Instead, quasi-experimental designs typically allow assignment to treatment condition to proceed how it would in the absence of an experiment.
In other words, 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).
[5]: 242 It does, however, require large numbers of study participants and precise modeling of the functional form between the assignment and the outcome variable, in order to yield the same power as a traditional experimental design.
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.
However, such bias can be controlled for by using various statistical techniques such as multiple regression, if one can identify and measure the confounding variable(s).
On their own, quasi-experimental designs do not allow one to make definitive causal inferences; however, they provide necessary and valuable information that cannot be obtained by experimental methods alone.
Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition.
We can run a linear regression to determine if there is a positive correlation between parents' spanking and their children's aggressive behavior.
It is usually easily conducted as opposed to true experiments, because they bring in features from both experimental and non-experimental designs.
Additionally, utilizing quasi-experimental designs minimizes threats to ecological validity as natural environments do not suffer the same problems of artificiality as compared to a well-controlled laboratory setting.
[10] 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.
[1] In the example above, a variation in the children's response to spanking is plausibly influenced by factors that cannot be easily measured and controlled, for example the child's intrinsic wildness or the parent's irritability.
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 in terms of internal validity.
This deficiency in randomization makes it harder to rule out confounding variables and introduces new threats to internal validity.
[11] Because randomization is absent, some knowledge about the data can be approximated, but conclusions of causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment.
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 extraneous variables.
[14] Therefore, the more relevant question is whether treatment effects generalize "across" subpopulations that vary on background factors that might not be salient to the researcher.
External validity depends on whether the treatments studies have homogeneous effects across different subsets of people, times, contexts, and methods of study or whether the sign and magnitude of any treatment effects changes across subsets in ways that may not be acknowledged or understood by the researchers.
An important factor in dealing with person-by-treatment designs is that random assignment will need to be used in order to make sure that the experimenter has complete control over the manipulations that are being done to the study.