Novel sources of data, and the sizes of these datasets, present new opportunities for a data-driven approach to problems in a range of domains like public health, social service delivery, and mitigation of social problems and injustices. While techniques from artificial intelligence and machine learning bring great promise, they are also under increasing scrutiny for their potential to exacerbate or institutionalize existing biases. Faculty in this track are interested in the use of data-enabled and computational techniques to improve social outcomes, broadly defined, considering issues of efficiency, social justice, and equity. You can learn more about faculty and their research interests on the track faculty page.
Track Course Requirements
Students must complete a doctoral seminar series, including conceptual foundations of social science, advanced research methods, and a theory seminar, plus an advanced substantive course from an approved list in their area of interest. With permission from the co-directors, students may substitute core courses for substantive classes.
Doctoral seminar series
- BSDC 8000: Introduction to advanced research
- BSDC 8001: Conceptual foundations of social science research
- BSDC 8002: Seminar in Social Work Theory and Knowledge OR
- BSDC 8014: Theoretical orientations in public health sciences
Substantive course (select one)
- BSDC 8005: Applied Linear Regression Analysis
- BSDC 8006: Generalized Linear Models
- BSDC 8007: Social network analysis
- BSDC 8008: Structural equation modeling
- BSDC 8010: Multilevel and longitudinal modeling
- BSDC 8011: Propensity score analysis
- BSDC 8020: Issues and directions in intervention research
- BSDC 8021: Seminar in Mental Health & Addictions Services Research
- BSDC 8022: Mental Health Services Research
- PHCC 6004: Health economics
- PHCC 6005: Quantitative Methods for Health Policy Analysis
- PHEL 6006: Qualitative research methods
- SWPM 6074: Community Based System Dynamics
- SWPM 6076: Foundations of Geographic Information Systems (GIS) for the Applied Social Sciences
- SPGN 6016: Benefit-Cost Analysis
Patrick Fowler
Track Chair, Social Work & Public Health
Associate Professor, Brown School
PhD, Wayne State University
- Email: pjfowler@nospam.wustl.edu
Patrick Fowler’s research aims to inform developmentally sensitive public and programmatic policy that supports socioeconomically marginalized families. This work applies rigorous methodology to understand the mechanisms through which housing stabilization promotes healthy child development.