Chun Wang

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Contact Me

Dept of Psychology
N218 Elliott Hall

75 E River Rd


My scientific career is broadly situated in the field of psychological and educational measurement, with specific devotion to methodology advancement that leads to better assessment with higher reliability/fidelity, fairness, and security. I consider myself as a quantitative psychologist whose passion is the improvement of methods for measuring a wide range of psychological and educational 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 computerized adaptive testing (CAT) that is built on modern item response theory (IRT). 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.

Educational Background & Specialties

Educational Background

  • Ph.D.: Quantitative Psychology, University of Illinois at Urbana-Champaign, 2012.
  • M.S.: Statistics, University of Illinois at Urbana-Champaign, 2009.
  • B.S.: Psychology, Peking University, Beijing, China, 2007.


  • (Multidimensional) computerized adaptive testing
  • Cognitive diagnostic modeling
  • Response time modeling and analysis
  • Item response theory
Courses Taught
  • PSY 8814 - Analysis of Psychological Data
  • PSY 3801 - Introduction to Psychological Measurement and Data Analysis
  • PSY 8882 - Statistical computing in latent variable models
  • Wang, C. , & Weiss, D. (2017). Multivariate hypothesis testing methods for evaluating significant individual change. Applied Psychological Measurement.
  • Zheng, C., &Wang, C. (2017). Application of Binary Searching for Item Exposure Control in Cognitive Diagnostic Computerized Adaptive Testing. Applied Psychological Measurement.
  • Wang, C., Song, T., Wang, Z., & Wolfe, E. (2017). Essay Selection Methods for Adaptive Rater Monitoring. Applied Psychological Measurement, 41, 60-79.
  • Wang, C., Xu, G., & *Shang, Z. (2017). A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing. Psychometrika, 1-32.
  • Chen, P., Wang, C., Xin, T., & Chang, H. (2017). Developing new online calibration methods for multidimensional computerized adaptive testing. British Journal of Mathematical and Statistical Psychology, 70, 81-117
  • Zhou, A., Whealin, J., Wang, C., & Lee, R. (Accepted). A Measure of Perceived Family Stigma: Validity in a Military Sample. Psychological Assessment.
  • Xu, G., Wang, C., & *Shang, Z. (2016). On initial item selection in cognitive diagnosis computerized adaptive testing (CD-CAT). British Journal of Mathematical and Statistical Psychology, 69, 291-315.
  • Jiang, S*. , Wang, C., & Weiss, D. (2016). Sample Size Requirements for Estimation of Item Parameters in the Multidimensional Graded Response Model. Frontiers in Psychology (Quantitative Psychology and Measurement). (* indicates graduate student) doi: 10.3389/fpsyg.2016.00109
  • Chen, P., &Wang, C. (2016). A new online calibration method for multidimensional computerized adaptive testing. Psychometrika, 81, 674-701.(co-first authors)
  • Huebner, A., Wang, C., Quinlan, K., & Seubert, L. (2016). Item exposure control for multidimensional computerized adaptive testing under maximum likelihood and expected a posterior estimation. Behavior Research Methods, 48, 1443-1453.
  • Wang, C., Kohli, N., & Henn, L. (2016). A second-order longitudinal model for binary outcomes: Item response theory versus structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23, 455-465.
  • Chow, P., Berenbaum, H., &Wang, C. (2015). Examining the validity and reliability of an abridged version of the Perceived Affect Utility Scale (PAUSe). European Journal of Psychological Assessment.
  • Wang, C., & Xu, G. (2015). A Mixture Hierarchical Model for Response Times and Response Accuracy. British Journal of Mathematical and Statistical Psychology, 68, 456-477. (co-first authors)
  • Wang, C., Shu, Z., Shang, Z., & Xu, G. (2015). Assessing Item-level Fit for the DINA Model. Applied Psychological Measurement, 39, 525-538.
  • Wang, C. (2015). On latent trait estimation in multidimensional compensatory item response models. Psychometrika, 80, 428-449.
  • Kohli, N., Hughes, J., Wang, C., Zopluoglu, C., & Davison, M. L. (2015). Fitting a linear–linear piecewise growth mixture model with unknown knots: A comparison of two common approaches to inference. Psychological Methods, 20, 259-275.
  • Wang, C.& Nydick, S. (2015). Comparing Two Algorithms for Calibrating the Restricted Non-Compensatory Multidimensional IRT Model. Applied Psychological Measurement , 39, 119-134.
  • Hong, H., Wang, C., Lim, Y., & Douglas, J. (2015). Efficient models for cognitive diagnosis with continuous and mixed-type latent variables. Applied Psychological Measurement, 39, 31-43
  • He, J. Choi, W., McCarley, J.S., Chaparro, B., &Wang, C. (2015). Texting while driving using Google Glass: Promising but not distraction-free. Accident Analysis & Prevention, 81,218-229.
  • Wang, C. (2014). Improving measurement precision of hierarchical latent traits using adaptive testing. Journal of Educational and Behavioral Statistics, 39, 452-477.
  • Wang, C., Zheng, C., & Chang, H. (2014). An Enhanced Approach to Combine Item Response Theory with Cognitive Diagnosis in Adaptive Testing. Journal of Educational Measurement, 51, 358-380.
  • Cheville, A. L., Wang, C., Ni, P. S., Jette, A. M., & Basford, J. R. (2014). Age, sex, and symptom intensity influence test taking parameters on functional patient reported outcomes. American Journal of Physical Medicine and Rehabilitation, 93(11), 931-937.
  • Wang, C., Zheng, Y. & Chang, H. (2014). Does Standard Deviation Matter? Using “Standard Deviation” to Quantify Security of Multistage Testing. Psychometrika, 79, 154-174.
  • Wang, C .(2013). Mutual information item selection method in cognitive diagnostic computerized adaptive testing with short test length. Educational and Psychological Measurement, 73, 1017-1035.
  • Wang, C., Fan, Z., Chang, H., & Douglas, J. (2013). A Semiparametric Model for Jointly Analyzing Response Times and Accuracy in Computerized Testing. Journal of Educational and Behavioral Statistics, 38, 381--417.
  • Wang, C.., Chang, H. & Boughton, K. (2013). Deriving stopping rules for multidimensional computerized adaptive testing. Applied Psychological Measurement, 37, 99-122.
  • Tay, L., Vermunt, J., &Wang, C.. (2013). Assessing the item response theory with covariate (IRT-C) procedure for ascertaining differential item functioning. International Journal of Testing, 13, 201--222.
  • Wang, C., Chang, H.-H., & Douglas, J. (2013). The linear transformation model with frailties for the analysis of item response times. British Journal of Mathematical and Statistical Psychology, 66, 148-168.
  • Wang, C., Chang, H., & Douglas, J. (2012). Combining CAT with cognitive diagnosis: A weighted item selection approach. Behavior Research Methods, 44, 95--109.
  • Fan, Z., Wang, C., Chang, H.-H., & Douglas, J. (2012). Utilizing response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37, 655-670.
  • Chen, P., Xin, T., Wang, C., Chang, H. (2012). On-line calibration methods in cognitive diagnostic computerized adaptive testing. Psychometrika, 77, 201--222.
  • Wang, C., Chang, H., & Huebner, A. (2011). Restrictive stochastic item selection methods in cognitive diagnostic CAT. Journal of Educational Measurement, 48, 255--273.
  • Wang, C., Chang, H., & Boughton, K. (2011). Kullback-Leibler information and its applications in multi-dimensional adaptive testing. Psychometrika, 76, 13--39.
  • Wang, C., & Chang, H. (2011). Item selection in multidimensional computerized adaptive tests|Gaining information from different angles. Psychometrika, 76, 363--384.
  • Huebner, A., &Wang, C. (2011). A note on comparing examinee classification methods for cognitive diagnosis models. Educational and Psychological Measurement, 71, 407--419.
  • Chang, H., &Wang, C. (2011) Book review: Mark D. Reckase’s Multidimensional Item Response Theory. Psychometrika, 76, 504--506.
  • McKnight Presidential Fellow (2017-2020)
  • Psychometric Society Early Career Award, 2017
  • Outstanding Reviewer Award (awarded by the Psychometric Society), 2016
  • Outstanding Reviewer Award (Awarded by Journal of Educational and Behaviroal Statistics, American Educational Research Association), 2015
  • AERA Division D Early Career Award, 2015
  • Early Career Researcher Award (Awarded by the International Association for Computerized Adaptive Testing), 2014
  • Jason Millman Promising Measurement Scholar Award (Awarded by National Council on Measurement in Education), 2014
  • Alicia Cascallar Award (Awarded by National Council on Measurement in Education), 2013
  • Outstanding Reviewer Award (Awarded by Journal of Educational and Behaviroal Statistics, American Educational Research Association), 2013
  • Jeffrey Tanaka Award (for outstanding original research or scholarship), UIUC, 2012
  • Nancy Hirschberg Award (for outstanding original research or scholarship), UIUC, 2011
  • Knowledge-for-All Lecturer Award (for outstanding research), UIUC, 2010
  • John Wallace Dallenbach Memorial Fellowship, UIUC, 2007