My research focuses on the development and application of advanced modeling and data mining approaches for the analysis of complex psychological data, and considers ways in which various substantive domains within psychology can be studied through these analytic approaches. I have developed and utilized data mining techniques to fit optimal structural equation and longitudinal models. In addition to data mining techniques, another aspect of my research is in estimation issues within structural equation modeling (SEM). I am also particularly interested in applying modeling techniques to study developmental and educational processes, with an emphasis on economically disadvantaged immigrant children. My collaborative work with applied researchers in the Minnesota Population Center recently received 5-years of funding from the National Institute of Health to investigate ​​the complex associations among parenting, marginalization, and well-being during the COVID-19 pandemic.

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Dr. Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program in the Department of Psychology at the University of Minnesota, Twin-Cities.  She is also a member of the Minnesota Population Center and was recently elected Co-Chair of the Structural Equation Modeling Special Interest Group for the American Educational Research Association. Prior to her arrival at UMN, she was an Assistant Professor in the Research and Evaluation Methodology Program in the College of Education and a member of the Informatics Institute at the University of Florida.  She received her B.A. in Psychology with a minor in Education from the University of California, Santa Barbara and her M.A. in Quantitative Psychology from the University of California, Davis.  She completed her Ph.D. in Quantitative Psychology from Arizona State University.

Education

• PhD: Quantitative Psychology, Arizona State University
• MA: Quantitative Psychology, University of California, Davis
• BA: Psychology Major, Education Minor, University of California, Santa Barbara

Teaching Subjects

• Honors Introduction to Psychological Measurement and Data Analysis
• Structural Equation Modeling for the Social and Behavioral Sciences
• Multilevel Modeling for Psychological Data
• Data Mining (graduate course)
• dvanced Quantitative Foundations of Educational Research (Regression Analysis; graduate course)

Honors and Awards

• UMN Center for Educational Innovation, Thank a Teacher Program, Letter of Recognition, 2024
• Elected Member of the Society of Multivariate Experimental Psychology , (SMEP; 65 members under the age of 65), 2023
• UMN New Faculty Program, Letter of Recognition , 2023
• UMN Center for Educational Innovation, Thank a Teacher Program, Letter of Recognition, 2022
• Association for Psychological Science (APS) Rising Star Award, 2021
• International Communication Association, Interpersonal Communication Division Top Paper Award, 2021
• National Communication Association, Health Communication Division Top Paper Award, 2016
• International Communication Association Top Paper Award, 2017

Selected Publications

• Wang, S., Marcoulides, K. M., Tang, J., & Yuan, K.-H. . Comparison of Minimal-Effect Testing, Equivalence Testing, and the Conventional Null Hypothesis Testing for the Analysis of Bi-factor Models. Structural Equation Modeling, 1-15.
• Marcoulides, K. M., & Trinchera, L. (2024). A novel approach for identifying unobserved heterogeneity in longitudinal growth trajectories using natural cubic smoothing splines. Journal of Behavioral Data Science, 4(1), 1-18. DOI:10.35566/jbds/marcoulides
• Marcoulides, K. M., & Yuan, K. -H. (2023) Testing structural equation model fit in psychological studies: A replication study using equivalence testing. Quality & Quantity, 58, 3417–3433.
• Torunsky*, N.T., Knauz*, S., Vilares, I., Marcoulides, K. M., & Koutstaal, W. (2023). Latent trait mediation analysis of alexithymia, experiential avoidance, and psychological distress: A latent analysis using three alexithymia questionnaires. Personality and Individual Differences, 214, 1-26. DOI:10.1016/j.paid.2023.112308.
• Marcoulides, K. M. (2023). Integration of historical data for the analysis of multiple assessment studies. Measurement: Interdisciplinary Research and Perspectives, 21(3), 181–193. DOI:10.1080/15366367.2022.2115250.
• Fisk*, C., Harring, J., Shen*, Z., Leite, W. L., Seun*, K.Y. & Marcoulides, K. M. (2023). Using simulated annealing to investigate sensitivity to external model misspecification in SEM. Educational and Psychological Measurement, 83(1), 73-92. DOI:10.1177/00131644211073121
• Marcoulides, K. M., Quan*, J., & Wright*, E. (2022). The impact of sample size on exchangeability in the Bayesian synthesis approach to data fusion.. Journal of Behavioral Data Science.
• Marcoulides, K. M. & Trinchera, L. (2021). Residual-based algorithm for detecting unobserved heterogeneity in latent growth models: A Monte Carlo simulation study. Frontiers in Psychology, 12, 1-7. Quantitative Psychology and Measurement Section, Special issue on Advances in Mixture Modeling. DOI:10.3389/fpsyg.2021.618647.
• Marcoulides, K. M. (2021). Latent growth curve model selection with Tabu search. International Journal of Behavioral Development, 45(2), 153–159. DOI:10.1177/0165025420941170.
• Kam, J. A., Marcoulides, K. M., Steuber, K. R., Mendez Murillo*, R., & Cornejo*, M. (2021). Latina/o/x immigrant youth’s motivations for disclosing their family-undocumented experiences to a teacher(s): A latent transition analysis. Journal of Communication, 71(1), 27-55. DOI:10.1093/joc/jqaa036.
• Marcoulides, K. M. & Trinchera, L. (2021). Residual-based algorithm for detecting unobserved heterogeneity in latent growth models: A Monte Carlo simulation study. Frontiers in Psychology, Quantitative Psychology and Measurement Section, Special issue on Advances in Mixture Modeling. DOI:10.3389/fpsyg.2021.618647.
• Marcoulides, K. M., & 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. Special Issue: The Contribution of Multilevel Structural Equation Modeling to Contemporary Trends in Educational Research. DOI:10.1080/1743727X.2020.1795113.
• Raborn*, A. W., Leite, W. L., & Marcoulides, K. M. (2020). A comparison of metaheuristic optimization algorithms for scale short form development. Educational and Psychological Measurement, 80(5), 910-931. DOI:10.1177/0013164420906600.
• Marcoulides, K. M., Foldnes, N., & Grønneberg, S. (2020). Assessing Model Fit in Structural Equation Modeling using Appropriate Test Statistics. Structural Equation Modeling. 27(3), 369-379. DOI: 10.1080/10705511.2019.1647785
• Marcoulides, K. M. (2019). Reliability estimation in longitudinal studies using latent growth curve modeling. Measurement: Interdisciplinary Research and Perspectives, 17(2), 67-77. DOI:10.1080/15366367.2018.1522169
• Marcoulides, K. M., & Raykov, T. (2019). Evaluation of variance inflation factors in regression models using latent variable modeling methods. Educational and Psychological Measurement, 79(5), 874-882. DOI:10.1177/0013164418817803
• Marcoulides, K. M. & Trinchera, L. (2019). Detecting unobserved heterogeneity in latent growth curve models. Structural Equation Modeling, 26(3), 390-401. DOI:10.1080/10705511.2018.1534591
• Marcoulides, K. M. & Khojasteh, J. (2018). Analyzing longitudinal data using natural cubic smoothing splines. Structural Equation Modeling, 25(6), 965-971. DOI:10.1080/10705511.2018.1449113.
• Deng, L., *Yang, M., & Marcoulides, K. M. (2018). SEM with many variables: Issues and developments. Frontiers in Psychology, Quantitative Psychology and Measurement Section, Advances and Practice in Psychometrics Research Topic. DOI:10.3389/fpsyg.2018.00580.
• Marcoulides, K. M. (2018). Careful with those priors: A note on Bayesian estimation in two-parameter logistic item response theory models. Measurement: Interdisciplinary Research and Perspectives, 16(2), 92-99. DOI:10.1080/15366367.2018.1437305.
• Marcoulides, K. M. (2018). Automated latent growth curve model fitting: A Segmentation and knot selection approach. Structural Equation Modeling, 25(5), 687-699. DOI:10.1080/10705511.2018.1424548.
• Marcoulides, K. M. & Falk, C. (2018). Model specification searches in structural equation modeling with R. Structural Equation Modeling, 25(3), 484-491. DOI:10.1080/10705511.2017.1409074.
• Kam, J. A., Marcoulides, K. M., & Merolla, A. J. (2017). Using an acculturation-stress-resilience framework to explore latent profiles of Latina/o language brokers. Journal of Research on Adolescence, 27(4), 842-861. DOI:10.1111/jora.12318
• Marcoulides, K. M. (2017). A Bayesian synthesis approach to data fusion using data-dependent priors. Multivariate Behavioral Research, 52(1), 111-112. DOI:10.1080/00273171.2016.1263927.
• Marcoulides, K. M. & Yuan, K. -H. (2017). New ways to evaluate goodness of fit: A note on using equivalence testing to assess structural equation models. Structural Equation Modeling, 24(1), 148-153. DOI: 10.1080/10705511.2016.1225260.
• Marcoulides, K. M. & Grimm, K. J. (2017). Data integration approaches to longitudinal growth modeling. Educational and Psychological Measurement, 77(6) 971–989. DOI:10.1177/0013164416664117.
• Grimm, K. J. & Marcoulides, K. M. (2016). Individual change and the onset of significant life events: methods, models, and assumptions. International Journal of Behavioral Development, 40(1), 87-96. DOI:10.1177/0165025415580806.