Contact Information
M/C 374
Champaign, IL 61820
Research Interests
Psychometrics
Latent Variable Models
Multivariate Categorical Data Analysis
Research Description
Assistant Professor and Associate Professor, Biostatistics and Medical Informatics, University of Wisconsin-Madison (1995-2001)
- Associate Editor, Statistics and Its Interface (2007-)
- Associate Editor, Psychometrika (2003-)
- Co-director of Educational and Psychological Measurement Lab
Education
Statistics, PhD, University of Illinois Urbana-Champaign
Mathematics, BS, University of Illinois Urbana-Champaign
Awards and Honors
Associate Editor, Statistics and Its Interface (2007-)
Associate Editor, Psychometrika (2003-)
Co-director of Educational and Psychological Measurement Lab
Additional Campus Affiliations
Professor, Statistics
Professor, Psychology
Professor, Educational Psychology
External Links
Recent Publications
He, S., Culpepper, S. A., & Douglas, J. A. (2024). A sparse latent class model incorporating response times. British Journal of Mathematical and Statistical Psychology. Advance online publication. https://doi.org/10.1111/bmsp.12380
He, S., Culpepper, S. A., & Douglas, J. (2023). A Sparse Latent Class Model for Polytomous Attributes in Cognitive Diagnostic Assessments. In L. Andries van der Ark, W. H. M. Emons, & R. R. Meijer (Eds.), Essays on Contemporary Psychometrics (pp. 413-442). (Methodology of Educational Measurement and Assessment). Springer. https://doi.org/10.1007/978-3-031-10370-4_21
Yu, A., & Douglas, J. A. (2023). IRT Models for Learning With Item-Specific Learning Parameters. Journal of Educational and Behavioral Statistics, 48(6), 866-888. https://doi.org/10.3102/10769986231193096
Yigit, H. D., & Douglas, J. A. (2021). First-Order Learning Models With the GDINA: Estimation With the EM Algorithm and Applications. Applied Psychological Measurement, 45(3), 143-158. https://doi.org/10.1177/0146621621990746
Benjamin, A. S., Griffin, M. L., & Douglas, J. A. (2019). A nonparametric technique for analysis of state-trace functions. Journal of Mathematical Psychology, 90, 88-99. https://doi.org/10.1016/j.jmp.2019.03.006