Structural equation modeling
Bayesian data analysis
ESRM 5393: Statistics in Education and Health Profession
ESRM 6403: Educational Statistics and Data Processing
ESRM 6513: Hierarchical Linear Modeling
ESRM 699V: Advanced Sturctural Equation Modeling
Ph.D., Measurement and Statistics, Florida State University, 2014
B.S., Information and Computing Science, Zhejiang Gongshang University, China, 2007
(* indicates student co-author)
Liang, X., & Jacobucci, R. (2019). Regularized structural equation modeling to detect measurement bias: Evaluation of lasso, adaptive lasso and elastic net. Structural Equation Modeling: A Multidisciplinary Journal. Advance Online Publication.
Liang, X., & Luo, Y. (2019). A comprehensive comparison of model selection methods for testing factorial invariance. Structural Equation Modeling: A Multidisciplinary Journal. Advance Online Publication.
Wu, H., †Liang, X., Yurekli, H., Becker, B., Paek, I., & Salih, B. (2019). Assessing the fine-grained attributes in a large-scale mathematics test: A DINA model application with two Q-matrices. Journal of Psychoeducational Assessment. Advance Online Publication. (†corresponding author)
Luo, Y., & Liang, X. (2019). Simultaneously modeling differential testlet functioning and differential item functioning: Addressing variance heterogeneity with a multigroup one-Parameter testlet model. Measurement: Interdisciplinary Research and Perspectives, 17(2), 93-105.
Liang, X., Yang, Y., & *Huang, J. (2018). Evaluation of structural relationships in autoregressive cross-lagged models under longitudinal approximate invariance: A Bayesian analysis. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 558-572.
Liang, X., Koo, J., Yurekli, H., Paek, I., Becker, B.J., Binici, S., & Fukuhara, H. (2017). An empirical investigation of item-pool and year-to-year equating plans using large-scale assessment data. Florida Journal of Educational Research, 55(1), 1-18.
Liang, X., & Yang, Y. (2016). Confirmatory factor analysis under violations of structural and distributional assumptions: A comparison of robust maximum likelihood and Bayesian estimation methods. Journal of Psychological Science, 39(5), 1256-1267. doi:10.16719/j.cnki.1671-6981.20160536
Liang, X., & Yang, Y. (2014). An evaluation of WLSMV and Bayesian methods for confirmatory factor analysis with categorical indicators. International Journal of Quantitative Research in Education, 2(1), 17-38.
Darabi, A., Liang, X., Suryavanshi, R., & Yurekli, H. (2013). Effectiveness of online discussion strategies: A Meta-Analysis. American Journal of Distance Education, 27(4), 228-241.
Yang, Y., & Liang, X. (2013). Confirmatory factor analysis under violations of distributional and structural assumptions. International Journal of Quantitative Research in Education, 1(1), 61-84.
Darabi, A., Arrastia, M.C., Nelson, D.W., Cornille, T., & Liang, X. (2011). Cognitive presence in asynchronous online learning: A comparison of four discussion strategies. Journal of Computer Assisted Learning, 27(3), 216-227.
Editorial Board Member: Journal of Psychoeducational Assessment (2016-present)
Program Chair (2018-2020), Annual Meeting Program Review Committee - AERA Division D Section 2: Statistical Theory and Quantitative Methodologies
Significant Research Award, RHRC, University of Arkansas (2019)
Nominee, Significant Research Award, COEHP, University of Arkansas (2019)
Nominee, Rising STAR (Research, Teaching, Advising, Service), COEHP, University of Arkansas (2018)
New Faculty Commendation for Teaching Commitment (2017).
Distinguished Paper Award, Consortium of State and Regional Educational Research Associations (SRERA), American Educational Research Association (2015).
Distinguished Paper of the Year Award, Florida Educational Research Association (2014).