Multilevel modeling myths.

The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their origin, and suggest approaches to dealing with certain issues. Misunderstandings related to the use of the intraclass correlation, design effects, minimum sample size, multilevel factor structures, model R², and the misestimation of standard errors are reviewed. Many of the cited myths have much truth in them—though at times, researchers may not be aware of the exceptions to the rules that prevent their overall generalization. Although nesting should be accounted for, researchers should realize that MLM, which is a powerful and flexible technique, is not the only method that can be used to account for the clustering effect. (PsycINFO Database Record (c) 2018 APA, all rights reserved)