
The LEAP (Learning, Exploring, and Advancing Psychometrics) Lab was established in 2021 and is located in the Mary Frances Early College of Education at the University of Georgia. We are a team of researchers interested in psychometrics, educational measurement and assessment, and quantitative methodologies with a focus on applications in educational research. We emphasize the L-E (learning and exploring) in all lab activities, including applied and methodological research, qualitative fieldwork, professional development, conference travel, and interviews with various scholars. Contact me if you are interested in getting involved with the lab.

Name: Matthew Madison (Lab Director; he/him/his)
Research interests: diagnostic measurement, longitudinal measurement, K - 12 assessment
Hobbies: golf, basketball, exercise, puzzles, true crime
Hometown/country: Columbia, South Carolina, United States
Contact: mjmadison@uga.edu

Name: Nancy Alila (she/her/hers)
Education: B.Sc. in Mathematics and Economics (Karatina University, Kenya); M.Sc. in Applied Statistics (West Chester University of Pennsylvania)
Program: Ph.D. in Measurement, Data Science, and Statistics
Research Interests: Bayesian statistics, structural equation modeling, diagnostic classification models, the application of machine learning and data mining techniques in psychometric research
Hobbies: exercising, traveling, reading, and playing tennis
Hometown/country: Migori, Kenya
Contact: nancy.alila@uga.edu

Name: Ashley Heady (she/her)
Education: B.S. In Supply Chain, Information, and Analytics (Purdue University); M.Ed. in Measurement and Evaluation (Teachers College, Columbia University)
Program: Ph.D. in Measurement, Data Science, and Statistics
Research Interests: item response theory, psychometrics, and diagnostic classification models
Hobbies: weather watching, card games, cooking, sewing, & traveling
Hometown/Country: Arlington, Indiana, United States
Contact: ashley.heady@uga.edu

Name: Beltrán Pantoja De Prada (he/him/his)
Education: B.A. in Mathematics Teaching (Universidad Alberto Hurtado, Chile), M.Ed. in Learning Assessment (Pontificia Universidad Católica de Chile)
Program: Ph.D. in Measurement, Data Science, and Statistics
Research Interests: educational measurement, diagnostic classification models
Hobbies: reading, drawing, watching tv shows.
Hometown/country: Santiago, Chile
Contact: bpp09644@uga.edu
Diagnostic Instrument for Teachers' Content and Pedagogical Content Knowledge of Numbers and Operations (2024 - 2028). Institute of Education Sciences.
Generalized, Multilevel, and Longitudinal Psychometric Models for Evaluating Educational Interventions (2022 – 2026). Institute of Education Sciences.
CT-STEM Pop-Ups4All: An RPP for Agile Learning (2020 – 2024). National Science Foundation.
Native STEM Portraits: A Longitudinal, Mixed-Methods Study of the Intersectional Experiences of Native Learners and Professionals in STEM (2020 – 2025). National Science Foundation.
A Family of Diagnostic Models for Evaluating Learning Progressions (2019 – 2024). National Science Foundation.
Doubly Mixture Psychometric Models for Modeling Learning Progressions (2021 – 2023). UGA Faculty Seed Grants.
Publications
Madison, M. J., Jeon, M. Cotterell, M. E., Haab, S., & Zor, S. (2025). TDCM: An R package for estimating longitudinal diagnostic classification models. Multivariate Behavioral Research, 60(3), 518 – 527. https://doi.org/10.1080/00273171.2025.2453454.
Schellman, M., & Madison, M. J. Estimating the reliability of skill transition in longitudinal diagnostic classification models. (2024). Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986241256032.
Madison, M. J., Wind, S. A., Maas, L., Yamaguchi, K., & Haab, S. (2024). A one-parameter diagnostic classification model with familiar properties. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12390.
Maas, L., Madison, M. J., Brinkhuis, M. J. S. (2024). Properties and performance of the one-parameter log-linear cognitive diagnosis model. Frontiers in Education. https://doi.org/10.3389/feduc.2024.1287279.
Presentations
Heady, A., Madison, M. J., & Thais, S. (2026, July). Psychometric Models & the Evaluation of Generative AI Performance. Paper presented at the 2026 Annual Meeting of the Psychometric Society in Seoul, Republic of Korea.
Heady, A., Pantoja, B., Alila, N., & Madison, M. J. (2026, July). Systematic review of Q-matrix validation methods for diagnostic classification models. Poster presented at the 2026 Annual Meeting of the Psychometric Society in Seoul, Republic of Korea.
Pantoja, B., Heady, A., & Madison, M. J. (2026, July). Empirical blueprint in unidimensional item response theory. Poster presented at the 2026 Annual Meeting of the Psychometric Society in Seoul, Republic of Korea.
Mireles, N., & Madison, M. J. (2025, April). Evaluating 1-PLCDM Test Blueprints Robustness to DIF. Paper presented at the 2026 Annual Meeting of the National Council on Measurement in Education in Los Angeles, CA.
Madison, M. J.,Adeleye, O., Alila, N., Heady, A., & Pantoja, B. (2026). On the robustness of the one-parameter log-linear cognitive diagnosis model. Paper presented at the 2026 Annual Meeting of the National Council on Measurement in Education in Los Angeles, CA.
Alila, N. & Madison, M. J. (2026). Developing adaptive fit index thresholds for diagnostic classification models using machine learning. Paper presented at the 2026 Annual Meeting of the National Council on Measurement in Education in Los Angeles, CA.
Schellman, M., & Madison, M. J. (2025, April). Enhancing accuracy in student growth measurement: Adjusting diagnostic growth percentiles. Paper presented at the 2025 Annual Meeting of the National Council on Measurement in Education in Denver, CO.
Madison, M. J., Alila, N. (2025, April). Diagnostic assessment design with blueprint specifications. Paper presented at the 2025 Annual Meeting of the National Council on Measurement in Education in Denver, CO.
Morris, A., & Madison, M. J. (2024, April). Exploring the impact of culturally-relevant assessment material on black students' reading comprehension. Poster presented at the 2024 Annual Meeting of the National Council on Measurement in Education in Philadelphia, PA.
Haab, S., & Madison, M. J. (2024, April). Continuous effect loglinear diagnostic classification model: Incorporating general continuous effects in the LCDM. Poster presented at the 2024 Annual Meeting of the National Council on Measurement in Education in Philadelphia, PA.
Sergio Haab, M.A. 2023; Doctoral student, University of Iowa
Lientje Maas, Ph.D. 2023 (Utrecht University); Psychometric Researcher, CITO, The Netherlands
Selay Zor, Ph.D. 2023; National Education Expert, Ministry of Education, Turkey
Armani Morris, M.A. 2024; Statistical and Research Analyst, Children’s Law Center, University of South Carolina
Ye Yuan, Ph.D. 2024; Associate Psychometrician, Graduate Management Admission Council
Madeline Schellman, Ph.D. 2024; Research & Psychometric Services Analyst, Navvy Education
Oluwatosin Adeleye, M.A. 2025; Assessment Specialist, Action Behavior Centers
Al Moore, M.A. 2025; Educational Research Consultant
Anastasia Kreisel, M.A. 2026 (expected); Academic Coordinator, Northeastern University