Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS
By N. Sommet, Davide Morselli. 2017
551 citations recorded by the source corpus.
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. 1
Subfields: Applied Psychology, Social Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/67f6685138ce57f57b52ad544d2d03cc6e513f9a
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Citations in the corpus
- Green gentrification or ‘just green enough’: Do park location, size and function affect whether a place gentrifies or not? (2020, methodology): “In these mixed models, we use random effect intercepts to account for variations among the 10 cities that are not described by the fixed effects (Raudenbush and Bryk, 2002; Sommet and Morselli, 2017).”