Despite lacking the random assignment found in true experiments, the quasi-experimental design can still offer valuable insights by comparing groups based on pre-existing variables, such as demographic characteristics or specific behaviors. However, when employing this methodology, the potential for selection bias and confounding variables should be carefully considered, as they may impact the generalizability and causal interpretations of the results.
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Quasi-Experimental Design – In a Nutshell
- Due to the manipulation of the independent variable, quasi-experimental research eliminates the problem of directionality.
- A quasi-experimental design manipulates an independent variable without randomly assigning individuals to conditions or ordering conditions.
- A quasi-experimental design is a distinctive research technique because it is distinguished by what it lacks.
Definition: Quasi-experimental design
A quasi-experimental design, like a true experiment, seeks to create a cause-and-effect link between an independent and dependent variable.
In contrast to a true experiment, a quasi-experimental design isn’t reliant on random assignment. Non-random criteria are used to assign subjects to groups.
Quasi-experiment vs. true experiment
There are numerous contrasts between true and quasi-experimental designs.
| True Experimental Design | Quasi-Experimental Design | |
| Assignment to treatment | Treatment and control groups are randomly assigned by a researcher. | Some other non-random method is used to give subjects to groups. |
| Control over treatment | Typically, the treatment is designed by the researcher. | Naturally, the researcher does not influence the treatment; instead, they investigate pre-existing groups that got various treatments after the fact. |
| Use of control groups | Requires the employment of treatment and control groups. | The use of control groups is not mandatory. |
Example of a quasi-experimental design vs. true experimental design
Suppose you’re curious about how a new psychological treatment affects people with depression.
True experimental design:
To conduct a true experiment, one must randomly assign the new treatment to half of the patients in a mental health clinic. The other half of the sample, the control group, receives the ordinary course of depression treatment.
Patients fill out a symptom sheet every few months to determine if the new treatment is considerably more effective (or less effective) than the traditional treatment.
However, due to ethical considerations, the administrators of the mental health center may deny you permission to randomly allocate their patients to treatments. In this situation, a true experiment cannot be conducted.
Instead, you might utilize a quasi-experimental design.
Quasi-experimental design:
You discover that some of the clinic’s psychotherapists have opted to test the new treatment, while others who handle comparable patients have decided to stay with the standard approach.
You can utilize these pre-existing groups to compare the symptom development of patients getting the new therapy to those receiving the usual treatment.
Even if the groups were not assigned randomly, if you correctly account for any systematic variations between them, you could be pretty confident that any differences must be attributable to the treatment and not to other confounding variables.
3 Types of a quasi-experimental design
One may distinguish between the three most prevalent types of a quasi-experimental design. In the following, we will delve into nonequivalent group design, regression discontinuity, and natural experiments.
Nonequivalent group design
Nonequivalent group designs are a hybrid of experimental designs and quasi-experimental methods. This is because it leverages both of their qualities. Similar to a true experiment, nonequivalent group design implements pre-existing groups: treatment and control groups that are believed to be comparable. However, it lacks the randomization that defines a quasi-experimental design.
Researchers ensure that any third or confounding variables do not impact them throughout the grouping process. Consequently, the groupings are as comparable as possible.
Example
- You believe that an after-school program may lead to better grades.
- You select two groups of children, one attending a school that implements the new program and the other does not.
- You can determine if the program affects grades by comparing the students attending it to those who don’t.
Regression discontinuity
Regression discontinuity design, or RDD, is a quasi-experimental method for calculating the impact of a treatment or intervention. It accomplishes this by using a mechanism known as a “cutoff” that assigns treatment based on eligibility.
Therefore, participants above the cutoff are assigned to a treatment group, while those below the cutoff are not. The distinction between these two divisions is negligible.
Example
- Some high schools in the U. S. are reserved for high-achieving pupils, who must get a minimum score on an entrance exam to attend. Those who pass the assessment differ systematically from those that do not.
- However, because the exact cutoff score is arbitrary, students who are close to the cutoff, who just barely pass the tests, and those who fail by a small margin—tend to have scores that are quite similar with small discrepancies between them being mainly attributable to random chance. Therefore, you can assume that any outcomes disparities are due to the schools the students attended.
- You may examine the long-term outcomes of these two groups of kids to determine the effect of attending a selective school.
Natural experiments
In both laboratory and field tests, researchers are often in charge of assigning individuals to a particular group. During a natural experiment, individuals are assigned to the treatment group at random or in a random-like manner by an external occurrence or situation.
Because natural experiments are observational, they are not regarded as true experiments despite some using random assignments.
Researchers can use the independent variable, even when they have no control over it, to study the treatment’s effect.
Example
- One of the most well-known natural experiments is the Oregon Health Study. In 2008, Oregon agreed to allow additional low-income persons to participate in Medicaid, the United States’ low-income health insurance program.
- As they couldn’t afford to cover all who qualified for the program, they used a random lottery to allocate program slots.
- Researchers evaluated the program’s effect by using those recruited as a randomly assigned treatment group and the individuals who were eligible but did not win the lottery as a control group.
When is a quasi-experimental design relevant?
Although true experiments have greater internal validity, you may opt for a quasi-experimental design due to ethical or practical considerations.
Ethical reasons
Occasionally, offering or withholding treatment at random would be unethical, making it impossible to conduct a true experiment. In this situation, a quasi-experimental design can be used to examine the exact causal link without ethical concerns.
A noteworthy example is the Oregon Health Study. It’d be unethical to randomly grant health insurance to specific individuals while excluding others from having coverage for the sole purpose of research.
Practical reasons
Despite their greater internal validity, true experiments can be expensive. In addition, a sufficient number of participants is necessary to justify the experiment. When conducting a quasi-experimental design, on the other hand, already collected data may be used.
Pros and cons of a quasi-experimental design
| ✓ Pros | ✗ Cons |
| It provides researchers control over variables by allowing them to manipulate them. | It has a lower level of internal validity than true experiments. |
| The quasi-experiment method is compatible with various experimental procedures. | It is susceptible to human error. |
| It provides a greater degree of transferability. | Allows the researcher's bias to enter the equation. |
FAQs
A quasi-experiment is a research design that aims to prove a cause-and-effect link.
Random assignment is used in experimental research to divide your subjects into distinct groups randomly.
This strategy ensures that every sample member is randomly assigned to either a control or an experimental group.
A quasi-experimental design is most beneficial in instances where conducting a true experiment would be either unethical or impractical.
The internal validity of a study based on quasi-experimental design is lower than actual experiments, but their external validity is frequently greater since they employ real-world interventions rather than contrived laboratory conditions.