C4D: Analyse data

What is it?

Analysing descriptive data (data about what has happened or is happening) means looking for patterns and themes, making sense of and summarising the data. It is an important part of every RM&E system or study. Techniques for analysis should be selected alongside the selection of methods in the design of a research study or evaluation. There are two basic categories of analysis methods for descriptive questions: qualitative data analysis and quantitative data analysis.

General information

The Rainbow Framework includes detailed information on a range of analysis methods.  In addition, one of the UNICEF Methodological Briefs Overview: Data Collection and Analysis Methods in Impact Evaluation, by the UNICEF Office of Research, Florence covers data collection and analysis. These pages are recommended background reading before considering options to apply to C4D. 

Applying the C4D principles


Additional resources may be required for analysing qualitative data (words-based data i.e. spoken or written, stories, interviews, questionnaires, focus group discussions, videos etc.). In C4D, qualitative data is often critical to understanding contexts and changes. Qualitative data analysis (summarising and looking for patterns and themes) can be more time consuming compared to quantitative data, and requires different sets of skills. 


The C4D Evaluation Framework encourages involvement of partners, institutions and community groups in the analysis process. Some methods/approaches have participatory analysis processes built in. A participatory approach to analysing data can reveal new findings and meanings, and support mutual learning.


The data analysis process should involve looking for differences, exceptions, and a critical analysis of power. To reveal these differences it is useful to involve a diversity of perspectives in the interpretation and meaning-making process.


Simple averages, frequency tables and graphs will not be enough to represent complicated and complex aspects of C4D interventions.  At the very least, there should be disaggregation in tables and diagrams to show differential effects on different sub-groups.  Timelines can be important for showing non-linear change over time.

Recommended methods and adaptations for C4D


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