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Eldad DavidovView profile

Professor

• Quantitative Methods
• Empirical Social Research
• Cross-Cultural Comparisons
• +5 more

Eldad Davidov is a Professor of Methods of Comparative Cross-National Social Research at the Institute of Sociology and Social Psychology, University of Cologne. He previously held professorships at the University of Zurich (2009-2016) and served as President of the European Survey Research Association (ESRA, 2015-2017). His research focuses on quantitative methods, cross-cultural comparisons, structural equation modeling, and attitudes/values analysis. Davidov has held visiting professorships at Mannheim and Cologne, and his work emphasizes methodological rigor in social research. PhD: Social Sciences, University of Giessen (2004) MA: Economics, Tel Aviv University (1996) BA: Economics, Tel Aviv University (1995) His research interests include: Measurement equivalence in cross-national surveys Value orientations and their societal implications Immigration attitudes and prejudice mechanisms Longitudinal survey data analysis Key publication themes involve analyzing immigration opposition, human values measurement, and methodological advancements in cross-cultural research. His work bridges theory and practice, contributing to the European Social Survey and policy-relevant studies on integration policies. Davidov is affiliated with the Center for Social and Economic Behavior (C-SEB) and contributes to the ECONtribute Cluster of Excellence. His research addresses societal challenges through interdisciplinary collaboration, emphasizing evidence-based approaches to public policy.

• Educational Psychology
• Quantitative Research Methods
• Educational Effectiveness
• +6 more

Benjamin Nagengast is Professor of Educational Psychology and Deputy Director of the Hector Institute for Empirical Educational Research at the University of Tübingen. His work focuses on educational psychology, quantitative research methods, and motivation theory, with a particular emphasis on academic self-concept, intervention programs, and causal analysis in educational settings. He previously served as co-director of the LEAD Graduate School & Research Network and head of the LEADing Research Center until June 2022. His research spans topics including: Educational effectiveness and policy evaluation Motivational dynamics and value-belief interactions Multilevel modeling of educational outcomes Personality-achievement correlations Longitudinal studies of developmental trajectories While not explicitly listing scientific awards, his publications highlight methodological innovations and large-scale empirical studies. He mentors students through collaborative research projects, though individual advisees aren't named. His work frequently appears in top journals like Psychological Science and Journal of Educational Psychology , with recent articles examining detracking effects, twin comparison processes, and the interplay between self-concept and achievement. He maintains active collaborations with institutions like the University of Oxford and University of Jena.

Tasha BeretvasView profile

Professor

• Quantitative Methods
• Psychometrics
• Multilevel Modeling
• +3 more

Tasha Beretvas is a Professor of Quantitative Methods in the Department of Educational Psychology and Associate Dean of Research and Graduate Studies at the College of Education, University of Texas at Austin. She holds the John L. and Elizabeth G. Hill Professorship and has led faculty affairs as Senior Vice Provost for Faculty Affairs. Research focuses on psychometric models, multilevel modeling with complex data structures (e.g., student mobility), and meta-analytic techniques for single-case designs. Key contributions include methodological advancements in multilevel modeling, meta-analysis of dependent effect sizes, and synthesis of single-subject experimental data. Her work appears in journals like Multivariate Behavioral Research , Psychological Methods , and Journal of Educational Psychology . Recent articles emphasize meta-analysis of single-case designs, handling missing data in meta-regression, and cluster wild bootstrapping for dependent effect sizes. She has pioneered tools like MultiSCED for multilevel analysis of single-case data. Scientific Awards Regents Award for Excellence in Undergraduate Teaching Grants Principal Investigator (PI) or Co-PI on multiple Institute of Education Sciences (IES) grants Leadership Elected member, Society for Research Synthesis Methodology Past Associate Editor, Journal of School Psychology and Journal of Educational Psychology Member, Design and Analysis Committee for the National Assessment of Educational Progress (NAEP)

Ehri RyuView profile

Associate Professor

• Statistical Methodology
• Structural Equation Modeling
• Multilevel Analysis
• +4 more

Ehri Ryu is an Associate Professor in the Department of Psychology at Boston College. She specializes in developing and evaluating statistical methods for multilevel multivariate data analysis, with a focus on structural equation modeling (SEM) frameworks. Her research addresses methodological challenges such as model fit assessment, factorial invariance, and indirect effect testing, applied to domains like consumer behavior, cognitive neuroscience, and traumatic brain injury outcomes. Education: Ph.D., Arizona State University Research Interests: Ryu's work bridges statistical methodology and applied research. She explores advanced SEM techniques for hierarchical data, collaborating on topics ranging from social media marketing to neurorehabilitation. Her methodological contributions enhance cross-disciplinary applications in psychology, neuroscience, and consumer sciences. Publications Trends: Her publications emphasize multilevel modeling innovations, consumer behavior dynamics during crises, and psychometric rigor in latent variable analysis. Recent work highlights societal impacts like pandemic-era charitable behavior and influencer marketing efficacy. Awards/Grants: No specific awards listed in provided text. Active research grants likely tied to methodology development but not explicitly stated. Labs/Teams: Collaborates across disciplines including cognitive neuroscience and consumer psychology through applied statistical method development. Supervises students in advanced quantitative methods training.

• Psychometrics
• Statistical Modeling
• Bayesian Methods
• +3 more

Roy Levy is a Professor at Arizona State University (ASU), holding appointments in the Department of Psychology and the T. Denny Sanford School of Social & Family Dynamics. His research focuses on quantitative methodology, including psychometrics, statistical modeling, Bayesian approaches, item response theory, and structural equation modeling. He specializes in applications such as assessment design and simulation-based evaluations. Levy teaches courses on Bayesian modeling, statistical analyses, and measurement methodologies. His work emphasizes methodological advancements to address interpretational challenges in factor analysis, missing data, and model validation. He has authored influential publications and a textbook on Bayesian psychometric modeling. Notably, his research bridges theoretical frameworks with practical applications in education and digital learning environments. Levy collaborates widely and maintains an active presence in academic publishing, contributing to journals like Structural Equation Modeling and Educational Assessment . Education and career details are not explicitly listed, but his academic trajectory is evident through his publications and roles. His research interests extend to dynamic Bayesian networks, educational measurement theory, and the integration of evidence-centered design principles in assessments. He advocates for rigorous methodological practices to enhance the reliability and validity of psychometric tools. Levy’s advising and grants are not detailed here, but his extensive publication record and textbook indicate significant contributions to training future researchers. His work on psychometric isomorphism and Bayesian network modeling highlights innovations in linking assessment design with cognitive processes. He is a key figure in advancing quantitative methods for evaluating complex educational and psychological constructs.

Elena PapanastasiouView profile

Professor

• Educational Research
• Quantitative Methods
• Assessment
• +8 more

Professor Elena Papanastasiou is the Dean of the School of Education at the University of Nicosia, Cyprus, where she teaches Educational Research , Quantitative Methods , and Assessment . She holds a PhD in Measurement and Quantitative Methods from Michigan State University (2001) and an Honors B.Sc. in Elementary Education from Pennsylvania State University (1996). Her research focuses on educational assessment , structural equation modeling , multilevel modeling , and mixed methods research , with applications in international assessments like TIMSS and IEA . Recent publications analyze generative AI in education , test-taking metacognition , student well-being , and international schooling contexts in Cyprus, Europe, and the USA. She serves as Cyprus’ General Assembly representative in IEA and is a member of the IEA Standing Committee and Publications Committee , as well as the Professional Development Committee of the Association for Educational Assessment. Her consulting work on methodological frameworks supports research teams globally, emphasizing advanced statistical analysis and educational policy development.

• Intergroup Relations
• Gender Studies
• Race and Ethnicity
• +7 more

Andrew L. Stewart is an Associate Professor in the Psychology Department at Clark University's Hiatt School of Psychology. Specializing in social psychology and political psychology, his work focuses on intergroup relations across gender, race, ethnicity, nationality, sexual orientation, and class dimensions. Ph.D. and M.A. in Social Psychology from University of Connecticut (2014) B.S. in Psychology and Mathematics from Colorado State University (2009) Stewart's research investigates how societal beliefs about social groups perpetuate violence and discrimination, while also developing interventions to mitigate these issues. His work spans: Sexist ideology and gender-based violence Hegemonic masculinity norms Collective action motivations Sexual assault prevention programming Advanced multilevel statistical modeling Recent publications examine power dynamics in tight/loose cultures and cross-cultural parenting differences. Stewart has received funding from the Spencer Foundation for his work on critical consciousness measurement and actively consults with universities on violence prevention initiatives. Measurement of Parent and Teacher Critical Consciousness (Spencer Foundation) As a committed educator, he teaches courses including: Advanced Social Psychology Psychology of Prejudice Structural Equation Modeling Masculinities and Violence

Kristopher J. PreacherView profile

Professor

• Structural Equation Modeling
• Multilevel Modeling
• Mediation and Moderation Analysis
• +3 more

Kristopher J. Preacher is a Professor in the Quantitative Methods (QM) program at Vanderbilt University's Department of Psychology & Human Development. He specializes in bridging theoretical frameworks with multivariate statistical techniques for analyzing complex longitudinal and correlational data. Lois Autrey Betts Chair in Education & Human Development Associate Chair, Department of Psychology & Human Development Editorial Board Member: Behavior Research Methods, Organizational Research Methods His methodological research focuses on: Structural Equation Modeling (SEM) Multilevel Modeling (MLM) Mediation and Moderation Effects Model Fit Evaluation Factor Analysis Continuous Time Modeling Recent publications emphasize model selection strategies, nonlinear treatment effects in cluster trials, and challenges in power analysis for complex models. Key collaborations span nutrition science, educational interventions, and clinical psychology applications. Scientific contributions include: Developing open-source statistical tools (e.g., semTools) Advancing multilevel SEM frameworks Promoting best practices for variable centering He advises graduate students and teaches courses in: Applied Nonparametric Statistics Multilevel Modeling

• Statistical Inference
• Data Modelling (Environmental events)
• Bayesian Statistics
• +7 more

Dr. Pragalathan (Prahan) Apputhurai is a Lecturer in Applied Statistics at Swinburne University of Technology's School of Health Sciences . With a PhD in Applied Statistics from Swinburne and prior degrees from the University of Peradeniya, Sri Lanka, his expertise spans Bayesian Statistics , Structural Equation Modeling , and Extreme Events Data Modelling . He teaches undergraduate and postgraduate courses in Applied Statistics and supervises PhD/Master's students in areas including Factor Analysis and Social Determinants of Health. Education: PhD in Applied Statistics, Swinburne University of Technology, Australia Bachelor of Science & Applied Statistics, University of Peradeniya, Sri Lanka Research Interests: Specializing in statistical methods for health and social sciences, Dr. Apputhurai's work addresses issues including intimate partner violence, mental health in chronic disease populations, and empathetic AI in healthcare. His recent publications focus on structural equation modeling applications, psychometric validation in gastrointestinal disorders, and conversational agent empathy assessment. Grants: Rome Foundation (2025): Dietary interventions for IBS pain management Civil Aviation Safety Authority (2022-2024): Bayesian methodology in aviation safety Department of Communities and Justice (2018-2019): Indigenous women's reintegration through empowerment pathways Professional Recognition: Vice Chancellor's Teaching Excellence Award (2016) SUPRA Scholarship Recipient (2009) Academic Excellence in B.Sc. Examinations (2003) Academic Supervision: Currently supervising research projects in inflammatory bowel disease mental health support, conversational agent development, and trauma impact on quality of life across various health conditions.

• Educational Assessment
• Student Motivation
• Educational Policy
• +4 more

Nicolas Hübner is an Assistant Professor at the Institute of Education, University of Tübingen. He holds a PhD from the Hector Research Institute of Education Sciences and Psychology (2017) and completed studies in Psychology and Education Sciences at the Universities of Erfurt, Jyväskylä (Finland), and Tübingen. He is a Bradshaw Fellow at Durham University (2019) and a member of the College for Interdisciplinary Educational Research (CIDER). His research focuses on student assessment, educational policy reforms, and teacher professional development, employing advanced quantitative methods like longitudinal structural equation modeling and multilevel analysis. Education: PhD in Education Sciences (University of Tübingen, 2017), M.Sc. in Educational Science and Psychology (University of Tübingen, 2014–2017), B.A. in Teaching, Learning, and Training Psychology (University of Erfurt, 2008–2012). Professional experience includes roles at the National Educational Panel Study (NEPS) and research fellowships at the Hector Institute. Research interests include the comparability of grading systems, STEM education outcomes, and the impact of educational reforms. He leads projects like the "Schulversuch Lernförderliche Leistungsrückmeldungen" and investigates teacher feedback mechanisms in secondary education. Awards include the Dissertation Award (2018) and the NEPS Publication Award (2020). Key grants: German Research Foundation (DFG), Baden-Württemberg Ministry of Science (projects on grade inflation and teacher professional development). Active in interdisciplinary collaborations, including studies on socioemotional skills and international education systems.

• Educational Research Methods
• Evaluation in Education
• Learning Analytics
• +7 more

Cristian Molla Esparza is an Assistant Professor in the Department of Research and Evaluation Methods in Education at the Faculty of Philosophy and Educational Sciences, University of Valencia. He holds a PhD from the same institution, where his research focused on adolescent sexting behaviors and the development of the A-SextS scale. He is actively involved in research on learning analytics, text mining, meta-analysis, and socioemotional competencies in higher education. Research Interests: His work centers on educational research methodologies, particularly quantitative and mixed methods. Key areas include sexting behavior in youth, learning analytics, text mining for conceptual learning, and systematic reviews. He applies advanced statistical techniques such as multilevel modeling, mediation analysis, and structural equation modeling in meta-analysis. Recent Publications: His recent articles (2020–2025) reflect a strong focus on digital behaviors in adolescents, academic performance analysis using learning analytics, and methodological advancements in meta-research. Trends include the use of text mining to understand learning processes, scoping and systematic reviews on socioemotional and risk behaviors, and the development of psychometric tools. Scientific Projects: Minería de textos para mejorar el aprendizaje conceptual en las universidades (2024–2025) Valoración de la calidad del reporte de estudios cuantitativos no experimentales (2023–2027) Evidence-driven teaching and learning analytics (2023–2024) How to Use ChatGPT for University Learning (2023–2024) Teaching and Supervision: He teaches courses in educational research, data analysis, and thesis supervision across bachelor's, master's, and doctoral programs at the University of Valencia and other institutions. He has coordinated teaching innovation projects and delivered training on research methods and educational technology. Research Stays: He has conducted research at the University of Murcia (systematic reviews), Autonomous University of Madrid (instrument development), and Universidad Autónoma de Encarnación, Paraguay (doctoral teaching and research).

Brisa SanchezView profile

Adjunct Professor

• Statistical Methodology
• Environmental Epidemiology
• Social Epidemiology
• +3 more

Brisa Sanchez is an Adjunct Professor of Biostatistics at the University of Michigan. She earned her PhD in Biostatistics from Harvard University in 2006 and joined the University of Michigan in 2006 as an Assistant Research Professor, advancing to Assistant Professor in 2008. Her research focuses on statistical methodology for environmental and social epidemiology, particularly health disparities. PhD, Biostatistics, Harvard University (2006) M.Sc., Biostatistics, Harvard University (2003) M.S., Statistics, University of Texas at El Paso (2001) B.S., Mathematics, University of Texas at El Paso (2000) Sanchez develops robust statistical methods, including Structural Equation Models and functional data analysis, for environmental health studies (e.g., lead exposure effects on child development) and longitudinal research (e.g., salivary cortisol as a stress biomarker in health disparities). Her collaborative work spans cardiovascular disease, stroke, BMI distribution, and school-based interventions to reduce childhood obesity. Recent publications emphasize statistical innovations for handling sparse environmental data, modeling salivary cortisol profiles, and integrating survey datasets to study neighborhood impacts on health. Methodological contributions include diagnostics for latent variable models and applications to gene-environment interactions. Contact: brisa@umich.edu

Tom SmithView profile

Teaching Professor

• Quantitative Research Methods
• Educational Statistics
• Multilevel Modeling
• +7 more

Tom Smith is a Teaching Professor in the Department of Educational Technology, Research and Assessment at Northern Illinois University’s College of Education. He is actively involved in teaching and research, focusing on quantitative methodologies and statistical analysis in educational contexts. Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign, 2000 M.S. in Statistics, University of Illinois at Urbana-Champaign, 1997 B.A. in Psychology, University of St. Thomas (St. Paul, MN), 1988 Dr. Smith's research centers on advanced statistical methods in education, including multilevel modeling, latent variable analysis, categorical data techniques, and robust estimation under non-normality. He conducts workshops on hierarchical linear modeling, survey construction, and factorial invariance, contributing significantly to methodological training in education. His recent publications reflect a strong trend in applying sophisticated statistical models to educational data, particularly in assessing model fit, improving logistic regression techniques, and enhancing computer-assisted learning environments. These works span journals in applied statistics, educational technology, and geography education, indicating interdisciplinary engagement. While no specific scientific awards are listed, his title as Presidential Teaching Professor suggests recognition for excellence in pedagogy. He teaches core courses such as ETR 520 (Research Methods), ETR 521/522 (Educational Statistics), and advanced seminars in multivariate analysis and latent variable modeling. Dr. Smith advises students through graduate workshops and field experiences, though no named advisees are listed. He has been involved in projects related to cognitive tutoring systems and technology-enhanced learning environments, often collaborating with researchers across disciplines. He contributes to outreach through the Learn-IT Conference and Project SEED partnership with School District 203. He is affiliated with the User Experience (UX) Lab and other resource labs within the department, supporting innovative research in learning analytics and educational assessment.

Bedřich SousedíkView profile

Associate Professor

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +5 more

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

Zachary K. CollierView profile

Assistant Professor

• Causal Machine Learning
• Structural Equation Modeling
• Educational Measurement
• +3 more

Zachary K. Collier is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut. He holds a Ph.D. in Measurement and Statistics from the University of Florida (2018) and a B.S. in Special Education from Winthrop University (2013). His research integrates Causal Machine Learning and Structural Equation Modeling with critical perspectives to advance equitable data analysis methods. He developed the CritSEM framework to examine how social and structural inequalities shape measurement constructs. Founded the MUDD Lab (Methods for Unstructured and Difficult to use Data) in 2020 Secured over $9 million in external funding His work emphasizes fairness, data ethics, and methodological transparency across education, public health, and social sciences. Selected publications demonstrate innovative applications of machine learning to educational measurement challenges. Scientific recognition includes: Emerging Scholar Featured Mathematician