Titre
A primer on fixed-effects and fixed-effects panel modeling using R, Stata, and SPSS
Type
autre
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Sommet, Nicolas
Auteure/Auteur
Lipps, Oliver
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
Langue
anglais
Résumé
Fixed-effects modeling is a powerful tool for estimating within-cluster associations in cross-sectional data and within-participant associations in longitudinal data. Although commonly used by other social scientists, this tool remains largely unknown to psychologists. To address this issue, we offer a pedagogical primer tailored for this audience, complete with R, Stata, and SPSS scripts. This primer is organized into three parts. In PART 1, we show how fixed-effects modeling applies to clustered cross-sectional data. We introduce the concepts of ‘cluster dummies’ and ‘demeaning,’ and provide scripts to estimate the within-school association between sports and depression in a fictional dataset. In PART 2, we show how fixed-effects modeling applies to longitudinal data, and provide scripts to estimate the within-participant association between sports and depression over time in a fictional four-wave dataset. In this part, we cover three additional topics. First, we explain how to calculate effect sizes and offer simulation-based sample size guidelines to detect median-sized within-participant effects with sufficient power. Second, we show how to test two possible interactions: between a time-constant and a time-varying predictor and between two time-varying predictors. Third, we introduce three relevant extensions: first-difference modeling (estimating changes from one wave to the next); time-distributed fixed-effects modeling (estimating changes before, during, and after an individual event); and within-between multilevel modeling (estimating both within- and between-participant associations). In PART 3, we discuss two limitations of fixed-effects modeling: time-varying confounders and reverse causality. We conclude with reflections on causality in nonexperimental data.
PID Serval
serval:BIB_41F1D19666F9
Date de création
2024-12-11T12:10:58.427Z
Date de création dans IRIS
2025-05-20T19:46:46Z
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Nom
Fixed-Effects and Fixed-Effects Panel Modeling (Preprint).pdf
Version du manuscrit
preprint
Taille
1.93 MB
Format
Adobe PDF
PID Serval
serval:BIB_41F1D19666F9.P001
URN
urn:nbn:ch:serval-BIB_41F1D19666F90
Somme de contrôle
(MD5):fcc4e4ef9f2c32ac5c14023cb4c468f0