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Washington State University
College of Education

Shenghai Dai

Shenghai Dai

Assistant Professor
Educational Psychology

Pullman Campus
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, and multilevel modeling in broad social and educational contexts. Currently, I am serving as a statistical and methodological advisor for the Journal of School Psychology, an associate editor for Frontiers in Education – Assessment, Testing and Applied Measurement, and sitting on the editorial board of the Journal of Experimental Education.

  • Psychometrics: Item response theory, cognitive diagnostic models, large-scale assessment, differential item functioning
  • Quantitative Methods: Structural equation modeling, multivariate/multilevel modeling, missing data issues
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

  • Statistics & Quantitative Methods
    • ED_PSYCH 508 Educational Statistics
    • ED_RES 565 Quantitative Research
    • ED_PSYCH 569 Multivariate Data Analysis
    • ED_PSYCH 521 Data Management & Visualization
  • 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
  • Others
    • ED_PSYCH 574 Seminar in Educational Psychology
Selected Accomplishments

Peer-Reviewed Journal Articles

  • Dai, S., French, B.F., Finch, W. H. (accepted). DIFplus: An R package for multilevel differential item functioning detection. Applied Psychological Measurement.
  • 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. https://www.frontiersin.org/articles/10.3389/feduc.2021.721963
  • 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
  • Dai, S., Wang, X., & Svetina, D. (2019). The application of minimum discrepancy estimation in the implementation of diagnostic classification models. Behaviormetrika. Advance online publication. https://doi.org/10.1007/s41237-019-00094-4
  • 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. Advance online publication. https://doi.org/10.1177/0743558419864021
  • 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. Advance online publication. https://doi.org/10.1037/apl0000402
  • 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. 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

Book Chapters

  • Dai, S., & Higheagle Strong, Z. (in press). Educational applications using large-scale assessment and survey data: Opportunities and challenges. In U. Luhanga & G. Allen (Ed.), Basic elements of survey research in education: Addressing the problems your advisor never told you about. North Carolina. Information Age Publishing.
  • Wang, X. & Dai, S. (in press). Extreme response style in survey research. In U. Luhanga & G. Allen (Ed.), Basic elements of survey research in education: Addressing the problems your advisor never told you about. North Carolina. Information Age Publishing.
  • Brown, N., Dai, S., & Svetina, D. (2016). Analyzing NAEP data at the item level. In P. Kloosterman, D. Mohr, & C. Walcott (Ed.), What mathematics do students know and how is that knowledge changing? evidence from the National Assessment of Educational Progress. North Carolina. Information Age Publishing.
  • Brown, N., Svetina, D., & Dai, S. (2016). Analyzing NAEP data at the construct level. In P. Kloosterman, D. Mohr, & C. Walcott (Ed.), What mathematics do students know and how is that knowledge changing? evidence from the National Assessment of Educational Progress. North Carolina. Information Age Publishing.
  • Kloosterman, P., Walcott, C., Brown, N. J. S., Mohr, D., Perez, A., Dai, S., Roach, M., Wilson, L. D., & Huang, H. (2015). Using NAEP to analyze 8th grade students’ ability to reason algebraically. In Middleton, J. A., Cai, J., Hwang, S., (Eds.), Large-scale studies in mathematics education. New York. Springer.

Software Packages