Dr. Yi-Hsin Chen
Associate Professor, educational measurement and research
Yi-Hsin Chen is an associate professor of Educational Research and Measurement. 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
Pham, T., Kromrey, J. D., Chen, Y.-H., Nguyen, D. T., Kim, E. S., & Wang, Y., (In press, 2020). ANOVA_Robust: A SAS® macro for various robust approaches to testing mean differences in one-factor ANOVA models. Journal of Statistical Software.
Hu, J., Serovich, J. M., Brown, M. J., Kimberly, J. A., & Chen, Y.-H. (2019). Validity study of the R-PLA, a resilience scale for people living with HIV. Journal of HIV and AIDS, 5(3): dx.doi.org/10.16966/2380-5536.170
Chen, Y.-H., Senk, S. L., Thompson, D. R., & Voogt, K. (2019). Examining psychometric properties and level classification of the van Hiele geometry test using CTT and CDM framework. Journal of Educational Measurement,54(4), 733-756.
Cao, C, Kim, E. S., Chen, Y.-H., Ferron, J, M., & Stark, S. (2019). Exploring the test of covariate moderation effects in multilevel MIMIC models. Educational and Psychological Measurement, 79(3), 512-544.
Wang, Y., Nguyen, D. T., Pham, T., Kim, E. S., Chen, Y.-H., Kromrey, J. D., Yi, Z., & Yue, Y. (2018). Evaluating the efficacy of conditional analysis of variance under non-normality and heterogeneity. Journal of Modern Applied Statistical Methods,17(2), eP2701.
Nguyen, D. T., Kim. E. S., & Chen, Y.-H. (2018). Can one test fit all? Responses to the article “Striving for simple but effective advice for comparing the central tendency of two populations” (Ruxton & Neuhauser, 2018). Journal of Modern Applied Statistical Methods, 17(2), eP2822.
Sengupta, S., Ergas, S., Cunningham, J., Goel, R., Feldman, A., & Chen, Y.-H. (2017). Concept inventory (CI) for fundamentals of environmental engineering courses: Findings from CI development and testing. Environmental Engineering Science, 34(12), 895-907.
Li, I. Y., Chen, Y.-H., Wang, Y., Rodriguez de Gil, P., Pham, T. V., Nguyen, D. T., Kim, E. S., & Kromrey, J. D. (2017). ANOVA_HOV: A SAS macro for testing homogeneity of variance in one-factor ANOVA models. Journal of Modern Applied Statistical Methods, 16(2), 506-539.
Hu, J., Serovich, J. M., Chen, Y.-H., Brown, M. J., & Kimberly, J. A. (2017). Psychometric evaluation of the HIV disclosure attitude scale: A Rasch Model. AIDS and Behavior, 21(1), 174-183.
Wang, Y., Rodriguez de Gil, P., Chen, Y.-H., Kromrey, J. D., Kim, E. S., Nguyen, D. T., Phah, T., & Romano, J. (2017). Comparing the performance of approaches for testing the homogeneity of variance assumption in one-factor ANOVA models. Educational and Psychological Measurement, 77(2), 305-329.
Nguyen, D. T., Kim, E. S., Rodriguez de Gil, P., Kellerman, A., Chen, Y.-H., Kromrey, J. D., & *Bellara, A. (2016). Parametric tests for two population means under normal and non-normal distributions. Journal of Modern Applied Statistical Methods, 15(1), Article 9.
Hu, J., Miller, M. D., Huggins-Manley, A. C., & Chen, Y.-H. (2016). Evaluation of model fit in cognitive diagnosis models. International Journal of Testing, 16(2), 119-141.