Research Interests
Chun Wang
Areas of Interest
- Multilevel/multidimensional/mixture Item response theory (IRT) models for both dichotomous and polytomous responses (e.g., Likert scales, nominal responses), with applications in achievement testing, aptitude testing, personality assessment, and health measurement
- Development of new scales/instruments
- Refine and validate scales (e.g., check for measurement invariance across different cultures, groups, etc)
- Computerized adaptive testing (CAT) and applications
- Cognitive diagnostic modeling and applications in classroom assessment and learning
- General statistical methods and applications including adaptive design, longitudinal models, etc.
My scientific career is broadly situated in the field of educational and psychological measurement, with specific devotion to methodology advancement that leads to better assessment with higher reliability/fidelity, fairness, and security. I am passionate about improving the methods for measuring a wide range of educational and psychological variables, as well as developing, refining, and extending methods for analyzing multivariate data that are widely used in the behavioral sciences. The first thrust of my work has been centered on resolving challenges emerged from the wide-ranging implementation of CAT. The second line of my core research agenda has been focused on developing innovative models/methods to better understand nonlinear relationships among observed and latent variables using state-of-art latent variable methods, including multidimensional and/or multilevel item response theory models, cognitive diagnostic models, and mixture models. I also look forward to any potential collaborations to apply the advanced psychometric methods in education, psychology, and health research broadly.
2020, Anne Anastasi Distinguished Early Career Contributions Award (American Psychological Association, Division 5)
2018, 2019, Best Reviewer Award for Psychometrika (Psychometric Society)
2017, McKnight Presidential Fellow, University of Minnesota
2017, Early Career Award, Psychometric Society
2016, Best Reviewer Award for Psychometrika (Psychometric Society)
2016, Outstanding Reviewer Award for Journal of Educational and Behavioral Statistics (AERA & ASA)
2015, Early Career Award, AERA Division D (Quantitative Research Methodology)
2014, Early Career Award, International Association for Computerized Adaptive Testing
2014, Post-doctoral Fellow, National Academy of Education/Spencer Foundation
2014, Jason Millman Promising Measurement Scholar Award, NCME
2013, State-of-the-Art Lecturer Award, Psychometric Society
2013, Alicia Cascallar Best Paper Award, NCME
Latest articles
* indicate student advised
† corresponding author other than the first author
Cho, A., Xiao, J.*, Wang, C.†, & Xu, G.† (2022). Regularized variational estimation for exploratory item factor analysis. Psychometrika.
Wang, C., Zhu, R.*, & Xu, G. (2022). Using lasso and adaptive lasso to identify DIF in multidimensional 2PL models. Multivariate Behavioral Research
Wang, C. (2021). Using penalized EM algorithm to infer learning trajectories in latent transition CDM. Psychometrika.
Wang, C., & Lu, J.* (2021). Learning attribute hierarchies from data: Two exploratory approaches. Journal of Educational and Behavioral Statistics.
Google Scholar page
https://scholar.google.com/citations?user=6j3ABHUAAAAJ&hl=en