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Data measures and explains what is being studied with numbers (e.g. counts, ratios, percentages, proportions, average scores, etc.). Quantitative methods tend to use structured approaches (e.g. coded responses to surveys) which provide precise data that can be statistically analysed and replicated for comparison. Depending on the characteristics being investigated we determine the most appropriate sampling method, sample selection and sample size that'll produce a robust and reliable results.
Qualitative data explains what is being studied with words (documented observations, representative case descriptions, perceptions, opinions of value, etc.) Qualitative methods use semi-structured techniques (e.g. observations, focus groups, in-depth or community informant, mini-groups, peer groups, durbars and town hall meetings) to provide in-depth understanding of attitudes, beliefs, motives and behaviours. However, because qualitative data is not an exact measurement of what is being studied, generalizations or comparisons are limited.
Both quantitative and qualitative methods have various levels of (biased) characteristics. Therefore, we often recommend & utilize mixed-methods approach because it allows us to harness the advantages of both: measuring what happened with quantitative data and examining how and why it happened with qualitative data. When used together, qualitative methods can uncover issues during the early stages of a project/programme that can then be further explored using quantitative methods. For example, interviews (a qualitative method) may reveal that people in a community are concerned about hunger, and a sample of infants' weights (a quantitative method) may substantiate that mass-wasting and malnutrition are indeed prevalent in the community.


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