# Ke-Hai Yuan

Professor

Ph.D., UCLA

- Quantitative

(574) 631-4619

Corbett Family Hall

Notre Dame, IN 46556

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

Professor Yuan is open to mentoring graduate students in the fall

## 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

**Yuan, K.-H.**, Yang, M., & Jiang, G. (in press). Empirically corrected rescaled statistics for SEM with small N and large p. Multivariate Behavioral Research.

**Yuan, K.-H.**, & Bentler, P. M. (2017). Improving the convergence rate and speed of Fisher-scoring algorithm: Ridge and anti-ridge methods in structural equation modeling. Annals of the Institute of Statistical Mathematics, 69, 571-597.

**Yuan, K.-H.**, Zhang, Z., & Zhao, Y. (2017). Reliable and more powerful methods for power analysis in structural equation modeling. Structural Equation Modeling, 24, 315-330.

Deng, L., Marcoulides, G., & **Yuan, K.-H. **(in press). Psychometric properties of measures of team diversity with Likert data. *Educational and Psychological Measurement*.

Deng, L., & **Yuan, K.-H.** (in press). Multiple group analysis for structural equation modeling with dependent samples. *Structural Equation Modeling. *

Patton, J., Cheng, Y., **Yuan, K.-H.**, & Diao, Q. (in press). Bootstrap standard errors for maximum likelihood ability estimates when item parameters are unknown*. Educational and Psychological Measurement. *

Tong, X., Zhang, Z., & **Yuan, K.-H. **(in press). Evaluation of test statistics for robust structural equation modeling with nonnormal missing data. *Structural Equation Modeling. *

**Yuan, K.-H.,** Cheng, Y., & Maxwell, S. (in press). Moderation analysis using a two-level regression model. *Psychometrika*.

**Yuan, K.-H.**, Tian, Y., & Yanagihara, H. (in press). Empirical correction to the likelihood ratio statistic for structural equation modeling with many variables. *Psychometrika. *

**Yuan, K.-H.**, Tong, X., & Zhang, Z. (in press). Bias and efficiency for SEM with missing data and auxiliary variables: Two-stage robust method versus two-stage ML.* Structural Equation Modeling. *

Jamshidian, M., &** Yuan, K.-H. **(2014). Examining missing data mechanisms via homogeneity of parameters, homogeneity of distributions, and multivariate normality. *Wiley Interdisciplinary Reviews: Computational Statistics*, 6, 56–73.

**Yuan, K.-H.**, Cheng, Y., & Patton, J. (2014). Information matrices and standard errors for MLEs of item parameters in IRT. Psychometrika, 79, 232–254.

**Yuan, K.-H.**, & Savalei, V. (2014). Consistency, bias and efficiency of the normal-distribution-based MLE: The role of auxiliary variables.* Journal of Multivariate Analysis, *124, 353–370.