C4D Hub: Combine qualitative and quantitative data

What is it?

M&E Frameworks and evaluation/study designs that include the collection both qualitative and quantitative data, are an important strategy for strong and balanced findings. It is important to plan in advance how the different types of data will be combined. Combining different kinds of data enriches findings, it can enables an examination of the generalisability of emerging hypothesis from qualitative data, qualitative data may offer explanations about patterns observed in quantitative data, and triangulation of data can confirm or reject findings from one source of data.

General Information

The main BetterEvaluation site includes detailed information on a range of options for combing different kinds of data. This page is recommended background reading before considering options to apply to C4D. 

Combining qualitative and quantitative data and C4D

Applying the C4D principles

Realistic 

As part of being realistic, the C4D Evaluation Framework advocates for the use of mixed-methods. This doesn't mean that every R,M&E activity must include both qualitative and quantitative data, however. For example, a qualitative study might be needed to fill gaps in quantitative data or indicators. 

Holistic 

Combining qualitative and quantitative data enables different paths into understanding the context. Combining data from different methods gives a more rounded, more holistic view of a context. 

Accountable 

A key part of being accountable is rigour. Combining data from different data collection methods boosts the rigour by providing different perspectives and ways to understand a problem.   

Recommended options and adaptations for C4D

Resources 

  • An overview of how triangulation helps with validity is available in Qualitative Research Methods for use in Equity Focused Monitoring (page 26)

Example

Retrospective Analysis study of Open Defecation in Nadia District, India: 

The Retrospective Analysis study of successful open defecation initiatives in Nadia, India, was specifically intended to fill gaps in knowledge. Existing quantitative surveys had confirmed that the initiatives had worked at a population level, and by using ethnographic and qualitative approaches, the study could answer questions about how and why the initiative had worked in holistic and contextualized ways. More information about how this study exemplifies the approaches advocated in the C4D Evaluation Framework will be available soon.

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