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However, such bias can be controlled for using various statistical techniques such as multiple regression, if one can identify and measure the confounding variable(s).
The primary drawback of quasi-experimental designs is that they cannot eliminate the possibility of confounding bias, which can hinder one's ability to draw causal inferences.
This drawback is often used to discount quasi-experimental results.
Factors such as cost, feasibility, political concerns, or convenience may influence how or if participants are assigned to a given treatment conditions, and as such, quasi-experiments are subject to concerns regarding internal validity (i.e., can the results of the experiment be used to make a causal inference? Quasi-experiments are also effective because they use the "pre-post testing".
This means that there are tests done before any data are collected to see if there are any person confounds or if any participants have certain tendencies.
Then the actual experiment is done with post test results recorded.
This data can be compared as part of the study or the pre-test data can be included in an explanation for the actual experimental data.
Any change in characteristics post-intervention is likely attributable to the intervention.
With quasi-experimental studies, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes.
As a result, differences between groups on both observed and unobserved characteristics would be due to chance, rather than to a systematic factor related to treatment (e.g., illness severity).
Randomization itself does not guarantee that groups will be equivalent at baseline.