*A revised version of this post is also available here.
I finally got around to reading a post that had been flagged to me awhile ago, written by Bruce Wydick. While I don’t think the general idea of taking sampling and representatives seriously is a new one, the spin of a ‘median narrative’ may be quite helpful in making qualitative and mixed work more mainstream and rigorous in (impact) evaluation.
Anyway, I got a bit long-winded in my comment on the devimpact blog site, so I am sticking it below as well, with some slight additions:
First, great that both Bruce and Bill (in the comments) have pointed out (again) that narrative has a useful value in (impact) evaluation. This is true not just for a sales hook or for helping the audience understand a concept — but because it is critical to getting beyond ‘did it work?’ to ‘why/not?’
I feel Bill’s point (“telling stories doesn’t have to be antithetical to good evaluation“) should be sharper — it’s not just that narrative is not antithetical to good evaluation but, rather, it is constitutive of good evaluation and any learning and evidence-informed decision-making agenda. And Bill’s right, part of the problem is convincing a reader that it is a median story that’s being told when an individual is used as a case study — especially when we’ve been fed outlier success stories for so long. This is why it is important to take sampling seriously for qualitative work and to report on the care that went into it. I take this to be one of Bruce’s key points and why his post is important.
I’d also like to push the idea of a median impact narrative a bit further. The basic underlying point, so far as I understand it, is a solid and important one: sampling strategy matters to qualitative work and for understanding and explaining what a range of people experienced as the result of some shock or intervention. It is not a new point but the re-branding has some important sex appeal for quantitative social scientists.
One consideration for sampling is that the same observable’s (independent vars) that drive sub-group analyses can also be used to help determine a qualitative sub-sample (capturing medians, outliers in both directions, etc). To the extent that theory drives what sub-groups are examined via any kind of data collection method, all the better. Authur Kleinman once pointed out that theory is what helps separate ethnography from journalism — an idea worth keeping in mind.
A second consideration is in the spirit of Lieberman’s call for nested analyses (or other forms of linked and sequential qual-quant work), using quantitative outcomes for the dependent variable to drive case selection, iterated down to the micro-level. The results of quantitative work can be used to inform sampling of later qualitative work, targeting those representing the range of outcomes values (on/off ‘the line’).
Both these considerations should be fit into a framework that recognizes that qualitative work has its own versions of representativeness (credibility) as well as power (saturation) (which I ramble about here).
Finally, in all of this talk about appropriate sampling for credible qualitative work, we need to also be talking about credible analysis and definitely moving beyond cherry-picked quotes as the grand offering from qualitative work. Qualitative researchers in many fields have done a lot of good work on synthesizing across stories. This needs to be reflected in ‘rigorous’ evaluation practice. Qualitative work is not just for pop-out boxes (I go so far as to pitch the idea of a qualitative pre-analysis plan).
Thanks to both Bruce and Bill for bringing attention to an important topic in improving evaluation practice as a whole — both for programmatic learning and for understanding theoretical mechanisms (as Levy-Paluck points out in her paper). I hope this is a discussion that keeps getting better and more focused on rigor and learning as a whole in evaluation, rather than quant v qual.