Instructor: Tamara Rushovich, PhD MPH, Postdoctoral Fellow, Urban Health Collaborative, Drexel Dornsife School of Public Health
Dates: Monday, June 23 - Friday, June 27
Times: 9:00 a.m. - 12:00 p.m. EST
Format: Hybrid instruction
This course introduces quantitative tools to evaluate programs and public health policies in the context of urban health. The course provides hands-on training in the theoretical approach and practical application of evaluation methodology, ranging from logic models and process evaluation to quasi-experimental designs (including novel applications of the Differences-in-Differences design). We will explore different publicly available datasets, discuss various study design and analytical approaches, and critically assess key examples from the literature. The course is designed to prepare practitioners and academics to identify appropriate data sources, approaches, and quantitative methods for evaluating programs and policies, and to determine when, how, and under what circumstances an evaluation should be conducted. Course activities will include discussions about the strengths and weaknesses of various methods, hands-on labs to explore datasets and practice analytic methods, and presentations that critically review published evaluation studies.
Learning objectives:
· Understand the fundamentals and applications of policy and program evaluation;
· Gain familiarity with common publicly available public health datasets;
· Understand the most common methodological approaches used to evaluate policies and programs in public health, and strengths and limitations of each approach;
· Critically assess the appropriate study design and analysis method, based on the policy context, data availability, stakeholder buy-in, and timeline.
Topics not covered related to Evaluation:
· Qualitative methods, stakeholder engagement or participatory methods, metric or survey creation, data collection and identifying target populations, measurement error.
Prerequisites: Basic understanding of statistics, probability, and regression techniques.
Continuing Education Credits*: 1.5 CEU or 15 CPH
