Department of Psychology
The University of Oklahoma
Ph.D. in Quantitative Psychology
The Department of Psychology houses a nationally recognized program in Quantitative Psychology, which serves many roles within the Department and the University. The program is designed to provide quantitative training to all Psychology Ph.D students and specifically to students in Quantitative Psychology. The Department has a strong commitment to Psychology as a Quantitative science. The program operates as a de facto Applied Methods program for the whole University, and students from many colleges and departments take our quantitative courses, including Business, Math, Nursing and Public Health, Education, Engineering, Communication, Sociology, Political Science, Computer Science, and many others.
Students in the Quantitative Program are trained in applied statistics, psychometrics, and mathematical modeling. Courses are offered in the following topics:
Behavior statistics Experimental design
Evaluation and quasi-experimental design Multivariate statistics
Multiple comparison procedures Nonparametric statistics
Structural equations modeling Factor analysis
Scaling and measurement Test theory
Exploratory data analysis Categorical data analysis
Longitudinal methods and data analysis Computer applications
Behavioral decision theory Mathematical modeling
Coursework: To complete the Ph.D. in Quantitative Psychology a student must complete 90 hours of coursework beyond the bachelor’s degree. The individual student, in consultation with a faculty advisory committee, will design a unique course of study that reflects the student’s interests and career goals. Each student will take two courses from the mathematics department: Calculus-based mathematical statistics and linear algebra. The pre-requisite calculus courses for these may be taken at OU if the student does not have them at the time of admission. Successful completion of all coursework, the Ph.D. general exams, and the dissertation is required for the Ph.D. A Master of Science degree is typically completed en route to the Ph.D., and requires 32 hours of coursework and the successful completion of a master’s thesis.
How to Apply
The Quantitative Program seeks students with a strong background in applied statistics and mathematics (some math coursework may be taken during training), an interest in quantitative techniques to analyze and model psychological data, and an enthusiastic appreciation of Psychology as a behavioral and social science. If that student sounds like you, then we would welcome your application!
To apply, simply complete the enclosed departmental application or visit our WEB site at:
Jorge Mendoza, Professor of Psychology. Dr. Mendoza received his Ph.D. in 1974 from the University of Oklahoma in the area of quantitative psychology. Dr. Mendoza’s current interests include the topics of selection, validation, validity generalization, and multivariate statistics.
Robert Terry, Associate Professor of Psychology. Dr. Terry received his Ph.D. in 1989 from the University of North Carolina at Chapel Hill in the area of quantitative psychology. Dr. Terry’s current interests include the measurement of individual differences, test construction and evaluation, and interpersonal perception.
Hairong Song, Assistant Professor of Psychology. Dr. Song's interests are in developing and applying statistical methods to analyze intra-individual variability and long-term change. Specifically, her research interests include (1) dynamic factor models and time series models from both Frequentist and Bayesian perspectives and (2) models for longitudinal data. Areas of substantive research concern various aspects of social-emotional development and academic achievement. She is also interested in the application of quantitative methods in education, health, and I/O related research.
Taehun Lee, Assistant Professor of Psychology. An underlying theme that runs through Dr. Lee’s research activities is an effort to investigate and quantify uncertainties involved in various aspects of statistical modeling e.g., parameter estimation and data-model fit evaluation in the context of multivariate/multilevel modeling with latent variables. Specific modeling frameworks investigated include structural equation modeling (SEM), item response theory (IRT), and multilevel modeling (MLM). At the University of Oklahoma, Dr. Lee is collaborating with researchers in various substantive fields (e.g. Cognitive, Education, Health Science, Industrial & Organizational Psychology, etc.).
Jennifer Kisamore is a faculty member in the Organizational Dynamics Program in Tulsa. Her research deals with issues of accuracy in measurement including methodological issues, faking, and cheating.
Lori Anderson Snyder's primary research interests include performance feedback, Occupational Health Psychology (OHP), and discrimination; interests related to reception of feedback include multisource performance appraisal, developmental assessment centers, and the experience of errors in performance. Within the realm of OHP her research has examined factors affecting the occurrence of workplace aggression, counterproductive work behavior, and safety-related outcomes, such as accidents and injuries. She also conducted research on the topics of workplace diversity and discrimination, primarily focusing on organizational diversity climates, attitudes toward affirmative action, and the experience of subtle discrimination.
Scott Gronlund, Professor of Psychology. Dr. Gronlund received his Ph.D. in 1986 from Indiana University in the area of cognitive and quantitative psychology. Dr. Gronlund’s current interest is in the empirical and theoretical evaluation of quantitative models of memory applied to eyewitness identification.
Larry Toothaker, Professor of Psychology. Dr. Toothaker received his Ph.D. in 1969 from the University of Wisconsin in the area of educational statistics. Dr. Toothaker’s current interests include research on MCPs, and specifically as this research interfaces with the areas of: 1) two-way ANOVA designs, notably repeated measures designs, and 2) nonparametric methods.