Email
wang4066@uw.edu
Phone
206-616-6306
Office
312E Miller Hall

Additional Appointments

Affiliate Faculty, Center for Statistics and the Social Sciences

Research Interests

Quantitative Research Methods

Chun Wang

Professor

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. 
Education
B.S. in Psychology, Peking University (Beijing, China)
M.S. in Statistics, University of Illinois at Urbana-Champaign
Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign
Research

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.

Fellowships, honors and awards

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

Publications

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

My Lab Webpage

https://sites.uw.edu/pmetrics/