Faculty Profiles

Dr. Eun Sook Kim

Dr. Eun Sook Kim, Assistant Professor

Kim headshot

Email: ekim3@usf.edu
Office: EDU 369
Curriculum Vitae

Eun Sook Kim has a broad interest in research methodology and psychometrics including structural equation modeling, multilevel modeling, latent class analysis, and factor mixture modeling. Her focal research interests include measurement invariance testing in multilevel and longitudinal data. She has recently focused on factor mixture approach to testing measurement invariance particularly with multilevel data and with a large number of groups. She has been involved in research groups studying propensity score analysis, multilevel confirmatory factor analysis, Bayesian estimation, and robust ANOVA in collaboration with faculty and graduate students.

Selected Publications

Kim, E. S., Cao, C., Wang, Y., & Nguyen, D. T. (in press). Measurement invariance testing with many groups: A comparison of five approaches. Structural Equation Modeling.
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Wang, Y., Rodriguez de Gil, P., Chen, Y-H., Kromrey, J. D., Kim, E. S., Nguyen, D. T., Pham, T. V., & Romano, J. (in press). Comparing the performance of approaches for testing the Homogeneity of Variance assumption in one-factor ANOVA models. Educational and Psychological Measurement.

Wang, Y., Kim, E. S., Dedrick, R., Ferron, J., & Tan, T. (in press). A multilevel bifactor approach to construct validation of the mixed-format Students Confident in Mathematics Scale. Educational and Psychological Measurement.

Kim, E. S., Dedrick, R. F., Cao, C. & Ferron, J. M. (2016). Multilevel factor analysis: Reporting guidelines and a review of reporting practices. Multivariate Behavioral Research, 51(6), 881-898.

Kim, E. S., Joo, S-H., Lee, P., Wang, Y., & Stark, S. (2016). Measurement invariance testing across between-level latent classes using multilevel factor mixture modeling. Structural Equation Modeling, 23(6), 870-887.

Kim, E. S., & Cao, C. (2015). Testing group mean differences of latent variables in multilevel data using multiple-group multilevel CFA and multilevel MIMIC modeling. Multivariate Behavioral Research, 50(4), 436-456.

Kim, E. S., Yoon, M., Wen, Y., Luo, W., & Kwok, O. (2015). Within-level group factorial invariance in multilevel data: Multilevel factor mixture and multilevel MIMIC models. Structural Equation Modeling, 22(4), 603-616.
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Rodriguez de Gil, P., Bellara, A. P., Lanehart, R. E., Lee, R. S., Kim, E. S., & Kromrey, J. D. (2015). How do propensity score methods measure up in the presence of measurement error: A Monte Carlo study. Multivariate Behavioral Research, 50(5), 520-532. 
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Kim, E. S., & Willson, V. L. (2014). Testing measurement invariance across groups in longitudinal data: Multi-group second-order latent growth model. Structural Equation Modeling, 21(4), 566-576.

Kim, E. S., & Willson, V. L. (2014). Measurement invariance across groups in latent growth models. Structural Equation Modeling, 21(3), 408-424.

Kim, E. S., Kwok, O., & Yoon, M. (2012). Testing factorial invariance in multilevel data: A Monte Carlo study. Structural Equation Modeling, 19(2), 250-267.

Kim, E. S., Yoon, M., & Lee. T. (2012). Testing measurement invariance using MIMIC: The likelihood ratio test with a critical value adjustment. Educational & Psychological Measurement, 72(3), 469-492.

Kim, E. S., & Yoon, M. (2011). Testing measurement invariance: A comparison of multiple-group categorical CFA and IRT. Structural Equation Modeling, 18(2), 212-228.

Thoemmes, F., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46(1), 90-118.