experimental research social work

21 13. Experimental design

Chapter outline.

• What is an experiment and when should you use one? (8 minute read)
• True experimental designs (7 minute read)
• Quasi-experimental designs (8 minute read)
• Non-experimental designs (5 minute read)
• Critical, ethical, and critical considerations  (5 minute read)

Content warning : examples in this chapter contain references to non-consensual research in Western history, including experiments conducted during the Holocaust and on African Americans (section 13.6).

13.1 What is an experiment and when should you use one?

Learning objectives.

Learners will be able to…

• Identify the characteristics of a basic experiment
• Describe causality in experimental design
• Discuss the relationship between dependent and independent variables in experiments
• Explain the links between experiments and generalizability of results
• Describe advantages and disadvantages of experimental designs

The basics of experiments

The first experiment I can remember using was for my fourth grade science fair. I wondered if latex- or oil-based paint would hold up to sunlight better. So, I went to the hardware store and got a few small cans of paint and two sets of wooden paint sticks. I painted one with oil-based paint and the other with latex-based paint of different colors and put them in a sunny spot in the back yard. My hypothesis was that the oil-based paint would fade the most and that more fading would happen the longer I left the paint sticks out. (I know, it’s obvious, but I was only 10.)

I checked in on the paint sticks every few days for a month and wrote down my observations. The first part of my hypothesis ended up being wrong—it was actually the latex-based paint that faded the most. But the second part was right, and the paint faded more and more over time. This is a simple example, of course—experiments get a heck of a lot more complex than this when we’re talking about real research.

Merriam-Webster defines an experiment   as “an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.” Each of these three components of the definition will come in handy as we go through the different types of experimental design in this chapter. Most of us probably think of the physical sciences when we think of experiments, and for good reason—these experiments can be pretty flashy! But social science and psychological research follow the same scientific methods, as we’ve discussed in this book.

As the video discusses, experiments can be used in social sciences just like they can in physical sciences. It makes sense to use an experiment when you want to determine the cause of a phenomenon with as much accuracy as possible. Some types of experimental designs do this more precisely than others, as we’ll see throughout the chapter. If you’ll remember back to Chapter 11  and the discussion of validity, experiments are the best way to ensure internal validity, or the extent to which a change in your independent variable causes a change in your dependent variable.

Experimental designs for research projects are most appropriate when trying to uncover or test a hypothesis about the cause of a phenomenon, so they are best for explanatory research questions. As we’ll learn throughout this chapter, different circumstances are appropriate for different types of experimental designs. Each type of experimental design has advantages and disadvantages, and some are better at controlling the effect of extraneous variables —those variables and characteristics that have an effect on your dependent variable, but aren’t the primary variable whose influence you’re interested in testing. For example, in a study that tries to determine whether aspirin lowers a person’s risk of a fatal heart attack, a person’s race would likely be an extraneous variable because you primarily want to know the effect of aspirin.

In practice, many types of experimental designs can be logistically challenging and resource-intensive. As practitioners, the likelihood that we will be involved in some of the types of experimental designs discussed in this chapter is fairly low. However, it’s important to learn about these methods, even if we might not ever use them, so that we can be thoughtful consumers of research that uses experimental designs.

While we might not use all of these types of experimental designs, many of us will engage in evidence-based practice during our time as social workers. A lot of research developing evidence-based practice, which has a strong emphasis on generalizability, will use experimental designs. You’ve undoubtedly seen one or two in your literature search so far.

The logic of experimental design

How do we know that one phenomenon causes another? The complexity of the social world in which we practice and conduct research means that causes of social problems are rarely cut and dry. Uncovering explanations for social problems is key to helping clients address them, and experimental research designs are one road to finding answers.

As you read about in Chapter 8 (and as we’ll discuss again in Chapter 15 ), just because two phenomena are related in some way doesn’t mean that one causes the other. Ice cream sales increase in the summer, and so does the rate of violent crime; does that mean that eating ice cream is going to make me murder someone? Obviously not, because ice cream is great. The reality of that relationship is far more complex—it could be that hot weather makes people more irritable and, at times, violent, while also making people want ice cream. More likely, though, there are other social factors not accounted for in the way we just described this relationship.

Experimental designs can help clear up at least some of this fog by allowing researchers to isolate the effect of interventions on dependent variables by controlling extraneous variables . In true experimental design (discussed in the next section) and some quasi-experimental designs, researchers accomplish this w ith the control group and the experimental group . (The experimental group is sometimes called the “treatment group,” but we will call it the experimental group in this chapter.) The control group does not receive the intervention you are testing (they may receive no intervention or what is known as “treatment as usual”), while the experimental group does. (You will hopefully remember our earlier discussion of control variables in Chapter 8 —conceptually, the use of the word “control” here is the same.)

In a well-designed experiment, your control group should look almost identical to your experimental group in terms of demographics and other relevant factors. What if we want to know the effect of CBT on social anxiety, but we have learned in prior research that men tend to have a more difficult time overcoming social anxiety? We would want our control and experimental groups to have a similar gender mix because it would limit the effect of gender on our results, since ostensibly, both groups’ results would be affected by gender in the same way. If your control group has 5 women, 6 men, and 4 non-binary people, then your experimental group should be made up of roughly the same gender balance to help control for the influence of gender on the outcome of your intervention. (In reality, the groups should be similar along other dimensions, as well, and your group will likely be much larger.) The researcher will use the same outcome measures for both groups and compare them, and assuming the experiment was designed correctly, get a pretty good answer about whether the intervention had an effect on social anxiety.

You will also hear people talk about comparison groups , which are similar to control groups. The primary difference between the two is that a control group is populated using random assignment, but a comparison group is not. Random assignment entails using a random process to decide which participants are put into the control or experimental group (which participants receive an intervention and which do not). By randomly assigning participants to a group, you can reduce the effect of extraneous variables on your research because there won’t be a systematic difference between the groups.

Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other related fields. Random sampling also helps a great deal with generalizability , whereas random assignment increases internal validity .

We have already learned about internal validity in Chapter 11 . The use of an experimental design will bolster internal validity since it works to isolate causal relationships. As we will see in the coming sections, some types of experimental design do this more effectively than others. It’s also worth considering that true experiments, which most effectively show causality , are often difficult and expensive to implement. Although other experimental designs aren’t perfect, they still produce useful, valid evidence and may be more feasible to carry out.

Key Takeaways

• Experimental designs are useful for establishing causality, but some types of experimental design do this better than others.
• Experiments help researchers isolate the effect of the independent variable on the dependent variable by controlling for the effect of extraneous variables .
• Experiments use a control/comparison group and an experimental group to test the effects of interventions. These groups should be as similar to each other as possible in terms of demographics and other relevant factors.
• True experiments have control groups with randomly assigned participants, while other types of experiments have comparison groups to which participants are not randomly assigned.
• Think about the research project you’ve been designing so far. How might you use a basic experiment to answer your question? If your question isn’t explanatory, try to formulate a new explanatory question and consider the usefulness of an experiment.
• Why is establishing a simple relationship between two variables not indicative of one causing the other?

13.2 True experimental design

• Describe a true experimental design in social work research
• Understand the different types of true experimental designs
• Determine what kinds of research questions true experimental designs are suited for
• Discuss advantages and disadvantages of true experimental designs

True experimental design , often considered to be the “gold standard” in research designs, is thought of as one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity and its ability to establish ( causality ) through treatment manipulation, while controlling for the effects of extraneous variable. Sometimes the treatment level is no treatment, while other times it is simply a different treatment than that which we are trying to evaluate. For example, we might have a control group that is made up of people who will not receive any treatment for a particular condition. Or, a control group could consist of people who consent to treatment with DBT when we are testing the effectiveness of CBT.

As we discussed in the previous section, a true experiment has a control group with participants randomly assigned , and an experimental group . This is the most basic element of a true experiment. The next decision a researcher must make is when they need to gather data during their experiment. Do they take a baseline measurement and then a measurement after treatment, or just a measurement after treatment, or do they handle measurement another way? Below, we’ll discuss the three main types of true experimental designs. There are sub-types of each of these designs, but here, we just want to get you started with some of the basics.

Using a true experiment in social work research is often pretty difficult, since as I mentioned earlier, true experiments can be quite resource intensive. True experiments work best with relatively large sample sizes, and random assignment, a key criterion for a true experimental design, is hard (and unethical) to execute in practice when you have people in dire need of an intervention. Nonetheless, some of the strongest evidence bases are built on true experiments.

For the purposes of this section, let’s bring back the example of CBT for the treatment of social anxiety. We have a group of 500 individuals who have agreed to participate in our study, and we have randomly assigned them to the control and experimental groups. The folks in the experimental group will receive CBT, while the folks in the control group will receive more unstructured, basic talk therapy. These designs, as we talked about above, are best suited for explanatory research questions.

Before we get started, take a look at the table below. When explaining experimental research designs, we often use diagrams with abbreviations to visually represent the experiment. Table 13.1 starts us off by laying out what each of the abbreviations mean.

Pretest and post-test control group design

In pretest and post-test control group design , participants are given a pretest of some kind to measure their baseline state before their participation in an intervention. In our social anxiety experiment, we would have participants in both the experimental and control groups complete some measure of social anxiety—most likely an established scale and/or a structured interview—before they start their treatment. As part of the experiment, we would have a defined time period during which the treatment would take place (let’s say 12 weeks, just for illustration). At the end of 12 weeks, we would give both groups the same measure as a post-test .

In the diagram, RA (random assignment group A) is the experimental group and RB is the control group. O 1 denotes the pre-test, X e denotes the experimental intervention, and O 2 denotes the post-test. Let’s look at this diagram another way, using the example of CBT for social anxiety that we’ve been talking about.

In a situation where the control group received treatment as usual instead of no intervention, the diagram would look this way, with X i denoting treatment as usual (Figure 13.3).

Hopefully, these diagrams provide you a visualization of how this type of experiment establishes time order , a key component of a causal relationship. Did the change occur after the intervention? Assuming there is a change in the scores between the pretest and post-test, we would be able to say that yes, the change did occur after the intervention. Causality can’t exist if the change happened before the intervention—this would mean that something else led to the change, not our intervention.

Post-test only control group design

Post-test only control group design involves only giving participants a post-test, just like it sounds (Figure 13.4).

But why would you use this design instead of using a pretest/post-test design? One reason could be the testing effect that can happen when research participants take a pretest. In research, the testing effect refers to “measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself” (Engel & Schutt, 2017, p. 444) [1] (When we say “measurement error,” all we mean is the accuracy of the way we measure the dependent variable.) Figure 13.4 is a visualization of this type of experiment. The testing effect isn’t always bad in practice—our initial assessments might help clients identify or put into words feelings or experiences they are having when they haven’t been able to do that before. In research, however, we might want to control its effects to isolate a cleaner causal relationship between intervention and outcome.

Going back to our CBT for social anxiety example, we might be concerned that participants would learn about social anxiety symptoms by virtue of taking a pretest. They might then identify that they have those symptoms on the post-test, even though they are not new symptoms for them. That could make our intervention look less effective than it actually is.

However, without a baseline measurement establishing causality can be more difficult. If we don’t know someone’s state of mind before our intervention, how do we know our intervention did anything at all? Establishing time order is thus a little more difficult. You must balance this consideration with the benefits of this type of design.

Solomon four group design

One way we can possibly measure how much the testing effect might change the results of the experiment is with the Solomon four group design. Basically, as part of this experiment, you have two control groups and two experimental groups. The first pair of groups receives both a pretest and a post-test. The other pair of groups receives only a post-test (Figure 13.5). This design helps address the problem of establishing time order in post-test only control group designs.

For our CBT project, we would randomly assign people to four different groups instead of just two. Groups A and B would take our pretest measures and our post-test measures, and groups C and D would take only our post-test measures. We could then compare the results among these groups and see if they’re significantly different between the folks in A and B, and C and D. If they are, we may have identified some kind of testing effect, which enables us to put our results into full context. We don’t want to draw a strong causal conclusion about our intervention when we have major concerns about testing effects without trying to determine the extent of those effects.

Solomon four group designs are less common in social work research, primarily because of the logistics and resource needs involved. Nonetheless, this is an important experimental design to consider when we want to address major concerns about testing effects.

• True experimental design is best suited for explanatory research questions.
• True experiments require random assignment of participants to control and experimental groups.
• Pretest/post-test research design involves two points of measurement—one pre-intervention and one post-intervention.
• Post-test only research design involves only one point of measurement—post-intervention. It is a useful design to minimize the effect of testing effects on our results.
• Solomon four group research design involves both of the above types of designs, using 2 pairs of control and experimental groups. One group receives both a pretest and a post-test, while the other receives only a post-test. This can help uncover the influence of testing effects.
• Think about a true experiment you might conduct for your research project. Which design would be best for your research, and why?
• What challenges or limitations might make it unrealistic (or at least very complicated!) for you to carry your true experimental design in the real-world as a student researcher?
• What hypothesis(es) would you test using this true experiment?

13.4 Quasi-experimental designs

• Describe a quasi-experimental design in social work research
• Understand the different types of quasi-experimental designs
• Determine what kinds of research questions quasi-experimental designs are suited for
• Discuss advantages and disadvantages of quasi-experimental designs

Quasi-experimental designs are a lot more common in social work research than true experimental designs. Although quasi-experiments don’t do as good a job of giving us robust proof of causality , they still allow us to establish time order , which is a key element of causality. The prefix quasi means “resembling,” so quasi-experimental research is research that resembles experimental research, but is not true experimental research. Nonetheless, given proper research design, quasi-experiments can still provide extremely rigorous and useful results.

There are a few key differences between true experimental and quasi-experimental research. The primary difference between quasi-experimental research and true experimental research is that quasi-experimental research does not involve random assignment to control and experimental groups. Instead, we talk about comparison groups in quasi-experimental research instead. As a result, these types of experiments don’t control the effect of extraneous variables as well as a true experiment.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention.  We’re able to eliminate some threats to internal validity, but we can’t do this as effectively as we can with a true experiment.  Realistically, our CBT-social anxiety project is likely to be a quasi experiment, based on the resources and participant pool we’re likely to have available. 

It’s important to note that not all quasi-experimental designs have a comparison group.  There are many different kinds of quasi-experiments, but we will discuss the three main types below: nonequivalent comparison group designs, time series designs, and ex post facto comparison group designs.

Nonequivalent comparison group design

You will notice that this type of design looks extremely similar to the pretest/post-test design that we discussed in section 13.3. But instead of random assignment to control and experimental groups, researchers use other methods to construct their comparison and experimental groups. A diagram of this design will also look very similar to pretest/post-test design, but you’ll notice we’ve removed the “R” from our groups, since they are not randomly assigned (Figure 13.6).

Researchers using this design select a comparison group that’s as close as possible based on relevant factors to their experimental group. Engel and Schutt (2017) [2] identify two different selection methods:

• Individual matching : Researchers take the time to match individual cases in the experimental group to similar cases in the comparison group. It can be difficult, however, to match participants on all the variables you want to control for.
• Aggregate matching : Instead of trying to match individual participants to each other, researchers try to match the population profile of the comparison and experimental groups. For example, researchers would try to match the groups on average age, gender balance, or median income. This is a less resource-intensive matching method, but researchers have to ensure that participants aren’t choosing which group (comparison or experimental) they are a part of.

As we’ve already talked about, this kind of design provides weaker evidence that the intervention itself leads to a change in outcome. Nonetheless, we are still able to establish time order using this method, and can thereby show an association between the intervention and the outcome. Like true experimental designs, this type of quasi-experimental design is useful for explanatory research questions.

What might this look like in a practice setting? Let’s say you’re working at an agency that provides CBT and other types of interventions, and you have identified a group of clients who are seeking help for social anxiety, as in our earlier example. Once you’ve obtained consent from your clients, you can create a comparison group using one of the matching methods we just discussed. If the group is small, you might match using individual matching, but if it’s larger, you’ll probably sort people by demographics to try to get similar population profiles. (You can do aggregate matching more easily when your agency has some kind of electronic records or database, but it’s still possible to do manually.)

Time series design

Another type of quasi-experimental design is a time series design. Unlike other types of experimental design, time series designs do not have a comparison group. A time series is a set of measurements taken at intervals over a period of time (Figure 13.7). Proper time series design should include at least three pre- and post-intervention measurement points. While there are a few types of time series designs, we’re going to focus on the most common: interrupted time series design.

But why use this method? Here’s an example. Let’s think about elementary student behavior throughout the school year. As anyone with children or who is a teacher knows, kids get very excited and animated around holidays, days off, or even just on a Friday afternoon. This fact might mean that around those times of year, there are more reports of disruptive behavior in classrooms. What if we took our one and only measurement in mid-December? It’s possible we’d see a higher-than-average rate of disruptive behavior reports, which could bias our results if our next measurement is around a time of year students are in a different, less excitable frame of mind. When we take multiple measurements throughout the first half of the school year, we can establish a more accurate baseline for the rate of these reports by looking at the trend over time.

We may want to test the effect of extended recess times in elementary school on reports of disruptive behavior in classrooms. When students come back after the winter break, the school extends recess by 10 minutes each day (the intervention), and the researchers start tracking the monthly reports of disruptive behavior again. These reports could be subject to the same fluctuations as the pre-intervention reports, and so we once again take multiple measurements over time to try to control for those fluctuations.

This method improves the extent to which we can establish causality because we are accounting for a major extraneous variable in the equation—the passage of time. On its own, it does not allow us to account for other extraneous variables, but it does establish time order and association between the intervention and the trend in reports of disruptive behavior. Finding a stable condition before the treatment that changes after the treatment is evidence for causality between treatment and outcome.

Ex post facto comparison group design

Ex post facto (Latin for “after the fact”) designs are extremely similar to nonequivalent comparison group designs. There are still comparison and experimental groups, pretest and post-test measurements, and an intervention. But in ex post facto designs, participants are assigned to the comparison and experimental groups once the intervention has already happened. This type of design often occurs when interventions are already up and running at an agency and the agency wants to assess effectiveness based on people who have already completed treatment.

In most clinical agency environments, social workers conduct both initial and exit assessments, so there are usually some kind of pretest and post-test measures available. We also typically collect demographic information about our clients, which could allow us to try to use some kind of matching to construct comparison and experimental groups.

In terms of internal validity and establishing causality, ex post facto designs are a bit of a mixed bag. The ability to establish causality depends partially on the ability to construct comparison and experimental groups that are demographically similar so we can control for these extraneous variables .

Quasi-experimental designs are common in social work intervention research because, when designed correctly, they balance the intense resource needs of true experiments with the realities of research in practice. They still offer researchers tools to gather robust evidence about whether interventions are having positive effects for clients.

• Quasi-experimental designs are similar to true experiments, but do not require random assignment to experimental and control groups.
• In quasi-experimental projects, the group not receiving the treatment is called the comparison group, not the control group.
• Nonequivalent comparison group design is nearly identical to pretest/post-test experimental design, but participants are not randomly assigned to the experimental and control groups. As a result, this design provides slightly less robust evidence for causality.
• Nonequivalent groups can be constructed by individual matching or aggregate matching .
• Time series design does not have a control or experimental group, and instead compares the condition of participants before and after the intervention by measuring relevant factors at multiple points in time. This allows researchers to mitigate the error introduced by the passage of time.
• Ex post facto comparison group designs are also similar to true experiments, but experimental and comparison groups are constructed after the intervention is over. This makes it more difficult to control for the effect of extraneous variables, but still provides useful evidence for causality because it maintains the time order[ /pb_glossary] of the experiment.
• Think back to the experiment you considered for your research project in Section 13.3. Now that you know more about quasi-experimental designs, do you still think it's a true experiment? Why or why not?
• What should you consider when deciding whether an experimental or quasi-experimental design would be more feasible or fit your research question better?

13.5 Non-experimental designs

Learners will be able to...

• Describe non-experimental designs in social work research
• Discuss how non-experimental research differs from true and quasi-experimental research
• Demonstrate an understanding the different types of non-experimental designs
• Determine what kinds of research questions non-experimental designs are suited for
• Discuss advantages and disadvantages of non-experimental designs

The previous sections have laid out the basics of some rigorous approaches to establish that an intervention is responsible for changes we observe in research participants. 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. We will discuss qualitative methods, which provide us with rich, contextual information, in Part 4 of this text. The designs we'll talk about in this section are sometimes used in [pb_glossary id="851"] qualitative research, but in keeping with our discussion of experimental design so far, we're going to stay in the quantitative research realm for now. Non-experimental 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, non-experimental designs do not strongly support causality and don't address threats to internal validity. However, that's not really what they're intended for. Non-experimental designs are useful for a few different types of research, including explanatory questions in program evaluation. Certain types of non-experimental design are also helpful for researchers when they are trying to develop a new assessment or scale. Other times, researchers or agency staff did not get a chance to gather any assessment information before an intervention began, so a pretest/post-test design is not possible.

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 require a comparison or control group, and as Engel and Schutt (2017) [3] point out, they "flow from a typical practice model of assessment, intervention, and evaluating the impact of the intervention" (p. 177). Thus, these designs are fairly intuitive for social workers, even when they aren't expert researchers. Below, we will go into some detail about the different types of non-experimental design.

One group pretest/post-test design

Also known as a before-after one-group design, this type of research design does not have a comparison group and everyone who participates in the research receives the intervention (Figure 13.8). 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 does provide weak support for causality.

Imagine, for example, 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 (O 1 ), implement the anti-drug program (X), and then immediately after the program ends, the researcher could once again measure students’ attitudes toward illegal drugs (O 2 ). You can see how this would be relatively simple to do in practice, and have probably 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 girls already had different attitudes about illegal drugs than children of other genders, and when we look at the class's results as a whole, we couldn't account for that influence using this design.

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 .)

After-only design

As the name suggests, this type of non-experimental design involves measurement only after an intervention. There is no comparison or control group, and everyone receives the intervention. I have seen this design repeatedly in my time as a program evaluation consultant for nonprofit organizations, because often these organizations realize too late that they would like to or need to have some sort of measure of what effect their programs are having.

Because there is no pretest and no comparison group, this design is not useful for supporting causality since we can't establish the 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.

Static-group design

A final type of non-experimental research is the static-group design. In this type of research, there are both comparison and experimental groups, which are not randomly assigned. There is no pretest, only a post-test, and the comparison group has to be constructed by the researcher. Sometimes, researchers will use matching techniques to construct the groups, but often, the groups are constructed by convenience of who is being served at the agency.

Non-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 have likely seen non-experimental research in your daily life or at your agency, and knowing the basics of how to structure such a project will help you ensure you are providing clients with the best care possible.

• Non-experimental designs are useful for describing phenomena, but cannot demonstrate causality.
• After-only designs are often used in agency and practice settings because practitioners are often not able to set up pre-test/post-test designs.
• Non-experimental designs are useful for explanatory questions in program evaluation and are helpful for researchers when they are trying to develop a new assessment or scale.
• Non-experimental designs are well-suited to qualitative methods.
• If you were to use a non-experimental design for your research project, which would you choose? Why?
• Have you conducted non-experimental research in your practice or professional life? Which type of non-experimental design was it?

13.6 Critical, ethical, and cultural considerations

• Describe critiques of experimental design
• Identify ethical issues in the design and execution of experiments
• Identify cultural considerations in experimental design

As I said at the outset, experiments, and especially true experiments, have long been seen as the gold standard to gather scientific evidence. When it comes to research in the biomedical field and other physical sciences, true experiments are subject to far less nuance than experiments in the social world. This doesn't mean they are easier—just subject to different forces. However, as a society, we have placed the most value on quantitative evidence obtained through empirical observation and especially experimentation.

Major critiques of experimental designs tend to focus on true experiments, especially randomized controlled trials (RCTs), but many of these critiques can be applied to quasi-experimental designs, too. Some researchers, even in the biomedical sciences, question the view that RCTs are inherently superior to other types of quantitative research designs. RCTs are far less flexible and have much more stringent requirements than other types of research. One seemingly small issue, like incorrect information about a research participant, can derail an entire RCT. RCTs also cost a great deal of money to implement and don't reflect “real world” conditions. The cost of true experimental research or RCTs also means that some communities are unlikely to ever have access to these research methods. It is then easy for people to dismiss their research findings because their methods are seen as "not rigorous."

Obviously, controlling outside influences is important for researchers to draw strong conclusions, but what if those outside influences are actually important for how an intervention works? Are we missing really important information by focusing solely on control in our research? Is a treatment going to work the same for white women as it does for indigenous women? With the myriad effects of our societal structures, you should be very careful ever assuming this will be the case. This doesn't mean that cultural differences will negate the effect of an intervention; instead, it means that you should remember to practice cultural humility implementing all interventions, even when we "know" they work.

How we build evidence through experimental research reveals a lot about our values and biases, and historically, much experimental research has been conducted on white people, and especially white men. [4] This makes sense when we consider the extent to which the sciences and academia have historically been dominated by white patriarchy. This is especially important for marginalized groups that have long been ignored in research literature, meaning they have also been ignored in the development of interventions and treatments that are accepted as "effective." There are examples of marginalized groups being experimented on without their consent, like the Tuskegee Experiment or Nazi experiments on Jewish people during World War II. We cannot ignore the collective consciousness situations like this can create about experimental research for marginalized groups.

None of this is to say that experimental research is inherently bad or that you shouldn't use it. Quite the opposite—use it when you can, because there are a lot of benefits, as we learned throughout this chapter. As a social work researcher, you are uniquely positioned to conduct experimental research while applying social work values and ethics to the process and be a leader for others to conduct research in the same framework. It can conflict with our professional ethics, especially respect for persons and beneficence, if we do not engage in experimental research with our eyes wide open. We also have the benefit of a great deal of practice knowledge that researchers in other fields have not had the opportunity to get. As with all your research, always be sure you are fully exploring the limitations of the research.

• While true experimental research gathers strong evidence, it can also be inflexible, expensive, and overly simplistic in terms of important social forces that affect the resources.
• Marginalized communities' past experiences with experimental research can affect how they respond to research participation.
• Social work researchers should use both their values and ethics, and their practice experiences, to inform research and push other researchers to do the same.
• Think back to the true experiment you sketched out in the exercises for Section 13.3. Are there cultural or historical considerations you hadn't thought of with your participant group? What are they? Does this change the type of experiment you would want to do?
• How can you as a social work researcher encourage researchers in other fields to consider social work ethics and values in their experimental research?
• Engel, R. & Schutt, R. (2016). The practice of research in social work. Thousand Oaks, CA: SAGE Publications, Inc. ↵
• Sullivan, G. M. (2011). Getting off the “gold standard”: Randomized controlled trials and education research. Journal of Graduate Medical Education ,  3 (3), 285-289. ↵

an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.

explains why particular phenomena work in the way that they do; answers “why” questions

variables and characteristics that have an effect on your outcome, but aren't the primary variable whose influence you're interested in testing.

the group of participants in our study who do not receive the intervention we are researching in experiments with random assignment

in experimental design, the group of participants in our study who do receive the intervention we are researching

the group of participants in our study who do not receive the intervention we are researching in experiments without random assignment

using a random process to decide which participants are tested in which conditions

The ability to apply research findings beyond the study sample to some broader population,

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief

An experimental design in which one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed

a type of experimental design in which participants are randomly assigned to control and experimental groups, one group receives an intervention, and both groups receive pre- and post-test assessments

A measure of a participant's condition before they receive an intervention or treatment.

A measure of a participant's condition after an intervention or, if they are part of the control/comparison group, at the end of an experiment.

A demonstration that a change occurred after an intervention. An important criterion for establishing causality.

an experimental design in which participants are randomly assigned to control and treatment groups, one group receives an intervention, and both groups receive only a post-test assessment

The measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself

a subtype of experimental design that is similar to a true experiment, but does not have randomly assigned control and treatment groups

In nonequivalent comparison group designs, the process by which researchers match individual cases in the experimental group to similar cases in the comparison group.

In nonequivalent comparison group designs, the process in which researchers match the population profile of the comparison and experimental groups.

a set of measurements taken at intervals over a period of time

Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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In This Article Expand or collapse the "in this article" section Experimental and Quasi-Experimental Designs

Introduction, introductory works.

• Manuals and Guides
• History of Experimental Design
• Appraising Experiments
• Statistical Principles and Analysis
• Cluster-Based Experiments
• Ethical Considerations
• Reporting Experiments
• Debate on Experimental Design

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• Community-Based Participatory Research
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Experimental and Quasi-Experimental Designs by Matthew Morton , Paul Montgomery LAST REVIEWED: 29 June 2011 LAST MODIFIED: 29 June 2011 DOI: 10.1093/obo/9780195389678-0053

In strengthening social work’s ability to improve lives and communities, experimental design can play a critical role in helping stakeholders better understand what works in achieving positive impacts. Experimental design studies aim to test whether a specific “intervention” (or “treatment”) causes change in specific outcomes. Experiments test for this cause-and-effect relationship by exposing a group of research participants to the intervention and observing for any differences in changes of behavior between the intervention group and another group that does not receive the intervention. The group that does not receive the intervention is typically called a “control” or “comparison” group. Notably some literature reserves the term “experimental design” for studies in which participants are randomly assigned to intervention or control groups. Other literature, however, defines the term more broadly to include what some would classify as “quasi-experimental” or “nonrandomized” trials in which an intervention is applied to one group in order to detect changes but assignment to groups occurs through a method of selection other than randomization. This bibliography will consider experimental design in the broader context of both randomized and nonrandomized trials, but it will also supply references that clarify the special ability of randomized controlled trials to reduce bias and strengthen the credibility of experimental findings that guarantee causality. The field of experimental design includes considerable diversity with respect to specific methods, applications, and perspectives. This bibliography aims to organize some of the foremost texts and papers concerning experimental design to provide readers with (a) useful introductions to experimental design and basic principles, (b) practical references for specific audiences or topics of interest, and (c) a rounded tour of the views and debates surrounding experimental design.

This section presents texts and papers that aim to introduce the purpose and principles of experimental design to a wider audience. Chalmers 2003 provides a good first read that articulates cause for the evidence-based practice movement from which experimental design has gained increasing momentum. Rubin and Babbie 2008 , particularly chapter 10, offers an introduction to experimental design and critical concepts with the intention of reaching a social work student audience. Baker 2000 provides similar material applied for use by developmental impact researchers. Eccles, et al. 2003 and Kendall 2003 , though geared toward a health care readership, provide useful summaries of key concepts in experimental design for unfamiliar readers. Oakley, et al. 2003 , Rosen, et al. 2006 , and Sibbald and Roland 1998 articulate nontechnical cases for general audiences for the applicability and value of experimental design. For the advanced student, Kirk 2003 is a most useful text, as it provides a more sophisticated presentation of the topic area.

Baker, Judy L. 2000. Evaluating the impact of development projects on poverty: A handbook for practitioners . Washington, DC: World Bank.

Available free online, this handbook offers a user-friendly overview of the impact of evaluation issues and approaches in which experimental design is often situated. Different types of experimental and quasi-experimental designs are discussed.

Chalmers, Iain. 2003. Trying to do more good than harm in policy and practice: The role of rigorous, transparent, up-to-date evaluations. Annals of the American Academy of Political and Social Science 589.1: 22–40.

DOI: 10.1177/0002716203254762

Chalmers articulates a case for increasing the development and use of rigorous, transparent, and up-to-date experimental designs to improve the processes by which we make decisions about whether and how to intervene in the lives of others. He further argues for systematically reviewing the state of research on a given topic prior to initiating new trials.

Eccles, Martin, Jeremy Grimshaw, Marion Campbell, and Craig Ramsay. 2003. Research designs for studies evaluating the effectiveness of change and improvement strategies. Quality and Safety in Health Care 12.1: 47–52.

DOI: 10.1136/qhc.12.1.47

This article briefly surveys different kinds of experimental designs for evaluation of more complex, behavioral interventions and in doing so introduces readers to key concepts and terms.

Kendall, Jonathan M. 2003. Designing a research project: Randomised controlled trials and their principles. Emergency Medicine Journal 20.2: 164–168.

DOI: 10.1136/emj.20.2.164

This article provides a basic, nontechnical summary introduction to the features and applicability of randomized designs for an unfamiliar audience.

Kirk, Roger E. 2003. Experimental design. In Handbook of psychology , Vol. 2, Research methods in psychology . Edited by John A. Schinka, and Wayne F. Velicer, 3–32. Hoboken, NJ: Wiley.

Though brief, this overview introduces readers to more complex categories of experimental design (for example, hierarchical designs in which multiple treatments are nested within each other) relevant to readers interested in a more advanced introduction to approaches. Kirk characterizes experimental design by random assignment of participants.

Oakley, Ann, Vicki Strange, Tami Toroyan, Meg Wiggins, Ian Roberts, and Judith Stephenson. 2003. Using random allocation to evaluate social interventions: Three recent U.K. examples. Annals of the American Academy of Political and Social Science 589.1: 170–189.

DOI: 10.1177/0002716203254765

Oakley and colleagues argue for the applicability of robust experimental design to social interventions as it has been popularly used in health and medicine. The paper provides three examples of randomized controlled trials with social interventions in the United Kingdom to illustrate strategies for conducting successful experimental trials.

Rosen, Laura, Orly Manor, Dan Engelhard, and David Zucker. 2006. In defense of the randomized controlled trial for health promotion research. American Journal of Public Health 96.7: 1181–1186.

DOI: 10.2105/AJPH.2004.061713

This paper discusses the value of experimental design in evaluating health promotion interventions and responds to common criticisms of experimental design with suggestions for tailoring strategies and approaches to meet different conditions rather than abandoning experimental design altogether.

Rubin, Allen, and Earl R. Babbie. 2008. Research methods for social work . 6th ed. Belmont, CA: Thomson Brooks Cole.

This textbook, which can serve as a general textbook for graduate and upper-level undergraduate social work students on research methods, dedicates chapter 10 to experimental design, which could be used as an introductory read to the topic. Unique to this edition from previous versions, the authors make explicit links to the material throughout the book to the evidence-based practice movement.

Sibbald, Bonnie, and Martin Roland. 1998. Understanding controlled trials: Why are randomised controlled trials important? British Medical Journal 316.7126: 201.

This brief note discusses the features of experimental design that make it useful and authoritative for evaluating intervention impacts.

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Experimental research designs in social work.

Theory and Applications

Bruce A. Thyer

Columbia University Press

Pub Date: August 2023

ISBN: 9780231201179

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I wish I had this book years ago! This comprehensive and beautifully written volume will be useful to both aspiring and seasoned experimental researchers. Experimental Research Designs in Social Work will be a work I reach to again and again as I plan and execute my next experimental trial. Joseph Himle, University of Michigan

Bruce Thyer has been a long-term advocate for clients receiving careful appraisals of potential service outcomes. Experimental Research Designs in Social Work is an important contribution for its extensive account of social work experiments and an up-to-date description of how to discover opportunities to evaluate practices and policies, adding to our knowledge about outcomes. Eileen Gambrill, University of California Berkeley

Thyer skillfully presents the key principles and importance of experimental designs to social work. Easy to follow yet intellectually invigorating, this book seamlessly integrates theory, practice, and research methods. Harold Briggs, University of Georgia

Outcome studies are essential to know whether or not an intervention is effective. This text—written by one of the foremost experts in experimental research—is an invaluable resource for anyone interested in designing, conducting, or evaluating intervention research. David R. Hodge, Arizona State University

Thyer has helped to take the “experimenting” out of teaching experimental research design. This text provides a clear blueprint for teaching and learning about experimental research design. I trust that this book will be an invaluable resource for social work scholars for many years to come. Javonda Williams, University of Tennessee, Knoxville

Social work students will greatly benefit from this excellent book....The text provides a clear integration of theory, practice, and research methods. Journal of Human Behavior in the Social Environment

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Experimental Research Design

Ojmarrh Mitchell

Experimental research design is centrally concerned with constructing research that is high in causal (internal) validity. Randomized experimental designs provide the highest levels of causal validity. Quasi-experimental designs have a number of potential threats to their causal validity. Yet, new quasi-experimental designs adopted from fields outside of criminology offer levels of causal validity that rival experimental designs.

The design of research is fraught with complicated and crucial decisions. Researchers must decide which research questions to address, which theoretical perspective will guide the research, how to measure key constructs reliably and accurately, who or what to sample and observe, how many people/places/things need to be sampled in order to achieve adequate statistical power, and which data analytic techniques will be employed. These issues are germane to research of all types (exploratory, explanatory, descriptive, evaluation research). However, the term “research design” typically does not refer to the issues discussed above.

The term “experimental research design” is centrally concerned with constructing research that is high in causal (or internal) validity. Causal validity concerns the accuracy of statements regarding cause and effect relationships. For example, does variable 1 cause variation in variable 2? Or does variable 2 cause variation in variable 1? Or does variable 3 cause variation in both variables 1 and 2? And what is the magnitude of the causal relationships among the variables? Thus, research design as used herein is a concern of explanatory and evaluation research but generally does not apply to exploratory or descriptive research.

The importance of making causal inferences in criminology is hard to overstate. A central issue in many criminological debates concerns whether correlates of offending are causally related to offending. The correlates of offending are well known: bad parenting, deviant friends, prior delinquency behavior, youthful age (i.e., adolescents and young adults), being male, deviant attitudes, personality traits such as impulsivity and psychopathy, and so forth. Criminologists largely agree on these correlates of offending. Yet, “correlation does not imply causation.” The field of criminology is filled with debates about which of these relationships are causal in nature. Perhaps the best known of these debates focuses on association between deviant peers on offending. Social learning theorists assert that having numerous, close relationships with those involved in deviance causes one's own level of deviance to increase. On the other hand, social control theorists argue that this relationship is noncausal; instead, the positive relationship between having deviant peers is the result of “homophily” (the tendency of individuals to associate with similar others) – “birds of a feather flock together.” Likewise, there is disagreement over whether the relationship between prior offending and future offending is causal. Theorists such as Gottfredson and Hirschi ( 1990 ) argue that this relationship is spurious, as both prior and future offending are caused by low self-control. Other theories, such as Sampson and Laub's ( 1993 ) age-graded theory of informal social control, assert that involvement in crime and contact with the criminal justice system increase the likelihood of future offending because these experiences diminish bonding to important sources of informal social control (e.g., marriage, employment).

Debates concerning causal inference are not confined to theory. The effectiveness, or causal effect, of many criminal justice – based interventions on measures of offending are hotly debated. Evaluations of criminal justice interventions (e.g., reentry programs, drug court, and domestic violence programs) often find that program participants have less recidivism than nonparticipants. Yet, most evaluations have difficulty proving that the observed differences were actually caused by program participation.

Simply put, research design is a central concern in criminology because carefully designed research that is implemented with high fidelity can establish causal validity/causal inferences.

Criteria for Establishing Causal Inferences

The three classic criteria necessary to support a causal inference, according to the philosopher John Stuart Mill, are: (1) association (correlation), (2) temporal order, and (3) nonspuriousness. The criterion of association requires that there is a systematic relationship between the cause and effect variables. This criterion is by far the easiest to determine. The second criterion of temporal order is a bit more complicated. The temporal order criterion requires that the cause, or more precisely variation in the cause variable, must occur before the observed variation in the effect variable. The third criterion of nonspuriousness is by far the most difficult to achieve. This criterion requires that the observed relationship between the cause and the effect variables must not be due to other omitted or unmeasured third variables. Using the relationship between delinquent peers and offending as an example, this criterion requires that this relationship cannot be due to homophily or any other potential explanation. Because there are usually many, many potentially relevant third variables and many of these third variables are unobserved, the criterion of nonspuriousness can be quite difficult to achieve.

Types of Experiments

Shadish, Cook, and Campbell ( 2002 ) define an experiment as “a study in which an intervention is deliberately introduced to observe its effects” (p. 12). Shadish and colleagues distinguish two broad types of experiments: randomized experiments and quasi experiments. The central difference between these two types of techniques is the use of random assignment to the levels of the hypothesized cause variable.

The hallmark of all randomized experiments is the use of random assignment to experimental conditions. In randomized experiments, research subjects are randomly assigned to different levels of the hypothesized cause variable (i.e., experimental conditions) by the researchers. Random assignment can be achieved in many different ways, such as by flipping a coin, using a table of random numbers, or using numbers randomly generated by a computer. The method of randomization is largely arbitrary, but the use of some form of randomization is the crucial element of a randomized experiment.

Randomized experiments come in many forms or designs. The most common form of a randomized experiment involves randomly assigning research subjects, all of whom have been screened for eligibility, to either the treatment group that receives the experimental intervention of interest or the control group that does not; the control group, instead, typically receives no treatment, standard care, or a placebo. Randomized experiments involving the use of a no-treatment control group are often referred to as “randomized controlled trials.” Randomized controlled trials are considered by many to be the gold standard of evaluation research for their high causal validity. There are many variations on this basic design. One common variation involves multiple treatment groups that receive varying doses of the experimental intervention. Another common variation involves “blinding” – procedures designed to prevent research subjects, treatment providers, and/or researchers from knowing which experimental condition a research subject was assigned. Double-blind randomized control trials typically attempt to prevent research subjects and researchers from learning which research subjects were assigned to the control group, until after all data have been collected. Blinding is intended to prevent various kinds of bias from contaminating the research results. Randomized experiments are increasingly common in criminology (see, e.g., Farrington & Welsh, 2005 ), but double-blind randomized experiments are extremely rare.

Quasi experiments do not use randomization to assign research subjects to experimental conditions; instead, some other method of assignment is utilized. Often research subjects voluntarily choose to participate or not to participate in the treatment of interest. Thus, the actions and wishes of the research subjects typically affect assignment.

Quasi experiments utilize a wide variety of designs. The two most common involve one-group and two-group designs. The simplest and least rigorous quasi-experimental research design involves one group of research subjects who participated in some treatment of interest. These research subjects are observed before and after the administration of treatment of interest. And the observed changes in the outcome of interest are causally attributed to participation in the treatment. Another widely used quasi-experimental design involves the use of two groups. Typically, two-group quasi experiments involve a comparison group that does not receive the treatment of interest and a treatment group that does receive the treatment. These groups are compared, often while controlling for any observed differences, and the remaining differences are causally attributed to the treatment.

Randomized experiments and quasi experiments are capable of clearly establishing the first two criteria for causal inferences (association and temporal order); yet, they differ sharply in their ability to establish nonspuriousness. Randomized experiments are able to convincing establish nonspuriousness because of their use of random assignment. Random assignment ensures that research subjects will be equal in expectation on all variables –  both observed and unobserved variables  – prior to the administration of the experimental intervention. The phrase “equal in expectation” does not mean that the research subjects assigned to each of the experimental conditions will be perfectly equal on all variables. Instead, equal in expectation means that if we could repeat this assignment process an infinite number of times, the population means on all variables would be equal for each of the experimental conditions. Therefore, any differences between research subjects assigned to the various experimental conditions are due to chance. Because there are no systematic differences between the experimental groups on any variable besides the experimental condition, randomized experiments are able to rule out all potential third variables as alternative explanations for the observed differences on the outcome variable(s) of interest.

Quasi experiments have much greater difficulty in establishing nonspuriousness. In quasi experiments the actions and/or wishes of those involved in the research affect which experimental condition they eventually receive. This is highly problematic, as research subjects who choose to participate in a particular level of the experimental condition often differ from other research subjects on observed and/or unobserved variables. If research subjects in various levels of the experimental condition (e.g., program participants vs. nonparticipants) differ only on observed variables, then it would be easy to control for these observed differences by using statistical techniques such as multiple regression. However, in the absence of random assignment, how does one establish convincingly that participants and nonparticipants differ only on observed variables? It stands to reason that if the groups differ on observed variables, then they also differ on unobserved variables as well. Further, even if participants and nonparticipants are equal on observed variables, this does not mean that these groups are also equal on important unobserved variables. This is the crucial issue, because it is these unobserved differences that cause selection bias. Selection bias refers to inaccuracies in the estimated relationship between variables that is caused by omitted or unmeasured variables. Because quasi experiments do not establish that research subjects are equal in expectation on all variables, especially unobserved variables, prior to the administration of the experimental intervention, selection bias is a persistent problem in quasi-experimental research designs .

In the language of research methods, in randomized experiments, the assignment of research subjects to experimental conditions is exogenous . Exogenous in this context means outside or external to everyone involved in the experiment including the research subjects, treatment providers, and researchers – only randomization affects experimental assignment. Research subjects have no influence on which level of the experimental condition they will be assigned. However, in quasi experiments, the actions and wishes of those involved in the research including research subjects, their families, treatment providers, criminal justice officials, and researchers among many others may affect assignment; and therefore, assignment in quasi experiments is endogenous  – meaning that the assignment process is affected by factors internal to the experiment. Endogeneity is highly problematic because accurate estimation of causal relationships requires the cause variable to be at least partially exogenous.

Threats to Causal Validity

Randomized experiments.

The use of randomized experimental research designs ensures that the research subjects in each of the experimental conditions are equal in expectation before the administration of the experimental treatment. However, the use of randomized experimental designs does not ensure that the experiment will remain bias-free after randomization. Randomized experiments must be carefully planned and implemented to avoid various biases affecting their results postrandomization. In particular, there are three primary threats (i.e., sources of bias) that must be guarded against for randomized experiments to achieve high levels of causal validity. The first potential threat is contamination . Contamination occurs in situations where research subjects assigned to different levels of the experiment (e.g., participants and nonparticipants) come into direct contact or interact in other ways. Contamination occurs when nonparticipants end up receiving the treatment via interactions with participants. For example, if nonparticipants learn ideas/techniques discussed in the experimental treatment, then this knowledge may attenuate the size of the treatment effect because in essence nonparticipants received some of the experiment treatment vicariously. Cross-overs are a second potential threat to the causal validity of randomized experimental designs. Cross-overs refers to research subjects assigned to one condition who end up in some other experimental condition. For example, if some nonparticipants end up receiving the treatment because of an error or deliberate actions, then these individuals have “crossed-over.” Cross-overs, particularly as their numbers rise, may attenuate the magnitude of the treatment effect and thereby negatively affect the experiment's causal validity. The third potential threat to randomized experimental research designs is attrition . Attrition is the loss of research subjects due to factors such as being unable to locate the subjects for follow-up interviews/assessments, subjects declining to participate, death of research subjects, and so forth. Attrition becomes an increasingly potent problem as the length of the tracking period grows. Attrition is problematic in two ways. First, general attrition (i.e., attrition across experimental conditions) undermines external validity, the ability to generalize research findings beyond the sample. Second and more problematic in terms of causal validity is differential attrition (i.e., attrition rates differ markedly between experimental conditions), as differential attrition has the potential to undo the equating of groups accomplished via random assignment.

Quasi Experiments

Quasi experiments face a host of issues that threaten the causal validity of findings derived from these designs. The particular threats depend on the specific design features of the quasi experiment. Quasi experiments using one-group designs face the most serious threats to the causal validity of their findings. These threats include maturation (i.e., changes due to aging), regression to the mean (i.e., the tendency of research subjects who scored unusually high and low scores in initial assessments to regress toward less extreme scores in later assessments), testing (i.e., the tendency of research subjects to respond differently in later assessments because they have been sensitized to the behaviors under investigation), and “history” (i.e., external events, besides the intervention, that cause changes in the behaviors under investigation). All of these threats are competing explanations for the results obtained from one-group quasi-experimental research designs. Given the number of threats challenging the causal validity of one-group designs, these designs are the weakest type of experiments.

Two-group quasi-experimental designs generally have fewer and different primary threats to their causal validity in comparison to one-group designs. Briefly, two-group quasi-experimental designs have all of the same threats as randomized experiments and the additional threat of selection bias. As discussed above, the nonrandom assignment of research subjects to experimental conditions leaves open the possibility that research subjects assigned to various levels of the experimental condition differed on observed and unobserved variables before the administration of the experimental treatment. As a result, the variable capturing treatment assignment is potentially endogenous, which is highly problematic because accurate estimation of the treatment effect requires the treatment assignment variable to be exogenous.

Exogeneity in Nonrandomized Experiments

The use of randomized experiments is not the only means of achieving exogeneity. There are several other research designs/research methods of achieving at least partial exogeneity. These designs/methods are more frequently utilized in fields outside of criminology and are making inroads in criminology. These quasi-experimental designs/methods include natural experiments, regression discontinuity, and instrumental variable estimation. These techniques allow researchers to accurately estimate causal relationships and draw causal inferences in certain situations.

Natural experiments are one means of establishing exogenous variation in a cause variable when researcher-led random assignment is not feasible. A natural experiment is study in which external factors such as natural events, serendipity, or policy changes “assign” research subjects to various experimental conditions of interest. Because the assignment process is external to the research subjects under observation, the assignment process is exogenous, or at least arguably exogenous. This exogenous variation allows researchers to accurately estimate causal relationships.

Natural experiments seem to be increasingly common in the social sciences (see Dunning, 2012 ). As an example of a natural experiment, Kirk ( 2009 ) wished to learn the causal effect of relocating previously incarcerated offenders from their old neighborhoods of residence to new less criminogenic neighborhoods. This is an important theoretical and practical issue because we know that many parolees return to the same criminogenic neighborhoods and social networks that contributed to their involvement in offending in the first place, and therefore we shouldn't be surprised that recidivism is often alarmingly high. While it is not impossible to conduct a randomized experiment on this issue, it would be difficult for a variety of reasons. However, a recent natural event, Hurricane Katrina, forced many parolees who resided in high-crime areas of New Orleans hard hit by the storm to move to other neighborhoods. In essence, Hurricane Katrina exogenously assigned some parolees to new neighborhoods, which made it possible to estimate the causal effect of relocation on recidivism. Kirk found that parolees who moved to a new area were substantially less likely to be reincarcerated within three years of release in comparison to parolees who did not move.

Another research design capable of establishing exogenous variation is the regression discontinuity design (see Murnane and Willett, 2011 ). The key element of this design is the use of some “forcing variable” that establishes a cut point (or threshold) that assigns research subjects below the cut point to one experimental condition and those above the cut point to another condition. The cut point is used as an exogenous source of variation; research subjects just below and just above the cut point are compared to estimate the causal relationship between the variables of interest. As an example of a regression discontinuity design, Berk and Rauma ( 1983 ) assessed the causal effect of providing financial assistance in the form of unemployment insurance to recently released former prison inmates. In order to qualify for the financial assistance former prison inmates had to have made at least $1,500 in the year prior to release; this criterion was used as the forcing variable used to assign former inmates to either the control (no financial assistance) or the treatment (financial assistance) conditions. Berk and Rauma found that financial assistance caused a 13% reduction in recidivism.

Outside of criminology, the most popular means of estimating causal relationships without the use of a random experiment is instrumental variable estimation. The logic of instrumental variable estimation begins by noting that if some part of the variation in some endogenous variable of interest could be established as exogenous, then this part of the variation in the variable of interest could be used to accurately estimate its causal effect on the outcome of interest. An “instrumental variable” can be used to identify exogenous variation. An instrumental variable is one that satisfies two assumptions: (1) it is uncorrelated with the error term of the regression of the outcome variable of interest on the endogenous independent variable of interest, and (2) it is correlated with the endogenous variable of interest. The first assumption means that the instrumental variable can have no effect on the outcome variable except via its indirect effect on the outcome through the endogenous independent variable. This is a strong assumption because the instrumental variable must only be related to the outcome variable through the endogenous independent (causal) variable and the instrumental variable cannot be correlated with other factors that affect the outcome variable. If an instrumental variable meeting these criteria can be found, then estimating the causal relationship between the endogenous variable and the outcome of interest is straightforward and can be accomplished using several statistical techniques, of which two-stage least stages is most popular.

Instrumental variable estimation has rarely been applied in criminology. Apel, Bushway, Paternoster, Brame, and Sweeten ( 2008 ) is one example of instrumental variable estimation in criminology. Apel and colleagues examine the relationship between hours worked by youth and delinquency. Prior research typically finds that youth who work more hours are more likely to be involved in delinquency; however, number of hours worked is likely to be endogenously related to delinquency, as youth who work more hours are likely to be different from other youth on a host of factors that are also related to delinquency. Apel and colleagues use variation in state child labor laws as an instrumental variable to identify exogenous variation in the number of hours worked. Contrary to prior research, these authors find that the number of hours worked by youth reduces delinquency.

Ojmarrh Mitchell is an associate professor and graduate director in the Department of Criminology at the University of South Florida. Professor Mitchell earned his PhD in criminal justice and criminology from the University of Maryland with a doctoral minor in measurement, statistics, and evaluation. His research interests include drugs and crime, corrections and sentencing, race and crime, and research methods.

SEE ALSO: Quasi‐Experimental Research Methods ; Research Methods ; Validity and Reliability

• Apel, R. , Bushway, S. D. , Paternoster, R. , Brame, R. , & Sweeten, G . ( 2008 ). Using state child labor laws to identify the causal effect of youth employment on deviant behavior and academic achievement . Journal of Quantitative Criminology , 24 , 337 – 362 . 10.1007/s10940-008-9055-5 PubMed Web of Science® Google Scholar
• Berk, R. A. , & Rauma, D. ( 1983 ). Capitalizing on nonrandom assignment to treatments: A regression-discontinuity evaluation of a crime-control program . Journal of the American Statistical Association , 78 , 21 – 27 . 10.1080/01621459.1983.10477917 Web of Science® Google Scholar
• Dunning, T. ( 2012 ). Natural experiments in the social sciences: A design-based approach . New York : Cambridge University Press. 10.1017/CBO9781139084444 Google Scholar
• Farrington, D P. , & Welsh, B. C. ( 2005 ). Randomized experiments in criminology: What have we learned in the past two decades? Journal of Experimental Criminology , 1 , 9 – 38 . 10.1007/s11292-004-6460-0 Google Scholar
• Gottfredson, M. R. , & Hirschi, T. ( 1990 ). A general theory of crime . Stanford, CA : Stanford University Press. 10.1515/9781503621794 Google Scholar
• Kirk, D. S. ( 2009 ). A natural experiment on residential change and recidivism: Lessons from Hurricane Katrina . American Sociological Review , 74 , 484 – 505 . 10.1177/000312240907400308 Web of Science® Google Scholar
• Murnane, R. J. , & Willett, J. B. ( 2011 ). Methods matter: Improving causal inference in educational and social science research . New York: Oxford . Google Scholar
• Sampson, R. J. , & Laub, J. H. ( 1993 ). Crime in the making: Pathways and turning points through life . Cambridge, MA : Harvard University Press. 10.1177/0011128793039003010 Google Scholar
• Shadish, W. R. , Cook, T. D. , & Campbell, D. T. ( 2002 ). Experimental and quasi-experimental designs for generalized causal inference . Belmont, CA : Wadsworth Cengage Learning. Web of Science® Google Scholar

Further Readings

• Morgan, S. L. , & Winship, C. ( 2007 ). Counterfactuals and causal inference: Methods and principles for social research . New York: Cambridge . 10.1017/CBO9780511804564 Web of Science® Google Scholar

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14.2 True experiments

Learning objectives.

Learners will be able to…

• Describe a true experimental design in social work research
• Understand the different types of true experimental designs
• Determine what kinds of research questions true experimental designs are suited for
• Discuss advantages and disadvantages of true experimental designs

A true experiment , often considered to be the “gold standard” in research designs, is thought of as one of the most rigorous of all research designs. In this design, one or more independent variables (as treatments) are manipulated by the researcher, subjects are randomly assigned (i.e., random assignment) to different treatment levels, and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its ability to increase internal validity and help establish causality through treatment manipulation, while controlling for the effects of extraneous variables. As such they are best suited for explanatory research questions.

In true experimental design, research subjects are assigned to either an experimental group, which receives the treatment or intervention being investigated, or a control group, which does not.  Control groups may receive no treatment at all, the standard treatment (which is called “treatment as usual” or TAU), or a treatment that entails some type of contact or interaction without the characteristics of the intervention being investigated.  For example, the control group may participate in a support group while the experimental group is receiving a new group-based therapeutic intervention consisting of education and cognitive behavioral group therapy.

After determining the nature of the experimental and control groups, the next decision a researcher must make is when they need to collect data during their experiment. Do they take a baseline measurement and then a measurement after treatment, or just a measurement after treatment, or do they handle data collection another way? Below, we’ll discuss three main types of true experimental designs. There are sub-types of each of these designs, but here, we just want to get you started with some of the basics.

Using a true experiment in social work research is often difficult and can be quite resource intensive. True experiments work best with relatively large sample sizes, and random assignment, a key criterion for a true experimental design, is hard (and unethical) to execute in practice when you have people in dire need of an intervention. Nonetheless, some of the strongest evidence bases are built on true experiments.

For the purposes of this section, let’s bring back the example of CBT for the treatment of social anxiety. We have a group of 500 individuals who have agreed to participate in our study, and we have randomly assigned them to the control and experimental groups. The participants in the experimental group will receive CBT, while the participants in the control group will receive a series of videos about social anxiety.

Classical experiments (pretest posttest control group design)

The elements of a classical experiment are (1) random assignment of participants into an experimental and control group, (2) a pretest to assess the outcome(s) of interest for each group, (3) delivery of an intervention/treatment to the experimental group, and (4) a posttest to both groups to assess potential change in the outcome(s).

When explaining experimental research designs, we often use diagrams with abbreviations to visually represent the components of the experiment. Table 14.2 starts us off by laying out what the abbreviations mean.

Figure 14.1 depicts a classical experiment using our example of assessing the intervention of CBT for social anxiety.  In the figure, RA denotes random assignment to the experimental group A and RB is random assignment to the control group B. O 1 (observation 1) denotes the pretest, X e denotes the experimental intervention, and O 2 (observation 2) denotes the posttest.

The more general, or universal, notation for classical experimental design is shown in Figure 14.2.

In a situation where the control group received treatment as usual instead of no intervention, the diagram would look this way (Figure 14.3), with X i denoting treatment as usual:

Hopefully, these diagrams provide you a visualization of how this type of experiment establishes temporality , a key component of a causal relationship. By administering the pretest, researchers can assess if the change in the outcome occured after the intervention. Assuming there is a change in the scores between the pretest and posttest, we would be able to say that yes, the change did occur after the intervention.

Posttest only control group design

Posttest only control group design involves only giving participants a posttest, just like it sounds. But why would you use this design instead of using a pretest posttest design? One reason could be to avoid potential testing effects that can happen when research participants take a pretest.

In research, the testing effect threatens internal validity when the pretest changes the way the participants respond on the posttest or subsequent assessments (Flannelly, Flannelly, & Jankowski, 2018). [1] A common example occurs when testing interventions for cognitive impairment in older adults. By taking a cognitive assessment during the pretest, participants get exposed to the items on the assessment and get to “practice” taking it (see for example, Cooley et al., 2015). [2] They may perform better the second time they take it because they have learned how to take the test, not because there have been changes in cognition. This specific type of testing effect is called the practice effect . [3]

The testing effect isn’t always bad in practice—our initial assessments might help clients identify or put into words feelings or experiences they are having when they haven’t been able to do that before. In research, however, we might want to control its effects to isolate a cleaner causal relationship between intervention and outcome. Going back to our CBT for social anxiety example, we might be concerned that participants would learn about social anxiety symptoms by virtue of taking a pretest. They might then identify that they have those symptoms on the posttest, even though they are not new symptoms for them. That could make our intervention look less effective than it actually is. To mitigate the influence of testing effects, posttest only control group designs do not administer a pretest to participants. Figure 14.4 depicts this.

A drawback to the posttest only control group design is that without a baseline measurement, establishing causality can be more difficult. If we don’t know someone’s state of mind before our intervention, how do we know our intervention did anything at all? Establishing time order is thus a little more difficult. The posttest only control group design relies on the random assignment to groups to create groups that are equivalent at baseline because, without a pretest, researchers cannot assess whether the groups are equivalent before the intervention. Researchers must balance this consideration with the benefits of this type of design.

Solomon four group design

One way we can possibly measure how much the testing effect threatens internal validity is with the Solomon four group design. Basically, as part of this experiment, there are two experimental groups and two control groups. The first pair of experimental/control groups receives both a pretest and a posttest. The other pair receives only a posttest (Figure 14.5). In addition to addressing testing effects, this design also addresses the problems of establishing time order and equivalent groups in posttest only control group designs.

For our CBT project, we would randomly assign people to four different groups instead of just two. Groups A and B would take our pretest measures and our posttest measures, and groups C and D would take only our posttest measures. We could then compare the results among these groups and see if they’re significantly different between the folks in A and B, and C and D. If they are, we may have identified some kind of testing effect, which enables us to put our results into full context. We don’t want to draw a strong causal conclusion about our intervention when we have major concerns about testing effects without trying to determine the extent of those effects.

Solomon four group designs are less common in social work research, primarily because of the logistics and resource needs involved. Nonetheless, this is an important experimental design to consider when we want to address major concerns about testing effects.

Key Takeaways

• True experimental design is best suited for explanatory research questions.
• True experiments require random assignment of participants to control and experimental groups.
• Pretest posttest research design involves two points of measurement—one pre-intervention and one post-intervention.
• Posttest only research design involves only one point of measurement—after the intervention or treatment. It is a useful design to minimize the effect of testing effects on our results.
• Solomon four group research design involves both of the above types of designs, using 2 pairs of control and experimental groups. One group receives both a pretest and a posttest, while the other receives only a posttest. This can help uncover the influence of testing effects.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

• Think about a true experiment you might conduct for your research project. Which design would be best for your research, and why?
• What challenges or limitations might make it unrealistic (or at least very complicated!) for you to carry your true experimental design in the real-world as a researcher?
• What hypothesis(es) would you test using this true experiment?

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Imagine you are interested in studying child welfare practice. You are interested in learning more about community-based programs aimed to prevent child maltreatment and to prevent out-of-home placement for children.

• Think about a true experiment you might conduct for this research project. Which design would be best for this research, and why?
• What challenges or limitations might make it unrealistic (or at least very complicated) for you to carry your true experimental design in the real-world as a researcher?
• Flannelly, K. J., Flannelly, L. T., & Jankowski, K. R. B. (2018). Threats to the internal validity of experimental and quasi-experimental research in healthcare. Journal of Health Care Chaplaincy, 24 (3), 107-130. https://doi.org/10.1080/08854726.20 17.1421019 ↵
• Cooley, S. A., Heaps, J. M., Bolzenius, J. D., Salminen, L. E., Baker, L. M., Scott, S. E., & Paul, R. H. (2015). Longitudinal change in performance on the Montreal Cognitive Assessment in older adults. The Clinical Neuropsychologist, 29(6), 824-835. https://doi.org/10.1080/13854046.2015.1087596 ↵
• Duff, K., Beglinger, L. J., Schultz, S. K., Moser, D. J., McCaffrey, R. J., Haase, R. F., Westervelt, H. J., Langbehn, D. R., Paulsen, J. S., & Huntington's Study Group (2007). Practice effects in the prediction of long-term cognitive outcome in three patient samples: a novel prognostic index. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists, 22(1), 15–24. https://doi.org/10.1016/j.acn.2006.08.013 ↵

An experimental design in which one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief

A demonstration that a change occurred after an intervention. An important criterion for establishing causality.

an experimental design in which participants are randomly assigned to control and treatment groups, one group receives an intervention, and both groups receive only a post-test assessment

The measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself

improvements in cognitive assessments due to exposure to the instrument

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56 12.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. Students in my research methods classes often use the term experiment to describe all kinds of research projects, but in social scientific research, the term has a unique meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three key features: independent and dependent variables, pretesting and posttesting, and experimental and control groups. In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ).

In some cases, it may be immoral to withhold treatment from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a comparison group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their comparison group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information. However, using a comparison group is a deviation from true experimental design and is more associated with quasi-experimental designs.

Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

In an experiment, the independent variable is the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must measure the number of episodes that existed prior to the intervention, which is the pretest , and after the intervention, which is the posttest .

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. 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. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your posttest to both groups to observer any changes in your dependent variable. Together, this is known as the classic experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 12.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003)  [1] study of peoples’ 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. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. 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. Upon measuring depression scores during the posttest period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

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).  [2] 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 a participant’s 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. That knowledge can cause them to answer differently on the posttest than they otherwise would. Participants are not stupid. They are actively trying to figure out what your study is about.

In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests so they may assess change over time within both the experimental and control groups. Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and posttest. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the posttest. Table 12.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them. Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs can be used. However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Comparison group- a group in quasi-experimental designs that receives “treatment as usual” instead of no treatment
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

Image attributions

exam scientific experiment by mohamed_hassan CC-0

• McCoy, S. K., & Major, B. (2003). Group identification moderates emotional response to perceived prejudice. Personality and Social Psychology Bulletin , 29, 1005–1017. ↵
• Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth; Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research . Chicago, IL: Rand McNally. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental research designs in social work: theory and applications (paperback).

Description

Experimental research is of great value to social work. Well-designed studies help social workers understand which approaches are most effective, with implications for both practice with individual clients and social policy more broadly. Many social work practitioners conduct studies that randomly assign clients to specific interventions and various control groups in order to assess policy outcomes. However, social work programs often do not teach experimental methods. Critics continue to assert that true experiments are impractical, unethical, or simply too blunt a tool to evaluate the effects of social work practices and policies.

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30 8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  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.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. 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. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

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. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. 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. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

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. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. 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. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level 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).  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.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

Image attributions

exam scientific experiment by mohamed_hassan CC-0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Quasi-Experimental Research Designs

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Quasi-experimental research designs are the most widely used research approach employed to evaluate the outcomes of social work programs and policies. This new volume describes the logic, design, and conduct of the range of such designs, encompassing pre-experiments, quasi-experiments making use of a control or comparison group, and time-series designs. An introductory chapter describes the valuable role these types of studies have played in social work, going back to the 1930s, and continuing to the present. Subsequent chapters describe the major features of individual quasi-experimental designs, the types of questions they are capable of answering, and their strengths and limitations. Each discussion of these designs presented in the abstract is subsequently illustrated with descriptions of real examples of their use as published in the social work literature and related fields. By linking the discussion of quasi-experimental designs in the abstract to actual applications to evaluate the outcomes of social services, the usefulness and vitality of these research methods comes alive to the reader. While this volume could be used as a research textbook, it will also have great value to practitioners seeking a greater conceptual understanding of the quasi-experimental studies they frequently read about in the social work literature. Human service professionals planning to undertake a program evaluation of their own agency's services will find this book of immense help in understanding the steps and actions needed to adopt a quasi-experimental strategy. It is usually the case that ethical and pragmatic considerations preclude the use of randomly assigning social work clients to experimental and comparative treatment conditions, and in such instances, the practicality of employing a quasi-experimental method becomes an excellent alternative.

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Experimental Research Designs in Social Work: Theory and Applications Paperback – August 22, 2023

• Print length 400 pages
• Language English
• Publisher Columbia University Press
• Publication date August 22, 2023
• Dimensions 6 x 1 x 9 inches
• ISBN-10 0231201176
• ISBN-13 978-0231201179
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• Publisher ‏ : ‎ Columbia University Press (August 22, 2023)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 400 pages
• ISBN-10 ‏ : ‎ 0231201176
• ISBN-13 ‏ : ‎ 978-0231201179
• Item Weight ‏ : ‎ 1.25 pounds
• Dimensions ‏ : ‎ 6 x 1 x 9 inches
• #4,137 in Social Sciences Research
• #7,656 in Social Work (Books)

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The University of Chicago The Law School

Center menu, malyi research awards 2024.

Professor Michael Albertus is a Professor of Political Science at the University of Chicago. His research examines democracy and dictatorship, inequality and redistribution, property rights, and civil conflict. The Malyi Center has awarded Professor Albertus research funding for his project, Consequences of the Legal Recognition of Ethnic Community Institutions: Evidence from Peru .

This project seeks to advance research on the consequences of the legal recognition of Indigenous community institutions. It does so in Peru, where thousands of indigenous communities gained legal recognition against a backdrop of civil war in the 1980s and 1990s. The project will geolocate communities, identify population centers within these communities, assign administrative data on the timing of community recognition and land titling, and match to communities event-level data from Peru’s Truth and Reconciliation Commission to examine how community legal recognition impacted local conflict and contestation during the country’s civil war. The project has important implications for understanding how the process of legal recognition of Indigenous communities and their institutions impacts social inclusion and contestation. It also stands to advance our understanding of conflicts where ethnic identity and legal/institutional recognition claims are at the heart of insurgencies and contested by the state and among communities themselves. 

Pegah Banihashemi is a JSD candidate at the University of Chicago Law School, where she also completed an LLM in 2022. Pegah is currently working on the history of the formation of the Iranian Constitution both before and after the Islamic Revolution of 1979. In this project, she explores the problems of implementing the constitution in Iran. Part of Pegah's project is dedicated to the issue of comparing the constitutions of different countries, and she intends to present in her work a suitable format of a constitution for Iran that can be more stable over time.

The Center assisted Pegah with research funds to gain access to materials and resources for her research.

Lingnan He is a PhD candidate in the Department of Political Science. The Malyi Center awarded Lingnan research funding for her project, Nudge through Judges: Judicial Reform as Persuasion .

How do authoritarian leaders credibly alleviate foreign economic actors’ concerns about the local business environment? This project formalizes the idea that policymakers can strategically leverage systematic judicial reforms to persuade outsider investors to act in the regime’s interest. In particular, the foreign actor is concerned about protectionism-induced distortions in the host country. Judicial reforms mobilize judges to make more pro-nonlocal firm decisions. The foreign actor then infers the level of distortions through the ruling outcomes and makes decisions about market entry. In the equilibria, the level of market optimism and the foreign actor’s entry costs across industries jointly impact the effectiveness of judicial reform as a persuasion device. Empirically, combining data from China’s civil lawsuits, tariff changes, and firm locations, the research evaluates how exposure to the trade war shapes policymakers’ incentive to implement judicial reforms and the ruling outcomes. The analysis shows that a raised trade barrier likely drives out productive domestic firms with more overseas connections, which disincentivizes policymakers from pursuing judicial independence.

Professor Jon C. Rogowski is a Professor of Political Science at the University of Chicago. His research interests are in American politics, where he studies representation and accountability, political institutions, and American political history. The Malyi Center awarded Professor Rogowski research funding for his project, The Institutionalization of the American Administrative State .

How well does the state serve its constituents? While U.S. politicians, commentators, and citizens express concerns about declining bureaucratic capacity and the politicization of personnel selection, neither these concerns nor the proposed remedies are unique to our contemporary era. On the contrary, some of the most important institutional changes in U.S. governance concern the organizational capacity of the federal bureaucracy and the role of politics in selecting its personnel. This project documents the evolution of the administrative state between the Civil War and the Cold War and examine its consequences for American governance. It examines the composition of bureaucratic positions before civil service reform, studies the consequences of merit-based protections for the selection of personnel, and identifies how changes in organizational capacity affect bureaucratic outputs. This research provides comprehensive evidence about historical changes in the American state while also addressing theoretical questions relevant to contemporary political debates.

Professor Susan Stokes is the Tiffany and Margaret Blake Distinguished Service Professor in the Department of Political Science. Her research and teaching interests include democratic theory and how democracy functions in developing societies, distributive politics, and comparative political behavior. The Malyi Center awarded Professor Stokes research funding for her project, Countering Mistrust of Democratic Institutions .

Political leaders in many countries worldwide have been elected and then undermined their democracies, a phenomenon known as democratic erosion. These leaders use public statements to reduce public confidence in institutions like election administration bodies. How can confidence be restored? The research team has used survey experiments to understand one example of this process: the Mexican president’s rhetorical attacks on a key Mexican institution, the National Electoral Institute. Our experimental findings indicate that presidential criticisms do undermine democratic confidence. But we also show that rebuttals of the president’s statements can restore this confidence.

Shih-An Wang is a JSD candidate at the University of Chicago Law School. Her current research project aims to analyze how judiciaries in countries with geopolitical tensions facilitate dialogues regarding the conflicts between national security and constitutional freedoms, focusing on the Constitutional Courts of South Korea, Taiwan, and Lithuania. Her research interest is in comparative constitutional law and democracy, executive power and regulations, and East Asian comparative law. In addition to her academic work, Shih-An serves as a Project Commissioner and Director of the North American Taiwan Studies Association.

The center awarded Shih-An with research funds to finance her participation in the Law and Society Association 2024 Annual Conference in Denver, Colorado. While attending the conference, Shih-An presented her research in a talk titled "Judicial Strategies under Geopolitical Distress: The Constitutional Court of Taiwan." She also served as a moderator and discussant on a panel titled, “ Constitutionalism Outside of Courts: Foundational Norms and Principles from a Comparative Perspective”.