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Qualitative and Quantitative Methods: Can They Be Joined?
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the election call could not have been affected by it. Now while we do not have good studiesof how many people vote right as the polls close, Brady does come up with a plausiblenumber. But surely this is not a DSO. Then, based on prior purely quantitative work onmedia attention, turnout, and the effect of previous early calls, he shows that the maximaleffect of the early call on turnout in the Panhandle must have been small.
Now what of the claim that the carefully structure sequence of necessary conditions
makes the Brady study the successful adjoining of CPOs to DSOs. If this simply meansthat Brady has thought about what it would take for the early call to have mattered, thenwe all agree that CPOs are important. But what this has to do with “smoking guns” or“process tracing” is beyond me. To make the issue simpler, let us return to epidemiology.Imagine we are interested in how many lives will be saved by a new screening process. Sowe do a standard experimental study (surely no CPOs here), randomly giving some peoplethe screening process, some a placebo process (let us not worry about any details here). Wethen can compute both the false positive rate (how many unnecessary operations will bedone) and the difference between the true positive and false negative rates, telling us howmany lives will be saved. We then compute how many deaths are caused by unnecessaryoperations, and perhaps do another quantitative study on how many people will choose to bescreened. We can then, using purely quantitative tools, and only DSOs, compute how manylives the screening program will save (or cost!). Now if by CPO we mean an understandingthat in life many will not choose to be screened, or that false positives are costly, then ofcourse we need CPOs. But this is not the usage of CPO as defined by BCS. Similarly, thisanalysis is multiplicative, not additive, and based on necessary conditions (one cannot diefrom a procedure not undertaken). But what does this have to do with CPOs? So whileBrady did an admirable study, I fail to see how this is an instance of successfully adjoiningCPOs to DSOs. I hope that being committed to DSOs and quantitative analysis does notmean that I also must be committed to being a Martian political scientist!
What then can we make of the general argument.
Brady, Collier and Seawright
ask, and
then answer why “are CPOs inherently important?”
“We argue that a major part of the answer is found in the empirical and theo-
retical limitations of DSOs. DSOs are especially useful for detecting probabilisticrelationships when there are many observations of comparable units and whenthe relevant causal pathways and interactions are well understood or controlledthrough randomized experiments. Yet it is often hard, if not impossible, to in-crease the number of analytically relevant DSOs, and we seldom have adequateunderstanding or control of these causal pathways or interactions.
In many situations, only a few similar units or events can be studied. .... In
these situations, adding cases may be impossible (or at least foolhardy) becausenew cases will differ fundamentally from the original universe of concern. Fur-thermore, in some situations, social scientists – and also natural scientists – wishto explain a singular event .... In these situations, large-N studies may not berelevant.
The second limitation is even more serious. Increasing the number of DSOs
can provide additional leverage when the major inferential problem is lack of sta-
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| | Authors: Beck, Nathaniel. |
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the election call could not have been affected by it. Now while we do not have good studies
of how many people vote right as the polls close, Brady does come up with a plausible
number. But surely this is not a DSO. Then, based on prior purely quantitative work on
media attention, turnout, and the effect of previous early calls, he shows that the maximal
effect of the early call on turnout in the Panhandle must have been small.
Now what of the claim that the carefully structure sequence of necessary conditions
makes the Brady study the successful adjoining of CPOs to DSOs. If this simply means
that Brady has thought about what it would take for the early call to have mattered, then
we all agree that CPOs are important. But what this has to do with “smoking guns” or
“process tracing” is beyond me. To make the issue simpler, let us return to epidemiology.
Imagine we are interested in how many lives will be saved by a new screening process. So
we do a standard experimental study (surely no CPOs here), randomly giving some people
the screening process, some a placebo process (let us not worry about any details here). We
then can compute both the false positive rate (how many unnecessary operations will be
done) and the difference between the true positive and false negative rates, telling us how
many lives will be saved. We then compute how many deaths are caused by unnecessary
operations, and perhaps do another quantitative study on how many people will choose to be
screened. We can then, using purely quantitative tools, and only DSOs, compute how many
lives the screening program will save (or cost!). Now if by CPO we mean an understanding
that in life many will not choose to be screened, or that false positives are costly, then of
course we need CPOs. But this is not the usage of CPO as defined by BCS. Similarly, this
analysis is multiplicative, not additive, and based on necessary conditions (one cannot die
from a procedure not undertaken). But what does this have to do with CPOs? So while
Brady did an admirable study, I fail to see how this is an instance of successfully adjoining
CPOs to DSOs. I hope that being committed to DSOs and quantitative analysis does not
mean that I also must be committed to being a Martian political scientist!
What then can we make of the general argument.
ask, and
then answer why “are CPOs inherently important?”
“We argue that a major part of the answer is found in the empirical and theo-
retical limitations of DSOs. DSOs are especially useful for detecting probabilistic
relationships when there are many observations of comparable units and when
the relevant causal pathways and interactions are well understood or controlled
through randomized experiments. Yet it is often hard, if not impossible, to in-
crease the number of analytically relevant DSOs, and we seldom have adequate
understanding or control of these causal pathways or interactions.
In many situations, only a few similar units or events can be studied. .... In
these situations, adding cases may be impossible (or at least foolhardy) because
new cases will differ fundamentally from the original universe of concern. Fur-
thermore, in some situations, social scientists – and also natural scientists – wish
to explain a singular event .... In these situations, large-N studies may not be
relevant.
The second limitation is even more serious. Increasing the number of DSOs
can provide additional leverage when the major inferential problem is lack of sta-
9
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