Transparency and Replicability of Multilevel Modeling Applications: A Guideline for Improved Reporting Practices

Wen Luo, Eunkyeng Baek, Haoran Li

Research output: Chapter in Book/Report/Conference proceedingChapter


Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results. A recent systematic review of the reporting practices in MLM applications in education and psychology showed that the reporting practices still lack clarity and completeness in some areas, including reliability and validity of multilevel measures, model specifications, description of missing data mechanisms, power analyses, assumption checking, model comparisons, and effect sizes (Luo et al, 2021). In this chapter, we aim to provide a guideline for improved reporting practices in the identified areas to enhance the transparency and replicability of MLM applications. We will offer suggestions for what and how to report MLM results in those areas, use examples from real life research to illustrate the principles and guidelines, and provide readers with a checklist to describe the main points that should be thoroughly checked and clearly conveyed in reports when applying MLM.

Original languageEnglish (US)
Title of host publicationMethodology for Multilevel Modeling in Educational Research
Subtitle of host publicationConcepts and Applications
PublisherSpringer Nature
Number of pages23
ISBN (Electronic)9789811691423
ISBN (Print)9789811691416
StatePublished - Jan 1 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.


  • Multilevel modelling
  • Multilevel research
  • Reporting practice


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