Find Academic Supervisors

Katerina MarcoulidesView profile

Associate Professor

• Data Mining
• Structural Equation Modeling
• Longitudinal Data Analysis
• +3 more

Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.

Daniel J. BauerView profile

Professor

• Quantitative Psychology
• Latent Variable Models
• Developmental Psychopathology
• +6 more

Dr. Daniel J. Bauer is a Professor and Director of the Quantitative Psychology Program and L.L. Thurstone Psychometric Laboratory at the University of North Carolina at Chapel Hill. His research focuses on advancing quantitative modeling techniques for studying negative social behaviors, health outcomes, and psychopathology, with expertise in generalized and nonlinear latent variable models, including multilevel models, structural equation models, and mixture models. His work emphasizes methodological innovations such as Bayesian regularization, measurement invariance evaluation, and the integration of deep learning with psychometrics. Bauer advises doctoral students in quantitative psychology and collaborates with developmental psychology programs. He leads the Thurstone Laboratory, one of the oldest quantitative psychology training programs in the U.S., emphasizing rigorous methodological training and applications in behavioral sciences. Bauer’s research trends span computational advancements in latent variable analysis, regularization for bias detection, and dynamic modeling of developmental processes. His advising includes over a dozen doctoral students now in academia and industry roles. He contributes to labs and initiatives like the Center for Developmental Science, advancing interdisciplinary research in psychological measurement and intervention evaluation.

Abhijit SarkarView profile

Professor

• Dynamics
• Probabilistic Mechanics
• Uncertainty Quantification
• +5 more

Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).

Jason RightsView profile

Associate Professor

• Quantitative Methods
• Multilevel/Hierarchical Data Analysis
• Statistical Methods
• +4 more

Dr. Jason Rights is an Associate Professor in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His office is located in Kenny Room 2017 at 2136 West Mall, Vancouver, BC. He leads The Rights Lab, a quantitative methods research group dedicated to improving statistical practice in scientific research. His educational background includes: B.S. in Psychology and Mathematics from the University of North Carolina at Chapel Hill (2011) M.S. in Psychology (Quantitative Methods) from Vanderbilt University (2015) Ph.D. in Psychology (Quantitative Methods) from Vanderbilt University (2019) Dr. Rights' research focuses on addressing methodological complexities in multilevel/hierarchical data contexts where observations are nested (e.g., patients within clinicians, students within schools). His work spans several interconnected programs including developing R-squared measures for multilevel models, addressing issues with level-specific effects, exploring connections between multilevel and mixture models, and advancing latent variable model selection techniques. Analysis of his publication record reveals a consistent focus on methodological innovations in quantitative psychology, with particular emphasis on improving statistical techniques for hierarchical data structures. His work bridges theoretical statistical development with practical applications across psychology and related fields. Dr. Rights actively develops open-source software in R to implement his methodological contributions, making advanced statistical techniques accessible to researchers. The Rights Lab serves as the hub for his ongoing research program in quantitative methods development.

Li CaiView profile

Professor

• Psychometrics
• Latent Variable Models
• Item Response Theory
• +3 more

Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.

Peter F. HalpinView profile

Associate Professor

• Psychometrics
• Educational Measurement
• Educational Technology
• +4 more

Peter F. Halpin is an Associate Professor in the Department of Learning Sciences and Psychological Studies at the University of North Carolina at Chapel Hill School of Education. He holds a PhD in Psychology (Theory and Methods) from Simon Fraser University and completed postdoctoral research at the University of Amsterdam. His research focuses on psychometric methodology, educational measurement, and statistical approaches to analyzing collaborative learning and teacher practices. Halpin has been recognized with awards including the National Academy of Education/Spencer Fellowship and NYU's High Merit Distinction in Research. Key research areas include developing statistical models for small group collaborations, analyzing educational technology data, and improving measurement tools for early childhood development (e.g., IDELA assessments). His work bridges theoretical psychometrics with applied educational research, addressing challenges in global education measurement and program evaluation. Halpin has authored over 20 peer-reviewed articles and contributed to open-source software projects like the scirt and hawkes R packages. He has advised numerous graduate students and led grants totaling over $2 million, including IES-funded studies on collaboration assessment and UNESCO-linked projects measuring educational outcomes in low-resource settings. Halpin also serves on editorial boards for journals like Psychometrika and Journal of Educational Measurement , and has presented globally at venues including the Psychometric Society and NCME conferences.

Ronny SchererView profile

Professor

• Meta-analysis
• Educational Measurement
• Digital Divides
• +6 more

Ronny Scherer is Center Director and Professor at CEMO (Center for Educational Measurement) and Deputy Director at CREATE (Center for Research on Equality in Education) at the University of Oslo's Faculty of Educational Sciences. His work bridges educational measurement, assessment, and evaluation with a focus on research syntheses and complex sampling surveys. Dr. Scherer's research spans two interconnected domains: substantive areas including digital divides, equity and equality in education, and measurement of complex cognitive skills (such as complex problem solving, adaptability, computational thinking, and executive functioning); and methodological areas focusing on advanced meta-analytic techniques, multilevel structural equation modeling, and spatial analysis of complex survey data. His work frequently utilizes international large-scale assessment data from PISA, ICILS, TIMSS, PIRLS, PIAAC, and TALIS. His publication record demonstrates a clear trajectory toward increasingly sophisticated meta-analytic approaches, with recent work focusing on second-order meta-analyses, AI-assisted screening methods, and advanced techniques for handling complex survey data. His research consistently addresses critical educational challenges related to equity, digital literacy, and measurement of 21st century skills. Dr. Scherer has secured significant research funding for projects including ARISE (Academic resilience in mathematics and science among vulnerable students), DiDiRes (Digital inequalities in education), and ADAPT21 (Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem solving situations). Co-director of CREATE (Centre for Research on Equality in Education) since 2023 Professor of Educational Assessment and Measurement at CEMO since 2019 Extensive experience with international large-scale assessments including ICILS, TALIS, and PIAAC As an educator, Dr. Scherer teaches advanced courses in measurement models, multilevel models, meta-analysis, and equity in education. He actively supervises graduate students interested in his research areas and has developed numerous workshops on structural equation modeling and meta-analytic methods for international audiences.

Patrick J. CurranView profile

Professor

• Quantitative Methods
• Longitudinal Data Analysis
• Developmental Psychopathology
• +3 more

Patrick J. Curran is a Professor in the Department of Psychology at the University of North Carolina, affiliated with the L.L. Thurstone Psychometric Laboratory and the Quantitude initiative. His research focuses on quantitative methodologies, particularly longitudinal data analysis and structural equation modeling, applied to developmental psychopathology and adolescent substance use. He collaborates on statistical education through YouTube tutorials, a blog (Help Desk), and workshops, alongside a podcast with Greg Hancock. His work bridges methodological innovation with substantive research in psychology and public health. Key contributions include advancing latent curve models with structured residuals, integrative data analysis techniques, and the dissemination of statistical tools. His research spans disciplines such as developmental psychology, clinical psychology, and epidemiology, addressing topics like alcohol use trajectories, chronic depression mechanisms, and pandemic-related disparities in healthcare workers’ mental health. Lab Affiliations: L.L. Thurstone Psychometric Lab, Quantitude Collaborations: Dan Bauer (statistical education), Greg Hancock (podcast), and interdisciplinary teams in public health and clinical research His publications emphasize methodological rigor and practical application, often addressing gaps in longitudinal data analysis and measurement equivalence. Notable work includes the latent curve model with structured residuals (LCM-SR) and integrative data harmonization strategies for prevention science.

• Educational Identity Development
• Adolescent Psychology
• School Transitions
• +3 more

Dr. Annabelle Christiaens serves as a Lecturer in the Department of Youth and Family at Utrecht University's Faculty of Social and Behavioural Sciences. She currently contributes to the "Pedagogy of Hope" project, where she investigates adolescents' attitudes toward climate and sustainability through the lens of eco-identity development. Her academic journey began with a Bachelor in Psychology at Tilburg University (2013-2017, with distinction), followed by a Research Master in Psychology: Individual Differences and Assessment (2017-2019, with honors). She completed her PhD at Utrecht University in 2024 with the dissertation "Navigating School Transitions: The Formation and Maintenance of Adolescents' Educational Identity," supervised by Dr. Stefanie Nelemans and Professor Dr. Susan Branje. Dr. Christiaens' research expertise centers on educational identity development during critical school transitions, with particular focus on how adolescents construct and maintain their educational identities. Her work employs sophisticated longitudinal methodologies and advanced statistical techniques including latent class/profile analysis, multilevel modeling, and structural equation modeling. She has made significant contributions to understanding identity processes through the development and validation of measurement tools like the Educational Identity Processes Scale (EIPS). Analysis of her publication record reveals a clear trajectory from foundational work on identity measurement and developmental patterns toward increasingly complex examinations of the interplay between identity formation, psychological needs, and environmental factors. Her recent work demonstrates growing attention to the role of basic psychological needs in identity construction and the reciprocal relationships between school environments and identity development. Dr. Christiaens maintains active engagement with the academic community through multiple service roles: Local Committee member for the ISRI conference 2024 Co-organizer of the CAS Studies Research Day Member of the CAS educational advisory committee Her commitment to methodological excellence is evident through her participation in numerous specialized training programs, including summer schools on multilevel analysis, SEM modeling in Mplus, and longitudinal analysis methods. Her current research on eco-identity represents an important expansion of her work into sustainability education, demonstrating the adaptability and relevance of identity frameworks to contemporary societal challenges.

• Multivariate Methods for Change Analysis
• Latent Class Models in Developmental Processes
• Machine Learning in Psychology
• +3 more

Kevin John Grimm is a Professor in the Department of Psychology at Arizona State University (ASU), serving as Director of Operations and Research within the College of Health Solutions. He holds a B.A. in Mathematics and Psychology from Gettysburg College (2000), and M.A. (2003) and Ph.D. (2006) in Psychology from the University of Virginia. Previously, he served as faculty at UC Davis before joining ASU in 2014. His research focuses on multivariate methods for analyzing developmental change, including nonlinear growth modeling, latent class analysis, and integrating machine learning with psychological data. Notable contributions include co-authoring the textbook *Growth Modeling: Structural Equation and Multilevel Modeling Approaches* (Guilford Press, 2017). Teaches courses like Structural Equation Modeling, Longitudinal Growth Modeling, and Machine Learning in Psychology at ASU. Active in professional service: Associate Editor of *Structural Equation Modeling: A Multidisciplinary Journal* since 2012. Recipient of NIH/NIDA grants for drug abuse/HIV prevention research and NICHD-funded studies on sleep health in children. His methodological work bridges quantitative innovation with substantive developmental research, emphasizing rigorous model specification and cross-disciplinary applications.

Keke LaiView profile

Associate Professor

• Quantitative Psychology
• Structural Equation Modeling
• Missing Data Analysis
• +5 more

Keke Lai is an Associate Professor of Quantitative Psychology at the University of California, Merced. Previously, she held positions at the University of Houston and Arizona State University. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2012), and a B.A. in English from China (2005). Her research focuses on advanced statistical methodologies in psychology and education, including structural equation modeling (SEM), robust statistical methods, model evaluation, longitudinal/multilevel data analysis, missing data techniques, and Bayesian statistics. She addresses critical gaps in SEM methodology, such as accurate estimation under missing data, nonnormal distributions, and model misspecification. Her recent work emphasizes improving confidence intervals for fit indices (e.g., RMSEA, CFI), comparing nonnested models, and developing methods for standardized parameters in misspecified models. These contributions enhance the reliability of statistical analyses in psychological research. Lai’s publications appear in top journals like Structural Equation Modeling and Psychometrika . She advises graduate students, including X. Zhang. Her website is https://sites.google.com/view/laikeke .

Laura M. StapletonView profile

Professor

• Multilevel Modeling
• Survey Methodology
• Educational Statistics
• +3 more

Laura M. Stapleton is Chair of the Department of Human Development and Quantitative Methodology and a Professor at the University of Maryland’s College of Education. She previously served as Interim Dean and Associate Dean for Research, Innovation, and Partnerships. Her academic roles include leadership in the NSF-funded Quantitative Research Methods Scholars Program (2019–2025) and membership on Maryland’s Accountability and Implementation Board for education reform. Education Background: Ph.D. in Measurement, Statistics, and Evaluation from the University of Maryland. Prior to academia, she worked as an economist at the Bureau of Labor Statistics and in educational research roles at the American Association of State Colleges and Universities and the University of Maryland’s institutional research department. Research focuses on complex survey data analysis, multilevel latent variable models, and mediation testing. Key interests include administrative data utilization, STEM education equity, and policy evaluation. Her work emphasizes bridging statistical rigor with practical educational challenges. Publications span methodological advancements in multilevel modeling, synthetic data strategies, and the integration of administrative datasets. Recent work addresses school-based prevention study attrition and the design effects of multilevel samples. Awards include AERA Fellowship (2023), election as President of the Society for Multivariate Experimental Psychology (2025), and recognition for mentoring and teaching excellence. She has led over $10M in grants, including NSF-funded initiatives to train early-career STEM equity researchers. Current projects include the BCSER Quantitative Research Methods Program (2022–2025) and collaborations on arts education impacts and intergroup relationship interventions. She advises on state-level longitudinal data systems and chairs the Maryland State Longitudinal Data System Center’s Research Branch (2013–2018).

Peter van der HeijdenView profile

Professor

• Categorical Data Analysis
• Data Science
• Multivariate Statistics
• +3 more

Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.

Gregory R. HancockView profile

Professor

• Structural Equation Modeling
• Latent Growth Models
• Latent Variable Analysis
• +5 more

Gregory R. Hancock is a Professor and Program Director of Quantitative Methodology: Measurement and Statistics at the University of Maryland, College Park. He also serves as Director of the Center for Integrated Latent Variable Research (CILVR) and is an Affiliated Professor at the Center for Advanced Study of Language. Holding a Ph.D. from the University of Washington (1991), his research focuses on structural equation modeling, latent growth models, experimental design, and power analysis. He has co-edited influential volumes such as Structural Equation Modeling: A Second Course and contributed to Psychometrika , Multivariate Behavioral Research , and other top journals. His awards include the Jacob Cohen Award for Teaching (2011), Fellowships from the APA and APS, and multiple recognition for mentorship. Hancock has led over 200 workshops globally and served on editorial boards for major journals. His work emphasizes methodological rigor in quantitative research, with contributions to latent variable models, measurement invariance, and longitudinal methods. Key Contributions: SEM applications, growth curve modeling, and statistical pedagogy Labs/Teams: Center for Integrated Latent Variable Research (CILVR) Funding/Grants: Not explicitly listed, but implied through extensive workshop leadership and editorial commitments

Pui-Wa LeiView profile

Professor

• Multivariate Statistical Analyses
• Structural Equation Modeling
• Item Response Theory
• +3 more

Dr. Pui-Wa Lei is a Professor in the Department of Educational Psychology, Counseling, and Special Education at Pennsylvania State University. Her research focuses on methodological issues in multivariate statistical analyses, structural equation modeling (SEM), and applications of item response theory (IRT). She teaches courses in statistics and measurement theories. Dr. Lei received her Ph.D. in Educational Measurement and Statistics from The University of Iowa (2001), following an M.S. (1997) and dual B.S./B.A. in Applied Psychology and Psychology from Bemidji State University (1994). Her work is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025) and the Pennsylvania State University's Social Science Research Institute (SSRI). She contributes to the Population Research Institute (PRI), which highlights her engagement in interdisciplinary research initiatives.