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Fei Gu

Fei Gu, Assistant Professor

Fei Gu
Fei Gu, Assistant Professor

School of Education
1750 Kraft Drive, Room 2106 (m/c 0302), Blacksburg, VA 24060
540-231-9734 |

Fei Gu is an assistant professor of educational research and evaluation in the School of Education at Virginia Tech. His area of interest includes latent variable models, associated methods in multivariate analysis, and statistical inference of rotated results. His scholarly work has been published in peer-reviewed journals including Applied Psychological Measurement, Behavior Research Methods, Behaviormetrika, British Journal of Mathematical and Statistical Psychology, Journal of Educational and Behavioral Statistics, Methodology, Multivariate Behavioral Research, Psychometrika, and Structural Equation Modeling.

Follow Fei Gu on ResearchGate.

  • Covariance structure analysis
  • Component analysis
  • Statistical inference of rotated results
  • Time series analysis by state space model
  • Statistical programming and computing
  • PhD in Educational Measurement and Statistics, University of Kansas, 2013
  • MS in Educational Measurement and Statistics, University of Kansas, 2008
  • BS in Statistics, Shanghai University of Finance and Economics, 2006

Selected Journal Articles

Gu, F., Wu, H., Yung, Y.-F., & Wilkins, J. L. M. (in press). Robust standard error estimates for rotated estimates of canonical correlation analysis: An implementation of the infinitesimal jackknife method. Behaviormetrika. DOI : 10.1007/s41237-020-00123-7

Gu, F., Yung, Y.-F., & Cheung, M. W.-L. (2019). Four covariance structure models for canonical correlation analysis: A COSAN modeling approach. Multivariate Behavioral Research, 54, 192-223. doi: 10.1080/00273171.2018.1512847

Gu, F., & Wu, H. (2018). Simultaneous canonical correlation analysis with invariant canonical loadings. Behaviormetrika, 45, 111-132. doi: 10.1007/s41237-017-0042-8

Gu, F. (2016). Analysis of correlation matrices using scale-invariant common principal component models and a hierarchy of relationships between correlation matrices. Structural Equation Modeling: A Multidisciplinary Journal, 23, 819-826. doi:10.1080/10705511.2016.1207180

Gu, F., & Wu, H. (2016). Raw data maximum likelihood estimation for common principal component models: A state space approach. Psychometrika, 81, 751-773. doi:10.1007/s11336-016-9504-2

Gu, F., Preacher, K. J., & Ferrer, E. (2014). A state space modeling approach to mediation analysis. Journal of Educational and Behavioral Statistics, 39, 117-143. doi:10.3102/1076998614524823

Gu, F., Preacher, K. J., Wu, W., & Yung, Y.-F. (2014). A computationally efficient state space approach to estimating multilevel regression models and multilevel confirmatory factor models. Multivariate Behavioral Research, 49, 119-129. doi:10.1080/00273171.2013.866537

  • EDRE 6634: Advanced Statistics for Education, Fall 2019
  • EDRE 6664: Application of Structural Equation in Education, Spring 2020

Dr. Gu is accepting new graduate students.

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