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Psychological Sciences

Psychological Sciences
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  3. Programs
  4. Quantitative Methods
  5. Quantitative Methods

Quantitative Methods

Program overview: Quantitative Methods & Data Analytics

Faculty in the Quantitative Methods & Data Analytics (QMDA) program train students in state-of-the-art statistical methods and engage in research that develops and applies such methods. Students in the QMDA doctoral program develop expertise in the principles of research design and in the theoretical foundations and application of advanced statistical models for human behavior. Students work closely on research projects with a faculty mentor throughout their graduate career, and often collaborate with other faculty and students. QMDA faculty collectively have expertise in factor analysis and structural equation modeling; network analysis; measurement and item response theory; exploratory data analysis; machine learning; mediation and moderation; longitudinal methods; multilevel modeling; mixture modeling; categorical data analysis; and generalized linear models. Quantitative faculty approach the study of these topics from a variety of angles, such as: developing analytic and computational tools to promote the use of new or existing methods; evaluating the performance of such methods under real-world conditions; and applying these methods in novel and sophisticated ways to solve substantive problems. Several QMDA faculty have substantive specializations in, for example, individual differences, personality psychology, clinical psychology, learning sciences, and developmental psychology, which facilitate intensive investigation of analytic approaches critical to those substantive domains. Students may pursue greater or lesser degrees of substantive psychological training, in addition to quantitative training, depending on their and their advisors' interests.

The QMDA program is housed within the Department of Psychology and Human Development at Peabody College--a top-five ranked school of education for the past ten years. This unique context exposes QMDA students to a variety of applications, methods, and statistical problems that arise in psychological and educational research, as well as the social sciences more generally.

Courses

QMDA faculty teach courses on a broad variety of fundamental and advanced topics in design and data analytics and also incorporate AI-literacy into the curriculum. These courses are attended by students from a variety of social science disciplines, as well as by QMDA students. QMDA students are encouraged to tailor their curriculum to maximize relevancy for their particular research interests, background, and career goals. QMDA course offerings include predictive analytics and regression; psychometric measurement; network analysis; multivariate statistics for data science; item response theory (IRT); advanced IRT; structural equation modeling (SEM); advanced SEM; behavioral data science; factor analysis; Bayesian analytics; multilevel modeling (MLM); advanced MLM; nonparametric analytics; statistical consulting incorporating AI; survival analysis; research methods; exploratory/graphical data analysis; latent growth curve modeling; categorical data analysis; mixture modeling; causal analysis in field experiments and quasi-experiments; analysis and design of experiments; and meta-analysis.

Additionally, many of our students get an optional Minor in Biostatistics. Students may also take courses in Scientific Computing, Data Science, Computer Science, and/or other areas of psychology and education. Several research centers on campus also provide QMDA students with training opportunities. Vanderbilt’s Data Science Institute (DSI) offers numerous workshops, short courses, and collaboration opportunities using data science methods and tools. QMDA faculty also serve as teaching faculty and/or faculty affiliates of the DSI. Also, the Vanderbilt Kennedy Center maintains a statistics and methodology core which provides a methodology lecture series as well as statistical consulting training and resources. Additionally, students gain presentation and research skills by participating in the Quantitative Methods & Data Analytics Colloquium (schedule below).

Core faculty

More information about individual faculty's research programs can be obtained from their websites by clicking on their names. Alternately, a list of QMDA faculty is available here. Prospective students are encouraged to contact core QMDA faculty with shared interests to ask questions about the program. Core QMDA faculty recruit and train Ph.D. students through the QMDA program.

  • Sun-Joo Cho (item response theory; generalized latent variable modeling; test development and validation)
  • Alex Christensen (network analysis; data science; psychometrics; measurement)
  • Shane Hutton (survival analysis; dynamical systems modeling)
  • David Lubinski (measurement; assessment; individual differences; intellectual talent development)
  • Kristopher Preacher (structural equation modeling; multilevel modeling; mediation and moderation)
  • Sonya Sterba (latent variable models; multilevel models; mixture models)
  • Chris Strauss (measurement and assessment; multilevel measurement; structural equation modelling)
  • Hao Wu (model evaluation; uncertainty quantification; robust and nonparametric methods; structural equation modeling)

         (* = interested in recruiting a QMDA Ph.D. student to start in the 2026-2027 academic year)

Emeritus faculty

  • David Cole (structural equation modeling; mediation analysis; longitudinal methods; developmental psychopathology)
  • Joseph Rodgers (general multivariate methods; exploratory/graphical data analysis; multidimensional scaling and measurement; behavior genetics; adolescent development)
  • Jim Steiger (structural equation modeling; model evaluation; inferential methods; statistical computing)
  • Andrew Tomarken (categorical data analysis; generalized linear models; longitudinal methods; clinical psychology)

Affiliated faculty

  • Li Chen (statistical consulting; quantitative pedagogy)
  • Scott Crossley (natural language processing)
  • Will Doyle (data science; education policy)
  • Kelly Goldsmith (business analytics, marketing, consumer psychology)

Facilities

The program maintains its own quantitative computer lab, and additionally individual faculty have labs and computing resources that support their research programs. There are also computing labs in the department and elsewhere in Peabody College that are supplied with statistical software often used for classroom teaching.  Special funds for research-related software and computing equipment, as well as external workshop and conference travel, are available to QMDA students.

Information for Prospective QMDA Applicants

QMDA doctoral program graduates are prepared for faculty positions in academic settings, methodology positions in basic or applied research centers, or methodological/analytics positions in industry. Students work together with their advisor and advisory committee to refine their career goals, and tailor their research, coursework, and teaching experiences accordingly. The American Psychological Association reports that there are far more jobs for doctoral students trained in quantitative methods and data analytics in psychology than there are applicants. Further information can be found here, here, and here.

The QMDA program is designed to lead to a Ph.D. degree within 5 years. In the first two years, students take a series of fundamental methods courses and begin working on research with their advisor. To build students' oral presentation skills, students present their research to the program on a yearly basis. Students who did not enter with a full year of calculus also complete such coursework in the Mathematics Department during this time. In their third year, students complete their masters thesis and continue research in collaboration with their advisor and others, while furthering their expertise with an individualized set of advanced coursework. Students take an exam in their third or fourth year that is based on reading lists related to content in completed coursework and research up until that point. In their fourth and fifth years students finish their coursework and conduct a dissertation project under the guidance of their advisor and other committee members, while building additional independent research and/or teaching skills relevant to their particular career goals.

Doctoral applicants admitted to the QMDA program receive a guaranteed 5 years of stipend and tuition support, which usually takes the form of a combination of research assistantships and/or teaching assistantships in quantitative courses (for instance, the introductory graduate statistics sequence). Additionally, QMDA students have a successful track record of obtaining prestigious NSF fellowships. Senior students routinely also may obtain other kinds of stipends as statistical analysts or consultants for various research projects and grants on campus; these opportunities serve as valuable supplementary training experiences. Some students also serve as teaching instructors for their own section of an undergraduate statistics course or undergraduate measurement course in order to deepen their teaching credentials. Application instructions are available here.

QMDA Masters Program

In Fall 2025 we launched a terminal M.S. in Quantitative Methods and Data Analytics (QMDA) for Human Behavior. This program is distinct from our longstanding Ph.D. program. More information about the goals and expectations for applicants to our M.S. program can be found here.

Graduate QMDA Minor

Doctoral students outside the QMDA program may elect to minor in quantitative methods & data analytics. This formal minor involves taking four advanced methods/analytics courses from the QMDA program beyond the first year required graduate statistics sequence (6 courses total). The minor requires a 3.5 average GPA (for all 6 minor courses), with no grade below a B. The minor provides students with exceptional training in the application of complex psychometric and statistical procedures and provides students with skills that can enhance the quality of their research program over the course of their career. Many students find that the credential of a graduate minor in quantitative methods & data analytics is a valuable asset in the pursuit of research-oriented academic positions or quantitatively-oriented industry positions after graduation. Detailed information on minor requirements can be obtained from the Psychological Sciences graduate student handbook. For more information, contact Chris Strauss.

Undergraduate QMDA Minor

The QMDA program offers an 18-credit undergraduate minor in quantitative methods & data analytics. For more information on our undergraduate QMDA minor, please see the program catalog or contact Chris Strauss.

Quantitative Methods & Data Analytics Colloquium Series

The QMDA program offers a weekly Quantitative Methods & Data Analytics Colloquium Series which covers novel methodological advances, cutting-edge applications of quantitative methods, inclusivity in QMDA, teaching pedagogy in QMDA, QMDA professional development activities, QMDA outreach, and QMDA workshops. The QMDA colloquium series features a mix of external speakers from different settings (e.g., academia and industry) and different stages of their careers in order to expose our QMDA students to a variety of career paths and perspectives. Each semester our QMDA forum also contains internal program speakers, QMDA students and QMDA faculty, to allow us to share our research with, and gain feedback from, our colleagues. For more information on the QMDA Colloquium please visit the Colloquium schedule.

Quantitative Methods & Data Analytics Outreach

The QMDA Colloquium Series periodically features an Open House where statistical consulting problems presented by Peabody faculty guest(s) receive a program-level discussion. Additionally, our QMDA program offers a yearly course on Statistical Consulting Integrating AI, and Peabody faculty can submit statistical problems to serve as student projects for this course. QMDA faculty also maintain a listserv (qmgroup@vanderbilt.edu) to which Peabody faculty can submit statistical problems that are limited in scope. Submitted questions will first be considered for open house or course project slots and secondarily for a graduate assistant to the QMDA faculty for further attention.

Fall 2025 QMDA Course Offerings

Graduate

  • PSY-GS 8850-01: Advanced Structural Equation Modeling. T 1:15 - 4:05p Preacher
  • PSY-GS 8850-02: Survival Analysis. M 10:10a - 1:00p Hutton
  • PSY-GS 8864-01: Analysis and Design of Experiments. TR 9:30a - 10:45a Wu
  • PSY-GS 8867-01: Multivariate Statistics for Data Science. TR 11:00a - 12:15p Wu
  • PSY-GS 8870-01 / PSY-PC 3735-01: Correlation and Regression. TR 11:00a - 12:15p Hutton
  • PSY-GS 8880-01 / PSY-PC 3738-01: Introduction to Item Response Theory. W 9:05a - 11:55a Cho
  • PSY-GS 8882-01: Multilevel Modeling. F 9:30a - 12:20p Preacher

Undergraduate

  • PSY-PC 2110-01: Introduction to Statistical Analysis. TR 9:30a - 10:45a Vinci-Booher
  • PSY-PC 2110-02: Introduction to Statistical Analysis. TR 11:00a - 12:15p Vinci-Booher
  • PSY-PC 2110-03: Introduction to Statistical Analysis. MWF 11:15a - 12:05p Chen
  • PSY-PC 2110-04: Introduction to Statistical Analysis. MWF 10:10a - 11:00a Chen
  • PSY-PC 2120-01: Statistical Analysis. WF 12:20p - 1:35p Sterba