The One-Shot Case Study

The one-shot case study design is shown in figure 4.1e. It is also called the ex post facto design because a single group of people is measured on some dependent variable after an intervention has taken place. This is the most common research design in culture change studies, where it is obviously impossible to manipulate the dependent variable. You arrive in a community and notice that something important has taken place. A clinic or a school has been built. You try to evaluate the experiment by interviewing people (O) and assessing the impact of the intervention (X).

With neither a pretest nor a control group, you can’t be sure that what you observe is the result of some particular intervention. Despite this apparent weakness, however, the intuitive appeal of findings produced by one-shot case studies can be formidable.

In the 1950s, physicians began general use of the Pap Test, a simple office procedure for determining the presence of cervical cancer. Figure 4.2 shows that since 1950, the death rate from cervical cancer in the United States has dropped steadily, from about 18

BOX 4.3


The posttest-only design, with random assignment, is not used as much as I think it should be, despite its elegance and its low cost. In June 2009, a search of PsycINFO turned up 1,110 examples of studies that used the pretest-posttest design, compared to 133 for studies that used the posttest-only design (with or without random assignment). This preference for the classic design is due partly to the appealing-but-mistaken idea that matching participants in experiments on key independent variables (age, ethnicity, etc.) is somehow better than randomly assigning participant to groups, and partly to the nagging suspicion that pretests are essential to the experimental method. That nagging suspicion (that we can do better than trust the outcome of events to randomness) has been the focus of a lot of research since a paper by Gilovich et al. in 1985 titled: ''The hot hand in basketball—on the misperception of random sequences.'' The hot-hand phenomenon—the belief that streaks (in sports and in money management, for example) are the result of nonrandom forces—is hard to break. By the same token, so is the belief that small samples, if drawn randomly, are sufficient to warrant generalizing to a population. On this one, see the 600+ citations to Tversky and Kahneman (1971) and chapters 6 and 7 on representative and nonrepresentative sampling (Further Reading: the posttest-only design).


FIGURE 4.1e.

The one-shot case study design.

per 100,000 women to about 11 in 1970, to about 8.3 in 1980, to about 6.5 in 1995 and to about 2.4 in 2005. If you look only at the data after the intervention (the one-shot case study X O design), you could easily conclude that the intervention (the Pap Test) was the sole cause of this drop in cervical cancer deaths. There is no doubt that the continued decline of cervical cancer deaths is due largely to the early detection provided by the Pap Test, but by 1950, the death rate had already declined by 36% from 28 per 100,000 in 1930 (B. Williams 1978:16).

Never use a design of less logical power when one of greater power is feasible. If pretest data are available, use them. On the other hand, a one-shot case study is often the best you can do. Virtually all ethnography falls in this category, and, as I have said before, nothing beats a good story, well told (Further Reading: case study methods).

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