Minjung Kim

Headshot of Minjung Kim

Associate Professor, Department of Educational Studies

Program Area: Quantitative Research, Evaluation and Measurement

(614) 247-8710


Minjung Kim is an Assistant Professor of Quantitative Research, Evaluation and Measurement in the Department of Educational Studies at The Ohio State University. She received her Ph.D. in Research, Measurement, and Statistics, in the department Educational Psychology at Texas A&M University in 2012. Her research interest is quantitative methods including Structural Equation Modeling (SEM) and Multilevel Modeling (MLM). Her current research focuses on evaluating the use of regression mixture models in SEM to identify the heterogeneous groups of subjects based on the effect of predictors on outcomes. Dr. Kim is also interested in applying the advanced statistical models to real data in social science research, including education and psychology.


  • Postdoc, Quantitative Psychology at the University of South Carolina (2013-2015)
  • PhD in Research, Measurement, and Statistics (RMS) in the department of Educational Psychology at Texas A&M University (2012)
  • MEd in Research, Measurement, and Statistics (RMS) in the department of Educational Psychology at Texas A&M University (2008)
  • BA in German Language and Literature at Hanyang University, Korea (2002)

Research Summary

Quantitative methods including: Structural Equation Modeling (SEM), Multilevel Modeling (MLM), Latent Growth Modeling, Regression Mixture Modeling, and Longitudinal Data Analysis.

Selected Publications

  • Jaki, T., Su, T., Kim, M., & Van Horn, L. (In press). An evaluation of the non-parametric bootstrap for model validation in mixture models. Communications and Statistics.
  • Goddard, Y. L. & Kim, M. (In press). Examining connections between teacher collaboration, differential instruction, and teacher efficacy. Teachers College Record.
  • Jung, S., Bishop, A., Kim, M., Hermann, J., & Lawrence, J. (In press). Nutritional status of rural older adults is linked to physical and emotional health. Journal of the Academy of Nutrition and Dietetics.
  • Chen, L., Chang, F., Kim, M., & Talwar, D., & Zhao, S. (2017). Genomic medicine practice among physicians in Taiwan. Personalized Medicine, 14(2), 109-121. DOI: 10.2217/pme-2016-0067 (Impact Factor, 2015: 1.000)
  • Jung, S.E., Bishop, A., Kim, M., Hermann, J., Kim, G., Lawrence, J. (2017). Does depressive affect mediate the relationship between self-care capacity and nutritional status among rural older adults?: A structural equation modeling approach. Journal of Nutrition in Gerontology and Geriatrics, 36(1), 63-74.
  • Kim, M., Vermunt, J., Bakk, Z., Jaki, T., & Van Horn, M. L. (2016). Modeling predictors of latent classes in regression mixture models. Structural Equation Modeling: A Multidisciplinary Journal23(4), 601-614.(Impact Factor, 2016: 6.933)
  • Kim, M., Lamont, E. A., Jaki, H., Feaster, D., Howe, G., & Van Horn, L. (2016). Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study. Behavior Research Methods, 48(2), 813-826. (Impact Factor, 2016: 3.098)
  • Kim, M., Lamont, E. A., Van Horn, L. (2016). Review of Structural Equation Modeling: Applications using Mplus, by Jichuan Wang and Xiaoqian Wang. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 476-477. (Impact Factor, 2016: 6.933)
  • Kim, M., Kwok, O., Yoon, M, Willson, V., & Lai, H. (2016). Specification search for identifying the correct mean trajectory in polynomial Latent Growth Models. Journal of Experimental Education, 84(2), 307-329. (Impact Factor, 2015: 1.638)
  • Van Horn, L., Feng, Y., Kim, M., Lamont, A., Feaster, D., & Jaki, T. (2016). Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects. Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 259-269.(Impact Factor, 2015: 6.933)
  • Miller, R. J., Goddard, R. D., Kim, M., Jacob, R., Goddard, Y., & Schroeder, P. (2016). Can professional development improve school leadership? Results from a randomized control trial assessing the impact of McREL’s Balanced Leadership Program on principals in rural Michigan schools. Educational Administration Quarterly, 52(4), 531-566. doi:10.1177/0013161X16651926. (Impact Factor, 2016: 1.118)
  • Goddard, Y. L.,Goddard, R. D., & Kim, M. (2015). School instructional climate and student achievement: An examination of group norms for differentiated instruction. American Journal of Education, 122(1), 111-131.(Impact Factor, 2013: 1.590)
  • Van Horn, L., Jaki, T., Masyn, K., Howe, G., Feaster, D., Lamont, E. A., George, M., & Kim, M.  (2015). Evaluating differential effects using regression interactions and regression mixture models. Educational and Psychological Measurement, 75(4), 677-714.(Impact Factor, 2015: 1.485)
  • Simmons, D., Kim, M., Kwok, O, Simmons, L., Oslund, E., & Coyne, M. (2015). Examining the effects of linking student performance and progression in a tier 2 kindergarten reading intervention. Journal of Learning Disabilities, 48(3), 255-270. (Impact Factor, 2014: 1.901)
  • Jacob, R., Goddard, R. D., Kim, M., Goddard, Y. L., & Miller, R. J. (2015). Exploring the proximal and distal impacts of the McREL Balanced Leadership Program for school leaders. Educational Evaluation and Policy Analysis, 37(3), 314-332.(Impact Factor, 2014: 1.688)
  • Chen, L. & Kim, M. (2014). Needs assessment in genomic education: a survey of health educators in the United States. Health Promotion Practice, 15(4), 592-598. PMID: 23545335.
  • Simmons, D. C., Taylor, A. B., Oslund, E. L., Hagan-Burke, S., Simmons, L. E., Coyne, M. D., Little, M. E., Rawlinson, D. M., Kwok, O., & Kim, M. (2013). Predictors of at-risk kindergarteners’ later reading difficulty: examining learner-by-intervention interactions. Reading and Writing, 27(3), 451-479.
  • Coyne, M. D., Little, M. E., Rawlinson, D. M., Simmons, D. C., Kwok, O., Kim, M., Simmons L., E., Hagan-Burke, S., & Civetelli, C. (2013). Replicating the impact of a supplemental beginning reading intervention: The role of instructional context. Journal of Research on Educational Effectiveness, 6(1), 1-23. doi: 10.1080/19345747.2012.706694
  • Coyne, M. D., Simmons, D. C., Hagan-Burke, S., Simmons, L. E., Kwok, O., Kim, M., Fogarty, M., Oslund, E., Taylor, A. B.Capozzoli-Oldham, A., Ware, S., Little, M. E., Rawlinson D. M.   (2013). Adjusting beginning reading intervention based on student performance: An experimental evaluation. Exceptional Children, 80(1), 25-44.  
  • Hagan-Burke, S., Coyne, M. D., Kwok, O., Simmons, D. C., Kim, M., Simmons, L. E., Skidmore, S. T., Hernandez, C. J., & Ruby, M. F. (2013). The effects and interactions of student, teacher, and setting variables on reading outcomes for kindergarteners receiving supplemental reading intervention. Journal for Learning Disabilities, 46(3), 260-277.