Trainers: Stephen Porter
Level: Intermediate/Advanced
Language: English
Workshop description:
Repurposing existing knowledge helps to support resilient national evaluation systems by enhancing the efficiency of the process, improving the validity of findings, and expanding the breadth of evidence that can be drawn upon. Using the potentially vast array of knowledge that synthesis techniques can draw upon with text and quantitative data is a challenge far beyond the ability of a team without applying data science techniques.
This workshop introduces techniques IEG has been developing to systematically undertake synthesis using qualitative, text analytics, and machine learning techniques to produce a new range of knowledge products and reports.
The workshop particularly lends itself to supporting evaluation practitioners, policymakers, and governments to use innovative methods using cutting-edge data science techniques. The workshop will work through an example of synthesis from start to finish drawing on IEG experience covering:
- Definition of synthesis
- Identification of knowledge for review
- Analysis of knowledge using synthesis techniques
- The process of relating and combining knowledge from a set of parts into a whole that forms new insights.
The one-day workshop will be delivered using a combination of lectures and hands-on examples. Participants should be experienced evaluators with an interest in working through and coding text the synthesize existing knowledge. The workshop will explain relevant data science techniques, but no previous knowledge of computer programming is required. Learning outcomes: Upon successfully completing this course, evaluators will be able to: • Define a synthesis process targeted at a project or policy demand; • Know how to identify a relevant conceptual framework, develop and apply a search taxonomy to select useful evidence; • Understand the application of a template-based coding structure or machine learning techniques for text data; and • Understand techniques for comparing analyses and interpreting text data.
Learning outcomes:
Upon successfully completing this course, evaluators will be able to:
- Define a synthesis process targeted at a project or policy demand;
- Know how to identify a relevant conceptual framework, develop and apply a search taxonomy to select useful evidence;
- Understand the application of a template-based coding structure or machine learning techniques for text data; and
- Understand techniques for comparing analyses and interpreting text data.