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Learners will take on the role of a staff member at the Middleton County Health Department who is tasked with helping to develop an evaluation plan for an obesity-prevention program recently launched in Middleton County. Using the CDC Program Evaluation Framework, learners will connect each step of the framework with a section of the evaluation plan, going into detailed discussion about: incorporating stakeholders, logic models and evaluation questions in the process; characteristics of different research designs for evaluation and considerations for choosing an appropriate design; options for data collection methods and sampling; and thinking through threats to validity. Additionally, the learner will review strategies for conducting rigorous evaluations within constraints of budget, time, and resources.
Advanced Program Evaluation builds on the knowledge and skills obtained from the Program Evaluation training series. See “Prerequisites” for additional information.
For a given public health program evaluation plan, trainees will be able to:
- Appreciate that a comprehensive evaluation plan addresses a program logic, stakeholders, evaluation questions and evaluation design.
- Assess advantages and limitations of evaluation designs, including randomized, quasi-experimental and pre-post designs.
- Appraise and compare options for data collection methods, measures and sampling strategies.
- Identify and address associated threats to validity.
- Identify strategies for addressing budget, time, data and political constraints in evaluation practice
Who should take this course?
- Public health practitioners who engage in planning of program evaluation
- Public health professionals from Tier 1 to Tier 3 may benefit from this course. It is most likely to correspond with activities of Tier 2 professionals with independent responsibilities for planning evaluation.
- MPH students
Estimated time to complete
Through prior training or experience, trainees are expected to have some background knowledge of the following topics:
- Social science research methods
- Logic models
- Process and outcome evaluation