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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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Forthcoming articles expand or collapse the "forthcoming articles" section.

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Experimental and Quasi-Experimental Designs by Matthew Morton , Paul Montgomery LAST REVIEWED: 01 May 2017 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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Part 3: Using quantitative methods

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 genderqueer person sitting on a couch, talking to a therapist in a brightly-lit room

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?

Media Attributions

Being kinder to yourself © Evgenia Makarova is licensed under a CC BY-NC-ND (Attribution NonCommercial NoDerivatives) license
therapist © Zackary Drucker is licensed under a CC BY-NC-ND (Attribution NonCommercial NoDerivatives) license
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 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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10 Experimental research

Experimental research—often considered to be the ‘gold standard’ in research designs—is 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 (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.

Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

$E = (O_{1} - O_{2})\,.$

The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

$2 \times 2$

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid experimental designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.

Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Quasi-experimental designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.

An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.

Perils of experimental research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee 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

Bruce A. Thyer

Columbia University Press

Experimental Research Designs in Social Work

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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13.2: True experimental design

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Matthew DeCarlo, Cory Cummings, & Kate Agnelli

Matthew DeCarlo, Cory Cummings, & Kate Agnelli
Open Social Work Education

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

Table 13.1 Experimental research design notations

Pretest and post-test control group design

Figure 13.1 Pretest and post-test control group design

Figure 13.2 Pretest and post-test control group design testing CBT an intervention

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).

Figure 13.3 Pretest and post-test control group design with treatment as usual instead of no treatment

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).

Figure 13.4 Post-test only control group design

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.

Solomon four group design

Figure 13.5 Solomon four-group design

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/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?

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ISBN-10 0231201168
ISBN-13 978-0231201162
Publisher Columbia University Press
Publication date August 22, 2023
Language English
Dimensions 6.5 x 1.25 x 9.25 inches
Print length 400 pages
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Publisher ‏ : ‎ Columbia University Press (August 22, 2023)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 400 pages
ISBN-10 ‏ : ‎ 0231201168
ISBN-13 ‏ : ‎ 978-0231201162
Item Weight ‏ : ‎ 1.74 pounds
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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.

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 ↵

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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2 Pre-Experimental Research Designs

Published: February 2012
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The simplest of the group research designs involve the assessment of the functioning of a single group of persons who receive social work services. These methods are called pre-experimental designs. Tightly controlled studies done in laboratory or special treatment settings are known as efficacy studies, and are used to demonstrate if a given treatment can produce positive results under ideal conditions. Outcome studies done with more clinically representative clients and therapists, in real world agency settings, are known as effectiveness studies. Ideally the latter are conducted after the former, under conditions of increasing complexity, so as to determine treatments that work well in real-world contexts. Among the pre-experimental designs are the one group posttreatment-only study and the one group pretest-posttest design. Various ways in which these designs can be strengthened are presented, along with descriptions of published articles illustrating their use in social work and other human service settings. The limitations of these designs are also discussed, as is a review of the major threats to internal validity that can inhibit causal inferences.

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Experimental Research Designs in Social Work

Theory and applications.

Bruce A. Thyer

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Creating a Corporate Social Responsibility Program with Real Impact

Emilio Marti,
David Risi,
Eva Schlindwein,
Andromachi Athanasopoulou

Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

EM Emilio Marti is an associate professor at the Rotterdam School of Management, Erasmus University. His research focuses on corporate sustainability with a specific focus on sustainable investing.
DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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COMMENTS

8.1 Experimental design: What is it and when should it be used?
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.
Experimental Research Designs in Social Work: Theory and ...
There are many uses of the word "experiment" as a noun and of "experimental" as an adjective. The definitions of these terms used in this book reflect the commonly accepted views of mainstream behavioral and social science. Box 2.1 presents selected definitions from the field of experimental design.
Experimental and Quasi-Experimental Designs
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.
13. Experimental design
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.
14.1 What is experimental design and when should you use it?
In social work research, experimental design is used to test the effects of treatments, interventions, programs, or other conditions to which individuals, groups, organizations, or communities may be exposed to. There are a lot of experiments social work researchers can use to explore topics such as treatments for depression, impacts of school ...
Experimental research
10 Experimental research. 10. Experimental research. Experimental research—often considered to be the 'gold standard' in research designs—is 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 ...
Experimental Research Designs in Social Work
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. Bruce Thyer has been a long-term advocate for clients receiving careful appraisals of potential service outcomes.
10: Experimental Research
Social Work and Human Services Social Science Research - Principles, Methods, and Practices (Bhattacherjee) 10: Experimental Research ... Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well ...
12.2: Pre-experimental and quasi-experimental design
Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally. ↵ Chicago, IL: Rand McNally. ↵ This page titled 12.2: Pre-experimental and quasi-experimental design is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by Matthew DeCarlo ( Open Social Work Education ) .
Experimental Research Designs in Social Work
An appendix contains a chronological listing of published studies authored by social workers who conducted experimental research. Accessible to social work undergraduate, graduate, and doctoral students alike and valuable for professionals from clinical workers to policy analysts, this book demonstrates the utility of experimental research ...
Experimental and Quasi- Experimental Design
Summary. Experimental and quasi-experimental research provides the foundation for all evidence-based practice systems that seek to identify and promote the use of social work practices of demonstrated effectiveness. This reflects the prevailing perspective that experimental research is the only definitive basis for claims that certain outcomes ...
8.3 The logic of experimental design
Alexander's work helps understand clients' experiences, but the explanation is incomplete. Human existence is more complicated than the experimental conditions in Rat Park. Social workers are especially attentive to how social context shapes social life. So, we are likely to point out a specific disadvantage of experiments.
12.1 Experimental design: What is it and when should it be used?
In an experiment, the independent variable is the intervention being tested. In social work, this could include a therapeutic technique, a prevention program, or access to some service or support. Social science research may have a stimulus rather than an intervention as the independent variable, but this is less common in social work research.
Experimental Research Designs in Social Work: Theory and Applications
Experimental Research Designs in Social Work: Theory and Applications B. A. Thyer, Columbia University Press, 2023, 400 pp., $140.00 Hardcover, ISBN: 9780231201162. Danny R. Dixon Social Work Service Line and Education/Training Program, Carl Vinson VA Medical Center, Dublin, Georgia, United States Correspondence [email protected]
13.2: True experimental design
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 ...
Experimental Research Designs in Social Work: Theory and Applications
Bruce Thyer is Distinguished Research Professor and former dean at the College of Social Work at Florida State University. He is the founding and current editor of the journal Research on Social Work Practice and coeditor of the Child and Adolescent Social Work Journal and the Journal of Evidence-based Social Work.He is a founding board member of the Society for Social Work and Research.
Research Practicum: An Experiential Model for Social Work Research
Despite the potential of RP, articulation of RP models in the literature is limited. To address this gap, this exploratory study aims to evaluate an RP model for undergraduate and graduate social work students in a western Canadian social work faculty to propose a comprehensive RP model that would accomplish several goals: provide opportunities to integrate research theory/methods with ...
A Bibliography of Randomized Controlled Experiments in Social Work
The faculty and students of a professional school of social work should together be engaged in using the great method of experimental research which we are just beginning to discover in our professional education programme, and which should be as closely knit into the work of a good school of social work as research has been embodied into the ...
14.2 True experiments
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.
Pre-Experimental Research Designs
The simplest of the group research designs involve the assessment of the functioning of a single group of persons who receive social work services. These methods are called pre-experimental designs. Tightly controlled studies done in laboratory or special treatment settings are known as efficacy studies, and are used to demonstrate if a given ...
Experimental Research Designs in Social Work: Theory and Applications
Semantic Scholar extracted view of "Experimental Research Designs in Social Work: Theory and Applications Experimental Research Designs in Social Work: Theory and Applications , B. A. Thyer, Columbia University Press, 2023, 400 pp., $140.00Hardcover, ISBN: 9780231201162." by Danny R. Dixon
Experimental Research Designs in Social Work: Theory and Applications
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. I wish I had this book years ago!
Experimental Research Designs in Social Work
Experimental Research Designs in Social Work. Published by Columbia University Press 2023.
Creating a Corporate Social Responsibility Program with Real Impact
Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful ...

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Experimental and Quasi-Experimental Designs by Matthew Morton , Paul Montgomery LAST REVIEWED: 01 May 2017 LAST MODIFIED: 29 June 2011 DOI: 10.1093/obo/9780195389678-0053

13. Experimental design

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

The basics of experiments

The logic of experimental design

Key Takeaways

13.2 True experimental design

Pretest and post-test control group design

Post-test only control group design

Solomon four group design

13.4 Quasi-experimental designs

Nonequivalent comparison group design

Time series design

Ex post facto comparison group design

13.5 Non-experimental designs

One group pretest/post-test design

After-only design

Static-group design

13.6 Critical, ethical, and cultural considerations

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