Research

With broad training in quantitative psychology, statistics, psychometrics, and data science — and years of experience in educational measurement — my research interests span multiple disciplines, crossing theoretical advances with practical applications. I am primarily interested in methodological issues in psychometrics, with latent variable modeling, Bayesian statistics, and mixed-effects models being the main focus of my current quantitative research program.

Areas of Focus

• Diagnostic Classification Models (DCMs) — model development, estimation, and applied diagnostic measurement. See the DCM Book companion site.
• Bayesian Psychometric Modeling — Bayesian formulations of confirmatory factor, IRT, and DCM models; estimation in JAGS, Stan, and nimble.
• Multilevel Measurement Models — hierarchical extensions of measurement models for clustered, longitudinal, and multi-rater data.
• Software & estimation methods — accessible implementations of contemporary psychometric models in R.

Publications

• ORCID: https://orcid.org/0000-0001-7616-0973 — full publication record.
• Google Scholar: Templin profile.

Books

• Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic Measurement: Theory, Methods, and Applications. New York, NY: Guilford Press. → Companion site

Software

• blatent — implements Bayesian Diagnostic Classification Models (DCMs). https://github.com/jonathantemplin/blatent

For course-related code repositories (Bayesian, SEM, missing data, multilevel measurement, multivariate, etc.), see my GitHub profile.