Quantitative Psychology
UVA Quantitative Psychology Doctoral Program Course Overview
Our department’s doctoral program in Quantitative Psychology provides rigorous training in the methods, techniques, and theories used to understand and analyze psychological phenomena from a quantitative perspective. The curriculum blends advanced coursework with hands-on research experience, preparing graduates for academic, governmental, or industry roles where expertise in statistical modeling, psychometrics, and computational methods is essential. Students gain a deep understanding of experimental design, machine learning, and mathematical modeling, equipping them with the tools to address complex psychological questions through data-driven approaches.
Earning a doctorate in Quantitative Psychology from the University of Virginia offers unparalleled opportunities for leadership and interdisciplinary collaboration. With access to world-class faculty, state-of-the-art research facilities, and a strong professional network, graduates from our program are well-positioned to carry out innovative research, develop sophisticated analytical tools, developing in silico models, and for solving real-world problems in fields ranging from behavioral science to artificial intelligence. This rigorous training ensures that our graduates are not only proficient in quantitative methods but also capable of advancing the science of human behavior through data-driven discovery.
In what follows, we provide a brief overview of what is covered in this rigorous program of graduate study:
Core Knowledge and Skills
Quantitative psychology doctoral programs build a foundation in psychological theory and research, covering diverse fields such as cognitive, social, developmental, and clinical psychology. This background is integrated with advanced statistical knowledge, including techniques like regression, multivariate analysis, structural equation modeling (SEM), Bayesian methods, Network Science, Network Psychometrics, and Machine Learning. Students learn psychometrics to develop, validate, and improve psychological assessments, mastering classical test theory (CTT) and item response theory (IRT). They also gain expertise in experimental design to craft robust, generalizable studies, and develop computational skills in programming languages such as R, Python, C, or MATLAB for data analysis and modeling.
Research and Applied Training
Students actively engage in collaborative research, applying statistical methods to real-world psychological questions in diverse domains such as education and health. Through dissertation research, they design and conduct studies addressing significant theoretical or applied issues, often working across multidisciplinary teams. Training emphasizes innovative approaches like mathematical modeling of behavior, time-series analysis, and machine learning, preparing students for complex research challenges.
Ethics and Scientific Communication
The program emphasizes ethical practices in data collection, analysis, and reporting, ensuring integrity in research. Communication skills are a key focus, with students learning to clearly and effectively present complex statistical findings to non-expert audiences, bridging the gap between quantitative analyses and practical application.
Specializations
Formally beginning in their third years, students begin to pursue specialized areas, including cognitive modeling, neuroimaging data analysis, social network analysis, or computational psychology. These niche focuses align advanced methodological training with specific research interests, equipping graduates with skills tailored to academic, governmental, or industry roles requiring expertise in quantitative psychological methods.
Core Methodological Areas
The program is built around fundamental quantitative approaches that form the backbone of modern psychological research methodology. Students receive comprehensive training in both theoretical foundations and practical applications, with emphasis on developing expertise in cutting-edge statistical techniques.
- Advanced structural equation modeling (both linear and non-linear)
- Dynamic systems modeling and analysis
- Optimal research design methodologies
- Quantitative neuroimaging analysis techniques
- Multivariate statistical methods
- Longitudinal data analysis approaches
Structural Equation Modeling Focus
The program places significant emphasis on advanced SEM techniques, particularly their application to longitudinal studies and behavior genetics. Students learn to model complex relationships between variables while accounting for measurement error and latent constructs.
- Linear and non-linear modeling approaches
- Longitudinal latent variable models
- Applications to behavior genetics research
- Family studies of ability differences
- Individual and group difference analysis
- Integration with developmental research
Dynamic Systems and Temporal Analysis
This area focuses on understanding how psychological processes evolve over time, incorporating sophisticated mathematical modeling techniques to capture complex temporal relationships and developmental patterns.
- Mathematical modeling of psychological processes
- Temporal dynamics in cognitive development
- Complex variable interactions across time
- Growth curve modeling techniques
- Developmental trajectory analysis
- Individual difference patterns in development
Research Design and Methodology
Students learn to optimize research approaches through advanced statistical and methodological techniques, ensuring efficient and effective study designs that maximize statistical power while minimizing resource usage.
- Experimental design optimization
- Statistical power analysis methods
- Sample size determination techniques
- Cost-benefit analysis in research
- Measurement occasion optimization
- Sampling methodology development
Quantitative Neuroimaging Analysis
The program includes cutting-edge approaches to analyzing neuroimaging data, incorporating modern statistical techniques for handling complex, high-dimensional data from brain imaging studies.
- Statistical analysis of brain imaging data
- High-dimensional data processing techniques
- Neural signal analysis methods
- Spatial and temporal brain data modeling
- Integration of multiple imaging modalities
- Advanced visualization techniques
Multivariate Analysis and Estimation
Students develop expertise in handling complex multivariate relationships and creating new approaches to estimation and visualization of psychological data.
- Numerical estimation technique development
- Graphical estimation methods
- Complex multivariate relationship modeling
- Multiple data source integration
- Advanced visualization approaches
- Statistical inference in high dimensions
Behavior Genetics Applications
The program integrates statistical methodology with behavior genetics research, providing students with tools to understand the complex interplay between genetic and environmental factors in psychological traits.
- Genetic and environmental influence modeling
- Twin and family data analysis techniques
- Heritability analysis methods
- Environmental contribution assessment
- Integration of statistical and genetic approaches
- Longitudinal genetic influence analysis
THE FOLLOWING FACULTY HOPE TO ADMIT GRADUATE STUDENTS TO THE QUANTITATIVE AREA FOR AUGUST 2025:
Please Note: The application for the PhD programs in the Department of Psychology has eliminated the GRE requirement, beginning with the applications for the 2021 admissions.