Quantitative Methods & Data Analytics for Human Behavior (M.S.)

It is the use of statistics, measurement, and computation to study people and organizations. You learn to design studies, build valid measures, and analyze data to test ideas, make predictions, and evaluate programs. Methods and analytics include regression and predictive modeling, psychometrics, multilevel and longitudinal modeling, introductory and advanced structural equation modeling, introductory and advanced item response theory, machine learning, Bayesian approaches, network analysis, survival analysis, and experimental design. The goal is trustworthy answers that decision makers can use in research, healthcare, business, government, science, and education settings. AI-literacy is incorporated into our curriculum; you learn to use AI with care after you understand the methods so you can ensure that your results are accurate and carefully explained.

Data science programs often focus more exclusively on coding, pipelines, and generic machine learning. While our program also covers such topics, we add a strong emphasis on how to quantify human behavior, test theoretically-driven hypotheses, and build statistical models to accurately reflect complex processes. If desired, you can build a data science emphasis in our program through QMDA courses like Behavioral Data Science, Multivariate Statistics for Data Science, and Statistical Consulting Integrating AI - plus a flexible internship in a setting you choose. The result is training that pairs sound study design and valid measurement with modern analytics.

You will use coding in most courses, beginning in R and SAS for running analyses, but optionally expand into other software programs during elective courses and on internship. We start from the basics and provide labs, examples, and templates, so students without any coding background can succeed. If you want more depth, electives and internship opportunities let you spend extra time on data scraping/wrangling, statistical modeling, creating AI-enhanced analytics tools, and/or creating dashboards for user-visualization. The focus is on skillfully applying, precisely justifying, and carefully interpreting your advanced psychometric modeling and data analyses.

Internships are flexible in topic, site, and timing. Students may also propose an internship at their current job with approval. Recent projects include: 

• Evaluating Learning Assistants in large introductory STEM courses to test effects on students’ sense of belonging, self-efficacy, and science identity. 

• Assessing depressive symptoms in autistic adults with item response theory and differential item functioning to detect item bias. 

• Evaluating the Future FLO chatbot in nursing education by comparing its Gantt chart output to a default chatbot with AI and human review. 

• Linking state policy indicators to maternal mental health with fixed effects and building a Shiny app for paid leave benefits. 

• Modeling alcohol use and working memory over time with random intercept cross lagged panel models in twin data. 

• Examining resource scarcity, competition, and permissible behavior using World Values Survey Wave 7 with principal component analysis and hierarchical regression. 

• Identifying ICU mortality risk by inflammatory subphenotype with latent class analysis and Cox models. 

• Estimating time to clinically significant change in outpatient therapy using discrete time survival analysis on PHQ 9 and GAD 7. 

• Comparing liver and kidney markers for GLP 1 users versus DPP 4 users with propensity score matching and regression. 

• Estimating long term effects of Tennessee’s Voluntary Pre-K on academic progress using four level multilevel growth curve models from a randomized trial subsample. 

No. We start by covering the basics of study design, measurement, and analytics. Strong applicants show readiness through prior statistics or math coursework, class projects, or any experience working with data. The internship gives you real-world experience during the program, so you can build a portfolio before you graduate.

Very flexible. You can select seven electives with many options inside the QMDA program, and the opportunity to take relevant courses outside the department to match your goals. The internship is individually-tailored and flexible in topic and site, so you can target research, healthcare, business, policy, education, or science labs, for instance. You can follow a 17-month or 22-month full-time plan, or study part time.

This program has a longstanding track-record of successfully placing graduates in their chosen industry settings or in competitive Ph.D. programs for over a decade. Students benefit from the resources and career networking associated with the highly-ranked Vanderbilt University. Students learn from a QMDA faculty that is among the largest in the country, works at the cutting edge of their field, and serves as editors of major methodological journals. The QMDA master’s program ensures that students acquire expertise in the combination of measurement, statistical modeling, data analytics, and AI-literacy. Another distinctive feature is a required internship that provides real-world experience in settings such as business, social and behavioral science, medicine, healthcare, policy, or education. Students benefit from strong connections established between the QMDA program and industry, medical, academic, and nonprofit partners. Students also responsibly learn to incorporate AI into their work after mastering the underlying methods so they can accurately justify their modeling choices and results with confidence. Finally, Vanderbilt’s QMDA program is situated in vibrant Nashville, which has been ranked #1 Best City to live in in the U.S., #1 American’s Favorite City, #3 Most Cultured City, one of the Most Walkable Cities in the U.S., #2 Best Big Airport, and #7 Best Food Destinations in the U.S. 

You will build strong, practical skills in study design, measurement, predictive analytics, psychometric methods, and advanced statistical modeling, while mastering how to clearly and accurately communicate results. Through electives, the consulting course, and the internship, you will practice solving real-world analytics problems—building the solid foundation needed for critically evaluating and responsibly assessing future AI-accelerated workflows. Graduates step into and excel in roles such as data analyst, quantitative researcher, data scientist, psychometrician, statistical programmer, biostatistician, data visualization engineer—or enter competitive Ph.D. programs. Graduates work across healthcare, business, government, education, and research settings with recent placements at organizations like Cleveland Clinic, HCA Healthcare, Tennessee Department of Health, Capital One, RAND Corporation, Vanderbilt University Medical Center, Habitat for Humanity, etc. Many also continue to doctoral study, with alumni admitted to Ph.D. programs at Harvard, Notre Dame, UNC Chapel Hill, University of Maryland, Arizona State, University of Minnesota, and Michigan State, to name a few. 

You will learn in person with a tight-knit cohort and small classes, so you get to know peers and faculty well. The Quantitative Methods and Data Analytics Forum brings students together to share and gain feedback on ongoing work, hear speakers on state-of-the-art techniques, acquire professional development skills, and build professional connections. Team projects are involved in many QMDA courses including the Statistical Consulting Integrating AI class, which creates regular chances to collaborate. The internship adds another circle of mentors and colleagues in settings like healthcare, business, policy, science, or education. Electives across campus also connect you with students and faculty in related fields. 

Yes. The M.S. in Quantitative Methods and Data Analytics for Human Behavior is a STEM-designated program, which allows eligible F-1 students to apply for 12 months of Optional Practical Training (OPT) after graduation, plus a 24-month STEM OPT extension—for up to 36 months of post-graduation work authorization in the United States. 

Students may also gain authorized off-campus experience during the program through Curricular Practical Training (CPT), such as internships, applied projects, or external research that are integral to the curriculum. CPT eligibility typically begins after the first academic year. For detailed CPT/OPT procedures, forms, and advising, visit Vanderbilt International Student & Scholar Services (ISSS). 

A B.A. or equivalent undergraduate degree is required.

No.

Yes. Students from all backgrounds have succeeded in our program and are welcome to apply. Students have entered our program with diverse backgrounds such as social/behavioral science, business, engineering, humanities, data science, medical/life sciences, education, computer science, physics, and public health, to name a few. Given the applied nature of the program, we encourage students with substantive interests that use quantitative methods and data analytics to apply.

10 to 12 students, which fosters a tight-knit, collaborative cohort environment.

The program is typically completed in 17 months. Students who complete their internship during the summer between their first and second year can finish in 17 months and graduate in December. Some students choose to follow a 22-month timeline, completing their internship during the spring semester of their second year, and graduating in May. Still other students choose to complete the program on a part-time basis or enroll in our Early Start option which allows Vanderbilt students to start completing requirements while simultaneously finishing their undergraduate degree.

While the program offers some flexibility in coursework, most classes are scheduled during the morning and afternoon. Thus, for students holding concurrent jobs, this program is most compatible with ongoing employment that has a flexible, remote component or has evening/weekend/part-time hours.

Yes. Full time enrollment is defined as 9 credit hours per semester. Students wishing to enroll with fewer credits can work with the Program Director and the Graduate School to determine eligibility as well as implications for timeline, visa status for international students, and financial aid.

A number of assistantships, university positions, and community employment options are available to help students finance their studies. Additional information and strategies for managing expenses can be found on the financial resources page.