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This piece is meant to help you understand and master two-level linear modeling in an accessible, swift, and fun way (while being based on rigorous and up-to-date research). It is divided into four parts: PART 1 presents the three key principles of two-level linear modeling. PART 2 presents a three-step procedure for conducting two-level linear modeling using SPSS, Stata, R, or Mplus (from centering variables to interpreting the cross-level interactions). PART 3 presents the results from a series of simulations comparing the performances of SPSS, Stata, R, and Mplus. PART 4 gives a Q&A addressing multilevel modeling issues pertaining to statistical power, effect sizes, complex design, and nonlinear two-level regression. The empirical example used in this tutorial is based on genuine data pertaining to ʼ90s and post-ʼ00s boy band member hotness and Instagram popularity. In reading this paper, you will have the opportunity to win a signed picture of Justin Timberlake.

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

Mugny and his colleagues have shown that conflict is sometimes detrimental for learning, but other times beneficial, depending on how it is regulated. Yet, it is assumed that laypeople perceive conflict as 'uniformly' negative. We argue that the valence of these lay perceptions depends on the mode of conflict regulation. Epistemic and relational protective conflict regulation behaviors (integrative and submissive response, respectively) can be described as more focused on the other than relational competitive conflict regulation (self-confirmatory response); thus, they should be perceived as more socially desirable. Moreover, epistemic and competitive regulations can be described as more focused on the self than protective regulation; thus, they should be perceived as more socially useful. First-year psychology students ('N' = 119) participants evaluated three bogus respondents allegedly regulating conflict in an epistemic, competitive, or protective manner. Results supported both hypotheses, suggesting that conflict is not to be avoided per se and can be positively valued as a function of its regulation.

This article details a correction to the article: Sommet, N. & Morselli, D., (2017). Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS. 'International Review of Social Psychology'. 30(1), pp. 203–218. DOI: https://doi.org/10.5334/irsp.90