Ke-Hai Yuan

Ke-Hai Yuan

Professor

Ph.D., UCLA

  • Quantitative

(574) 631-4619

kyuan@nd.edu

Corbett Family Hall

Notre Dame, IN 46556

Statistical Methods for Real Data Lab

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Developing better or more valid methods for analyzing messy data or nonstandard samples in social and behavioral sciences.

Profile

Ke-Hai Yuan's research has been around developing better or more valid methods for analyzing messy data or non-standard samples in social and behavioral sciences. Most of his work is on factor analysis, structural equation modeling, and multilevel modeling. He has also worked on correlations, regression, combining effect sizes, mean comparison and power, classical and modern testing theory, statistical computation, estimating equations, and big data. His teaching interests include psychometric theory, structural equation modeling, item response theory, missing data, asymptotics and simulation-based research methodology. You may visit Ke-Hai's lab here: https://smrd.nd.edu/

Recent Publications

Gomer, B., & Yuan, K.-H. (in press). Subtypes of the missing not at random missing data mechanism. Psychological Methods.

Liu, H., & Yuan, K.-H. (in press). New measures of effect size in moderation analysis. Psychological Methods. doi: 10.1037/met0000371

Liu, H., Yuan, K.-H., Gan, K. (in press). Two-level mediated moderation models with single level data and new measures of effect sizes. Acta Psychologica Sinica, 53(3), 322–336. doi: 10.3724/SP.J.1041.2021.00322

Liu, H., Yuan, K.-H., Wen. Z. (in press). Two-level moderated mediation models with single level data and new measures of effect sizes. Behavior Research Methods.

Yuan, K.-H., & Deng, L. (in press). Equivalence of partial-least-squares SEM and the methods of factor-score regression. Structural Equation Modeling.

Yuan, K.-H., & Gomer, B. (in press). An overview of applied robust methods. British Journal of Mathematical and Statistical Psychology.

Yuan, K.-H., Gomer, B., & Marcoulides, K. (in press). Smoothed quantiles for chi-square type test statistics with applications. Multivariate Behavioral Research. doi: 10.1080/00273171.2020.1858018 

Yuan, K.-H., & Liu, F. (in press). Which method is more reliable in performing model modification: Lasso regularization or Lagrange multiplier test? Structural Equation Modeling. doi: 10.1080/10705511.2020.1768858

Yuan, K.-H., Liu, H., & Han, Y. (in press). Differential item functioning analysis without a priori information on anchor items: QQ plots and graphical test. Psychometrika.

Liu, H., Yuan, K.-H., & Liu, F. (2020). A two-level moderated latent variable model with single level data. Multivariate Behavioral Research, 55(6), 873–893. doi: 10.1080/00273171.2019.1689350

Marcoulides, K., & Yuan, K.-H. (2020). Using equivalence testing to evaluate goodness of fit in multilevel structural equation models. International Journal of Research & Method in Education, 43(4), 431–443. doi: 10.1080/1743727X.2020.1795113 

Yuan, K.-H., Wen, Y., & Tang, J. (2020). Regression analysis with latent variables by partial least squares and four other composite scores: Consistency, bias, and correction. Structural Equation Modeling, 27(3), 333–350. doi: 10.1080/10705511.2019.1647107