Publications

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.
• Danielson, R.W., Gale, M.S., Dai, S., Seyranian, V., Heddy, B., Marsh, J., & Polikoff, M.S. (2023). The development and validation of the elementary activity interest measure. Journal of Experimental Education. Advanced online publication. https://doi.org/10.1080/00220973.2023.2276933
• Svetina Valdivia, D. & Dai, S. (2023). Number of response categories and sample size requirements in polytomous IRT models. Journal of Experimental Education. 92 (1), 154-185. 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

• Education
  • Vo, T., Dai, S., & French, B.F. (2024). Black girls’ mathematics and science identities using large-scale assessment and survey data: A QuantCrit framework. Methods in Psychology. Advanced online publication. https://doi.org/10.1016/j.metip.2024.100158
  • Kangas, S., Dai, S., & Ardasheva, Y. (2024). The Intersection of language and disability: Progress of English learners with disabilities on NAEP reading. The Journal of Special Education, 58(2), 88-99. https://doi.org/10.1177/00224669231213054. 
  • Zhang, X., Dai, S., Ardasheva, Y., & Hong, Y. (2023). Relationships among English language proficiency, self-efficacy, motivation, motivational Intensity, and achievement in an ESP/EAP Context. Journal of Psycholinguist Research, 58, 3019-3038. https://doi.org/10.1007/s10936-023-10034-9.
  • Ramazan, O., Dai, S., Danielson, R., Ardasheva, Hao, T., & Y. Austin, B., (2023). Students’ 2018 PISA reading self-concept: Identifying predictors and examining model generalizability for emergent bilinguals. Journal of School Psychology. 101, 101254. https://doi.org/10.1016/j.jsp.2023.101254.
  • 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. 36, 2763-2795.https://doi.org/10.1007/s11145-022-10357-4. 
  • 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.
  • 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.
• Psychology
  • Schmitter-Edgecombe, M., Luna, C., Dai, S., & Cook Diane. (2024). Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment. The Clinical Neuropsychologist. Advanced online publication. https://doi.org/10.1080/13854046.2024.2330143.
  • 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. 50, 563–583. https://doi.org/10.1007/s41237-022-00191-x. 
  • 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.
• Kinesiology 
  • Stewart, B.C., Dai, S., Havens, K., Eggleston, J.D., Bagwell, J., Deering, R.E., Little, E.E., & Catena, R. (2023). Determining fall risk change throughout pregnancy: The accuracy of postpartum survey and relationship to fall efficacy. Ergonomics. Advanced online publication. https://doi.org/10.1080/00140139.2023.2296827.

Book Chapters

• Dai, S., & Higheagle Strong, Z. (2021). 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 (pp. 747-776). North Carolina. Information Age Publishing.
• Wang, X. & Dai, S. (2021). 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 (pp. 607-623). 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 (pp. 33-44). 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 (pp. 315-334). 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 (pp. 179-208). New York. Springer.

Software Packages

• Dai, S., Kehinde, O.J., French, B.F., Schmitter-Edgecombe, M. (2020). ROCpsych: Compute and compare diagnostic test statistics across groups. (R package version 1.3) [Computer software]. https://CRAN.R-project.org/package=ROCpsych.
• Dai, S., French, B.F., Finch, W. H. (2020). DIFplus: Multilevel Mantel-Haenszel statistics for differential item functioning detection. (R package version 1.1) [Computer software].
  https://cran.r-project.org/package=DIFplus.
• Dai, S., Wang, X., & Svetina, D. (2022). subscore: Subscore computing functions in classical test theory. (R package version 3.3) [Computer software]. http://CRAN.R-project.org/package=subscore.
• Dai, S., Wang, X., & Svetina, D. (2021). TestDataImputation: Missing item responses imputation for test and assessment data. (R package version 2.2) [Computer software]. http://CRAN.R-project.org/package=TestDataImputation.