Introduction to Impact Evaluation for Public Management (1° ed)

The introductory course on impact evaluation will allow you to acquire the key concepts to understand how experimental and quasi-experimental methodology can be used with the aim of making better decisions within an organization and ultimately promoting a process of learning and continuous improvement.

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Objective: At the end of the course, the student will have understood what a reliable impact evaluation with scientific credibility consists of and will have learned how impact evaluations help make better decisions about resources and public management. In turn, they will know the experimental evaluation method and the technical requirements to carry out impact evaluations and will have acquired the ability to identify opportunities for experimental evaluation within public policy, as well as the recognition of the elements (logistical, institutional and ethical) to take into account when designing an evaluation.

Methodology: The course will last approximately 6 weeks and will allow its participants to get started in the field of impact evaluation. In addition, this course uses numerous practical examples and a didactic style to explain what an impact evaluation consists of, its technical elements, and how it differs from other types of evaluations. Additionally, its content and activities are available through the MiríadaX platform 24 hours a day, 7 days a week, providing flexibility in time and space to consult the material and learn at your own pace based on the calendar of work of each participant.

Modules:

Module 1 - Importance of impact evaluation
Module 2 - Basic concepts of impact evaluation and the experimental method
Module 3 - Theory of change
Module 4 - Management Assessments: Assessing the how rather than the what
Module 5 - How to start thinking about an evaluation: technical, logistical, institutional and ethical challenges
Module 6 - Closing and conclusions

Recommended prior knowledge: Although not required, basic knowledge of statistical inference is recommended.