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Russel-Research-Method-in-Anthropology

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<strong>Research</strong> Design: Experiments and Experimental Th<strong>in</strong>k<strong>in</strong>g 125<br />

In the 1950s, physicians began general use of the Pap Test, a simple office<br />

procedure for determ<strong>in</strong><strong>in</strong>g the presence of cervical cancer. Figure 5.2 shows<br />

that s<strong>in</strong>ce 1950, the death rate from cervical cancer <strong>in</strong> the United States has<br />

30<br />

20<br />

10<br />

1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br />

Figure 5.2. Death rate from cervical cancer, 1930–1995.<br />

SOURCE: Adapted from B. Williams, A Sampler on Sampl<strong>in</strong>g, figure 2.1, p. 17. Repr<strong>in</strong>ted with permission of<br />

Lucent Technologies Inc./Bell Labs.<br />

dropped steadily, from about 18 per 100,000 women to about 11 <strong>in</strong> 1970, to<br />

about 8.3 <strong>in</strong> 1980, to about 6.5 <strong>in</strong> 1995 and to about 4.6 <strong>in</strong> 2000. If you look<br />

only at the data after the <strong>in</strong>tervention (the one-shot case study X O design),<br />

you could easily conclude that the <strong>in</strong>tervention (the Pap Test) was the sole<br />

cause of this drop <strong>in</strong> cervical cancer deaths. There is no doubt that the cont<strong>in</strong>ued<br />

decl<strong>in</strong>e of cervical cancer deaths is due largely to the early detection provided<br />

by the Pap Test, but by 1950, the death rate had already decl<strong>in</strong>ed by<br />

36% from 28 per 100,000 <strong>in</strong> 1930 (Williams 1978:16).<br />

Never use a design of less logical power when one of greater power is feasible.<br />

If pretest data are available, use them. On the other hand, a one-shot case<br />

study is often the best you can do. Virtually all ethnography falls <strong>in</strong> this category,<br />

and, as I have said before, noth<strong>in</strong>g beats a good story, well told.<br />

The One-Group Pretest-Posttest<br />

The one-group pretest-posttest design is shown <strong>in</strong> figure 5.1f. Some variables<br />

are measured (observed), then the <strong>in</strong>tervention takes place, and then the<br />

variables are measured aga<strong>in</strong>. This takes care of some of the problems associated<br />

with the one-shot case study, but it doesn’t elim<strong>in</strong>ate the threats of history,<br />

test<strong>in</strong>g, maturation, selection, and mortality. Most importantly, if there<br />

is a significant difference <strong>in</strong> the pretest and posttest measurements, we can’t<br />

tell if the <strong>in</strong>tervention made that difference happen.<br />

The one-group pretest-posttest design is commonly used <strong>in</strong> evaluat<strong>in</strong>g tra<strong>in</strong><strong>in</strong>g<br />

programs. The question asked is: Did the people who were exposed to this

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