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Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design.
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Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments. For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.
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Whilst much more complex to set up and analyze, this design type combats many of the internal validity issues that can plague research. It allows the researcher to exert complete control over the variables and allows the researcher to check that the pretest did not influence the results. Growing up in Jamaica in very humble beginnings, I am sure, helped prepare me for my chosen vocation as an interior designer.
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This is just one of many examples of social scientific experimental research. You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals. For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change. There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).
For instance, the students get to see the types of questions that appear on the test and get to experience the pressure and stress of the test environment. These and other potential factors may cause the students to perform differently the next time they take the test. Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment. This design contains two extra control groups, which serve to reduce the influence of confounding variables and allow the researcher to test whether the pretest itself has an effect on the subjects.
Example of a study that used the Solomon four-group design
Quasi-experimental designs try to approximate experiments but lack a key component, random assignment. For instance, in a nonequivalent 2-group comparative design, cases are divided into two groups, one an experimental group and a comparison group that is meant to be like a control group. In a design known as matching, cases are matched together on multiple variables with the only variation being that of the treatment variable. While it may be possible to match on identified variables, it is difficult to discern whether variables that are not observed are also evenly distributed.
Experimental and control groups
An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels.
Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group.
Therefore, the study will suffer from a generalizability (a.k.a. an external validity) problem. Consider a pretest that contains questions that non-deliberately make participants more aware and concerned regarding the consequences of smoking. Such pretest may sensitize participants to make them more responsive to a smoking cessation intervention.
In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963). The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects, in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression.
