A war since the beginning of time, well at least certainly since my exposure to data concepts, is that which is fought between quantitative and qualitative data proponents. This is certainly true for social research, but even more so in evaluation that occurs within a political and organizational context. Quantitative advocates suggest that you cannot make any decisions for a group based on subjective data of a few persons; while qualitative supporters say that human behaviour is not static, and there is always bias present in any data collection. However, what we have found, which may be a hard truth for some, is that there is no one “right” way to do an evaluation. Furthermore, quantitative data is not automatically robust because it has numbers, and qualitative methodology is not instantly less valuable because it tells stories. The type of data that is the “right” data in your context depends entirely on your programme objectives and the indicators you have selected as your measures; and often, particularly for evaluation, you may need both to provide a comprehensive understanding.
The defining feature of quantitative data is that it measures things that can be quantified and are thus numerical in nature. These include rates, ratios, proportions, percentages, and measurement scales. It assumes that there is a relatively stable and observable reality that can be counted and that will provide useful insights on your programme once you have a representative sample. The methodology is designed to be objective and free from the personal bias of the evaluator and some of its biggest strengths are its replicability and ease of analysis. However, data collection can be expensive, and often you can “get caught up” in the numbers and lose sight of the bigger picture and the reason for doing the evaluation in the first place when the quantification becomes an end in and of itself.
Qualitative data collectors, on the other hand, insist that understanding is not so black and white, and life cannot be measured on a physical scale. The generalizations that quantitative data seek to provide negates humans’ unique capacity to interpret their experiences, construct their own meanings, and act on these. Qualitative methodologies such as key informant interviews, focus group discussions, case studies, participant observations, and ripple effect mapping allow evaluators to get an insider’s view on what has happened which enhances understanding of complexities. This insight helps evaluators to identify possible relationships and dynamic interactions with external and individual contexts, and better identify unintended as well as intended consequences. The aim is not to be able to generalize but to try to appreciate the full depth and breadth of experiences. As such, a unique perspective that emerges is as critical in qualitative data analysis as common themes.
As a #project manager, you need to be able to appreciate both types of data. You need to understand which type will help you figure out what works well and how to make it even better. At EvaluCore, we have the perfect complement of #quantitative and #qualitative expertise to guide you through this. Contact us if you still feel unsure about which data is the “right” data for you or if you just want help to collect and analyze it.
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