Does your program work? How can you be sure? This workshop is a user-friendly introduction to rigorous quantitative impact evaluation methods (experimental & quasi-experimental), their scope and limitations. It provides a guided tour of complex methodologies for those who do not have – nor want to acquire – advanced training in statistics, but need to grasp fundamentals of impact evaluation methodology.
Are experimental methods the gold standard of evaluation? Are other methods valid and useful to answer questions about impact? What does that mean and why should we care? If you are interested in impact evaluation, but too afraid (of math and statistical formulae) to ask, this is the workshop for you.
The participants will
- Master the difference between impact evaluation and other types of evaluation.
- Understand the difference between experimental and quasi-experimental methods, and why they matter.
- Identify the need and the usefulness of rigorous quantitative impact evaluation.
- Identify main requirements to perform and/or commission an impact evaluation.
- Understand and assess the quality and usefulness of quantitative impact evaluation reports.
- Realize why people using these methods to learn “what works” in the development field do win Nobel Prizes, yet still critically assess their usefulness.
- Understand the must-know technical terminology required to pursue more advanced training in impact evaluation.
Emerging evaluators, commissioners, policy-makers and development activists.
The workshop is intended to provide essential knowledge to professionals who are at the start of or in the early phases of their career in evaluation and to more experienced professionals who need a good refresher of quantitative methodologies for impact evaluation. It is designed for people with no advanced training in statistics that need a jargon-free roadmap to understand these methods in order to deepen their understanding of impact evaluation, its scope and limits.
The workshop is mid-level.
Participants should have some familiarity with the evaluation field (i.e. types of evaluation, principles of evaluation, uses of evaluation), but no advanced training in statistics or statistical software is required.