Program of study

Individualized coursework

Beyond the required core courses, you may tailor your coursework to meet your individual research needs. You are expected to sign up for research credits (regular, thesis, and dissertation) each semester.

You should also take elective seminars that complement or enhance your specializations. You are also encouraged to take the teaching seminar to prepare yourself for future roles as a teaching faculty member.

The quantitative psychology Ph.D. program is structured so that all requirements for the degree can be completed in five years. This entails satisfying all the requirements listed in the Psychology General Requirements Document and the Graduate Bulletin.

Required research milestones

To complete the program within five years, we recommend the following sequence:

• First-year project — Completed at the end of the first year
• Master's thesis — Proposed and defended during the second or third year
• Preliminary exam — Completed during the third or fourth year
• Dissertation — Proposed and defended during the fourth or fifth year

Candidates in the program will earn their master's degree following the defense of the master's thesis. We do not have a terminal master's program.

Recently offered courses

This schedule will be updated if necessary due to faculty leaves. The goal is to repeat the schedule every two years.

(R) = Required for quantitative students

(A) = Every year

(B) = Every other year

Note: Statistical Inference I and II are taught through the Department of Applied and Computational Math and Statistics (ACMS). Note that Statistical Inference I is called Applied Probability. There is also a summer course offered by ACMS that can serve as an alternative to Applied Probability to fulfill the Statistical Inference I requirement. Statistical Inference II is called Statistical Inference.

Spring 2023

• 60101 - Quantitative Methods II (R)(A) 
• 60109 - R for Data Science (B)
• 60122 - Machine Learning for Social/Behavioral (B)
• 60124 - Psychological Measurement (R)(B)
• 60130 - Structural Equation Modeling (R)(A)
• 60155 - Longitudinal Data Analysis (R)(A)

Fall 2022

• 60100 - Quantitative Methods I (R)(A)
• 60123 - Linear Model (R)(B)
• 60125 - Multivariate Statistics (R)(A)

Spring 2022

• 60101 - Quantitative Methods II (R)(A) 
• 60109 - R for Data Science (B)
• 60130 - Structural Equation Modeling (R)(A)
• 60152 - Item Response Theory (B)
• 60155 - Longitudinal Data Analysis (R)(A)

Fall 2021

• 60100 - Quantitative Methods I (R)(A)
• 60125 - Multivariate Statistics(R)(A)
• 60145 - Computational Statistics (R)(B)