Graduate Coursework

The Department of Psychology offers several courses in quantitative methods. Many students seek out elective courses beyond program requirements and may choose to complete a Concentration in Quantitative Methods. Upcoming opportunities for continuing education (e.g., power analysis workshop, Quant Club training events) are posted on our calendar.

Psychology 941: Fundamentals of Research Design & Data Analysis 1

  • Usual Instructor: Cal Garbin
  • Pre-Requisites: Instructor permission
  • Course Format: 3 credit hours; lecture activity
  • Notes: PSYC 941 is usually offered every fall
  • Description: An integration of fundamental methodological and data analytic models commonly employed in psychological and behavioral research. Univariate and bivariate statistical analyses and research hypothesis testing using parametric and nonparametric models are covered, along with an introduction to research synthesis and meta-analysis.
  • Current Online Course Materials

Psychology 942: Fundamentals of Research Design & Data Analysis 2

  • Usual Instructor: Cal Garbin
  • Pre-Requisites: PSYC 941 and instructor permission
  • Course Format: 3 credit hours; lecture activity
  • Notes: PSYC 942 is usually offered every spring
  • Description: Multiple regression and related models for multivariate analyses, statistical control, mediation & path analysis are introduced. Factorial research designs and ANOVA models for examining conditionality, interactions & moderation are covered. The integration of these "two statistical families" as the general linear model (GLM) is the heart of the course.
  • Current Online Course Materials

Psychology 944: Multilevel Modeling in the Behavior Sciences

  • Usual Instructor: Becca Brock
  • Pre-Requisites: PSYC 941 & 942 and instructor permission. Please email the instructor for a permission code when registration opens. 
  • Course Format: 3 credit hours; lecture activity
  • Notes: PSYC 944 is usually offered every other fall
  • Description: Applications of the multilevel model (hierarchical linear model, general linear mixed model) for analyzing nested data with a particular focus on longitudinal data analysis.

Psychology 948: Structural Equation Modeling in the Behavioral Sciences

  • Usual Instructor: Becca Brock or Anna Jaffe
  • Pre-Requisites: PSYC 941 & 942 and instructor permission. Please email the instructor for a permission code when registration opens. 
  • Course Format: 3 credit hours; lecture activity
  • Notes: PSYC 948 is usually offered every spring
  • Description: Fundamentals and foundations of SEM (model specification and identification, data preparation, model estimation, model respecification, reporting results) and specific applications of path analysis (moderation, mediation, and moderated mediation) and latent variable modeling (standard and nonstandard CFA models, integrating latent variables into path models, multiple group analysis).

Psychology 949: Longitudinal Structural Equation Modeling

  • Usual Instructor: Anna Jaffe
  • Pre-Requisites: PSYC 941, 942, 948 and instructor permission. Please email the instructor for a permission code when registration opens. 
  • Course Format: 3 credit hours; lecture activity
  • Notes: PSYC 949 is usually offered every other fall
  • Description: Applications of structural equation models to longitudinal designs for analyzing repeated measures data. Topics will include evaluation of measurement invariance, panel models, latent growth curve models, and other extensions of SEM for longitudinal data.

Psychology 930: Advanced Design and Analysis Modules

  • Usual Instructors: Cal Garbin
  • Pre-Requisites: Will vary by topic
  • Course Format: 1 credit hours lecture activity
  • Notes: Modules will usually, but not always, be offered in the summer
  • Description: Descriptions of advanced modules are listed at the bottom of the main "Quant Training" page.
  • Current offerings:

PSYC 971: Data Science and Visualization in R

  • Usual Instructor: Jeff Stevens
  • Pre-Requisites: None
  • Course Format: 3 credit hours; lecture activity
  • Notes: Scheduled for Spring 2023
  • Description: Introduction to the fundamental concepts and methods used in the R statistical software package to prepare, visualize, and disseminate data.