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

Realistic

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. 

Participatory

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.

Critical

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.

Complexity

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

  • Equal access participatory monitoring and evaluation toolkit

    Module 5 of this resource 'Doing qualitative data analysis' is a useful guide to doing qualitative data analysis. It covers the basic steps involved in undertaking qualitative data analysis, explains the difference between description and interpretation, suggests ways to get feedback on analysis, and discusses how to use triangulation to increase the trustworthiness of findings. It is consistent with the C4D Evaluation Framework in the following ways:

    • Realistic: the module sets out the ideal steps for data analysis, and also offers more 'rapid' alternatives.  
    • Holistic: this module is particularly aimed at helping people deal with and make sense of 'messy' data that comes from more open-ended, holistic data collection approaches. 
  • Ethnographic Action Research Toolbox

    A brief, web-resource that provides guidance on data management, labelling and analysis, particularly useful for qualitative and ethnographic data. The analysis section includes examples of themes and coding. It is consistent with the C4D Evaluation Framework in the following ways:

    • Holistic: this resource is particularly focused on analysing 'messy' data that comes from ethnographic approaches, which are more open-ended and unstructured.
    • Learning-based: the resource connects the processes of analysis to learning by linking analysis with developing findings for planning and action. 
  • The IDEAS guide and facilitators' guide

    Module 9 of the IDEAS Guide provides a guide to doing qualitative and quantitative analysis using sticky notes to summarise and sort data into themes. This guide is consistent with the C4D Evaluation Framework in the following ways:

    • Participatory: the resource outlines a group-based, visual process for analysing data, and is designed to be accessible for people with little or no prior experience of M&E. 
    • Realistic: the resource uses simplified processes similar to coding. Ideally, participants would be familiar with the data, or sometimes should be allowed for familiarisation with data during the workshop.  
    • Holistic:  this resource guides processes of analysing both qualitative 'messy' data and quantitative data.
  • Participatory rural communication appraisal starting with the people

    Section 6.4 of the Participatory Rural Communication Appraisal (PRCA) Handbook provides guidance on how to conduct analysis with community groups and other stakeholders. It is consistent with the C4D Evaluation Framework in the following ways:

    • Participatory: The guidance encourages the inclusion of community groups in processes of reflection and analysis.
    • Critical: The guidance includes a list of key questions to be asking through the analysis process which bring attention to the differences in experiences among different groups.
    • Realistic: The processes outlined are all quite practical and feasible, getting to the heart of what is important for a participatory approach to analysis.  

Example

  • KAP action research study on violence against children

    A study exploring Knowledge Attitudes and Practices relating to Violence Against Children in Tanzania used a technical reference group, including community researchers and child-peer researchers, to take a participatory approach to analysis. It is consistent with the C4D Evaluation Framework in relation to this task in the following ways:

    • Participatory: university researchers, community researchers and community groups were all involved in data analysis processes
    • Learning-based: the involvement of a range of groups enabled mutual learning and for insight to inform recommendations.

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