Quasi-Experimental Design is a unique research methodology because it is characterized by what is lacks. For example, Abraham & MacDonald (2011) state: Show
This type of research is often performed in cases where a control group cannot be created or random selection cannot be performed. This is often the case in certain medical and psychological studies. For more information on quasi-experimental design, review the resources below: Here’s a table that summarizes the similarities and differences between an experimental and a quasi-experimental study design: A quasi-experimental design is a non-randomized study design used to evaluate the effect of an intervention. The intervention can be a training program, a policy change or a medical treatment. Unlike a true experiment, in a quasi-experimental study the choice of who gets the intervention and who doesn’t is not randomized. Instead, the intervention can be assigned to participants according to
their choosing or that of the researcher, or by using any method other than randomness. Having a control group is not required, but if present, it provides a higher level of evidence for the relationship between the intervention and the outcome. (for more information, I recommend my other article: Understand Quasi-Experimental Design Through an Example). Examples of quasi-experimental designs include:
What is an experimental design?An experimental design is a randomized study design used to evaluate the effect of an intervention. In its simplest form, the participants will be randomly divided into 2 groups:
Randomization ensures that each participant has the same chance of receiving the intervention. Its objective is to equalize the 2 groups, and therefore, any observed difference in the study outcome afterwards will only be attributed to the intervention – i.e. it removes confounding. (for more information, I recommend my other article: Purpose and Limitations of Random Assignment). Examples of experimental designs include:
When to choose an experimental design over a quasi-experimental design?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. Another problem with quasi-experiments is the natural progression of the disease or the condition under study — When studying the effect of an intervention over time, one should consider natural changes because these can be mistaken with changes in outcome that are caused by the intervention. Having a well-chosen control group helps dealing with this issue. So, if losing the element of randomness seems like an unwise step down in the hierarchy of evidence, why would we ever want to do it? This is what we’re going to discuss next. When to choose a quasi-experimental design over a true experiment?The issue with randomness is that it cannot be always achievable. So here are some cases where using a quasi-experimental design makes more sense than using an experimental one:
Further reading
What is the difference between QuasiExperimental designs demonstrate that the independent variable is related to the outcome, and quasi-experimental studies allow for independent variables that stand alone.
What is the difference between QuasiQ. What is the difference between true experimental research and quasi-experimental research? Quasi-experimental research does not describe variables while true experimental research understands a cause-effect relationship.
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