Quantitative

The goal of the graduate program in Quantitative Psychology is to produce researchers able to develop, evaluate, and apply advanced methodological techniques to psychological research questions. The program offers considerable diversity in faculty research and coursework offerings; Our substantive interests span clinical, social, health, developmental, and cognitive psychology. Quantitative areas of expertise cover a range of linear and non-linear approaches to modeling, including categorical techniques, structural equation modeling, time series, state-space models, and issues in large scale data management.

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Coursework

Students in the Quantitative program complete coursework in mathematical statistics, experimental design, and measurement, as well as courses in quantitative methods both within the Department of Psychological Sciences as well as other departments on campus. Program requirements are flexible, and students with particular interests a substantive area of Psychology are encouraged to take advanced courses in that area. Quantitative course offerings focus both on classic analytic methods as well as advanced techniques such as structural equation modeling, multilevel modeling, and multivariate models. Strong ties exist between the Department of Psychological Sciences and the Statistics Department, and students may opt to complete a Masters Degree in Statistics as they progress through the Quantitative program. In addition, students trained in the program will have the opportunity to gain experience as statistical consultants through specific coursework in this area.

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Research

The University of Missouri-Columbia is a Research I institution, and faculty are strongly committed to the research mission. Students in the quantitative program work with faculty on research projects throughout their graduate tenure. Research laboratories are well equipped with computational resources. Ongoing projects conducted by the faculty include research in meta-analytic and secondary analysis techniques, structural equation modeling, particularly as applied to longitudinal models of change and growth, multilevel modeling, and mathematical and statistical models of cognition and perception. Faculty research is often supported though federal grants and other extramural sources.

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Minor in Psychological Statistics and Methods

The quantitative training area also administers the Minor in Psychological Statistics and Methods, a certification of training in the area of statistics and methods which has proven quite popular. The menu on the right side of the page includes requirements for completion of the minor, as well as the relevant forms to use for applying for the minor. The information in these files supersedes all previous information.

Please direct any further questions concerning application to the Quantitative Psychology program or questions regarding the minor in Psychological Statistics to the training area director, Phil Wood (phillipkwood@gmail.com).

Lab Director Location
Decision Making and Data Science Lab

I study how individuals make risky choices and whether they do so in consistent, predictable ways that reveal their motivations and goals.  My lab uses the language of mathematics and statistical modeling to make these concepts precise and testable.   In collaboration with other labs, I seek to better understand decision making across many experimental and observational settings, with a particular focus on risky decisions involving alcohol and addictions.  In tandem with our empirical investigations, my lab develops and applies advanced quantitative methods to make sound statistical inferences and predictions.   This scope of work includes Bayesian cognitive modeling, order-constrained statistical inference, network modeling, and machine learning. The lab has received funding from the National Institutes for Health and the National Science Foundation.  I support transparency in science by making data, code, and related materials publicly available on established file sharing websites.

Clintin P. Davis-Stober
Medical Decision Research Lab (MDRL)

We broadly seek to apply decision psychology and behavioral economics to inform practical problems in medical decision making. Recent work has focused on applications to Clinical Decision Support Systems and Patient Decision Aids.


Medical Decision Research Lab flyer
Victoria Shaffer 220 Noyes

Current Quantitative Psychology Faculty

Professor
28A McAlester Hall
Professor
208 McAlester Hall
573-882-9094
Professor
21 McAlester Hall
Professor
220 McAlester Hall
573-445-2754