About
Katerina 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. Her research focuses on the development and application of advanced modeling and data mining approaches for the analysis of complex psychological data, with particular interest in how various substantive domains within psychology can be studied through these analytic approaches. She has developed and utilized data mining techniques to fit optimal structural equation and longitudinal models, and her work also addresses estimation issues within structural equation modeling (SEM). Additionally, her research emphasizes applying modeling techniques to study developmental and educational processes, especially concerning economically disadvantaged immigrant children. Her collaborative work with applied researchers has received funding from the National Institute of Health to investigate the complex associations among parenting, marginalization, and well-being during the COVID-19 pandemic.
Research topics
- Machine Learning
- Data Mining
- Computer Science
- Artificial Intelligence
- Developmental psychology
- Mathematics
- Statistics
- Psychology
- Algorithm
- Mathematical optimization
- Engineering
Selected publications
Structural Equation Modeling A Multidisciplinary Journal · 2026-05-15
article1st authorCorrespondingStructural Equation Modeling A Multidisciplinary Journal · 2026-01-26
articleStructural Equation Modeling A Multidisciplinary Journal · 2025-06-18
article1st authorCorrespondingCorrelations between coaching quality and teacher change in social-emotional teaching practices
Early Childhood Research Quarterly · 2025-01-01 · 2 citations
articleCapturing fluctuations in multivariate intensive longitudinal data
Methods in Psychology · 2025-09-25
articleOpen access1st authorCorrespondingThis paper introduces a novel method for intensive longitudinal data incorporating dimension reduction and time series analyses. The method capitalizes on the notion of determining distance or similarity parameters in the data. The method is a three-phase approach where, 1) distance parameters are determined for each individual, 2) optimal distances between the variables are computed across all participant and time points, and 3) a one-dimensional solution is computed across all time-points for each participant. A first-order autoregressive model was fit to each individual's solution vector to examine intra-individual dynamics and allow for comparisons of inter-individual trajectories. The method constructs a one-dimensional representation at each time-point while preserving the structure of the relationships between variables.
Journal of Behavioral Data Science · 2024-05-12 · 1 citations
articleOpen access1st authorCorrespondingA novel algorithmic modeling method is proposed to determine dissimilarities between subjects for longitudinal data clustering using natural cubic smoothing splines. Although various modeling techniques have to date been suggested for conducting such analyses, a major problem with many of these approaches is that they often impose overly restrictive assumptions. As a consequence, potentially problematic interpretations of data clustering regarding both the number and the nature of the growth trajectory patterns can occur. The proposed method is shown to be highly effective in identifying heterogeneity of growth trajectories in settings with data exhibiting complex nonlinear longitudinal patterns and without imposing potentially problematic constraints on the model.
Structural Equation Modeling A Multidisciplinary Journal · 2024-05-28 · 2 citations
articleA necessary step in applying bi-factor models is to evaluate the need for domain factors with a general factor in place. The conventional null hypothesis testing (NHT) was commonly used for such a purpose. However, the conventional NHT meets challenges when the domain loadings are weak or the sample size is insufficient. This article proposes using minimal-effect testing (MET) and equivalence testing (ET) to analyze bi-factor models. A key element in conducting MET and ET is the minimal size of factor loadings that can be regarded as noteworthy in practice, termed as minimal noteworthy size. This article presents two approaches to formulating the minimal noteworthy size and compares the pros and cons of MET, ET, and the conventional NHT. Analysis shows that MET, ET, and the conventional NHT are complementary. Combining them to test the noteworthiness of domain loadings can help researchers make a comprehensive judgment. Real and simulated datasets illustrate the applications of the three methods. Monte Carlo results show that MET and ET can control type I errors reasonably well while maintaining good statistical power.
An examination of the coaching quality checklist using confirmatory factor analysis
International Journal of Mentoring and Coaching in Education · 2024-12-26 · 1 citations
articlePurpose We developed and studied an approach to measuring the quality of coaching meetings. Coaching is a professional development approach that has been implemented in education settings for several decades to support teachers and other practitioners in providing effective instruction. As coaching has become more prevalent, it has become clear that the field needs tools to measure coaching quality. Design/methodology/approach The coaching quality checklist (CQC) is a measure based on the empirical and theoretical literature on coaching. It has 26 items designed to measure three constructs: foundational, supportive and change-oriented coaching skills. In this study, we conducted a confirmatory factor analysis of the CQC. Findings We found the one-factor model fit the data well. The hypothesized higher-order three subfactor model fit the data better but not significantly so. Additional research is needed to further validate the CQC using a larger sample and examine different types of validity. Originality/value The CQC is a promising tool for measuring coaching quality, which can help ensure that teachers are provided with high-quality professional development to support their use of interventions.
Personality and Individual Differences · 2023-07-13 · 16 citations
articleOpen accessAlexithymia is a clinically relevant personality trait characterized by poor emotional awareness and associated with several psychological and physical health concerns. Individuals with high alexithymia tend to engage in experiential avoidance and this may mediate psychological distress. However, little is known about what specific processes of experiential avoidance are involved, and the nature of the relation between alexithymia, experiential avoidance, and psychological distress remains unclear at a latent construct level. To examine this relationship at the latent construct level, a representative sample of 693 U.S. adults completed alexithymia (TAS-20, BVAQ, PAQ), general distress (DASS-21), multi-dimensional experiential avoidance (MEAQ), and general health (PROMIS-G-10) questionnaires. Structural equation modeling revealed that alexithymia significantly predicted experiential avoidance (β = 0.966, t = 82.383, p < .01), experiential avoidance significantly predicted general distress (β = 0.810, t = 2.017, p < .05), and experiential avoidance fully mediated the relationship between alexithymia and general distress (βindirect = −0.159, t = −0.398, p > .05). Correlations between alexithymia and experiential avoidance subfactors revealed a strong relationship to the repression and denial subfactor. Experiential avoidance is a promising target for clinical interventions, though longitudinal research is necessary to elucidate how the relationship between alexithymia and experiential avoidance unfolds over time.
Quality & Quantity · 2023-12-27 · 3 citations
article1st authorCorresponding
Frequent coauthors
- 7 shared
Ke‐Hai Yuan
University of Notre Dame
- 6 shared
Walter L. Leite
University of Florida
- 4 shared
Katherine Lust
University of Minnesota
- 4 shared
Wilma Koutstaal
- 4 shared
Sara Knauz
Twin Cities Orthopedics
- 4 shared
Patricia Frazier
University of Minnesota
- 4 shared
Laura Trinchera
NEOMA Business School
- 4 shared
Nathan Torunsky
University of Minnesota
Education
B.A., Psychology
University of California, Santa Barbara
M.A., Quantitative Psychology
University of California, Davis
Ph.D., Quantitative Psychology
Arizona State University
Awards & honors
- Elected Member of the Society of Multivariate Experimental P…
- UMN Center for Educational Innovation, Thank a Teacher Progr…
- UMN Center for Educational Innovation, Thank a Teacher Progr…
- Association for Psychological Science (APS) Rising Star Awar…
- International Communication Association Top Paper Award, 202…
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