Quasi-experimental design
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A quasi-experiment is a scientific research method primarily used in the social sciences. “Quasi” means likeness or resembling, so therefore quasi-experiments share characteristics of true experiments which seek interventions or treatments. The key difference in this empirical approach is the lack of random assignment. Another unique element often involved in this experimentation method is use of time series analysis: interrupted and non-interrupted.
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[edit] Design
The first part of creating a quasi-experimental design is to identify the variables. The quasi-independent variable will be the x-variable. This is the variable that is manipulated in order to affect the outcome. “X” is generally a grouping variable with different levels. Grouping means two or more groups such as a treatment group and a placebo group. The predicted outcome is the dependent variable which is the y-variable. In a time series analysis, the dependant variable is observed over time for any changes that may take place. Once the variables have been identified and defined, a procedure should then be implemented and group differences should be examined.[1]
[edit] Advantages
Since quasi-experimental designs are used when randomization is impossible and/or impractical, they are typically easier to set up than true experimental designs;[2] it takes much less effort to study and compare subjects or groups of subjects that are already naturally organized than to have to conduct random assignment of subjects. Additionally, utilizing quasi-experimental designs minimizes threats to external validity.[3] Since quasi-experiments are natural experiments, findings in one may be applied to other subjects and settings, allowing for some generalizations to be made about population. Also, this experimentation method is efficient in longitudinal research that involves longer time periods which can be followed up in different environments.
[edit] Disadvantages
The control allowed through the manipulation of the x-variable can lead to unnatural circumstances. Also, the lack of random assignment in the quasi-experimental design method may allow studies to be more feasible, but this also poses many challenges for the investigator. This deficient in randomization makes it harder to rule out confounds and introduces new threats to internal validity.[4] Because randomization is absent, some knowledge about the data can be approximated, but cause-effect conclusions are difficult to determine. Moreover, even if these threats to internal validity are assessed, causation still cannot be fully established because the experimenter does not have total control over variables.[5]
