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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.

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

  • ED_RES 565 Quantitative Research (Syllabus)
  • ED_PSYCH 569 Seminar in Quantitative Techniques in Education
    • Multivariate Data Analysis (flyer)
  • ED_PSYCH 521 Topics in Educational Psychology
    • Data Management & Visualization (flyer)
  • ED_PSYCH 577 Item Response Theory (flyer)
  • ED_PSYCH 574 Seminar in Educational Psychology (Syllabus)
Selected Accomplishments

Peer Reviewed Journal Articles

  • Dai, S., Svetina, D., & Chen, C. (2018). Investigation of Missing Responses in Q-Matrix Validation. Applied Psychological Measurement. doi: 10.1177/0146621618762742
  • Wang, X., Svetina, D., & Dai, S. (2018). Exploration of factors affecting the necessity of reporting test subscores. Journal of Experimental Education. doi:10.1080/00220973.2017.1409182
  • 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). doi: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. doi: 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. doi: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), 530-544doi: 10.1177/0146621617707507.

Book Chapters

  • 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

Conference Presentations

  • Dai, S., Wang, X., Svetina, D., Underhill, S., & Feng, Y. (2017, April). The application of minimum discrepancy estimation in the implementation of cognitive diagnostic models. Paper presented at the annual meeting of the American Educational Research Association, San Antonio, TX.
  • Dai, S., & Svetina, D. (2017, April). Investigation and treatment of missing responses in the implementation of cognitive diagnostic models. Paper presented at the annual meeting of the American Educational Research Association, San Antonio, TX.
  • Dai, S., Svetina, D., & Chen, C. (2017, April). Dealing with missingness in cognitive diagnostic models when the Q-matrix is misspecified. Paper presented at the annual meeting of the National Council on Measurement in Education, San Antonio, TX.
  • Dai, S., Svetina, D., & Wang, X. (2017, April). Making multi-level diagnostic inferences in large-scale assessments. Paper presented at the annual meeting of the National Council on Measurement in Education, San Antonio, TX.
  • Chen, C., Dai, S., & Zhang, J. (2017, April). Comparison of three Q-matrix validation methods for developing cognitive diagnostic assessments. Paper presented at the annual meeting of the National Council on Measurement in Education, San Antonio, TX.
  • Wang, X., Svetina, D., Dai, S. & Zhang, O. (2017, April). How much can we gain from collateral information for subscore reporting?. Paper presented at the annual meeting of the National Council on Measurement in Education, San Antonio, TX.
  • Chen, C, Zhang, J., & Dai, S. (2017, April). The Effects of Q-matrix Misspecification on Classification Accuracy and Consistency for Cognitive Diagnostic Assessment. Paper presented at the annual meeting of the American Educational Research Association, San Antonio, TX.
  • Svetina, D., Valdivia, R., Underhill, S., Dai, S., & Wang, X. (2016, April). Parameter recovery in multidimensional item response theory models under complexity and nonnormality. Paper presented at the annual meeting of the National Council on Measurement in Education, Washington, D.C.
  • Wang, X., Svetina, D., & Dai, S. (2016, April). Exploration of factors affecting the necessity of reporting test subscores. Paper presented at the annual meeting of the National Council on Measurement in Education, Washington, D.C.
  • Dai, S., Svetina, D., & Brown, N. J. S. (2015, April). Predicting skipping behavior in NAEP mathematics assessment: A multilevel modeling approach. Poster presented at the annual meeting of the National Council on Measurement in Education, Chicago, IL.
  • Maltese, A., Ross, H., & Dai, S. (2015, April). Assessing multinational interest in STEM. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL.
  • Brown, N., Dai, S. & Svetina, D., (2014, April). Predictors of omitted responses on the 2009 National Assessment of Educational Progress (NAEP) mathematics assessment. Poster presented at the annual meeting of the American Educational Research Association, Philadelphia, PA.
  • Brown, N., Svetina, D., & Dai, S. (2014, April). Impact of methods of scoring omitted responses on achievement gaps. Paper presented at the annual meeting of the National Council on Measurement in Education, Philadelphia, PA.
  • Chen, C., Zhang, J., & Dai, S. (2014, April). A case study of optimizing Q-matrix in cognitive diagnostic assessment. Paper presented at the annual meeting of the National Council on Measurement in Education, Philadelphia, PA.

Other Presentations

  • Howland, A. & Dai, S (2017). The Indiana Maternal, Infant, Early Childhood Home Visiting Program (IN-MIECHV) Quarterly Benchmark Reports for FY15. Report discussions presented to Indiana State Department of Health, Indiana Department of Child Services, Healthy Families Indiana, and Indiana Nurse Family Partnership, Indianapolis, IN.
Washington State University