Dr Katerina M Marcoulides

My research focuses on the development and application of advanced data mining and modeling approaches, particularly for the analysis of complex longitudinal data. My research interests also include longitudinal data analysis, item response theory, and data fusion techniques, especially as they pertain to developmental processes and educational research. Some of my current research utilizes integrative data analysis/data fusion and parallel analysis methods to allow for the fitting of various longitudinal models that might not otherwise have been possible to fit to a single data set. I have also extended this data fusion approach to the Bayesian framework (Bayesian Synthesis). My substantive interests focus on the application of advanced modeling techniques to the study of developmental processes, with an emphasis on economically disadvantaged immigrant children.

Educational Background & Specialties

Educational Background

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

Curriculum Vitae

Specialties

  • Statistical learning and data mining
  • Longitudinal data analysis
  • models for longitudinal data
  • big data analytics and modeling
  • multilevel modeling
  • factor analysis and structural equation modeling
  • Item response theory
  • Data fusion
  • Research Methodology and Statisitcal Analysis
  • R programming and data analysis
  • Integrative data analysis
  • growth and development
Courses Taught
  • Honors Psychological Measurement and Data Analysis
  • Advanced Quantitative Foundations of Educational Research (Regression Analysis; graduate course)
  • Multilevel Modeling (graduate course)
  • Data Mining in Educational Research (graduate course)
Publications
  • 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.
  • 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. (2021). Latent growth curve model selection with Tabu search. International Journal of Behavioral Development, 45(2), 153–159. DOI:10.1177/0165025420941170.
  • 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.
Awards
  • Association for Psychological Science (APS) Rising Star Award, 2021
  • International Communication Association, Interpersonal Communication Division Top Paper Award, 2021
  • International Communication Association Top Paper Award, 2017
  • National Communication Association, Health Communication Division Top Paper Award, 2016