Quasi-experimental Research: What It Is, Types & Examples

Much like an actual experiment, quasi-experimental research tries to demonstrate a cause-and-effect link between a dependent and an independent variable. A quasi-experiment, on the other hand, does not depend on random assignment, unlike an actual experiment. The subjects are sorted into groups based on non-random variables.

What is Quasi-Experimental Research?

“Resemblance” is the definition of “quasi.” As a result, quasi-experimental research is research that appears to be experimental but is not. Individuals are not randomly allocated to conditions or orders of conditions, even though the regression analysis is changed.

The directionality problem is avoided in quasi-experimental research since the regression analysis is altered before the multiple regression is assessed. However, because individuals are not randomized at random, there are likely to be additional disparities across conditions in quasi-experimental research.

As a result, in terms of internal consistency, quasi-experiments fall somewhere in between correlational research and actual experiments.

The key component of a true experiment is randomly allocated groups. This means that each person has an equivalent chance of being assigned to the experimental group or the control group, depending on whether they are manipulated or not.

Simply put, a quasi-experiment is not a real experiment. A quasi-experiment does not feature randomly allocated groups since the main component of a real experiment is randomly assigned groups. Why is it so crucial to have randomly allocated groups, given that they constitute the only distinction between quasi-experimental and actual experimental research?

Let’s use an example to illustrate our point. Let’s assume we want to discover how new psychological therapy affects depressed patients. In a genuine trial, you’d split half of the psych ward into treatment groups. With half getting the new psychotherapy therapy and the other half receiving standard depression treatment.

And the physicians compare the outcomes of this treatment to the results of standard treatments to see if this treatment is more effective. Doctors, on the other hand, are unlikely to agree with this genuine experiment since they believe it is unethical to treat one group while leaving another untreated.

A quasi-experimental study will be useful in this case. Instead of allocating these patients at random, you uncover pre-existing psychotherapist groups in the hospitals. Clearly, there’ll be counselors who are eager to undertake these trials as well as others who prefer to stick to the old ways.

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. 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.

As we mentioned before, quasi-experimental research entails manipulating an independent variable by randomly assigning people to conditions or sequences of conditions. Non-equivalent group designs, pretest-posttest designs, and regression discontinuity designs are only a few of the essential types.

Quasi-experimental research types

There are many different sorts of quasi-experimental designs. Three of the most popular varieties are described below: Design of non-equivalent groups, Discontinuity in regression, and Natural experiments.

Design of Non-equivalent Groups

The researcher picks existing groups that look comparable, but only one of the groups receives the therapy in a non-equivalent group design. When employing this design, researchers attempt to accommodate for any confounding factors by adjusting for them in their study or selecting groups that are as comparable as feasible. The most prevalent sort of quasi-experimental design is this one.

Example: Design of Non-equivalent Groups

You believe that the new after-school activity will result in improved academic performance. You pick two comparable groups of students from separate classes, one of which uses the new program and the other does not.

You can see if the program influences grades by comparing students who participate to those who do not.

Discontinuity in regression

Many of the prospective therapies that researchers want to investigate are based on a basic arbitrary cutoff, with those who fall over the threshold receiving treatment and those who fall below it not. At this point, the group differences are frequently so minor that they are almost non-existent. As a result, researchers can utilize people who are under the limit as a reference group and people who are just beyond it as an intervention group.

Example: Discontinuity in regression

In the United States, certain high schools are reserved for pupils who achieve a specified level of achievement on a test. Those who succeed in this exam are likely to vary from those who do not in a systematic way.

However, because the precise cutoff number is arbitrary, students near the limit who barely pass exams and those who fail by a razor-thin margin tend to be extremely similar, with the minute variations in their results owing primarily to chance. As a result, any disparities in outcomes must be due to their educational experiences.

You may look at the long-term outcomes of these two groups of kids to see how attending a selective school affects them.

Natural experiments

Researchers usually choose which group the individuals are allocated to in both lab and outdoor tests. A random or irregular assignment of patients to the control treatment occurs in a natural experiment because of an external occurrence or scenario (“nature”). Natural experiments are not actual experiments since they are observational, even though some employ random assignments.

Example: Natural experiments

One of the best-known natural experiments is the Oregon Health Study. In 2008, Oregon voted to increase the number of low-income people enrolled in Medicaid, America’s low-income public health care program.

However, because they couldn’t afford to pay everyone who qualified for the program, they had to use a random lottery to distribute slots.

Experts were able to investigate the program’s impact by utilizing enrolled people as a treatment group and those who were qualified but did not play the jackpot as an experimental group.

Conclusion on quasi-experimental research:

The true experimental design may be impossible to accomplish or just too expensive, especially for researchers with little resources. 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 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.

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