Measurement and construct representation
Psychometric theory, educational measurement, and factor and component models for representing constructs and interpreting scores.
Psychological & learning sciences
The PsycheMatrica Lab develops and evaluates psychometric and statistical methods for studying the human psyche—its nature, structure, dynamics, and development. Our name combines the Greek psychē (mind or soul) with matrica, a coined form inspired by the Latin matrix (source or underlying structure). The name also evokes the mathematical matrix, reflecting our use of data and mathematical models to represent and analyze psychological and learning phenomena. Together, these meanings express our mission to develop models that enable rigorous measurement and empirical investigation of how people think, feel, believe, learn, and act. We make these methods accessible to researchers and pursue collaborative empirical research with substantive-area experts.

How we work
Psychometric theory, educational measurement, and factor and component models for representing constructs and interpreting scores.
Structural equation modeling for relationships, dynamics, and change across time, levels, and heterogeneous data.
We aim to combine AI with statistical and psychometric methods to develop game-based and simulation-based systems that measure psychological change, decision-making, and learning, and provide personalized feedback.
Making methods accessible through research software, practical tutorials, workshops, and collaboration with substantive-area experts.
Selected work
Structural Equation Modeling: A Multidisciplinary Journal
Journal DOI: 10.1080/10705511.2026.2674637
Structural Equation Modeling: A Multidisciplinary Journal, 32(6), 1057–1068
Journal DOI: 10.1080/10705511.2025.2537946
Research software
Free standalone software with a graphical interface for structured factor analysis.
Methods, downloads & documentation ↗Free graphical software for structural equation modeling using generalized structured component analysis methods.
Methods, downloads & documentation ↗Supplementary tool: TestSEM Pro for Monte Carlo evaluation of SEM estimators.
A longer view
We seek to help people understand themselves, learn well, and exercise independent judgment in the age of AI.
Privacy, autonomy, and careful empirical evaluation guide our long-term research vision.
Our mission and vision ↗News & announcements
We are pleased to announce the launch of the official PsycheMatrica Lab website. The website introduces our mission and research directions in quantitative methodology for psychological …
“A Practical Guide to Conditional Mediation Analysis with GSCA: Method, Simulation, and Software” was published online in Structural Equation Modeling: A Multidisciplinary Journal. A prac…
A public preprint of “Data-Matrix-Based Overall Goodness-of-Fit Testing for Confirmatory Factor Analysis: In-Sample Prediction Error for Observed Variables” is available. A public preprin…
People
Led by GyeongCheol Cho, Assistant Professor, Quantitative Psychology at The Ohio State University.
Meet the lab ↗Connections
We collaborate with domain experts to develop and evaluate methods in the contexts where they matter.
Research with us ↗