Dr. Yi-Hsin Chen
Professor, educational measurement and research
Yi-Hsin Chen is a Professor of Educational Measurement and Research. Dr. Chen's primary research focuses are in the following related areas:
- Applications of cognitive psychometric models (e.g., generalized DINA, LCDM, RUMM) which incorporate cognitive information into psychometric models.
- Exploration of validating approaches (e.g., DIF, internal and external score validity) for Q-matrices and cognitive diagnostic profiles.
- Examination of accuracy and precision of robust statistical approaches for traditional t-tests and ANOVA tests.
- Applications of multilevel modeling (MLM) approaches (e.g., IRT, Rasch, and SEM) to achievement data and psychological data.
Dr. Chen's work has been published in Journal of Educational Measurement, Educational and Psychological Measurement, International Journal of Testing, Journal of Experimental Education, Journal of Modern Applied Statistical Methods, and Educational Research and Evaluation as well as in Monograph series.
- EDF 6432: Foundations of Measurement (Online)
- EDF 6407: Statistical Analysis for Educational Research I (Online)
- EDF 7439: Item Response Theory
- EDF 7469: Computer-Based Testing
- EDF 7436: Rasch Models
Warrican, S. J., Alleyne, M., Smith, P., Esperat, T., Zaidi, R., Chen, Y.-H., & Yin, Y. (2021, Accepted). Cultural alloys and heterogenous mixes: Contextualized and comparative language differences in the literacy assessment of U.S. and Canadian youth. Research in Comparative and International Education. [Impact Factor: 1.50]
Chen, Y.-J., Chen, Y.-H., Anthony, J., & Erazo, N. (2021, Accepted). Evaluation of the computer-based orthographic processing assessment: An application of cognitive diagnostic modeling. Journal of Psychoeducational Assessment.
Cao, C., Kim, E. S., Chen, Y.-H., & Ferron, J. (2021). Examining the impact of and sensitivity of fit indices to omitting covariates interaction effects in multilevel MIMIC models. Educational and Psychological Measurement, 81(5), 817-846. https://doi.org/10.1177/0013164421992407 [SSCI, SCIE | 2018IF = 2.21 | 2018SJR = 1.747 | Q1]
Yi, Z., Chen, Y.-H. Yin, Y., Cheng, K., Wang, Y., Nguyen, D. T., Pham, T., & Kim, E. S. (2020; OnlineFirst). Brief research report: A comparison of robust tests for homogeneity of variance in factorial ANOVA. The Journal of Experimental Education. [SSCI | 2019IF:2.107 | 2019SJR:1.28 | h-index:54 | Q1] https://doi.org/10.1080/00220973.2020.1789833
Wu, Y.-J., Kiefer, S., & Chen, Y.-H. (2020). The relationships of learning strategies with self-efficacy and mathematics performance: A cross-cultural comparison. International Journal of School and Educational Psychology, 8(S1), 91-103. [2018IF: 1.64 | 2019SJR: 0.34 | h-index:11 | Q2] https://doi.org/10.1080/21683603.2019.1566104
Pham, T., Kromrey, J. D., Chen, Y.-H., Nguyen, D. T., Kim, E. S., & Wang, Y., (2020). ANOVA_Robust: A SAS® macro for various robust approaches to testing mean differences in one-factor ANOVA models. Journal of Statistical Software, 95, Code Snippet 2. [SCIE | 2018IF = 10.30 | 2018SJR = 17.569 | Q1] https://www.jstatsoft.org/article/view/v095c02
Rosengrant, D., Matthews, G., Feldman, A., Chen, Y.-H., & Alsultan, J. (2021 August). Preliminary results on a video-based force concept inventory. Physics Education Research Conference, Poster. Virtual Format. https://www.per-central.org/perc/2021/detail.cfm?ID=8440
Yin, Y., Chen, Y.-H., & Dedrick, R. (2020, April). Exploring the moderation effects of technology on Gender DIF of reading comprehension. NCME Annual Meeting, Poster. San Francisco, California.
Chen, Y.-H., Yi, Z., & Thompson, D. R. (2020, April). Investigating attribute hierarchical relations with multilevel mediation measurement modeling. NCME Annual Meeting, Paper Session. San Francisco, California.
Hsu, C.-L., Wu, Y.-J., & Chen, Y.-H. (2020, April). Exploring skill hierarchical relationships of the van Hiele theory: An application of diagnostic classification modeling. AERA Annual Meeting, Poster Session. San Francisco, CA.
Cao, C., Kim, E., Chen, Y.-H., & Ferron, J. M. (2020, April). Exploring the impact of omitting covariates interaction effect in multilevel multiple indicators, multiple causes (MIMIC) models. AERA Annual Meeting, Paper Session. San Francisco, CA.