Quant Hub: Summary-Statistics-Based Power Analyses for Mixed-Effects Modeling

For applied researchers, statistical power analysis with mixed-effects modeling (or multilevel modeling) poses a big challenge, because it requires substantive expertise on modeling, use of special software, and a number of input parameters which are usually not available in published work. The current talk proposes an easy and practical method to conduct statistical power analysis for mixed-effects modeling, called summary-statistics-based power analysis. The proposed method bases its logic on conditional equivalence of the summary-statistics approach and mixed-effects modeling, paring back the power analysis for mixed-effects modeling to that for a simpler statistical analysis (e.g., one-sample t test). Accordingly, the proposed method allows us to conduct power analysis for mixed-effects modeling using popular software such as G*Power or the pwr package in R and, with minimum input from relevant prior work (e.g., t value). I also provide a shinny app to make the approach even more accessible to applied researchers (https://koumurayama.shinyapps.io/summary_statistics_based_power/).

Kou Murayama is Professor for Educational Psychology at the Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Germany. In 2020, he has been awarded with the prestigious Alexander von Humboldt Professorship. He is also Co-Director of the LEAD Graduate School & Research Network

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