My PhD Journey at St. Louis University

A required 3‑credit course designed to equip social work PhD students with the knowledge and skills to engage in effective descriptive, explanatory, and intervention research. The course accentuates the application of optimal methodologies and measurement techniques vis-a-vis a particular research question or study objectives. It focuses on conceptualizing and designing a research project culminating in a research proposal.

This required 3‑credit course is the second of two required courses on quantitative analyses for doctoral students at the School of Social Work. The course is designed to equip students with the knowledge and skills to apply linear regression to empirical social work research. In the course students will learn the concepts of regression, simple regression, multivariate regression, regression diagnostics, and the goodness of fit. The course also involves categorical predictors, transformation of variables, collinearity, variable selection, and generalized linear models

This graduate-level course introduces applied regression analysis as a core tool for examining social, educational, psychological, and developmental phenomena using sample data to draw population-level inferences. Students learn to model and interpret relationships among multiple variables, evaluate competing explanations, and assess the limits of statistical inference in real-world research contexts. Emphasis is placed on using regression to answer substantive research questions, critically evaluating published studies, and clearly communicating findings through written narratives, tables, and figures for scholarly audiences. The course prioritizes practical data analysis using Stata or comparable statistical software and assumes prior completion of an introductory statistics course, with instruction focused on application and interpretation rather than theoretical derivation.