7 3 Quasi-Experimental Research Research Methods in Psychology

Table Of Content

• Case Study – Methods, Examples and Guide
• What are the different quasi-experimental study designs?
• Example Comparing A True Experiment And Quasi-Experiment
• Workplace financial education and change in financial knowledge: A quasi-experimental approach (Horwitz et al.,
• Part 3: study design features and their role in disambiguating study design labels
• Part 2: “quasi-experimental” designs used by health care evaluation researchers
• Types of Quasi-Experimental Designs

We conclude with a brief discussion of incorporating additional design elements to capture the full range of relevant implementation outcomes in order to maximize external validity. 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. This would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them. Because they allow better control for confounding variables than other forms of studies, they have higher external validity than most genuine experiments and higher internal validity (less than true experiments) than other non-experimental research. While both quasi-experimental designs and true experiments aim to uncover cause-and-effect relationships, they differ in terms of control over variables, randomization, and ethical considerations.

Case Study – Methods, Examples and Guide

However, it doesn't use randomization, the lack of which is a crucial element for quasi-experimental design. This design involves collecting data on the dependent variable(s) over a period of time, both before and after an intervention or event. The researcher can then determine whether there was a significant change in the dependent variable(s) following the intervention or event. This design involves selecting two groups of participants that are similar in every way except for the independent variable(s) that the researcher is testing. The two groups are then compared to see if there are any significant differences in the outcomes.

What are the different quasi-experimental study designs?

Because in this design, subjects may serve as their own controls, this may yield greater statistical efficiency with fewer numbers of subjects. This design adds a third posttest measurement (O3) to the one-group pretest-posttest design and then removes the intervention before a final measure (O4) is made. The advantage of this design is that it allows one to test hypotheses about the outcome in the presence of the intervention and in the absence of the intervention. Thus, if one predicts a decrease in the outcome between O1 and O2 (after implementation of the intervention), then one would predict an increase in the outcome between O3 and O4 (after removal of the intervention).

Example Comparing A True Experiment And Quasi-Experiment

Quasi-experimental designs enable you to investigate an issue by utilizing data that has already been paid for or gathered by others (often the government). Because the assignments are not random, it is harder to draw conclusions about cause and effect than in a real experiment. However, quasi-experimental designs are still useful when randomization is not possible or ethical.

Explanatory Research – Types, Methods, Guide

A quasi-experiment, on the other hand, does not depend on random assignment, unlike an actual experiment. 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. This research design is common in laboratory and field experiments where researchers control target subjects by assigning them to different groups. Researchers randomly assign subjects to a treatment group using nature or an external event or situation.

Workplace financial education and change in financial knowledge: A quasi-experimental approach (Horwitz et al.,

Researchers also use more conventional epidemiological designs, sometimes called observational, that exploit naturally occurring variation. Instrumental variable estimation using data from a randomized controlled trial to estimate the effect of treatment in the treated, when there is substantial nonadherence to the allocated intervention, is a particular instance of this approach [37], [38]. Most health interventions are delivered by discrete care provider units, typically organized hierarchically (e.g., hospitals, family practices, practitioners); this makes clustering important, except when allocation is randomized, because interventions are chosen by care provider units in complex ways. The aim of the study was to extend a previously published checklist of study design features to include study designs often used by health systems researchers and economists. Our intention is to help review authors in any field to set eligibility criteria for studies to include in a systematic review that relate directly to the intrinsic strength of the studies in inferring causality. We also seek to clarify key equivalences and differences in terminology used by different research communities.

The order in which clusters receive the intervention can be assigned randomly or using some other approach when randomization is not possible. For example, in settings with geographically remote or difficult-to-access populations, a non-random order can maximize efficiency with respect to logistical considerations. Again, if students in the treatment condition become more negative toward drugs, this could be an effect of the treatment, but it could also be a matter of history or maturation. Some of the earliest CCT programs randomly assigned clusters (communities of households) and used longitudinal household survey data collected by researchers to estimate the effects of CCTs on the health of both adults and children [21]. The design and analysis of a cluster-randomized controlled trial of this kind is familiar to health care researchers [29]. The use of both a pretest and a comparison group makes it easier to avoid certain threats to validity.

Part 2: “quasi-experimental” designs used by health care evaluation researchers

For example, a research study shows that a new curriculum improved reading comprehension of third-grade children in Iowa. To assess the study's external validity, you would ask whether this new curriculum would also be effective with third graders in New York or with children in other elementary grades. If any substantial variations between them can be well explained, you may be very assured that any differences are attributable to the treatment but not to other extraneous variables. These pre-existing groups can be used to compare the symptom development of individuals who received the novel therapy with those who received the normal course of treatment, even though the groups weren’t chosen at random. As a result, in terms of internal consistency, quasi-experiments fall somewhere between correlational research and actual experiments.

One caveat is that if the intervention is thought to have persistent effects, then O4 needs to be measured after these effects are likely to have disappeared. For example, a study would be more convincing if it demonstrated that pharmacy costs decreased after pharmacy order-entry system introduction (O2 and O3 less than O1) and that when the order-entry system was removed or disabled, the costs increased (O4 greater than O2 and O3 and closer to O1). In addition, there are often ethical issues in this design in terms of removing an intervention that may be providing benefit.

This latter design is often very applicable to medical informatics where new technology and new software is often introduced or made available gradually. The main advantage of this design is that it controls for potentially different time-varying confounding effects in the intervention group and the comparison group. In our example, measuring points O1 and O2 would allow for the assessment of time-dependent changes in pharmacy costs, e.g., due to differences in experience of residents, preintervention between the intervention and control group, and whether these changes were similar or different. The reader should note that with all the studies in this category, the intervention is not randomized.

Quasi-experimental design, a fascinating method in the realm of research, offers a unique approach to uncovering cause-and-effect relationships. Unlike traditional experiments, where researchers randomly assign participants to groups, studies work with real-world constraints, employing non-random criteria for group allocation. This flexibility makes it a practical choice for exploring complex scenarios where strict experimental controls aren’t feasible or ethical. Question 7 asks about the variables that were measured and available to control for confounding in the analysis. The two broad classes of variables that are important are the identification and collection of potential confounder variables and baseline assessment of the outcome variable(s).

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The design involved matching clinics by size and an inverse roll-out, to balance out the sizes across the four groups. The inverse roll-out involved four strata of clinics, grouped by size with two clinics in each strata. The roll-out was sequenced across these eight clinics, such that one smaller clinics began earlier, with three clinics of increasing size getting the intervention afterwards. This was then followed by a descending order of clinics by size for the remaining roll-out, ending with the smallest clinic. This inverse roll-out enabled the investigators to start with a smaller clinic, to work out the logistical considerations, but then influence the roll-out such as to avoid clustering of smaller or larger clinics in any one step of the intervention. It can be useful to obtain pre-test data or baseline characteristics to improve the comparability of the two groups.

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. There are three key challenges when trying to communicate study designs that do not use randomization to evaluate the effectiveness of interventions. First, study design labels are diverse or ambiguous, especially for cluster-allocated designs; moreover, there are key differences between research fields in the way that similar designs are conceived. Terms such as quasi-experimental, natural experiment, and observational cause particular ambiguity.

The top panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment worked. The bottom panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment did not work. A quasi-experimental design is used when it's not logistically feasible or ethical to conduct randomized, controlled trials.