What about AI that could take images, text, videos etc and which produce written answers to the insight problem, drawing on knowledge not included in the data being reviewed. But it is true that why is usually a question asked by qual, and why is often not the key element of quant analysis. Conversely, most of the behavioural economics findings from Kahneman, Tversky, Ariely, Thaler et al (that explain why people do things they do) are based on quantitative experiments and observations.
For example, you might do an ethnographic study to gain a description of what is happening, but it might not answer the why. It might be very good, it might fully answer the problem, but it is an algorithm generating numbers. Note, if you change the researcher, you will normally (to some extent) change the interpretation – because the additional information they bring and their specific way of seeing patterns will differ.Īre qual and quant becoming the same thing? No! People quote the example of applying tools such as AI to photos, videos, text etc to produce analyses such as sentiment. The most common reason for using Qualitative Analysis is that the problem being addressed can’t be answered by simply applying an algorithm to generate numbers. it is a form of Constructivism/Interpretivism). In Qualitative Analysis the analyst/researcher/insighter combines the data with their insight to join the dots to ‘create’ meaning (i.e. In theory, but often not in practice, if you change the analyst in a quant project you will get the same numbers*. To an extent, we can think of Quant Analysis as ‘discovering’ things (i.e.
For example, to find the mean, the values are added up and divided by the number of values used – that is an algorithm. In general, an algorithm is used to produce the output. Quantitative Analysis is where numbers are produced that meaningfully describe something of interest from data. Let’s start with Quant Analysis because I think the definition is narrower. The first thing I would stress is that we need to focus on Qual Analysis and Quant Analysis, because the data used is neither a sufficient or necessary criterion. So, here is another go at producing an answer. This is a perennial question and it came up in conversation earlier today.
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