An introduction to quasi-experimental designs
However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria.
Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons.
Differences between quasi-experiments and true experiments
There are several common differences between true and quasi-experimental designs.
|True experimental design||Quasi-experimental design|
|Assignment to treatment||The researcher randomly assigns subjects to control and treatment groups.||Some other, non-random method is used to assign subjects to groups.|
|Control over treatment||The researcher usually designs the treatment.||The researcher often does not have control over the treatment, but instead studies pre-existing groups that received different treatments after the fact.|
|Use of control groups||Requires the use of control and treatment groups.||Control groups are not required (although they are commonly used).|
Example of a true experiment vs a quasi-experiment
However, for ethical reasons, the directors of the mental health clinic may not give you permission to randomly assign their patients to treatments. In this case, you cannot run a true experiment.
Instead, you can use a quasi-experimental design.
Types of quasi-experimental designs
Many types of quasi-experimental designs exist. Here we explain three of the most common types: nonequivalent groups design, regression discontinuity, and natural experiments.
Nonequivalent groups design
In nonequivalent group design, the researcher chooses existing groups that appear similar, but where only one of the groups experiences the treatment.
In a true experiment with random assignment, the control and treatment groups are considered equivalent in every way other than the treatment. But in a quasi-experiment where the groups are not random, they may differ in other ways—they are nonequivalent groups.
When using this kind of design, researchers try to account for any confounding variables by controlling for them in their analysis or by choosing groups that are as similar as possible.
This is the most common type of quasi-experimental design.
Many potential treatments that researchers wish to study are designed around an essentially arbitrary cutoff, where those above the threshold receive the treatment and those below it do not.
Near this threshold, the differences between the two groups are often so minimal as to be nearly nonexistent. Therefore, researchers can use individuals just below the threshold as a control group and those just above as a treatment group.
In both laboratory and field experiments, researchers normally control which group the subjects are assigned to. In a natural experiment, an external event or situation (“nature”) results in the random or random-like assignment of subjects to the treatment group.
Even though some use random assignments, natural experiments are not considered to be true experiments because they are observational in nature.
Although the researchers have no control over the independent variable, they can exploit this event after the fact to study the effect of the treatment.
When to use quasi-experimental design
Although true experiments have higher internal validity, you might choose to use a quasi-experimental design for ethical or practical reasons.
Sometimes it would be unethical to provide or withhold a treatment on a random basis, so a true experiment is not feasible. In this case, a quasi-experiment can allow you to study the same causal relationship without the ethical issues.
The Oregon Health Study is a good example. It would be unethical to randomly provide some people with health insurance but purposely prevent others from receiving it solely for the purposes of research.
However, since the Oregon government faced financial constraints and decided to provide health insurance via lottery, studying this event after the fact is a much more ethical approach to studying the same problem.
True experimental design may be infeasible to implement or simply too expensive, particularly for researchers without access to large funding streams.
At other times, too much work is involved in recruiting and properly designing an experimental intervention for an adequate number of subjects to justify a true experiment.
In either case, quasi-experimental designs allow you to study the question by taking advantage of data that has previously been paid for or collected by others (often the government).
Advantages and disadvantages
Quasi-experimental designs have various pros and cons compared to other types of studies.
- Higher external validity than most true experiments, because they often involve real-world interventions instead of artificial laboratory settings.
- Higher internal validity than other non-experimental types of research, because they allow you to better control for confounding variables than other types of studies do.
- Lower internal validity than true experiments—without randomization, it can be difficult to verify that all confounding variables have been accounted for.
- The use of retrospective data that has already been collected for other purposes can be inaccurate, incomplete or difficult to access.
Frequently asked questions about quasi-experimental designs
- What is a quasi-experiment?
- What is random assignment?
- When should I use a quasi-experimental design?