Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS.

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Ressource 1Télécharger: Sommet_and_Morselli_2017_IRPS.pdf (1327.62 [Ko])
Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_B64BDD5DB9AF
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS.
Périodique
International Review of Social Psychology
Auteur(s)
Sommet Nicolas, Morselli Davide
ISSN
2397-8570
Statut éditorial
Publié
Date de publication
2017
Volume
30
Pages
203-218
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.
Open Access
Oui
Création de la notice
15/03/2018 12:07
Dernière modification de la notice
21/08/2019 6:10
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