(I tried last night to post a suggestion that debates in comparative politics might provide a few interesting ideas on standards for causal inference in case studies; but the comment was lost to the ether. But since I’m back, I was thinking, for instance, of Gerring’s “What is a Case Study and What is it Good For?”; George and Bennett’s Case Studies and Theory Development in the Social Sciences; as well as Collier and Mahoney 1996.)

What Popper was also right about, however, and you may disagree here, is that there is no such thing as simply “adding to the total stock of knowledge”. The specifics of any given case will be interesting (to science) in so far as a theory makes predictions about it. It is unscientific to investigate something in detail just out of curiosity and with the aim of compiling a list of true sentences about it.

One way to make up for such unscientific research is generate hypotheses, as you say. But these become scientific not from their basis in the case, but from their further testing.

Science happens when we approach phenomena as specific cases OF more general truths.

That said, I don’t think organization studies should be “scientific” every second of the day.

(I tried last night to post a suggestion that debates in comparative politics might provide a few interesting ideas on standards for causal inference in case studies; but the comment was lost to the ether. But since I’m back, I was thinking, for instance, of Gerring’s “What is a Case Study and What is it Good For?”; George and Bennett’s Case Studies and Theory Development in the Social Sciences; as well as Collier and Mahoney 1996.)

If you weren’t a falsificationist, the single case couldn’t challenge assumptions.

I don’t know what his position on statistics was specifically, but I’ve never connected Popperianism with large data sets. Given a good theory, you need only a few intelligently chosen observations for the purposes of testing.

Then again, maybe this is just another classic case of “At least Popper wasn’t a Popperian!” (You may know some “Popperians” that really do need to be corrected on this point.)

It is a separate question as to what the next step can be. Using a case study to motivate an ad hoc amendment is logically valid but highly uninformative. A better move is to dig deeper and think about how platypuses and other mammals fit together, even if they differ in one very important anatomical feature.

But let me emphasize – we wouldn’t even know the relative importance of reproduction in biological classifications if we didn’t take the time to do case studies. And it is a different question how the puzzles raised by case studies are to be resolved.

Jay – I don’t have the energy to fight Weberians after cleaning up the Popperians…

It seems mammals are not distinguished by reproductive characteristics today. Probably because “live birth” was falsified. It looks like “sweat glands” is the clincher. The other is: “two knobs at the base of the skull which fit into the topmost neck vertebra” (which has somethign to do with hearing, I think.)

Suppose someone explained Google’s dominance by an incentive system not used in any other industry’s dominant firms? Wouldn’t we prefer something else?

Popper would find the “or egg laying” dubious because it suggests an “ad hoc” way of getting around evidence to contrary. What you are really sayign is “live birth or egg laying or …” whatever might turn up. Well that’s not really a theory. That’s just a strategy for accomodating any observation.

Falsificationism forces us to say “Eggs?” “Really?” “Are you sure?” “A mammal?” and then find some way to make sense of it.

Let’s get back to the platypus. Before the discovery of the platypus, Western science said “being a mammal is inconsistent with egg laying.” After it’s discovery, the new general statement is “being a mammal is consistent with either live birth or egg laying.”

Seems like a generalization to me and it was based on a single species.

I used to dismiss Popper as, I guess, “falsified”. But today I find falsificationism to be one of the most useful heuristics for making sense of scientific claims: what is X saying will NOT happen on the basis of the theory X is defending?

And that’s really the difficulty with a straight case study. What do the “facts of the case” tell us will not, cannot, or should not happen? Often very little.

Singular events are not just “difficult” to falsify, they aren’t really what falsification was about. Falsification was about theories, i.e., general claims (preferably universal ones).

Popper wasn’t talking about what you can or cannot “study”, he was talking about the scientific basis of theories. As long as we agree that a field study of a single platypus does not give as a basis to generalize about life on earth, there is no problem. Popper would say you can “study” whatever you like.

But if you’re going to say something like “All mammals give birth to live offspring” (or not) then you’re going to need more than a case study. The platypus could be used to defend the claim that “Not all mammals give birth to live offspring.” But how interesting is that? It’s like saying, “Some mammals have two legs.” A case study could determine that, but what have we learned?

I think it is very difficult to say what finding life on Mars would teach us about life in general. I don’t think we’re testing any of our biological theories on Mars today. I think we’re just curious about Mars. The scientific mission there is a bit questionable. But the adventure is beyond dispute.

In the end, I think the hypothetical Popperian’s issue might be to what extent falsifiable theories can be made concerning rare cases. Some claims are falsifiable while others are not. It is the nature of those claims that makes them science or not science. The ones that cannot be falsified would not be science per se (or they would be on the fringe). But the hypothetical Popperion could still be interested or even persuaded by such work. Eg, Popper himself was a philosopher of science who argued in favor of democracy though those views are not exactly falsifiable.

If there’s a good Popperian out there, please jump in!

… you never have “too much (or too many) data,” any more than you have too much money. If you have too much money you buy fancier things, you move to a nicer neighborhood, eventually you start giving it away to worthy causes–you never have too much. Similarly, if you have an overflow of data, you can perform estimates for subgroups, you can estimate nonlinear effects, etc. That’s one reason you almost never see estimates that are 10 se’s away from zero: if you had that kind of data, you’d subdivide and learn more.