Similar to a true experiment, a quasi-experimental design aims to establish a causal relationship between an independent and dependent variable. However, unlike true experiments, quasi-experiments do not utilize random assignment of participants to treatment and control groups. Instead, participants are assigned to groups based on pre-existing characteristics or circumstances, rather than through random selection.
What does quasi experimental mean? Quasi-experimental designs are valuable research tools when conducting true experiments is not feasible or ethical due to practical or ethical constraints. They allow researchers to study cause-and-effect relationships in real-world situations where random assignment or manipulation of variables is challenging or impossible.
Differences between quasi experiments and true experiments
Here’s a table highlighting the differences between true experimental designs and quasi-experimental designs in terms of assignment to treatment, control over treatment, and the use of control groups:
| Aspect | True Experimental Design | Quasi-Experimental Design |
| Assignment to Treatment | Participants are randomly assigned to treatment and control groups. | Participants are not randomly assigned to groups; they are assigned based on pre-existing characteristics or circumstances. |
| Control over Treatment | The researcher has complete control over the treatment or intervention. | The researcher has limited or no control over the treatment or intervention, as it may be naturally occurring or predetermined. |
| Use of Control Groups | Control groups are used, and participants are randomly assigned to these groups. | Control groups may or may not be used, and if used, participants are not randomly assigned to them. |
Example of a true experiment vs a quasi-experiment
Assume you are interested in studying the effects of a new tutoring program on student academic performance.
True Experiment:
A researcher wants to study the effect of a new teaching method on student performance in mathematics. The researcher randomly assigns students from the same school and grade level to either the treatment group (receives the new teaching method) or the control group (receives the traditional teaching method).
The researcher has control over the implementation of the teaching methods and ensures that all other factors, such as curriculum, instructional time, and classroom environment, are kept consistent between the two groups.
Quasi-Experiment:
A researcher wants to study the effect of a new school policy that provides additional tutoring services on student performance in reading. However, the researcher cannot randomly assign students to groups. Instead, the researcher selects two schools: one school that has implemented the new tutoring policy (treatment group) and another school that has not implemented the policy (control group).
The researcher has no control over the implementation of the tutoring services or other factors that may differ between the two schools, such as teacher quality, socioeconomic status of the student population, or school resources.
In the true experiment, the random assignment of participants to groups and the researcher’s control over the treatment ensure that any observed differences in student performance can be attributed to the new teaching method, minimizing the influence of confounding variables.
In the quasi-experiment, the lack of random assignment and the researcher’s limited control over the treatment (tutoring policy) and other factors introduce potential confounding variables that may influence student performance. The researcher must account for these potential confounding variables in the analysis to strengthen the validity of the findings and draw more reliable conclusions about the effect of the tutoring policy.
Types of quasi experimental designs
Quasi-experimental designs allow researchers to study phenomena and interventions in situations where true experiments are not feasible or ethical due to practical or ethical constraints.The three different types are:
Nonequivalent groups design
In this quasi design, two or more groups are compared, but the participants are not randomly assigned to the groups. The groups may differ on important characteristics, and the researcher must account for these differences in the analysis.
Example: A researcher wants to study the effect of a new tutoring program on academic performance. Two existing classes are selected: one class receives the tutoring program (treatment group), and the other class does not (control group). Since the classes already exist and students were not randomly assigned to them, this is a nonequivalent groups design.
Regression discontinuity
This design is used when participants are assigned to treatment or control groups based on a specific cutoff score or threshold on a continuous variable.
Example: A school district implements a new reading intervention program for students who score below a certain threshold on a standardized reading test. Students just below the cutoff score receive the intervention (treatment group), while students just above the cutoff do not (control group). The researcher can compare the reading scores of the two groups to evaluate the effectiveness of the intervention.
Natural experiments
These designs take advantage of naturally occurring events or circumstances that resemble experimental treatments. The researcher does not have control over the treatment or assignment to groups.
Example: A researcher wants to study the effect of a new state law that raises the minimum wage. Some cities in the state have already implemented the higher minimum wage (treatment group), while others have not (control group). The researcher can compare economic indicators, such as employment rates and consumer spending, between the two groups of cities to evaluate the impact of the minimum wage increase.
When to use quasi-experimental design
Quasi-experimental designs are often used when true experiments are not feasible or ethical due to practical or ethical constraints.
Ethical
In some situations, it may be unethical or undesirable to randomly assign participants to treatment or control groups, especially when the treatment or intervention being studied involves potential risks or benefits. Quasi-experimental designs are suitable in these cases because they do not require random assignment.
For example, in medical research, it would be unethical to randomly assign participants to receive a potentially harmful treatment or to withhold a potentially beneficial treatment. In such cases, researchers may use a quasi-experimental design to study the effects of an existing treatment or intervention without randomly assigning participants.
Practical
In other cases, it may be difficult or impossible to randomly assign participants or manipulate the treatment due to practical constraints. Quasi-experimental designs are useful in these situations because they allow researchers to study phenomena in real-world settings or with pre-existing groups.
For instance, in educational research, it may not be feasible to randomly assign students to different teaching methods or interventions due to logistical or administrative constraints. In such cases, researchers may use a quasi-experimental design to study the effects of an educational program or policy by comparing existing groups of students or schools.
advantages and disadvantages of quasi experimental design
Despite their limitations, quasi-experimental designs are valuable research methods when true experiments are not feasible or ethical. Here are some advantages and disadvantages:
Advantages
- Allow researchers to study phenomena that cannot be manipulated experimentally due to ethical or practical constraints.
- Provide insights into real-world situations and naturalistic settings, enhancing external validity.
- Generally less expensive and time-consuming than true experiments, as they do not require extensive experimental controls or setups.
Disadvantages
- Lack of random assignment and control over treatment can introduce confounding variables and reduce internal validity, making it more difficult to establish cause-and-effect relationships.
- Potential for selection biases and other threats to validity due to the non-random assignment of participants to groups.
- Limited generalizability due to the specific context and sample used in the study, which may not be representative of the broader population.