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X2 O2 Where "X" is a particular treatment or intervention and "O" is a measured outcome, and "R" indicates whether participants were randomly assigned to treatment groups.
Why is the control group "nonequivalent" for the quasi-experimental design? Because we did not use random assignment to place subjects in treatment groups, we cannot assume that on the average the groups are the same, or even approximately equivalent on the average, to begin with.
However, two types of design often conducted more with quasi-experimental situations include the time series design sometimes called a "natural experiment" and the case study. In the time series design, there are several observations over time. While there may be some type of experimental intervention, often "nature" does the experimenting for you: A hurricane hits and you assess how well emergency personnel put new training and procedures in place.
New legislation requires all automobile drivers and passengers to wear seat belts. New state legislation mandates new testing for high school students. A state eliminates affirmative action considerations in collegiate admissions policies.
A new therapy is publicized that purports to help autistic children. In all these cases, we assume that there was a series of "pre-intervention" measures, or that a series of pre-intervention measures could be obtained, which the scholar then continues following the intervention.
In all likelihood, there isn't a control group let alone a randomized control group so you can't tease out specifically what it was about the intervention, legislation, or therapy that caused the observed outcomes. If you have enough advance warning, you may be able to have more groups, although without randomization of treatments to groups, you still have many of the threats to internal validity listed below.
Case studies occur with some frequency in medical, educational and therapeutic fields. Practitioners who work one on one such as counselors or with very small groups special education classes are the most likely to use case studies.
Subjects are not random, the case base is small, and there may be no control group. As you can guess, causal inference is much more difficult. Much of the research on such individuals is conducted by clinicians.
Their patients comprise the sample sometimes a sample of one and it uses a case study approach wherein inferences are made about the person's proneness to fantasy construction.
However, due to the lack of comparison groups, it can be difficult to ascertain what the true causal variables are. What's the best that can be done under such circumstances? Impose a time series of observations if possible.
If the intervention is under researcher control dispensing a new medication, for exampleimpose the intervention, remove it, impose it again, remove it, and so forth.Ali Azim is a practicing Internal Medicine doctor in Chicago, IL.
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Random assignment of participants to treatments is key in experiments and potentially gives experiments strong internal validity. If a study has different levels of "experimental treatments", and people or groups are assigned to these WITHOUT random assignment, we have a quasi-experiment.