You’ll be aware of the gist. Quantitative statistical models are great for generalizing, also data suitable for the stats tends to be quicker to analyze than qualitative data. More qualitative methods, such as interviewing, tend to provide much richer information, but generalization is very tricky and often involves coding up so the data can be fitted using the stats. How else can the two (crudely defined here!) approaches to analysis talk to each other?
I like this a lot:
“In the social sciences we are often criticized by the ethnographers and the anthropologists who say that we do not link in with them sufficiently and that we simply produce a set of statistics which do not represent reality.”
“… by using league tables, we can find examples of places which are perhaps not outliers but where we want to look for the pathways of influence on why they are not outliers. For example, one particular Bangladeshi village would have been expected to have high levels of immunization, whereas it was down in the middle of the table with quite a large confidence interval. This seemed rather strange, but our colleagues were able to attribute this to a fundamentalist imam. […] Another example is a village at the top of the league table, which our colleagues could attribute to a very enthusiastic school-teacher.”
“… by connecting with the qualitative workers, by encouraging the fieldworkers to look further at particular villages and by saying to them that we were surprised that this place was good and that one was bad, we could get people to understand the potential for linking the sophisticated statistical methods with qualitative research.” (Ian Diamond and Fiona Steele, from a comment on a paper by Goldstein and Spiegelhalter, 1996, p. 429)
Also reminds me of a study by Turner and Sobolewska (2009) which split participants on their Systemizing and Empathizing Quotient scores. Participants were asked, “What is inside a mobile phone?” Here’s what someone with high EQ said:
“It flashes the lights, screen flashes, and the buttons lights up, and it vibrates. It comes to life on the inside and it comes to life on the outside, and you talk to the one side and someone is answering on the other side”
And someone with high SQ:
“Many things, circuit boards, chips, transceiver [laughs], battery [pause], a camera in some of them, a media player, buttons, lots of different things. [pause] Well there are lots and lots of different bits and pieces to the phone, there are mainly in … Eh, like inside the chip there are lots of little transistors, which is used, they build up to lots of different types of gates…”
(One possible criticism is that the SQ/EQ just found students of technical versus non-technical subjects… But the general idea is still lovely.)
Would be great to see more quantitative papers with little excerpts of stories. We tried in our paper on spontaneous shifts of interpretation on a probabilistic reasoning task (Fugard, Pfeifer, Mayerhofer & Kleiter, 2011, p. 642), but we only squeezed in a few sentences:
‘Participant 34 (who settled into a conjunction interpretation) said: “I only looked at the shape and the color, and then always out of 6; this was the quickest way.” Participant 37, who shifted from the conjunction to the conditional event, said: “In the beginning [I] always [responded] ‘out of 6,’ but then somewhere in the middle . . . Ah! It clicked and I got it. I was angry with myself that I was so stupid before.” Five participants spontaneously reported when they shifted during the task, for example, saying, “Ah, this is how it works.”’
Fugard, A. J. B., Pfeifer, N., Mayerhofer, B., & Kleiter, G. D. (2011). How people interpret conditionals: Shifts towards the conditional event. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 635–648.
Goldstein, H. & Spiegelhalter, D. J. (1996). League tables and their limitations: statistical issues in comparisons of institutional performance. Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, 385–443.
Turner, P. & Sobolewska, E. (2009). Mental models, magical thinking, and individual differences. Human Technology 5, 90–113.