C4D: Generalise findings

What is it?

An evaluation usually involves some kind of generalisation of the findings to put forward an analysis that predicts how the findings of one initiative might relate to future programs, other places and contexts, or other groups of people. Often it is assumed that statistical generalisation is the only way to generalise, but there are a range of methods for undertaking this task.

General information

The Rainbow Framework includes comprehensive guidance on methods with links to resources, including both statistical and non-statistical generalisation methods. This page is recommended as background reading before considering methods to apply to C4D.  

Applying the C4D principles to generalising findings from C4D


Although there may rarely be a one-size-fits-all set of recommendations for C4D, there may be some key principles or insights about the kinds of contextual factors that have the most influence and can be generalised.


The knowledge of partners, communities and other stakeholders can be valuable in drawing out key principles or insights that can be used to consider whether the same initiative might work in other contexts (other times, places and people). 


Consider who the initiative has worked for and where (who has it not worked for) and how this might this translate to other contexts (places, people and groups). When using participatory approaches to generalising findings, consider whose perspectives are included and silenced in this process.


When Generalising Findings it is important to identify what the key social, political, economic, cultural and other systemic factors were, in that specific place and time, that affected whether it worked. This will help to predict what factors will need to be considered in other contexts.

Recommended methods and adaptations for generalising findings in C4D



'C4D: Generalise findings' is referenced in: