(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Jonathan Stray writes:
I read your “when do stories work” paper (with Thomas Basbøll) with interest—as a journalist stories are of course central to my field. I wondered if you had encountered the “process tracing” literature in political science? It attempts to make sense of stories as “case studies” and there’s a nice logic of selection and falsification that has grown up around this.
This article by David Collier is a good overview of process tracing, with a neat typology of story-based theory tests.
Besides being a good paper generally, section 6 of this paper by James Mahoney and Gary Goertz discusses why you want non-random case/story selection in certain types of qualitative research.
This paper by Jack Levy is another typology of the types and uses of case studies/stories.
I had not heard about process tracing, and I’ll have to take a look at these papers. I’m very interested in the connections between quantitative and qualitative research. Indeed, one of my themes when criticizing recent research boondoggles such as power pose and himmicanes has been the weakness of the connections between the qualitative and quantitative aspects of the work. And recently I got a taste of this criticism myself when I was presenting some of our findings regarding social penumbras: a psychologist in the audience pointed out that one reason our results were so weak was because there was only a very weak link between qualitative theories of changes in political attitudes, and the particular quantitative measures we were using. In short, I was doing what I often criticize in others, which was to gather data using a crude measuring instrument and then just hope for some results. We did find some things—I still think the penumbra work has been a successful research project—but we could’ve done much better, I’m sure, had we better tied qualitative to quantitative ideas.
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