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So You’re Thinking of Using Outcome Harvesting? Read This First.
Outcome Harvesting (OH) is a powerful approach for monitoring and evaluation, especially in complex, dynamic environments where change isn’t always linear. This article offers practical lessons learned from real organisations that have used OH to track advocacy, capacity-building, and social change efforts. Whether you’re new to OH or trying to refine your practice, this guide helps you avoid common mistakes like treating OH as a reporting checkbox or skipping substantiation.
3 min read
200 views


Webinar on Outcome Harvesting
Outcome Harvesting flips traditional evaluation on its head. Instead of measuring what you planned to achieve, it helps you uncover what actually happened, intended or not. Whether you're mapping policy shifts, grassroots influence, or unexpected wins, this webinar will show you how to surface real outcomes and make sense of them.
2 min read
1,124 views


How To Do Qualitative Data Analysis
If you have ever conducted an interview, a focus group or collected qualitative data, here is a way to go about the analysis.
1 min read
1,650 views


Types of Interviews
In-depth interviews are used a lot during research and evaluations. They can be structured, semi-structured or unstructured. Depending on...
1 min read
593 views


0 min read
492 views


4 Tips for Piloting a Survey
Piloting and testing a survey is important to spot errors and correct them before data collection begins. While the piloting can take...
1 min read
296 views


5 Ways Key Informant Interviews are Useful
Key Informant Interview (KII) is a qualitative data collection method that involves a conversation with a person(s) who are knowledgeable...
1 min read
4,487 views


Pros and Cons of Key Informant Interviews
Key Informant Interviews (KIIs), along with desk reviews, are usually conducted in nearly all evaluations. There is a good reason for...
1 min read
2,750 views


234 views


How to Strengthen Qualitative Data
One of the main criticisms of qualitative methods and data is that it lacks sufficient rigour (relative to quantitative data). Here are 3...
1 min read
689 views