Measurement, reliability, and validity 測量與信效度
Classical test theory, reliability, validity, scale development
In preparation
Textbooks, software, and teaching materials
Textbooks, software, and course materials from the lab for researchers and students.
A unified graphical grammar for statistical models: one lexicon of nodes, edges, and plates that renders CFA, SEM, IRT, multilevel, mixture, and Bayesian models consistently across TikZ, ggplot2, and DiagrammeR backends. Available on CRAN.
Dynamic missingness graphs and sensitivity analysis for informative nonresponse in ecological momentary assessment data: declare the nonresponse mechanism, check which estimands remain recoverable, test whether skipped prompts were informative, and profile estimates over a self-censoring parameter. Available on CRAN.
Models with discrete latent variables for categorical data: a common framework and estimation toolbox for latent class, latent transition, and multilevel extensions (Yu, 2013, Behavior Research Methods).
Course sites with the syllabus, schedule, and materials for each course.
Describing and modeling change: multilevel and latent growth models, intensive longitudinal data, and dynamic approaches, with hands-on analysis in R. The course site carries the syllabus, weekly schedule, datasets, and example scripts. [Chinese]
Foundations of psychological measurement: reliability, validity, item analysis, test construction, and evaluation. The course site carries the syllabus, weekly schedule, and assignment and exam guidelines. [Chinese]
Material on each topic will be added here.
Classical test theory, reliability, validity, scale development
In preparation
EFA, CFA, SEM, model fit
In preparation
IRT models, differential item functioning, measurement invariance
In preparation
Latent class, latent profile, latent transition, multilevel latent class models
In preparation
Random effects, centering, cross-classified models, multilevel SEM
In preparation
Growth models, panel models, EMA and diary data, dynamic models
In preparation
Network estimation, network comparison, factors versus networks
In preparation
Power, sample size planning, Monte Carlo simulation, single-case designs
In preparation
Meta-analysis, missing data, invalid responding
In preparation
Reporting standards, reproducibility, generative AI and inference
In preparation