@article{sommet_keep_2017,
	title = {Keep {Calm} and {Learn} {Multilevel} {Logistic} {Modeling}: {A} {Simplified} {Three}-{Step} {Procedure} {Using} {Stata}, {R}, {Mplus}, and {SPSS}},
	volume = {30},
	copyright = {Authors who publish with this journal agree to the following terms:    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a  Creative Commons Attribution License  that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.  Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.  Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See  The Effect of Open Access ).  All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on  Educational Fair Use , please see  this useful checklist prepared by Columbia University Libraries .   All copyright  of third-party content posted here for research purposes belongs to its original owners.  Unless otherwise stated all references to characters and comic art presented on this journal are ©, ® or ™ of their respective owners. No challenge to any owner’s rights is intended or should be inferred.},
	issn = {2397-8570},
	shorttitle = {Keep {Calm} and {Learn} {Multilevel} {Logistic} {Modeling}},
	url = {http://www.rips-irsp.com/articles/10.5334/irsp.90/},
	doi = {10.5334/irsp.90},
	abstract = {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},
	language = {eng},
	number = {1},
	urldate = {2018-03-16TZ},
	journal = {International Review of Social Psychology},
	author = {Sommet, Nicolas and Morselli, Davide},
	month = sep,
	year = {2017},
	keywords = {IP201, Justin Bieber, Logistic regression, NIRA, grand-mean centering and cluster-mean centering, intraclass correlation coefficient, likelihood ratio test and random random slope variance, liveswebsite, multilevel logistic modeling, three-step simplified procedure, year8}
}

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Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access ). All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on Educational Fair Use , please see this useful checklist prepared by Columbia University Libraries . All copyright of third-party content posted here for research purposes belongs to its original owners. 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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. 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Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access ). All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on Educational Fair Use , please see this useful checklist prepared by Columbia University Libraries . All copyright of third-party content posted here for research purposes belongs to its original owners. Unless otherwise stated all references to characters and comic art presented on this journal are ©, ® or ™ of their respective owners. No challenge to any owner’s rights is intended or should be inferred.},\n\tissn = {2397-8570},\n\tshorttitle = {Keep {Calm} and {Learn} {Multilevel} {Logistic} {Modeling}},\n\turl = {http://www.rips-irsp.com/articles/10.5334/irsp.90/},\n\tdoi = {10.5334/irsp.90},\n\tabstract = {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). 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