Skip to main content Skip to navigation
Washington State University
College of Education

Shenghai Dai

Shenghai Dai

Associate Professor
Educational Psychology

WSU Pullman
Cleveland Hall 354
Pullman, WA  99164-2136
509-335-0958
s.dai@wsu.edu

Curriculum Vitae || Research Gate || Linkedin

Research Interests

My research interests mainly lie in the investigations of the performance and utility of current and emerging measurement frameworks that can provide formative and diagnostic information about student learning and achievement in various assessment settings. Particularly, I am interested in both methodological and applied aspects of (multidimensional) item response theory models, cognitive diagnostic models (CDMs), subscore reporting, differential item functioning (DIF), and large-scale assessment. I am also interested in applying statistical methods, such as missing data analysis, structural equation modeling, multilevel modeling, and machine learning approaches in broad social and educational contexts. I am the director of the Large-Scale Data laboratory that is housed within the WSU Learning and Performance Research Center. Currently, I am serving as a statistical and methodological advisor of the Journal of School Psychology, an associate editor of Frontiers in Education – Assessment, Testing and Applied Measurement, and a consulting editor of the Journal of Experimental Education and Psych.

  • Psychometrics: Item response theory, large-scale assessment, cognitive diagnostic models, differential item functioning, missing data issues
  • Quantitative Methods: Structural equation modeling, multivariate/multilevel modeling, longitudinal data analysis, machine learning applications in education research
Education
  • Ph. D., Inquiry Methodology, Indiana University Bloomington
    • Specialization: Psychometrics & Quantitative Methodology
  • M. S., Applied Statistics, Indiana University Bloomington
  • M. A., Language Testing, Beijing Language and Culture University
  • B. A., Teaching Chinese as a Second Language, Beijing Language and Culture University
Teaching
  • Measurement & Psychometrics
    • ED_PSYCH 511 Classical and Modern Test Theory
    • ED_PSYCH 577 Item Response Theory
    • ED_PSYCH 578 Advanced Item Response Theory
    • ED_PSYCH 579 Large-Scale Surveys in Education
  • Statistics & Quantitative Methods
    • ED_PSYCH 508 Educational Statistics
    • ED_RES 565 Quantitative Research
    • ED_PSYCH 569 Multivariate Data Analysis
    • ED_PSYCH 576 Factor Analytic Procedures
    • ED_PSYCH 521 Data Management & Visualization
  • Others
    • ED_PSYCH 574 Seminar in Educational Psychology
Selected Accomplishments

Peer-Reviewed Journal Articles

  • Measurement & Psychometrics
    • Dai, S., French, B.F., Finch, W. H. (in press). DIFplus: An R package for multilevel differential item functioning detection. Applied Psychological Measurement.
    • Svetina Valdivia, D. & Dai, S. (2023). Number of response categories and sample size requirements in polytomous IRT models. Journal of Experimental Education. Advance online publication. https://doi.org/10.1080/00220973.2022.2153783
    • Kehinde, O.J., Dai, S., French, B. (2022). Item parameter estimation for multidimensional graded response model under complex structure. Frontiers in Education – Assessment, Testing and Applied Measurement. 7, 947581. https://www.frontiersin.org/articles/10.3389/feduc.2022.947581
    • Dai, S. & Svetina Valdivia, D. (2022). Dealing with missing responses in cognitive diagnostic modeling. Psych. 4(2), 318-341. https://doi.org/10.3390/psych4020028.
    • Dai, S., Vo, T., Kehinde, O.J., He, H., Xue, Y., Demir, C., & Wang, X. (2021). Performance of polytomous IRT models with rating scale data: An investigation over sample size, instrument length, and missing data. Frontiers in Education – Assessment, Testing and Applied Measurement. 6, 721963. https://www.frontiersin.org/articles/10.3389/feduc.2021.721963
    • Dai, S. (2021). Handling missing responses in psychometrics: Methods and software. Psych, 3, 673-693. https://doi.org/10.3390/psych3040043.
    • Wang, X., Svetina, D., & Dai, S. (2019). Exploration of factors affecting the necessity of reporting test subscores. Journal of Experimental Education, 87(2), 179-192. https://doi.org/10.1080/00220973.2017.1409182
    • Dai, S., Svetina, D., & Chen, C. (2018). Investigation of missing responses in Q-matrix validation. Applied Psychological Measurement. 42(8), 660–676. https://doi.org/10.1177/0146621618762742
    • Svetina, D., Feng, Y., Paulsen, J., Valdivia, M., Valdivia, A., & Dai, S. (2018). Examining DIF in the context of CDMs when the Q-matrix is Misspecified. Frontiers in Psychology (section Quantitative Psychology and Measurement), 9:696, 1-15. https://doi.org/10.3389/fpsyg.2018.00696
    • Dai, S., Svetina, D., & Wang, X. (2017). Reporting subscores using R: A software review. Journal of Educational and Behavioral Statistics, 42(2), 617-638. https://doi.org/10.3102/1076998617716462
    • Svetina, D., Dai, S., & Wang, X. (2017). Use of cognitive diagnostic model to study differential item functioning in accommodations. Behaviormetrika, 44(2), 313-349. https://doi.org/10.1007/s41237-017-0021-0
    • Svetina, D., Valdivia, A., Underhill, S., Dai, S., & Wang, X. (2017). Recovery of parameters in multidimensional item response theory models under complexity and nonormality. Applied Psychological Measurement, 41(7), 530-544. https://doi.org/10.1177/0146621617707507
    • Dai, S., Wang, X., & Svetina, D. (2019). The application of minimum discrepancy estimation in the implementation of diagnostic classification models. Behaviormetrika, 46, 453-481. https://doi.org/10.1007/s41237-019-00094-4
  • Statistics & Quantitative Methods
    • Ramazan, O., Dai, S., Danielson, R., Ardasheva, Hao, T., & Y. Austin, B., (accepted). Students’ 2018 PISA reading self-concept: Identifying predictors and examining model generalizability for emergent bilinguals. Journal of School Psychology. [Machine learning & multilevel modeling]
    • Dai, S., Hao, T., Ardasheva, Y., Ramazan, O., Danielson, R., & Austin, B. (2023). PISA reading achievement: Identifying predictors and examining model generalizability for multilingual students. Reading and Writing. Advance online publication. https://doi.org/10.1007/s11145-022-10357-4 [Machine learning & multilevel modeling]
    • Dai, S., Kehinde, O.J., Schmitter-Edgecombe, M., & French, B. (2022). Modeling daily fluctuations in everyday cognition and health behaviors at general and person-specific levels: A GIMME analysis. Behaviormetrika. Advance online publication. https://doi.org/10.1007/s41237-022-00191-x. [Intensive longitudinal data analysis]
    • Zhang, X., Dai, S., & Ardasheva, Y. (2020). Contributions of (de)motivation, engagement, anxiety in English listening and speaking. Learning and Individual Differences, 79, 1-13. https://doi.org/10.1016/j.lindif.2020.101856.  [Structural equation modeling]
    • Liu, Z., Roggio, R., Day, D., Zheng, C., Dai, S., & Bian, Y. (2019). Leader development begins at home: Over-parenting harms adolescent leader emergence. Journal of Applied Psychology, 104(10), 1226–1242. https://doi.org/10.1037/apl0000402. [Structural equation modeling]
    • Higheagle Strong, Z., McMain, E.M., Frey, K.S., Wong, R.M., Dai, S., & Jin, G., (2019). Ethnically diverse adolescents recount third-party actions that amplify their anger and calm their emotions after perceived victimization. Journal of Adolescent Research, 35(4), 461-488. https://doi.org/10.1177/0743558419864021. [Nonparametric]

Book Chapters

Software Packages