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Russel-Research-Method-in-Anthropology

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122 Chapter 5<br />

behavior. However, don’t let that bother you. Go ahead and shoot your free<br />

throws. (Leith 1988:61)<br />

What a wonderfully simple, utterly diabolic experiment. You can guess the<br />

result: There was a significantly greater probability of chok<strong>in</strong>g if you were<br />

among the groups that got that little pep talk, irrespective of whether they’d<br />

been given the warm-up pretest.<br />

The Two-Group Pretest-Posttest without Random Assignment<br />

Figure 5.1c shows the design for a quasi-experiment—an experiment <strong>in</strong><br />

which participants are not assigned randomly to the control and the experi-<br />

Time 1 Time 2<br />

Assignment Pretest<br />

Intervention Posttest<br />

Group 1 O X O<br />

1 2<br />

Group 2 O O<br />

Figure 5.1c. The classic design without randomization: The quasi-experiment.<br />

3<br />

mental condition. This compromise with design purity is often the best we can<br />

do.<br />

Program evaluation research is usually quasi-experimental. Consider a program<br />

<strong>in</strong> rural Kenya <strong>in</strong> which women farmers are offered <strong>in</strong>struction on apply<strong>in</strong>g<br />

for bank credit to buy fertilizer. The idea is to <strong>in</strong>crease corn production.<br />

You select two villages <strong>in</strong> the district—one that gets the program, and one<br />

that doesn’t. Before the program starts, you measure the amount of credit, on<br />

average, that women <strong>in</strong> each village have applied for <strong>in</strong> the past 12 months. A<br />

year later, you measure aga<strong>in</strong> and f<strong>in</strong>d that, on average, women <strong>in</strong> the program<br />

village have applied for more agricultural credit than have their counterparts<br />

<strong>in</strong> the control village.<br />

Campbell and Boruch (1975) show how this research design leads to problems.<br />

But suppose that the women <strong>in</strong> the program village have, on average,<br />

more land than the women <strong>in</strong> the control village have. Would you (or the<br />

agency you’re work<strong>in</strong>g for) be will<strong>in</strong>g to bet, say, $300,000 on implement<strong>in</strong>g<br />

the program across the district, <strong>in</strong>, say, 30 villages? Would you bet that it was<br />

the <strong>in</strong>tervention and not some confound, like the difference <strong>in</strong> land hold<strong>in</strong>gs,<br />

that caused the difference <strong>in</strong> outcome between the two villages?<br />

The way around this is to assign each woman randomly to one of the two<br />

4

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