Ph.D., Quantitative Methods and Evaluation, University of California, Berkeley
M.A., Statistics, University of California, Berkeley
M.A., Quantitative Methods, Measurement and Evaluation, Yonsei University
B.A., Education and Sociology, Yonsei University
Teaching & Research Interests
My research revolves around developing, applying, and estimating a variety of latent variable models for studying measurement and growth. I also have a keen interest in developing efficient computational algorithms and software packages. My recent research topics include respondent-item network analysis, dynamic feedback process modeling, and joint modeling of behavioral and biological process data.
255C: Advanced seminar on statistical modeling with latent variables
255C: Advanced seminar on cross-classified multilevel modeling
231A: Toolkit for Advanced quantitative methodology research
Jeon, M., Rijmen, F., & Rabe-Hesketh, S. (2018). CFA models with a general factor and multiple sets of secondary factors. Psychometrika, 83, 785-808.
Jin, I-H. & Jeon, M. (2018). [co-first-authors] A doubly latent space joint model for the analysis of item response data. Psychometrika. 84, 236-260.
Jeon, M. & De Boeck, P. (2019). An analysis of an item response strategy based on knowledge retrieval. Behavior Research Methods., 51, 697-719.
Jeon, M., Kaufman, C. & Rabe-Hesketh, S. (2019). Monte Carlo local likelihood approximation. Biostatistics, 20, 164–179.
Jeon, M. & De Boeck, P. (2017). Decision qualities of Bayes factor and p-value based hypothesis testing. Psychological Methods, 22, 340-360.
Jeon, M., Rijmen, F. & Rabe-Hesketh, S. (2017). A variational maximization-maximization algorithm for generalized linear mixed models with crossed random effects. Psychometrika, 82, 693-716.
Jeon, M. & Rabe-Hesketh, S. (2016). An autoregressive growth model for longitudinal item analysis. Psychometrika, 81, 830-850