Quasi Experimental Research, Design
The prefix ‘Quasi’ means resembling in Latin. Quasi experimental simply means that it is almost similar to true experiment. Like a true experiment, a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable. However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria. With respect to internal validity, they often appear to be inferior to randomized experiments. But there is something compelling about these designs; taken as a group, they are easily more frequently implemented than their randomized cousins. In the quasi-experimental design the researcher often does not have control over the treatment, but instead studies pre-existing groups that received different treatments after the fact. Like randomized experiments, control groups are not a pre-requisite although it is used commonly in the design.
Let us further explain with an example. Let us say we have to know the impact of new psychological therapy amongst the patients who is having depression. In a true experiment, you will have divide half of the mental health hospital into treatment group, where they receive the new psychotherapy treatment and rest of them are control where they receive the traditional treatment for depression. And the doctors note down the results of this treatment and the traditional treatment to know whether this treatment has a positive effect compared to other. But in these experiment, Doctors mostly will not agree to this true experiment because they would feel that it is not ethically right to treat one group and not to treat others. The quasi experimental design will come handy in these kind of situations. Instead of randomly assigning these patients, you discover the pre-existing groups of psychotherapists in the hospitals. Naturally there will be therapists who are willing to try these experiments and therapists who still want to follow traditional methods. You can use these pre-existing groups to study the symptom progression of the patients treated
with the new therapy versus those receiving the standard course of treatment. Although the groups were not randomly assigned, if you properly account for any systematic differences between them, you can be reasonably confident any differences must arise from the treatment and no other confounding variables.
As we discussed earlier, Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Among the important types are non-equivalent group’s designs, pretest-posttest, and regression discontinuity design.
Non-equivalent group design: In non-equivalent group design, the researcher chooses existing groups that appear similar, but where only one of the groups experiences the treatment. When using this kind of design, researchers try to account for any confounding variables by controlling for them in their analysis or by choosing groups that are as similar as possible. This is the most common type of quasi-experimental design.
Regression discontinuity design: Many potential treatments that researchers intend to study are designed around an essentially arbitrary cutoff, where those above the threshold receive the treatment and those below it do not. Near this threshold, the differences between the two groups are often so minimal as to be nearly non-existent. Therefore, researchers can use individuals just below the threshold as a control group and those just above as a treatment group.
Natural Experiments: In both laboratory and field experiments, researchers normally control which group the subjects are assigned to. In a natural experiment, an external event or situation (“nature”) results in the random or random-like assignment of subjects to the treatment group. Even though some use random assignments, natural experiments are not considered to be true experiments because they are observational in nature.
True experimental design may be infeasible to implement or simply too expensive, particularly for researchers without access to large funding streams. Quasi-experimental designs allow you to study the question by taking advantage of data that has previously been paid for or collected by others (often the government). It has higher external validity than most of the true experiments and higher internal validity (less than true experiments) than other non-experimental research because they allow you to better control for confounding variables than other types of studies do.