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.

多层建模（MLM）是一种统计技术，用于分析聚类数据。尽管其历史悠久，但该技术和随附的计算机程序正在迅速发展。考虑到多层次模型的复杂性，研究人员提供完整，透明的数据描述，统计分析和结果至关重要。自最初发布报告多级研究的指南以来已经过去了十年。这项研究回顾了传销的新进展，并回顾了过去十年传销中的报告实践。系统评价包括来自19个期刊的301篇文章，它们代表了教育和心理学的不同学科。结果显示，报告做法的某些方面有所改善，例如测试的模型数量，预测变量的中心，缺少数据处理，软件和方差分量的估计。但是，在模型规范，缺少的机制描述，功效分析，假设检查，模型比较和效果大小方面，不良做法仍然存在。介绍了有关报告多层次研究的指南的最新信息以及对MLM中未来方法学研究的建议。