Quasi-experiment


A quasi-experiment is a research design used to estimate the causal impact of an intervention. Quasi-experiments share similarities with experiments and randomized controlled trials, but specifically lack random assignment to treatment or control. Instead, quasi-experimental designs typically allow assignment to treatment condition to proceed how it would in the absence of an experiment.
The causal analysis of quasi-experiments depends on assumptions that render non-randomness irrelevant, and thus it is subject to concerns regarding internal validity if the treatment and control groups are not be comparable at baseline. In other words, it may be difficult to convincingly demonstrate a causal link between the treatment condition and observed outcomes in quasi-experimental designs. This is particularly true if there are confounding variables that cannot be controlled or accounted for.

Design

The first part of creating a quasi-experimental design is to identify the variables. The quasi-independent variable is the variable that is manipulated in order to affect a dependent variable. The predicted outcome is the dependent variable. In a time series analysis, the dependent variable is observed over time for any changes that may take place. One or more covariates are usually included in analyses, ideally variables that predict both the treatment group and the outcome. These are additional variables that are often used to address confounding, e.g., through statistical adjustment or matching. Once the variables have been identified and defined, a procedure should then be implemented and group differences should be examined.
In an experiment with random assignment, study units would have the same chance of being assigned to a given treatment condition. As such, random assignment ensures that both the experimental and control groups are equivalent. In contrast, in a quasi-experimental design, assignment to a given treatment condition is based on something other than random assignment. Depending on the type of quasi-experimental design, the researcher might have control over assignment to the treatment condition but use some criteria other than random assignment to determine which participants receive the treatment, or the researcher may have no control over the treatment condition assignment and the criteria used for assignment may be unknown. 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.
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. Quasi experiments have independent variables that already exist such as age, gender, eye color. These variables can either be continuous or they can be categorical. In short, naturally occurring variables are measured within quasi experiments.
There are several types of quasi-experimental designs, each with different strengths, weaknesses and applications. These designs include :
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". 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.
Though quasi-experiments are sometimes shunned by those who consider themselves to be experimental purists, they can be useful in areas where it is not feasible or desirable to conduct an experiment or randomized control trial. Such instances include evaluating the impact of public policy changes, educational interventions or large scale health interventions. 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 as an excuse to discount quasi-experimental results. 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. Such techniques can be used to model and partial out the effects of confounding variables techniques, thereby improving the accuracy of the results obtained from quasi-experiments. Moreover, the developing use of propensity score matching to match participants on variables important to the treatment selection process can also improve the accuracy of quasi-experimental results.
In fact, data derived from quasi-experimental analyses has been shown to closely match experimental data in certain cases, even when different criteria were used. In sum, quasi-experiments are a valuable tool, especially for the applied researcher. 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. Researchers, especially those interested in investigating applied research questions, should move beyond the traditional experimental design and avail themselves of the possibilities inherent in quasi-experimental designs.

Ethics

A true experiment would, for example, 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 spanking or not spanking categories may not be practical or ethical, because some parents may believe it is morally wrong to spank their children and refuse to participate.
Some authors distinguish between a natural experiment and a "quasi-experiment". A natural experiment may approximate random assignment, or involve real randomization not by the experimenters or for the experiment. A quasi-experiment generally does not involve actual randomization.
Quasi-experiments have outcome measures, treatments, and experimental units, but do not use random assignment. Quasi-experiments are often the design that most people choose over true experiments. It is usually easily conducted as opposed to true experiments, because they bring in features from both experimental and non-experimental designs. Measured variables as well as manipulated variables can be brought in. Usually quasi-experiments are chosen by experimenters because they maximize internal and external validity.

Advantages

Since quasi-experimental designs are used when randomization is impractical and/or unethical, they are typically easier to set up than true experimental designs, which require random assignment of subjects. 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. 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.
Other advantages of quasi experiments include the idea of having any manipulations the experimenter so chooses. In natural experiments, the researchers have to let manipulations occur on their own and have no control over them whatsoever. Also, using self selected groups in quasi experiments also takes away the chance of ethical, conditional, etc. concerns while conducting the study.

Disadvantages

Quasi-experimental estimates of impact are subject to contamination by confounding variables. 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. 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.
Disadvantages also include the study groups may provide weaker evidence because of the lack of randomness. Randomness brings a lot of useful information to a study because it broadens results and therefore gives a better representation of the population as a whole. Using unequal groups can also be a threat to internal validity. If groups are not equal, which is sometimes the case in quasi experiments, then the experimenter might not be positive about determining the causes of the results.