Quantitative research methods
Psychology PhD
Shape the future of psychological research through rigorous training in statistical modeling, measurement and data analysis in ASU’s PhD program in psychology with a concentration in quantitative research methods.
Program description
Degree awarded: Psychology (Quantitative research methods), PhD
Quantitative research methods is one of six training areas offered through ASU’s PhD program in psychology. This concentration prepares students to design, analyze and interpret complex psychological data through advanced training in statistical modeling, measurement and research methodology.
Students work alongside award-winning faculty to refine existing methods and create new approaches that enhance the rigor and reproducibility of psychological science. Research topics include:
- Statistical modeling and measurement theory
- Research design and evaluation methods
- Computational and data-driven approaches to analysis
- Development and validation of new methodological tools
- Applications of quantitative methods across psychology
The Department of Psychology fosters a collaborative, interdisciplinary learning environment. Students complete a core curriculum tailored to quantitative research methods while engaging with faculty and peers across all areas of psychology.
Important dates
- September 1: Fall 2026 application opens.
- December 5: Fall 2026 application deadline.
Students must submit ASU’s graduate application and the Department of Psychology’s Slideroom application to be considered for admission.
Faculty and research labs
Collaborate with globally recognized experts in quantitative research methods, gaining hands-on experience in methodological innovation, advanced data analysis and research techniques.
Dr. Samantha Anderson
Experimental Design and Analysis Lab
The Experimental Design and Analysis Lab investigates research methodology and meta-science, focusing on approaches that are both rigorous and practical. Work explores sample size planning for high statistical power, strategies to address replication, handling missing data and the impact of multiplicity on Type I error, effect size bias, power and heterogeneity.
Dr. Kevin Grimm
Longitudinal & Behavioral Data Science Lab
The Longitudinal & Behavioral Data Science Lab develops and evaluates statistical methods for understanding individual change over time. Research applies machine learning to latent variable and longitudinal models, including growth, mixed-effects, mixture, latent class and linear dynamic models, with applications in health, cognition and achievement outcomes.
Dr. Dan McNeish
Mixed Effects Modeling Lab
The Mixed Effects Modeling Lab develops, evaluates and applies statistical methods for analyzing correlated data structures, such as students within schools, patients within hospitals or repeated measures within individuals. Research emphasizes small sample data, missing data, Bayesian estimation and extending mixed effects models to novel scenarios.
Dr. Roy Levy
Psychometric and Latent Variable Modeling Lab
The Psychometric and Latent Variable Modeling Lab conducts methodological research in psychometrics and statistical modeling. Work focuses on item response theory, structural equation modeling, Bayesian networks and Bayesian inference, with interests in dimensionality assessment, model checking and model comparison. The lab also studies assessment design and applications in complex, simulation-based evaluations.
Dr. Mike Edwards
Psychometrics Lab
The Psychometrics Lab investigates measurement issues in the social sciences, focusing on item response theory (IRT) and factor analysis. Current research includes multidimensional IRT models, minimum detectable change, multi-stage adaptive testing, measurement models for multiple reporters, local dependence diagnostics and model fit assessment. The lab also explores applications of psychometrics in patient-reported outcomes (PROs).
Dr. David MacKinnon
Research and Prevention Lab
The Research in Prevention Lab studies prevention strategies to promote public health and healthy behaviors. The lab also conducts methodological research, developing innovative approaches for analyzing data across disciplines. Projects include adolescent obesity prevention, health promotion for first responders, steroid use prevention, drug testing in student athletes and evaluating alcohol warning labels.
Dr. Stephen West
Research Methods and Causal Inference Lab
The Research Methods and Causal Inference Lab focuses on methodological research in causal inference, experimental and quasi-experimental designs, multiple regression, structural equation modeling and longitudinal analysis. Substantive research examines personality and prevention-related issues in health, mental health and education, combining quantitative methods with applied social science to answer real-world questions.
Courses and electives
Concentration course. Students complete a course in structural equation modeling as part of their quantitative research methods training.
Core courses. A required core course — either Professional Issues in Psychology (3) or Multiple Regression in Psychological Research (4) — that supports students' work in quantitative research methods.
Elective breadth courses. Students collaborate with their supervisory committees to choose 22–23 credit hours of courses that support their research interests and goals.
Required courses. Students complete required courses in the quantitative research methods concentration covering topics such as:
- Intermediate statics
- Longitudinal growth modeling
- Missing data analysis
- ... and more!
Research activities. Milestone courses, involving independent study and regular meetings with a faculty member to discuss assignments and conduct research.
Dissertation. Supervised research including literature review, research, data collection and analysis, and writing.
Graduate students in the quantitative research methods concentration complete 84 credit hours and may earn a master’s degree along the way. Coursework blends foundational and specialized methods courses with flexible electives that match students’ research interests and goals. Students also gain hands-on experience through research projects and their dissertation, developing cutting-edge statistical skills to tackle complex research challenges.
Graduates of the quantitative research methods concentration leave the program prepared to advance psychological science through rigorous, data-driven research and evidence-based discovery. Students develop expertise in statistical and methodological approaches valuable across psychology and related fields such as education, health, neuroscience and marketing.
Faculty members guide students as they pursue careers in research, academia, industry and applied settings, including roles as consultants, data scientists, policy analysts, psychometricians and professors.
Get in touch
- For questions about the concentration, email the quantitive research methods area head, Dr. Mike Edwards, directly at [email protected].