Faculty in the Quantitative Methods (QM) program train students in state-of-the-art statistical methods and engage in research that develops and applies such methods. Students in the QM 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. QM faculty collectively have expertise in factor analysis and structural equation modeling; network analysis; measurement and item response theory; exploratory data analysis; 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 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 QM 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 QM program is housed within the Department of Psychology and Human Development at Peabody College--a top-ten ranked school of education for the past ten years. This unique context exposes QM 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.
QM faculty teach courses on a broad variety of fundamental and advanced topics in design and data analysis. These courses are attended by students from a variety of social science disciplines, as well as by QM students. QM students are encouraged to tailor their curriculum to maximize relevancy for their particular research interests, background, and career goals. QM course offerings include correlation and regression; analysis of variance; psychological and educational measurement; data science methods; multivariate analysis; psychological, field, and clinical research methods; item response theory (basic and advanced); exploratory/graphical data analysis; structural equation modeling; factor analysis; latent growth curve modeling; categorical data analysis; multilevel modeling; mixture modeling; nonparametric statistics; individual differences; causal analysis in field experiments and quasi-experiments; network analysis; statistical consulting; and meta-analysis. Additionally, many of our students get an optional Minor in Biostatistics. Students may also take courses in Scientific Computing, and/or other areas of psychology and education. Several research centers on campus also provide QM students with training opportunities. Vanderbilt’s new Data Science Institute (DSI) offers numerous workshops, short courses, colloquia, and collaboration opportunities using data science methods and tools. QM faculty also serve as teaching faculty and/or faculty affiliates of the DSI and are involved with the development, operations, and strategic goals 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 Forum (schedule below).
More information about individual faculty's research programs can be obtained from their websites by clicking on their names. Alternately, a list of QM faculty is available here. Prospective students are encouraged to contact core QM faculty with shared interests to ask questions about the program. Core QM faculty recruit and train Ph.D. students through the QM program.
- Sun-Joo Cho (item response theory; generalized latent variable modeling; test development and validation)
- *Alex Christensen (network analysis; data science; psychometrics; measurement)
- David Cole (structural equation modeling; mediation analysis; longitudinal methods; developmental psychopathology)
- 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 (mixture models; multilevel and longitudinal methods; latent variable 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 QM Ph.D. student to start in the 2024-2025 academic year)
- 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)
- Li Chen (statistical consulting; quantitative pedagogy)
- Scott Crossley (natural language processing)
- Will Doyle (data science; education policy)
- Kelly Goldsmith (business analytics, marketing, consumer psychology)
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 QM students.
Information for Prospective QM Applicants
QM doctoral program graduates are prepared for faculty positions in academic settings, methodology positions in basic or applied research centers, or methodology 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 in psychology than there are applicants. Further information can be found here, here, and here.
The QM 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 courses they have taken 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 QM 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, QM 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.
QM Masters Program
In Spring 2014, the QM program launched a terminal M.Ed. in Quantitative Methods. This program is distinct from our longstanding research-focused Ph.D. program. More information about the goals and expectations for applicants to our M.Ed. program can be found here.
Graduate QM Minor
Doctoral students outside the QM program may elect to minor in quantitative methods. This formal minor involves taking four advanced methods courses from the QM 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 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 Kris Preacher.
Undergraduate QM Minor
The QM program offers an 18-credit undergraduate minor in quantitative methodology. For information on our new undergraduate QM minor, please click here.
Quantitative Methods Colloquium Series
The QM program offers a weekly Quantitative Methods Colloquium Series which covers novel methodological advances, cutting-edge applications of quantitative methods, inclusivity in QM, teaching pedagogy in QM, QM professional development activities, QM outreach, and QM workshops. The QM 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 QM students to a variety of career paths and perspectives. Each semester our QM forum also contains internal program speakers, QM students and QM faculty, to allow us to share our research with, and gain feedback from, our colleagues. For more information on the QM Colloquium please visit the Colloquium schedule.
Quantitative Methods Outreach
At least once per year the QM Colloquium Series features an Open House where statistical consulting problems presented by Peabody faculty guest(s) receive a program-level discussion. Additionally, our QM program offers a statistical consulting course on a yearly basis to which Peabody faculty can submit statistical problems to serve as student projects. QM faculty also maintain a listserv (email@example.com) 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 QM faculty for further attention.
Spring 2024 QM Course Offerings
- PSY-GS 8350-01 / PSY-PC 7500-07: Individual Differences. T 4:15p - 7:05p Lubinski
- PSY-GS 8850-01: Advanced Structural Equation Modeling. T 9:30a - 12:20p Preacher
- PSY-GS 8864-01: Analysis and Design of Experiments. TR 9:30a - 10:45a Wu
- PSY-GS 8867-01: Multivariate Statistics. TR 1:15p - 2:30p Wu
- PSY-GS 8870-01 / PSY-PC 3735-01: Correlation and Regression. TR 11:00a - 12:15p Hutton
- PSY-GS 8875-01: Behavioral Data Science. W 12:10p - 3:10p Christensen
- PSY-GS 8880-01: Introduction to Item Response Theory. W 9:05a - 11:55a Cho
- PSY-GS 8882-01: Multilevel Modeling. F 9:30a - 12:20p Preacher
- PSY-PC 2110-01: Introduction to Statistical Analysis. MWF 1:25p - 2:15p Dunbar
- PSY-PC 2110-02: Introduction to Statistical Analysis. TR 9:30a - 10:45a Hutton
- PSY-PC 2110-03: Introduction to Statistical Analysis. TR 1:15p - 2:30p Strauss
PSY-PC 2110-04: Introduction to Statistical Analysis. MWF 9:05a - 9:55a Dunbar
PSY-PC 2110-05: Introduction to Statistical Analysis. TR 8:00a - 9:15a Vinci-Booher
PSY-PC 2110-06: Introduction to Statistical Analysis. MWF 10:10a - 11:00a Chen
PSY-PC 2110-07: Introduction to Statistical Analysis. MWF 11:15a - 12:05p Chen
- PSY-PC 2120-01: Statistical Analysis. WF 12:20p - 1:35p Sterba