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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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固定效应与随机效应的争论以及它与多层次建模中的中心化有何关系。

在许多学科中，研究人员使用纵向面板数据来研究2个变量之间的潜在因果关系。但是，各学科之间的约定和关注点差异很大。这里我们关注两个问题，即：（a）关于随机效应与固定效应的关注，这在（微观）计量经济学/社会学文献中很重要；（b）关于均值与群体（或人）均值居中的关注，这在与心理学和教育科学等学科相关的多层次文献中很重要。我们表明，这两个问题实际上正在解决相同的根本问题。我们讨论基于长格式数据的多级回归建模或宽格式数据的结构方程建模的多种建模方法，并将这些方法与模拟数据进行比较。我们用随机斜率扩展了多级模型，并讨论了其后果。随后，我们提供了有关如何在各种建模选项之间进行选择的指南。我们通过一个基于密集纵向数据的经验示例来说明这些准则的使用，其中我们同时考虑了时变协变量和时不变协变量。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。