We’ll begin this blog by stating the cardinal truth: Data is of the utmost importance. Whether you are a businessman, a researcher, or a homemaker, the significance of data and its pervasive cannot be ignored. Accurate, credible data is required not only to gauge the efficacy and progress of a project but also to determine the areas of improvement. This is precisely why we come to a pressing query concerning impact measurement—what should be given precedence during the impact measuring exercise of your social project? Qualitative or quantitative data?
The answer is simple, both go in tandem, hand in hand.
The thumb rule: Quantitative data will tell you about the problem areas in your impact project while qualitative data will help you understand the cause behind the problem.
Before we go on to understand the importance of quantitative and qualitative data, here’s a quick look at the definition of ‘impact’.
By ‘impact’, we refer to the long-term effects of a project or an initiative undertaken by an organization. Here, we are concerned with organizations, NGOs or individuals engaged in the creation of a socially meaningful impact. ‘Impact’ includes effects on immediate stakeholders and those directly involved with the project as well as those impacted indirectly.
Now we’ll turn to understand the difference between qualitative and quantitative data for impact measurement and why the two should go hand-in-hand.
According to the Governance and Social Development Resource Centre (GSDRC), University of Birmingham, UK, “the case for qualitative and combined methods is strong. Qualitative methods have an equal footing in impact evaluation and can generate sophisticated, robust, and timely data and analysis.”
Simply put, while calculable, measurable data is important to evaluate your impact and also ‘evidence’ it, some indicators cannot be measured in a conventional ‘quantifiable’ manner. These would include, for example, some aspects tied to poverty or standard of living such as self-respect, dignity, security, et cetera. Qualitative data solves this issue.
Qualitative and quantitative research employ different methods to arrive at the respective data. While the former is more quantifiable – can be statistically analyzed, modeled, calculated, and aggregated – the latter is contextual, based on judgments & perceptions.
While quantitative methods produce data that can be analyzed to describe and predict relationships, qualitative research can help probe those relationships and explain contextual differences in their quality.
The GSDRC, in its paper titled ‘Quantitative and Qualitative Methods in Impact Evaluation and Measuring Results’, emphasizes the importance of working collaboratively with quantitative and qualitative data for effective impact evaluation.
“…what is not quantifiable becomes unimportant while ‘what is measurable and measured then becomes what is real and what matters’ (Chambers, 1995, 8). Conversely, if qualitative research inductively throws up interesting, often surprising and sometimes counterintuitive relationships and patterns, quantitative research is then able to ask ‘how much?’ and establish how confident we can be in these ‘working hypotheses’. This iterative relationship between describing and explaining provides the key to an effective combination of methods and data.”
Qualitative and quantitative data are more powerful when combined. World Bank’s Soniya Carvalho and notable economist Howard White, in their book, ‘Combining the Quantitative and Qualitative Approaches to Poverty Measurement and Analysis: The Practice and the Potential’, state three ways of combining qualitative and quantitative approaches.
- Integrating methodologies
- Sequencing information
- Merging findings
The authors argue that integrating methodologies, sequencing information, and merging findings of quantitative and qualitative data can yield better measurement, analysis, and action-taking ability concerning impact evaluation.
“Combining qualitative research with quantitative instruments that have a greater breadth of coverage and generalisability can result in impact evaluations that make the most of their comparative advantages,” GSDRC.
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