MAP LAB

Research

Cognitive Diagnostic Modeling for Learning

  • Wang, C. & Lu, J. (2020). Learning attribute hierarchies from data: Two exploratory approaches. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998620931094

Source code: LVS functions    LVS divergent structure     RLCM Convergent

Source code  Study II Matlab code

Source code K=6+DINA+Unknown items;    K=10+ACDM+Known items

Multidimensional computerized adaptive testing

  • Wang, C., Weiss, D., & Shang, Z. (2018). Variable-length stopping rules for multidimensional computerized adaptive testing. Psychometrika.

Source code: MGRM_MCAT_code    Real MGRM item parameters  MGRM item parameters

  • Wang, C., Chen, P., & Jiang, S. (2019). Item Calibration Methods with Multiple Subscale Multistage Testing.  Journal of Educational Measurement.

Source code: (a) Data generation: data generation R code ;  (b) Single-group & Multiple-group MML/EM: Single_Multiple group MML R code

  • Chen, P., Engel, S., & Wang, C. (R&R). The multivariate adaptive design for efficient estimation of the time-course of perceptual adaptation. Behavior Research Methods.

Adaptive vision code

Multidimensional/Multilevel IRT models

  • Wang, C., Zhu, R., Crane, P., Choi, S., Jones, R., & Tommet, D. (2023+). Using Bayesian IRT for multi-cohort repeated measure design to estimate individual latent change scores. Psychological Methods.

Manuscript ADNI simulation manuscript_FINAL ; Supplemental material_online

  • Wang, C., Zhu, R., & Xu, G. (2022). Using lasso and adaptive lasso to identify DIF in multidimensional 2PL models. Multivariate Behavioral Research.

Source code  MIRT-RegDIF code  GitHub page RegDIF 

  • Wang, C., Weiss, D., Suen, K. (2020). Hypothesis testing methods for multivariate multi-occasion intra-individual change. Multivariate Behavioral Research.

Source code Code_for_MV-MO_Simulations (Conventional Test + CAT )

Real data and source code for handling real data Real data+Code

  • Jiang, S., Wang, C., & Weiss, D. (2016). Sample size requirement for estimation of item parameters in multidimensional graded response model. Front. Psychol, 7:109. doi:        10.3389/fpsyg.2016.00109

Source code: MGRM_simulation

  • 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.

Appendix Appendix 

Supplementary studies and results Supplementary files

  • Wang, C., Xu, G., & Zhang, X. (2019). Correction for item response theory latent trait measurement error in linear mixed effects models. Psychometrika.

Full manuscript Manuscript with Appendices

Code for  MIRT_LME (zipped file of MATLAB and R code)

  • Wang, C., & Nydick, S. (2019). On longitudinal item response theory models: A didactic. Journal of Educational and Behavioral Statistics.

Full manuscript Wang_Nydick_JEBS

Mplus code Longitudinal IRT Comparison – MPlus Code

Data simulation and analysis R code Longitudinal IRT – Simulation  Longitudinal IRT – Analysis

Mixture models for detecting aberrant test-taking behavior

  • Wang, C., Xu, G., Shang, Z. , & Kuncel, N. (2018). Detecting aberrant behavior and item pre-knowledge—A comparison of mixture modeling method and residual method. Journal of Educational and Behavioral Statistics, 43, 469-501.
  • Wang, C., Xu, G., &  Shang, Z. (2018). A two-stage approach to differentiating normal and aberrant behavior in computer based testing. Psychometrika, 83, 223-254.
  • 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.

Source code: Mixture model MCMC code in R

Miscellaneous Studies

  • Want to know how sampling weights should (or should not) be incorporated in item calibration? Please read this piece of analysis. If you would like to replicate the study, please check out the R code: MML with sampling weights