James O.UanhoroAssociate ProfessorEducational PsychologyFacultyJames.Uanhoro@unt.edu940-565-4988Matthews Hall 316-I

FacultyEducational PsychologyTabs Tab Style   Tab Shown on Load 1 Heading Content About James Uanhoro is an assistant professor in the Research, Measurement and Statistics program in the Department of Educational Psychology at the University of North Texas. He received his Ph.D. in 2021 in the Quantitative, Research, Evaluation and Measurement program in the Department of Educational Studies at Ohio State University. Uanhoro's work focuses on the similarities between multilevel regression models and commonplace measurement models — structural equation models, classical test theory and item response models. Specifically, he attempts to leverage insights from both groups of models to better understand patterns in complex data analysis contexts. Underlying much of this work is Bayesian data analysis, which he also applies in research collaborations with social scientists. Finally, James has an interest in building easy-to-use statistical tools that allow researchers better understand patterns in their data, and better communicate insights from their studies. Publications  Uanhoro, J. O. (In press). Meta-analytic structural equation modeling under dependent samples. Behavior Research Methods. https://dx.doi.org/10.3758/s13428-024-02340-4 Uanhoro, J. O. (In press). Bayesian structural equation models of correlation matrices. Structural Equation Modeling. https://dx.doi.org/10.1080/10705511.2024.2306532 Devine, S., Uanhoro, J. O., Otto, R., & Flake, J. K. (2024). Approaches for quantifying the ICC in multilevel logistic models: A didactic demonstration. Collabra: Psychology, 10(1), 94263. https://dx.doi.org/10.1525/collabra.94263 Uanhoro, J. O. (2024). Modeling misspecification as a parameter in Bayesian structural equation models. Educational and Psychological Measurement, 84(2), 245–270. https://dx.doi.org/10.1177/00131644231165306 Uanhoro, J. O. (2023). minorbsem: An R package for structural equation models that account for the influence of minor factors. Journal of Open Source Software, 8(86), 5292. https://dx.doi.org/10.21105/joss.05292 Uanhoro, J. O. (2023). Hierarchical covariance estimation approach to meta-analytic structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 30(4), 532–546. https://dx.doi.org/10.1080/10705511.2022.2142128 Uanhoro, J. O. (2023). Probability of superiority for comparing two groups of clusters. Behavior Research Methods, 55(2), 646–656. https://dx.doi.org/10.3758/s13428-022-01815-6 Uanhoro, J. O., & Stone-Sabali, S. (2023). Beyond group mean differences: A demonstration with scale scores in psychology. Collabra: Psychology, 9(1), 57610. https://dx.doi.org/10.1525/collabra.57610 Kim, M., Winkler, C., Uanhoro, J. O., Peri, J., & Lochman, J. (2022). Using a cross-classified multilevel mediation model (CC-M3) with longitudinal data having changes in cluster membership. Structural Equation Modeling: A Multidisciplinary Journal, 29(2), 218–232. https://dx.doi.org/10.1080/10705511.2021.1965886 Merkle, E. C., Fitzsimmons, E., Uanhoro, J. O., & Goodrich, B. (2021). Efficient Bayesian Structural Equation Modeling in Stan. Journal of Statistical Software, 100(6), 1–22. https://dx.doi.org/10.18637/jss.v100.i06 Programs  Research and Evaluation Research and Evaluation Master of Science Research, Measurement & Stats. Research, Measurement & Stats. Doctor of Philosophy