The previous sections have laid out the basics of some rigorous approaches to establish that an intervention is associated with, or even responsible for, changes we observe in research participants when comparing them to those who have not received the intervention. This type of evidence is extremely important to build an evidence base for social work interventions, but it’s not the only type of evidence to consider. Pre-experimental design is also often a stepping stone for more rigorous experimental design in the future, as it can help test the feasibility of your research. In general, pre-experimental designs do not support causality nor address threats to internal validity. However, that’s not really their intention. Pre-experimental designs are useful when researchers are developing new interventions, testing out new measurement instruments, or want to build toward more rigorous experimental designs.

A significant benefit of these types of designs is that they’re pretty easy to execute in a practice or agency setting. They don’t use comparison or control group, but they do examine outcomes for people who have gone through an intervention or been exposed to a condition.  Below, we will go into some detail about the different types of pre-experimental design.

One group pretest/posttest design

Also known as a before-after one-group design, this type of research design does not have a comparison group; everyone who participates in the research receives the intervention or is exposed to the experimental condition (Figure 14.8). This is a common type of design in program evaluation in the practice world. Controlling for extraneous variables is difficult or impossible in this design, but given that it is still possible to establish some measure of time order, it can begin to provide potential support for causality.

Imagine a researcher who is interested in the effectiveness of an anti-drug education program on elementary school students’ attitudes toward illegal drugs. The researcher could assess students’ attitudes about illegal drugs (O₁), implement the anti-drug program (X), and then immediately after the program ends, the researcher could once again measure students’ attitudes toward illegal drugs (O₂). You can see how this would be relatively simple to do in practice, and you may have been involved in this type of research design yourself, even if informally. But hopefully, you can also see that this design would not provide us with much evidence for causality because we have no way of controlling for the effect of extraneous variables. A lot of things could have affected any change in students’ attitudes—maybe something happened in the community while the program was underway that caused the change or maybe when the students took the pretest they were trying to impress the researchers, but once they got to know them, they felt more comfortable and were more honest about their attitudes.

All of that doesn’t mean these results aren’t useful, however. If we find that children’s attitudes didn’t change at all after the drug education program, then we need to think seriously about how to make it more effective or whether we should be using it at all. (This immediate, practical application of our results highlights a key difference between program evaluation and research, which we will discuss in Chapter 23.)

One group posttest-only design

As the name suggests, this type of pre-experimental design involves measurement only after an intervention. In fact, sometimes it is called the after-only design. As in other pre-experimental designs, there is no comparison or control group; everyone receives the intervention (Figure 14.9).

Because there is no pretest and no comparison group, this design is not useful for supporting causality since we can’t establish time order and we can’t control for extraneous variables. However, that doesn’t mean it’s not useful at all! Sometimes, agencies need to gather information about how their programs are functioning. A classic example of this design is satisfaction surveys—realistically, these can only be administered after a program or intervention. Questions regarding satisfaction, ease of use or engagement, or other questions that don’t involve comparisons are best suited for this type of design.

Conclusion

Pre-experimental research designs are easy to execute in practice, but we must be cautious about drawing causal conclusions from the results. A positive result may still suggest that we should continue using a particular intervention (and no result or a negative result should make us reconsider whether we should use that intervention at all). You will likely see pre-experimental research in your graduate research assistant (GRA) assignments or in the articles you read. Knowing the basics of how to structure such a project, will help you prepare for collaborative research in the future.