, in which we consider both a time-varying and a time-invariant covariate.Translational AbstractWhen it comes to the gold standard for modeling particular data, there can be stark differences across disciplines. In this article we discuss one such difference between psychology on the one hand, and disciplines like (micro)econometrics, sociology, and political sciences on the other. Specifically, our focus is on how to handle longitudinal data, which consists of repeated measurements of the same cases (e.g., individuals, couples, families, or companies), when the interest is in how one variable predicts-or even causes-another variable. We show that the concerns that are raised in these disciplines seem quite distinct, but are in fact identical. We show this analytically and through simulations, and provide guidelines for researchers to help them decide which modeling approach to use in practice.