quasi-experimental design | Definition

Quasi-experimental design is a research method that tests cause-effect relationships without randomly assigning participants to groups.

What Is a Quasi-Experimental Design?

A quasi-experimental design is a research strategy used to examine cause-and-effect relationships when random assignment is not possible. In social science research, this approach is often used in real-world settings like schools, workplaces, or communities where it may be unethical, impractical, or impossible to randomly assign people to treatment and control groups.

In a true experiment, researchers randomly assign participants to different groups to control for outside influences. However, in a quasi-experiment, participants are grouped based on existing characteristics or situations. Even though researchers do not use random assignment, they still attempt to control for bias and other variables to make valid conclusions.

Quasi-experimental designs are widely used in education, criminal justice, public policy, psychology, and health research. They are especially useful when studying interventions or programs that are already in place.

Key Features of Quasi-Experimental Designs

Lack of Random Assignment

The most important feature that sets a quasi-experimental design apart from a true experimental design is the absence of random assignment. Instead of randomly placing people into different groups, researchers use naturally occurring groups. For example, they might compare students from two different schools where one school has introduced a new teaching method and the other has not.

Use of Comparison Groups

Even without randomization, quasi-experiments still try to include a comparison group. This helps researchers evaluate the impact of an intervention by comparing outcomes between those exposed to the intervention and those who are not.

Attempts to Control for Confounding Variables

Because randomization is not used, there is a greater risk that other variables could influence the results. To address this, researchers often use statistical methods or study designs that try to minimize the impact of these outside variables, also known as confounders.

Real-World Settings

Quasi-experimental designs are often used outside of laboratories. They are commonly applied in schools, hospitals, workplaces, and communities, where real-life factors affect behavior and outcomes. This makes the findings more applicable to everyday situations.

Common Types of Quasi-Experimental Designs

Nonequivalent Groups Design

This is one of the most common quasi-experimental designs. In this approach, two or more groups are compared, but the groups are not randomly assigned. One group receives the treatment (intervention), and the other does not. Researchers try to ensure the groups are as similar as possible in other ways.

Example: A school introduces a new reading program for third-grade students. Researchers compare the reading test scores of these students with scores from another school that uses a traditional program.

Pretest-Posttest Design

In this design, researchers measure the outcome both before and after the intervention. This helps them see changes over time. There may or may not be a comparison group.

Example: A police department implements a new community policing strategy. Researchers measure crime rates before and after the strategy is put in place to see if it had an impact.

Interrupted Time Series Design

This design involves collecting data over time, both before and after the intervention. The goal is to look for patterns or changes that occur once the intervention begins.

Example: A new drug policy is introduced in a city. Researchers track drug-related arrests each month for several years before and after the policy takes effect to see if trends change.

Regression Discontinuity Design

This design is used when participants are assigned to a group based on a cutoff score on a pretest or other measure. People above the cutoff receive the treatment, and those below do not. This method allows for strong causal conclusions even without random assignment.

Example: Students who score just above a cutoff on a placement test are placed in advanced math classes. Researchers compare their outcomes to students who scored just below the cutoff to evaluate the program’s effects.

Strengths of Quasi-Experimental Designs

Practical and Ethical

Quasi-experimental designs are useful when random assignment is not possible due to ethical or practical reasons. For example, you can’t randomly assign some students to receive special education services and others to go without.

Realistic Settings

Because these studies often occur in real-world environments, the findings may be more generalizable to other similar settings.

Useful for Policy Evaluation

Policymakers often implement changes that affect large groups of people. Quasi-experimental designs help researchers evaluate the effects of those changes without needing full control over the situation.

Cost-Effective

Compared to controlled experiments, quasi-experiments can be less expensive and time-consuming. Researchers can often use existing programs and collect data from available records.

Limitations of Quasi-Experimental Designs

Risk of Selection Bias

Without random assignment, the groups being compared may differ in important ways. These differences can affect the results and lead to incorrect conclusions.

Example: If one group of students has more experienced teachers, their better test scores might be due to the teachers, not the intervention.

Confounding Variables

Other variables that were not measured or controlled can influence the results. These confounding variables can make it difficult to know whether the observed effects are due to the intervention.

Weaker Causal Inference

Because quasi-experiments lack randomization, they are not as strong as true experiments in proving cause-and-effect relationships. However, careful design and analysis can still provide valuable insights.

Difficulty in Replication

Since these studies are often based on specific real-world events or groups, they can be harder to replicate than controlled experiments.

Strategies to Improve Validity

To address some of the limitations, researchers use several strategies to improve the credibility of their quasi-experimental studies:

Matching

Researchers can match participants in the treatment and comparison groups based on characteristics like age, gender, income, or prior performance. This helps make the groups more similar.

Statistical Controls

Advanced statistical techniques, such as multiple regression or propensity score matching, can control for differences between groups.

Use of Multiple Measures

Collecting data from different sources or using different types of measurement (such as surveys, test scores, and observations) can help confirm findings.

Longitudinal Designs

Following the same group of people over time allows researchers to observe trends and reduce the effects of temporary factors.

Applications in Social Science Fields

Education

Researchers often use quasi-experimental designs to evaluate new teaching methods, school policies, or curriculum changes. For example, comparing student achievement across schools using different instructional strategies.

Criminal Justice and Criminology

These designs are used to study the impact of policy changes, policing strategies, or rehabilitation programs. For instance, comparing crime rates before and after implementing body cameras in a police department.

Public Policy

Governments introduce laws and programs that affect entire populations. Quasi-experiments help evaluate the effects of these interventions when randomized studies are not possible.

Psychology

Psychologists may study natural groups, such as people with or without certain experiences (e.g., trauma), to understand the effects on mental health or behavior.

Health and Social Work

Health researchers might compare outcomes for patients in different hospitals or communities when a new treatment or program is introduced. Social workers use quasi-experiments to examine outcomes of family support services or housing programs.

When to Use a Quasi-Experimental Design

This design is most useful when:

• Random assignment is not possible or ethical

• A real-world intervention is being studied

• You want to evaluate the impact of a policy or program

• You aim to balance practical constraints with scientific rigor

While not as strong as a true experiment, quasi-experiments offer a valuable middle ground between descriptive studies and randomized trials.

Comparing Quasi-Experimental and True Experimental Designs

Even though quasi-experiments are less controlled, they are highly respected in the social sciences when well-designed and properly analyzed.

Conclusion

Quasi-experimental design provides a valuable option for researchers who want to study cause-and-effect relationships in real-world settings without the use of random assignment. While it carries some limitations, careful planning, and rigorous analysis can make it a powerful method for evaluating policies, programs, and social interventions. Across education, health, psychology, public policy, and criminal justice, this design allows researchers to study meaningful questions in ways that are both ethical and practical.

Glossary Return to Doc's Research Glossary

Last Modified: 03/22/2025