Titre
Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
International Review of Social Psychology
Auteur(s)
Sommet, Nicolas
Auteure/Auteur
Morselli, Davide
Auteure/Auteur
Liens vers les personnes
ISSN
2397-8570
Statut éditorial
Publié
Date de publication
2017
Volume
30
Première page
203
Dernière page/numéro d’article
218
Langue
anglais
Résumé
This paper aims to introduce multilevel logistic regression analysis in a simple and practical way. First, we introduce the basic principles of logistic regression analysis (conditional probability, logit transformation, odds ratio). Second, we discuss the two fundamental implications of running this kind of analysis with a nested data structure: In multilevel logistic regression, the odds that the outcome variable equals one (rather than zero) may vary from one cluster to another (i.e. the intercept may vary) and the effect of a lower-level variable may also vary from one cluster to another (i.e. the slope may vary). Third and finally, we provide a simplified three-step “turnkey” procedure for multilevel logistic regression modeling:
-Preliminary phase: Cluster- or grand-mean centering variables
-Step #1: Running an empty model and calculating the intraclass correlation coefficient (ICC)
-Step #2: Running a constrained and an augmented intermediate model and performing a likelihood ratio test to determine whether considering the cluster-based variation of the effect of the lower-level variable improves the model fit
-Step #3 Running a final model and interpreting the odds ratio and confidence intervals to determine whether data support your hypothesis
Command syntax for Stata, R, Mplus, and SPSS are included. These steps will be applied to a study on Justin Bieber, because everybody likes Justin Bieber.
-Preliminary phase: Cluster- or grand-mean centering variables
-Step #1: Running an empty model and calculating the intraclass correlation coefficient (ICC)
-Step #2: Running a constrained and an augmented intermediate model and performing a likelihood ratio test to determine whether considering the cluster-based variation of the effect of the lower-level variable improves the model fit
-Step #3 Running a final model and interpreting the odds ratio and confidence intervals to determine whether data support your hypothesis
Command syntax for Stata, R, Mplus, and SPSS are included. These steps will be applied to a study on Justin Bieber, because everybody likes Justin Bieber.
PID Serval
serval:BIB_B64BDD5DB9AF
Open Access
Oui
Date de création
2018-03-15T11:07:32.583Z
Date de création dans IRIS
2025-05-21T02:49:53Z
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Nom
Sommet_and_Morselli_2017_IRPS.pdf
Version du manuscrit
preprint
Taille
1.3 MB
Format
Adobe PDF
PID Serval
serval:BIB_B64BDD5DB9AF.P001
URN
urn:nbn:ch:serval-BIB_B64BDD5DB9AF2
Somme de contrôle
(MD5):2e8bb8e1bd6bb9e4d0dba39feb715afa