A quasi-experiment is an empirical study used to estimate the causal impact of an intervention on its target population. Quasi-experimental research designs share many similarities with the traditional experimental design or randomized controlled trial, but they specifically lack the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment (e.g., an eligibility cutoff score).[1] In some cases, the researcher may have no control over assignment to treatment condition.
Quasi-experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, the treatment group will be statistically identical to the control group, on both observed and unobserved characteristics, at baseline (provided that the study has adequate sample size). Any change in characteristics post-intervention is due, therefore, to the intervention alone. With quasi-experimental studies, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes. This is particularly true if there are confounding variables that cannot be controlled or accounted for.[2]
There are several types of quasi-experimental designs, each with different strengths, weaknesses and applications. These designs include (but are not limited to)[3]:
Of all of these designs, the regression discontinuity design comes the closest to the experimental design, as the experimenter maintains control of the treatment assignment and it is known to “yield an unbiased estimate of the treatment effects”.[4] 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
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A true experiment would randomly assign children to a scholarship, in order to control for all other variables. 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.
As an example, suppose we divide households into two categories: Households in which the parents spank their children, and households in which the parents do not spank their children. We can run a linear regression to determine if there is a positive correlation between parents' spanking and their children's aggressive behavior. However, to simply randomize parents to spank or to not spank their children may not be practical or ethical.
Some authors distinguish between a natural experiment and a "quasi-experiment".[5][1] The difference is that in a quasi-experiment the criterion for assignment is selected by the researcher, while in a natural experiment the assignment occurs 'naturally,' without the researcher's intervention.
Quasi-experimental estimates of impact are subject to contamination by confounding variables.[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.
DiNardo, J. (2008). "Natural experiments and quasi-natural experiments". In Durlauf, Steven N.; Blume, Lawrence E. The New Palgrave Dictionary of Economics (Second ed.). Palgrave Macmillan. doi:10.1057/9780230226203.1162. http://www.dictionaryofeconomics.com/article?id=pde2008_N000142.