Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis
Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis
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用于因果分析的固定效应、随机效应和混合模型
DOI：
10.1007/978-94-007-6094-3_7
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发表时间：
2013
影响因子：
2.1
通讯作者：
Michael Massoglia
中科院分区：
文献类型：
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作者：
G. Firebaugh;Cody Warner;Michael Massoglia
纵向数据在社会科学研究中越来越普遍。在本章中，我们讨论了利用纵向数据的特征来研究因果关系的方法。我们讨论的方法一般称为固定效应和随机效应模型。我们首先讨论固定效应模型相对于传统回归方法的一些优点，然后提出固定效应模型的基本符号。这种符号还可以作为引入随机效应模型的基准，随机效应模型是固定效应方法的一种常见替代方法。在比较了固定效应和随机效应模型之后——特别注意它们的基本假设——我们描述了混合模型，它结合了每种模型的吸引人的特征。为了更深入地理解这些模型，并帮助研究人员在分析纵向数据时确定最合适的方法，我们提供了三个实证例子。我们还简要讨论了固定/随机效应模型的几个扩展。最后，我们提出了一些读者可能会觉得有用的其他文献。
Longitudinal data are becoming increasingly common in social science research. In this chapter, we discuss methods for exploiting the features of longitudinal data to study causal effects. The methods we discuss are broadly termed fixed effects and random effects models. We begin by discussing some of the advantages of fixed effects models over traditional regression approaches and then present a basic notation for the fixed effects model. This notation serves also as a baseline for introducing the random effects model, a common alternative to the fixed effects approach. After comparing fixed effects and random effects models – paying particular attention to their underlying assumptions – we describe hybrid models that combine attractive features of each. To provide a deeper understanding of these models, and to help researchers determine the most appropriate approach to use when analyzing longitudinal data, we provide three empirical examples. We also briefly discuss several extensions of fixed/random effects models. We conclude by suggesting additional literature that readers may find helpful.