The MPH in Quantitative Methods provides the professional training that is common to all MPH subtracks in the College of Public Health (the Core MPH requirements) as well as substantive and meaningful training in Biostatistics. This degree is designed to train public health professionals who can provide leadership in the analysis of public health data and the design of studies for public health investigations. Individuals with an interest in public health and with quantitative ability, but without advanced mathematics training, may find this an interesting career track.
Graduates of the MPH in Quantitative Methods will be able to:
- Demonstrate a broad knowledge and understanding of statistical techniques used in public health studies and investigations.
- Serve as an advocate for good statistical design in public health investigations
- Apply appropriate statistical methods for inference about public health related questions, and describe the results to public health professionals and educated lay audiences.
- Interpret the results of statistical analyses in public health related publications for public health professionals and educated lay audiences.
- Promote the use of sound statistical methods to answer open questions in public health practice.
- Function as a collaborator on public health projects, taking a leadership role in the design and implementation of projects.
- Assume responsibility for the design and implementation of analyses in investigations of public health questions.
- Manage the data for public health related projects such as large community surveys, laboratory investigations, and multi-center clinical trials
- Demonstrate effective written and oral communication skills when communicating quantitative information and statistical inferences to different audiences of public health professionals
An undergraduate degree is required. No specific major is required. Previous coursework or experience in statistical methods or data analysis is preferred. The cumulative grade point average should be a minimum of a 3.0 on a 4.0 scale. The applicant’s training should include basic course work in computer science and mathematics. The level of training required in each of these areas is described below.
Familiarity with the mathematics of single variable calculus and matrix algebra are required. These requirements can be satisfied by a one semester college course in calculus equivalent to AP Calculus AB and a high school algebra course involving matrices.
Knowledge of elementary computer programming is required. Programming in any commonly used modern programming language (e.g., Python, Java, C++) is acceptable.
Persons with deficiencies in any of the above areas may apply for admission with the understanding that they will gain such experience through self-study in the first semester of enrollment.
MPH Core Courses (18-19 s.h.)
The following course work is required for all MPH students. Students must earn ≥ B- (2.67) on each core course and must earn a ≥ B (3.0) cumulative grade point average on all core courses. When necessary, a student may repeat courses to achieve this standard.
|CPH:5100||Intro to Public Health||3 s.h.|
|BIOS:4120 BIOS:5710||Intro to Biostatistics OR Biostatistical Methods I||3 s.h. OR 4 s.h.|
|CBH:4105||Intro to Health Promotion and Disease Prevention||3 s.h.|
|EPID:4400||Epidemiology I: Principles||3 s.h.|
|HMP:4000||Intro to the US Healthcare System||3 s.h.|
|OEH:4240||Global Environmental Health||3 s.h.|
Required Biostatistics Courses (10 s.h.)
|BIOS:5120||Regression Modeling and ANOVA in the Health Sciences||3 s.h.|
|BIOS:6110||Applied Categorical Data Analysis||3 s.h.|
|Biostatistical Computing (Programming with R)||2 s.h.|
|Biostatistical Computing (Programming with SAS)*||2 s.h.|
*This course is waived if previously taken BIOS:5310 Research Data Management
Electives (students select 11-13 s.h. in consultation with advisor)
Electives may be chosen from the following list or may include any related course approved by the student’s advisor.
|BIOS:6310||Introductory Longitudinal Data Analysis||3 s.h.|
|BIOS:6610||Statistical Methods in Clinical Trials||3 s.h.|
|BIOS:6210||Applied Survival & Cohort Data Analysis||3 s.h.|
|BIOS:7600||Advanced Biostatistics Seminar||1-3 s.h.|
|BIOS:6810||Bayesian Methods and Design||3 s.h.|
|STAT:3100||Intro to Mathematical Statistics I||3 s.h.|
|STAT:3101||Intro to Mathematical Statistics II||3 s.h.|
|STAT:4100||Mathematical Statistics I||3 s.h.|
|STAT:4101||Mathematical Statistics II||3 s.h.|
|STAT:5100||Statistical Inference I||3 s.h.|
|STAT:5101||Statistical Inference II||3 s.h.|
|STAT:4200||Statistical Methods and Computing||3 s.h.|
|STAT:4520||Bayesian Statistics||3 s.h.|
|STAT:4540||Statistical Learning||3 s.h.|
|STAT:6560||Applied Time Series Analysis||3 s.h.|
|STAT:3210||Experimental Design & Analysis||3 s.h.|
|STAT:6540||Applied Multivariate Analysis||3 s.h.|
|IE:4172||Big Data Analysis||3 s.h.|
As specified by the Graduate College, a maximum of 6 s.h. may be transferred from another graduate or professional degree.
Practicum Requirement (3 s.h.)
The experience from this course, including a final written report and a poster presentation, constitutes the final examination for the MPH.
|CPH:7000||MPH Practicum Experience||3 s.h.|
Pre-Requisite to MPH Practicum:
Students must complete all of their MPH core courses and the majority of other MPH coursework prior to registering for the Practicum.
Summary of Hour Requirements
|MPH Core||18-19 s.h.|
|Required courses||10 s.h.|
* Advanced Biostatistics Sequence Substitution: A student with sufficient mathematical background can substitute: Biostatistics Methods I, Biostatistics Methods II, and Biostatistics Methods in Categorical Data (11 s.h.) in place of Introduction to Biostatistics, Design & Analysis of Biomedical Studies, and Applied Categorical Data Analysis (9 s.h.). This advanced sequence has the additional prerequisites of undergraduate multivariable calculus and linear algebra. The advanced sequence requires 2 s.h. fewer elective credits.
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; 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, adolescent health, mental health, clinical psychology, developmental psychology, or learning sciences, 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.
Psychological Sciences at Vanderbilt consists of two departments, one in Peabody College (Psychology and Human Development) and the other in the College of Arts and Sciences (Psychology). The graduate curriculum for Psychological Sciences at Vanderbilt is the same for both departments, and all graduate students enroll in a first year seminar together. 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. However, QM consists of faculty from both departments. 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. Within the QM program, course offerings include correlation and regression; analysis of variance; psychological and educational measurement; 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; behavior genetics methods; categorical data analysis; multilevel modeling; mixture modeling; nonparametric statistics; individual differences; causal analysis in field experiments and quasi-experiments; 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. 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. The Peabody Research Institute provides a lecture series and opportunities for student involvement in meta-analysis research. 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 faculty with shared interests to ask questions about the program.
- Sun-Joo Cho* (item response theory; generalized latent variable modeling; test development and validation)
- 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)
- Joseph Rodgers (general multivariate methods; exploratory/graphical data analysis; multidimensional scaling and measurement; behavior genetics; adolescent development)
- Sonya Sterba (latent variable models; longitudinal methods; mixture models; developmental psychopathology)
- Andrew Tomarken (categorical data analysis; generalized linear models; longitudinal methods; clinical psychology)
(* designates interest in recruiting a student to start in academic year 2018/2019)
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, 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, teaching assistantships in quantitative courses (for instance, the introductory graduate statistics sequence), and/or fellowships. Senior students routinely 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. 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 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 biweekly colloquium series, featuring talks on various aspects of methodology. Speakers include faculty and graduate students within the QM program and from across the university, as well as occasional invited speakers from other institutions. For information on the QM Colloquium please visit the Colloquium schedule.
Fall 2018 QM Course Offerings
- PSY 8310-01: Research Methods in Clinical Psychology. M 4:30p - 7:00p Tomarken
- PSY-GS 8861-01: Statistical Inference. TR 1:10p - 2:25p Hutton
- PSY-GS 8870-01 / PSY-PC 3735-01: Correlation and Regression. MW 8:45a - 10:00a Rodgers
- PSY-GS 8873-01: Structural Equation Modeling. TR 11:00a - 12:15p Cole
- PSY-GS 8876-01 / PSY-PC 3724-01: Psychological Measurement / Psychometrics. T 4:10p - 7:00p McCabe
- PSY-GS 8880-01: Introduction to Item Response Theory. W 1:10p - 4:00p Cho
- PSY-GS 8882-01: Multilevel Modeling. T 1:10p - 4:00p Preacher
- PSY-PC 2110-01: Introduction to Statistical Analysis. TR 9:35a - 10:50a Hutton
- PSY-PC 2110-02: Introduction to Statistical Analysis. TR 4:00p - 5:15p Garrison
- PSY-PC 2110-03: Introduction to Statistical Analysis. MWF 9:10a - 10:00a Osina
- PSY-PC 2110-04: Introduction to Statistical Analysis. MWF 11:10a - 12:00p Osina
- PSY-PC 2110-05: Introduction to Statistical Analysis. MW 11:10a - 12:25p Rodgers
- PSY-PC 2110-06: Introduction to Statistical Analysis. TR 9:35a - 10:50a TBA
- PSY-PC 2120-01: Statistical Analysis. TR 4:00p - 5:15p Novick