Michael Zyphur

Michael Zyphur is a Professor of Quantitative Methods at UQ Business School, University of Queensland. He graduated in 2006 with a PhD in industrial and organizational psychology from Tulane University (New Orleans). He is a former ARC Future Fellow and he has been the lead CI on multiple successful ARC Discovery Project applications. He is currently Professor of Quantitative Methods at the University of Queensland and Director of Instats, and he has held academic positions at the University of Melbourne, University of Washington, and the National University of Singapore. His research focuses on the application of quantitative methods to answer questions in the social and health sciences, particularly with latent variable methods including structural equation modeling (SEM), multilevel modeling (MLM), and multilevel structural equation modeling (MSEM). The new empirical dynamic modeling (EDM) Stata and R packages resulting from his collaborations are now available for download at https://edm-developers.github.io/EDM/ For over a decade Dr Zyphur has taught statistical modeling to over 10,000 researchers and PhD students in seminars and university classes across the globe. He is proficient in Mplus, Stata, SPSS, R, LISREL, and HLM, and he has published roughly 100 academic papers using these software packages in a variety of top-tier journals. His work on statistical modeling and related topics in the social sciences with over 10,000 citations, and Dr Zyphur has helped thousands of researchers and research teams across the globe with their research problems, including in universities, research institutes, corporations, non-profits, government agencies, and the military. Dr Zyphur is currently Director of the Society for Quantitative Methods (SQM), and the Director of Instats, the Institute for Statistical and Data Science (instats.org). application of quantitative methods to answer questions in the social and health sciences, particularly with latent variable methods including structural equation modeling (SEM), multilevel modeling (MLM), and multilevel structural equation modeling (MSEM)

[2026] The Institutional AI Readiness Pack: Self-Assessment and Implementation Tools for Responsible AI in Academic Research. Instats Inc.. doi: 10.61700/bv2nulyhht [2026] Variance Explained (Vx): A General, Model-Based Approach for Explaining and Partitioning Variance. Academy of Management Proceedings, 2026 (1) 14432. doi: 10.5465/amproc.2026.14432abstract [2026] Responsible AI in Academic Research: A Competency Framework for Research Training. Instats Inc.. doi: 10.61700/t31oy23grr [2026] SEM in R and Mplus with AI. Instats Inc.. doi: 10.61700/pxaed7af0kxpd2704 [2026] AI as a Research Method. Instats Inc.. doi: 10.61700/o5zxkw8ia9t772657 [2026] Shedding light on the black box: integrating prediction models and explainability using explainable machine learning. Organizational Research Me