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The lab welcomes master's and doctoral students in psychology, education, statistics, and related fields who want to work on quantitative methodology: psychological measurement and test theory, latent variable and multilevel models, psychometric networks, longitudinal and intensive longitudinal data, research synthesis, or the methodology of psychological research more broadly. Students with substantive interests who want to strengthen the quantitative side of their research are equally welcome, provided they are willing to invest in the methods.

What helps most is not prior expertise but a particular disposition: curiosity about why a method works, patience with simulation and code, and the discipline to write clearly. A solid course in statistics and some experience with R (or willingness to acquire it quickly) are expected; linear algebra and probability are assets.

Graduate students in the lab typically learn to design and run Monte Carlo simulation studies, to estimate models in R with packages such as lavaan, mirt, lme4, and brms, to write reproducible analysis code, and to present methodological work at conferences such as the Psychometric Society meeting and the Taiwan Psychological Society annual meeting.