Reporting Practice in Multilevel Modeling: A Revisit After 10 Years

Abstract

Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results. Ten years have passed since the guidelines for reporting multilevel studies were initially published. This study reviewed new advancements in MLM and revisited the reporting practice in MLM in the past decade. A total of 301 articles from 19 journals representing different subdisciplines in education and psychology were included in the systematic review. The results showed improvement in some areas of the reporting practices, such as the number of models tested, centering of predictors, missing data treatment, software, and estimates of variance components. However, poor practices persist in terms of model specification, description of a missing mechanism, power analysis, assumption checking, model comparisons, and effect sizes. Updates on the guidelines for reporting multilevel studies and recommendations for future methodological research in MLM are presented.