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The Two-Group Posttest-Only Design without Random Assignment

The two-group posttest-only design without random assignment design is shown in figure 4.1g. This design, also known as the static group comparison, improves on the


FIGURE 4.1g.

Two-group posttest only design without random assignment: Static group comparison design.

one-shot ex post facto design by adding an untreated control group—an independent case that is evaluated only at time 2. The relation between smoking cigarettes (the intervention) and getting lung cancer (the dependent variable) is easily seen by applying the humble ex post facto design with a control group for a second posttest.

In 1965, when the American Cancer Society did its first big Cancer Prevention Study, men who smoked (that is, those who were subject to the intervention) were about 12 times more likely than nonsmokers (the control group) to die of lung cancer. At that time, relatively few women smoked, and those who did had not been smoking very long. Their risk was just 2.7 times that for women nonsmokers of dying from lung cancer.

By 1988, things had changed dramatically. Male smokers were then about 23 times more likely than nonsmokers to die of lung cancer, and female smokers were 12.8 times more likely than female nonsmokers to die of lung cancer. Men’s risk had doubled (from about 12 to about 23), but women’s risk had more than quadrupled (from 2.7 to about 13) (National Cancer Institute 1997). The death rate for lung cancer has continued to fall among men in the United States, while the death rate for women has increased (http://

In true experiments run with the posttest-only design, participants are assigned at random to either the intervention or the control group. In the static group comparison design, the researcher has no control over assignment of participants. This leaves the static-group comparison design open to an unresolvable validity threat. There is no way to tell whether the two groups were comparable at time 1, before the intervention, even with a comparison of observations 1 and 3. Therefore, you can only guess whether the intervention caused any differences in the groups at time 2.

Despite this, the static-group comparison design is useful for evaluating natural experiments, where you have no control over the assignment of participants anyway (Further Reading: static group comparison design) (box 4.4).

BOX 4.4


Lambros Comitas and I wanted to find out if the experience abroad of Greek labor migrants had any influence on men's and women's attitudes toward gender roles when they returned to Greece. The best design would have been to survey a group before they went abroad, then again while they were away, and again when they returned to Greece. Since this was not possible, we studied one group of persons who had been abroad and another group of persons who had never left Greece. We treated these two groups as if they were part of a static-group comparison design (Bernard and Comitas 1978).

From a series of life histories with migrants and nonmigrants, we learned that the custom of giving dowry was under severe stress (Bernard and Ashton- Vouyoucalos 1976). Our survey confirmed this: Those who had worked abroad were far less enthusiastic about providing expensive dowries for their daughters than were those who had never left Greece. We concluded that this was in some measure due to the experiences of migrants in what was then West Germany.

There were threats to the validity of this conclusion: Perhaps migrants were a self-selected bunch of people who held the dowry and other traditional Greek customs in low esteem to begin with. But we had those life histories to back up our conclusion. Surveys are weak compared to true experiments, but their power is improved if they are conceptualized in terms of testing natural experiments and if their results are backed up with data from open-ended interviews.

Interrupted Time Series Design

The interrupted time series design, shown in figure 4.1h, can be very persuasive. It involves making a series of observation before and after an intervention. Figure 4.3 shows


FIGURE 4.1h.

The interrupted time series design.

the rate of alcohol deaths, per 100,000 population, in Russia, from 1956 to 2002 (Pride- more et al. 2007:281). The year 1992 marks the formal shift in Russia to a market economy from a centrally planned economy. As it turns out, homicide and suicide rates show very similar patterns—something that Pridemore et al. interpret as outcomes that are predictable from Durkheim’s (1933 [1893], 1951 [1897]) theory of anomie (Further Reading: interrupted time series).

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