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This design can be useful for determining whether the intervention or event had an effect, but it does not allow for control over other factors that may have influenced the outcomes. The quality of causal evidence presented in this study is low because the authors did not ensure that the groups being compared were similar before the intervention. This means we are not confident that the estimated effects are attributable to financial education in the workplace; other factors are likely to have contributed. An alternative explanation that refers to how the participants might have changed between the pretest and posttest in ways that they were going to anyway because they are growing and learning. In a true experiment, some participants would eat junk foods, while the rest would be in the control group, adhering to a regular diet.
Example 2: Determining the effectiveness of teaching modern leadership techniques in start-up businesses
The prefix quasi means “resembling.” Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. But because participants are not randomly assigned—making it likely that there are other differences between conditions—quasi-experimental research does not eliminate the problem of confounding variables. In terms of internal validity, therefore, quasi-experiments are generally somewhere between correlational studies and true experiments. Quasi-experimental designs are useful in situations where randomized controlled trials are not feasible or ethical. They provide researchers with an alternative method to evaluate the effectiveness of interventions, policies, and programs in real-life settings.
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Finally, if participants in this kind of design are randomly assigned to conditions, it becomes a true experiment rather than a quasi experiment. In fact, it is the kind of experiment that Eysenck called for—and that has now been conducted many times—to demonstrate the effectiveness of psychotherapy. Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows data from a hypothetical interrupted time-series study. The top panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment worked.
1. Pre-Post with Non-Equivalent Control Group
The strengths of pre-post designs are mainly based in their simplicity, such as data collection is usually only at a few points (although sometimes more). However, pre-post designs can be affected by several of the threats to internal validity of QEDs presented here. Imagine, for example, that students in one school are given a pretest on their attitudes toward drugs, then are exposed to an antidrug program, and finally are given a posttest.
Regression to the mean can be a problem when participants are selected for further study because of their extreme scores. Imagine, for example, that only students who scored especially low on a test of fractions are given a special training program and then retested. Regression to the mean all but guarantees that their scores will be higher even if the training program has no effect. A closely related concept—and an extremely important one in psychological research—is spontaneous remission. This is the tendency for many medical and psychological problems to improve over time without any form of treatment. If one were to measure symptom severity in 100 common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved.
Green and Glasgow (2006) (25) have outlined several useful criteria for gaging the extent to which an evaluation study also provides measures that enhance interpretation of external validity, for which those employing QEDs could identify relevant components and frameworks to include in reported findings. The system, trained on millions of examples of successful and unsuccessful conversations, provided suggestions that the agents could use, adapt, or reject. A nonequivalent groups design, then, is a between-subjects design in which participants have not been randomly assigned to conditions. However, even with random assignment, this research design cannot be called a true experiment since nature aspects are observational. Researchers can also exploit these aspects despite having no control over the independent variables. Quasi-experimental research designs are a type of research design that is similar to experimental designs but doesn’t give full control over the independent variable(s) like true experimental designs do.
Features of the Study
Researchers assessed the program's effectiveness by assigning the selected subjects to a randomly assigned treatment group, while those that didn't win the lottery were considered the control group. This is a hybrid of experimental and quasi-experimental methods and is used to leverage the best qualities of the two. Like the true experiment design, nonequivalent group design uses pre-existing groups believed to be comparable.
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Quasi-experimental Designs That Use Control Groups and Pretests
Such interventions typically include on one or more of the “7 Ps” (programs, practices, principles, procedures, products, pills, and policies) (9). Increasingly, both public health and clinical research have sought to generate practice-based evidence on a wide range of interventions, which in turn has led to a greater focus on intervention research designs that can be applied in real-world settings (2, 8, 9, 20, 25, 26, 10, 2). A quasi-experimental design is a non-randomized study design used to evaluate the effect of an intervention. In the four-year period of JAMIA publications that the authors reviewed, 25 quasi-experimental studies among 22 articles were published.
The reader should note that with all the studies in this category, the intervention is not randomized. Obtaining pretest measurements on both the intervention and control groups allows one to assess the initial comparability of the groups. The assumption is that if the intervention and the control groups are similar at the pretest, the smaller the likelihood there is of important confounding variables differing between the two groups. In medical informatics, what often triggers the development and implementation of an intervention is a rise in the rate above the mean or norm. For example, increasing pharmacy costs and adverse events may prompt hospital informatics personnel to design and implement pharmacy order-entry systems. However, often informatics personnel and hospital administrators cannot wait passively for this decline to occur.
This can be challenging as there may not be adequate information available to determine how ‘equivalent’ the comparison group is regarding relevant covariates. Although many statistical techniques can be used to deal with confounding in a quasi-experimental study, in practice, randomization is still the best tool we have to study causal relationships. Furthermore, general purpose technologies like AI are likely to experience a lag between their initial adoption and observable improvements in productivity. However, as these technological and organizational complements are gradually implemented, the productivity benefits of AI begin to materialize, marked by an upward trajectory in the J-curve. Another important threat to establishing causality is regression to the mean.12,13,14 This widespread statistical phenomenon can result in wrongly concluding that an effect is due to the intervention when in reality it is due to chance. The phenomenon was first described in 1886 by Francis Galton who measured the adult height of children and their parents.
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More broadly, qualitative methods (e.g. periodic interviews with staff at intervention and control sites) can help uncover key contextual factors that may be affecting study results above and beyond the intervention itself. RCTs in implementation science share the same basic structure as efficacy- or effectiveness-oriented RCTs, but typically feature important distinctions. In this section we will start by reviewing key factors that separate implementation RCTs from more traditional efficacy- or effectiveness-oriented RCTs. We will then discuss optimization trials, which are a type of experimental design that is especially useful for certain implementation science questions. We will then briefly turn our attention to single subject experimental designs (SSEDs) and on-off-on (ABA) designs.
The advantage of this design is that with multiple measurements both pre- and postintervention, it is easier to address and control for confounding and regression to the mean. In the example of a pharmacy order-entry system, O1 through O5 could represent monthly pharmacy costs preintervention and O6 through O10 monthly pharmacy costs post the introduction of the pharmacy order-entry system. Interrupted time-series designs also can be further strengthened by incorporating many of the design features previously mentioned in other categories (such as removal of the treatment, inclusion of a nondependent outcome variable, or the addition of a control group). This design involves the inclusion of a nonequivalent dependent variable (b) in addition to the primary dependent variable (a).
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