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Title: Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis
Resulting in 1 citation.
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Firebaugh, Glenn Warner, Cody Massoglia, Michael |
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Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis In: Handbook of Causal Analysis for Social Research. S. Morgan, ed., New York: Springer, 2013: 113-132 Cohort(s): NLSY79 Publisher: Springer Keyword(s): Modeling; Modeling, Fixed Effects; Modeling, Random Effects Permission to reprint the abstract has not been received from the publisher. 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. |
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Bibliography Citation
Firebaugh, Glenn, Cody Warner and Michael Massoglia. "Fixed Effects, Random Effects, and Hybrid Models for Causal Analysis" In: Handbook of Causal Analysis for Social Research. S. Morgan, ed., New York: Springer, 2013: 113-132
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