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Quasi-Experimental Research Design – Types, Methods

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Quasi-experimental research design is a widely used methodology in social sciences, education, healthcare, and other fields to evaluate the impact of an intervention or treatment. Unlike true experimental designs, quasi-experiments lack random assignment, which can limit control over external factors but still offer valuable insights into cause-and-effect relationships.

This article delves into the concept of quasi-experimental research, explores its types, methods, and applications, and discusses its strengths and limitations.

Quasi-Experimental Design

Quasi-experimental research design is a type of empirical study used to estimate the causal relationship between an intervention and its outcomes. It resembles an experimental design but does not involve random assignment of participants to groups. Instead, groups are pre-existing or assigned based on non-random criteria, such as location, demographic characteristics, or convenience.

For example, a school might implement a new teaching method in one class while another class continues with the traditional approach. Researchers can then compare the outcomes to assess the effectiveness of the new method.

Key Characteristics of Quasi-Experimental Research

• No Random Assignment: Participants are not randomly assigned to experimental or control groups.
• Comparison Groups: Often involves comparing a treatment group to a non-equivalent control group.
• Real-World Settings: Frequently conducted in natural environments, such as schools, hospitals, or workplaces.
• Causal Inference: Aims to identify causal relationships, though less robustly than true experiments.

Purpose of Quasi-Experimental Research

• To evaluate interventions or treatments when randomization is impractical or unethical.
• To provide evidence of causality in real-world settings.
• To test hypotheses and inform policies or practices.

Types of Quasi-Experimental Research Design

1. non-equivalent groups design (negd).

In this design, the researcher compares outcomes between a treatment group and a control group that are not randomly assigned.

• Example: Comparing student performance in schools that adopt a new curriculum versus those that do not.
• Limitation: Potential selection bias due to differences between the groups.

2. Time-Series Design

This involves repeatedly measuring the outcome variable before and after the intervention to observe trends over time.

• Example: Monitoring air pollution levels before and after implementing an industrial emission regulation.
• Variation: Interrupted time-series design, which identifies significant changes at specific intervention points.

3. Regression Discontinuity Design (RDD)

Participants are assigned to treatment or control groups based on a predetermined cutoff score on a continuous variable.

• Example: Evaluating the effect of a scholarship program where students with test scores above a threshold receive funding.
• Strength: Stronger causal inference compared to other quasi-experimental designs.

4. Pretest-Posttest Design

In this design, outcomes are measured before and after the intervention within the same group.

• Example: Assessing the effectiveness of a training program by comparing employees’ skills before and after the training.
• Limitation: Vulnerable to confounding factors that may influence results independently of the intervention.

5. Propensity Score Matching (PSM)

This method pairs participants in the treatment and control groups based on similar characteristics to reduce selection bias.

• Example: Evaluating the impact of online learning by matching students based on demographics and prior academic performance.
• Strength: Improves comparability between groups.

Methods of Quasi-Experimental Research

1. data collection.

• Surveys: Collect information on attitudes, behaviors, or outcomes related to the intervention.
• Observations: Document changes in natural environments or behaviors over time.
• Archival Data: Use pre-existing data, such as medical records or academic scores, to analyze outcomes.

2. Statistical Analysis

Quasi-experiments rely on statistical techniques to control for confounding variables and enhance the validity of results.

• Analysis of Covariance (ANCOVA): Controls for pre-existing differences between groups.
• Regression Analysis: Identifies relationships between the intervention and outcomes while accounting for other factors.
• Propensity Score Matching: Balances treatment and control groups to reduce bias.

3. Control for Confounding Variables

Because randomization is absent, quasi-experimental designs must address confounders using techniques like:

• Matching: Pair participants with similar attributes.
• Stratification: Analyze subgroups based on characteristics like age or income.
• Sensitivity Analysis: Test how robust findings are to potential biases.

4. Use of Mixed Methods

Combining quantitative and qualitative methods enhances the depth of analysis.

• Quantitative: Statistical tests to measure effect size.
• Qualitative: Interviews or focus groups to understand contextual factors influencing outcomes.

Applications of Quasi-Experimental Research

1. education.

• Assessing the impact of new teaching methods or curricula.
• Evaluating the effectiveness of after-school programs on academic performance.

2. Healthcare

• Comparing outcomes of different treatment protocols in hospitals.
• Studying the impact of public health campaigns on vaccination rates.

3. Policy Analysis

• Measuring the effects of new laws or regulations, such as minimum wage increases.
• Evaluating the impact of urban planning initiatives on community health.

4. Social Sciences

• Studying the influence of community programs on crime rates.
• Analyzing the effect of workplace interventions on employee satisfaction.

Strengths of Quasi-Experimental Research

• Feasibility: Can be conducted in real-world settings where randomization is impractical or unethical.
• Cost-Effectiveness: Often requires fewer resources compared to true experiments.
• Flexibility: Accommodates a variety of contexts and research questions.
• Generates Evidence: Provides valuable insights into causal relationships.

Limitations of Quasi-Experimental Research

• Potential Bias: Lack of randomization increases the risk of selection bias.
• Confounding Variables: Results may be influenced by external factors unrelated to the intervention.
• Limited Generalizability: Findings may not apply broadly due to non-random group assignment.
• Weaker Causality: Less robust in establishing causation compared to randomized controlled trials.

Steps to Conduct Quasi-Experimental Research

• Define the Research Question: Clearly articulate what you aim to study and why a quasi-experimental design is appropriate.
• Identify Comparison Groups: Select treatment and control groups based on the research context.
• Collect Data: Use surveys, observations, or archival records to gather pre- and post-intervention data.
• Control for Confounders: Employ statistical methods or matching techniques to address potential biases.
• Analyze Results: Use appropriate statistical tools to evaluate the intervention’s impact.
• Interpret Findings: Discuss results in light of limitations and potential confounding factors.

Quasi-experimental research design offers a practical and versatile approach for evaluating interventions when randomization is not feasible. By employing methods such as non-equivalent groups design, time-series analysis, and regression discontinuity, researchers can draw meaningful conclusions about causal relationships. While these designs may have limitations in controlling bias and confounding variables, careful planning, robust statistical techniques, and clear reporting can enhance their validity and impact. Quasi-experiments are invaluable in fields like education, healthcare, and policy analysis, providing actionable insights for real-world challenges.

• Cook, T. D., & Campbell, D. T. (1979). Quasi-Experimentation: Design and Analysis Issues for Field Settings . Houghton Mifflin.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference . Houghton Mifflin.
• Creswell, J. W. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Sage Publications.
• Bryman, A. (2016). Social Research Methods . Oxford University Press.
• Babbie, E. (2020). The Practice of Social Research . Cengage Learning.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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7.3 Quasi-Experimental Research

Learning objectives.

• Explain what quasi-experimental research is and distinguish it clearly from both experimental and correlational research.
• Describe three different types of quasi-experimental research designs (nonequivalent groups, pretest-posttest, and interrupted time series) and identify examples of each one.

The prefix quasi means “resembling.” Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 1979). Because the independent variable is manipulated before the dependent variable is measured, quasi-experimental research eliminates the directionality problem. But because participants are not randomly assigned—making it likely that there are other differences between conditions—quasi-experimental research does not eliminate the problem of confounding variables. In terms of internal validity, therefore, quasi-experiments are generally somewhere between correlational studies and true experiments.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention. There are many different kinds of quasi-experiments, but we will discuss just a few of the most common ones here.

Nonequivalent Groups Design

Recall that when participants in a between-subjects experiment are randomly assigned to conditions, the resulting groups are likely to be quite similar. In fact, researchers consider them to be equivalent. When participants are not randomly assigned to conditions, however, the resulting groups are likely to be dissimilar in some ways. For this reason, researchers consider them to be nonequivalent. A nonequivalent groups design , then, is a between-subjects design in which participants have not been randomly assigned to conditions.

Imagine, for example, a researcher who wants to evaluate a new method of teaching fractions to third graders. One way would be to conduct a study with a treatment group consisting of one class of third-grade students and a control group consisting of another class of third-grade students. This would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them. For example, the parents of higher achieving or more motivated students might have been more likely to request that their children be assigned to Ms. Williams’s class. Or the principal might have assigned the “troublemakers” to Mr. Jones’s class because he is a stronger disciplinarian. Of course, the teachers’ styles, and even the classroom environments, might be very different and might cause different levels of achievement or motivation among the students. If at the end of the study there was a difference in the two classes’ knowledge of fractions, it might have been caused by the difference between the teaching methods—but it might have been caused by any of these confounding variables.

Of course, researchers using a nonequivalent groups design can take steps to ensure that their groups are as similar as possible. In the present example, the researcher could try to select two classes at the same school, where the students in the two classes have similar scores on a standardized math test and the teachers are the same sex, are close in age, and have similar teaching styles. Taking such steps would increase the internal validity of the study because it would eliminate some of the most important confounding variables. But without true random assignment of the students to conditions, there remains the possibility of other important confounding variables that the researcher was not able to control.

Pretest-Posttest Design

In a pretest-posttest design , the dependent variable is measured once before the treatment is implemented and once after it is implemented. Imagine, for example, a researcher who is interested in the effectiveness of an antidrug education program on elementary school students’ attitudes toward illegal drugs. The researcher could measure the attitudes of students at a particular elementary school during one week, implement the antidrug program during the next week, and finally, measure their attitudes again the following week. The pretest-posttest design is much like a within-subjects experiment in which each participant is tested first under the control condition and then under the treatment condition. It is unlike a within-subjects experiment, however, in that the order of conditions is not counterbalanced because it typically is not possible for a participant to be tested in the treatment condition first and then in an “untreated” control condition.

If the average posttest score is better than the average pretest score, then it makes sense to conclude that the treatment might be responsible for the improvement. Unfortunately, one often cannot conclude this with a high degree of certainty because there may be other explanations for why the posttest scores are better. One category of alternative explanations goes under the name of history . Other things might have happened between the pretest and the posttest. Perhaps an antidrug program aired on television and many of the students watched it, or perhaps a celebrity died of a drug overdose and many of the students heard about it. Another category of alternative explanations goes under the name of maturation . Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a yearlong program, participants might become less impulsive or better reasoners and this might be responsible for the change.

Another alternative explanation for a change in the dependent variable in a pretest-posttest design is regression to the mean . This refers to the statistical fact that an individual who scores extremely on a variable on one occasion will tend to score less extremely on the next occasion. For example, a bowler with a long-term average of 150 who suddenly bowls a 220 will almost certainly score lower in the next game. Her score will “regress” toward her mean score of 150. Regression to the mean can be a problem when participants are selected for further study because of their extreme scores. Imagine, for example, that only students who scored especially low on a test of fractions are given a special training program and then retested. Regression to the mean all but guarantees that their scores will be higher even if the training program has no effect. A closely related concept—and an extremely important one in psychological research—is spontaneous remission . This is the tendency for many medical and psychological problems to improve over time without any form of treatment. The common cold is a good example. If one were to measure symptom severity in 100 common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved. This does not mean that the chicken soup was responsible for the improvement, however, because they would have been much improved without any treatment at all. The same is true of many psychological problems. A group of severely depressed people today is likely to be less depressed on average in 6 months. In reviewing the results of several studies of treatments for depression, researchers Michael Posternak and Ivan Miller found that participants in waitlist control conditions improved an average of 10 to 15% before they received any treatment at all (Posternak & Miller, 2001). Thus one must generally be very cautious about inferring causality from pretest-posttest designs.

Does Psychotherapy Work?

Early studies on the effectiveness of psychotherapy tended to use pretest-posttest designs. In a classic 1952 article, researcher Hans Eysenck summarized the results of 24 such studies showing that about two thirds of patients improved between the pretest and the posttest (Eysenck, 1952). But Eysenck also compared these results with archival data from state hospital and insurance company records showing that similar patients recovered at about the same rate without receiving psychotherapy. This suggested to Eysenck that the improvement that patients showed in the pretest-posttest studies might be no more than spontaneous remission. Note that Eysenck did not conclude that psychotherapy was ineffective. He merely concluded that there was no evidence that it was, and he wrote of “the necessity of properly planned and executed experimental studies into this important field” (p. 323). You can read the entire article here:

http://psychclassics.yorku.ca/Eysenck/psychotherapy.htm

Fortunately, many other researchers took up Eysenck’s challenge, and by 1980 hundreds of experiments had been conducted in which participants were randomly assigned to treatment and control conditions, and the results were summarized in a classic book by Mary Lee Smith, Gene Glass, and Thomas Miller (Smith, Glass, & Miller, 1980). They found that overall psychotherapy was quite effective, with about 80% of treatment participants improving more than the average control participant. Subsequent research has focused more on the conditions under which different types of psychotherapy are more or less effective.

In a classic 1952 article, researcher Hans Eysenck pointed out the shortcomings of the simple pretest-posttest design for evaluating the effectiveness of psychotherapy.

Wikimedia Commons – CC BY-SA 3.0.

Interrupted Time Series Design

A variant of the pretest-posttest design is the interrupted time-series design . A time series is a set of measurements taken at intervals over a period of time. For example, a manufacturing company might measure its workers’ productivity each week for a year. In an interrupted time series-design, a time series like this is “interrupted” by a treatment. In one classic example, the treatment was the reduction of the work shifts in a factory from 10 hours to 8 hours (Cook & Campbell, 1979). Because productivity increased rather quickly after the shortening of the work shifts, and because it remained elevated for many months afterward, the researcher concluded that the shortening of the shifts caused the increase in productivity. Notice that the interrupted time-series design is like a pretest-posttest design in that it includes measurements of the dependent variable both before and after the treatment. It is unlike the pretest-posttest design, however, in that it includes multiple pretest and posttest measurements.

Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows data from a hypothetical interrupted time-series study. The dependent variable is the number of student absences per week in a research methods course. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. The top panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment worked. There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. The bottom panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment did not work. On average, the number of absences after the treatment is about the same as the number before. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design. If there had been only one measurement of absences before the treatment at Week 7 and one afterward at Week 8, then it would have looked as though the treatment were responsible for the reduction. The multiple measurements both before and after the treatment suggest that the reduction between Weeks 7 and 8 is nothing more than normal week-to-week variation.

Figure 7.5 A Hypothetical Interrupted Time-Series Design

The top panel shows data that suggest that the treatment caused a reduction in absences. The bottom panel shows data that suggest that it did not.

Combination Designs

A type of quasi-experimental design that is generally better than either the nonequivalent groups design or the pretest-posttest design is one that combines elements of both. There is a treatment group that is given a pretest, receives a treatment, and then is given a posttest. But at the same time there is a control group that is given a pretest, does not receive the treatment, and then is given a posttest. The question, then, is not simply whether participants who receive the treatment improve but whether they improve more than participants who do not receive the treatment.

Imagine, for example, that students in one school are given a pretest on their attitudes toward drugs, then are exposed to an antidrug program, and finally are given a posttest. Students in a similar school are given the pretest, not exposed to an antidrug program, and finally are given a posttest. Again, if students in the treatment condition become more negative toward drugs, this could be an effect of the treatment, but it could also be a matter of history or maturation. If it really is an effect of the treatment, then students in the treatment condition should become more negative than students in the control condition. But if it is a matter of history (e.g., news of a celebrity drug overdose) or maturation (e.g., improved reasoning), then students in the two conditions would be likely to show similar amounts of change. This type of design does not completely eliminate the possibility of confounding variables, however. Something could occur at one of the schools but not the other (e.g., a student drug overdose), so students at the first school would be affected by it while students at the other school would not.

Finally, if participants in this kind of design are randomly assigned to conditions, it becomes a true experiment rather than a quasi experiment. In fact, it is the kind of experiment that Eysenck called for—and that has now been conducted many times—to demonstrate the effectiveness of psychotherapy.

Key Takeaways

• Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Among the important types are nonequivalent groups designs, pretest-posttest, and interrupted time-series designs.
• Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable. It does not eliminate the problem of confounding variables, however, because it does not involve random assignment to conditions. For these reasons, quasi-experimental research is generally higher in internal validity than correlational studies but lower than true experiments.
• Practice: Imagine that two college professors decide to test the effect of giving daily quizzes on student performance in a statistics course. They decide that Professor A will give quizzes but Professor B will not. They will then compare the performance of students in their two sections on a common final exam. List five other variables that might differ between the two sections that could affect the results.

Discussion: Imagine that a group of obese children is recruited for a study in which their weight is measured, then they participate for 3 months in a program that encourages them to be more active, and finally their weight is measured again. Explain how each of the following might affect the results:

• regression to the mean
• spontaneous remission

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.

Eysenck, H. J. (1952). The effects of psychotherapy: An evaluation. Journal of Consulting Psychology, 16 , 319–324.

Posternak, M. A., & Miller, I. (2001). Untreated short-term course of major depression: A meta-analysis of studies using outcomes from studies using wait-list control groups. Journal of Affective Disorders, 66 , 139–146.

Smith, M. L., Glass, G. V., & Miller, T. I. (1980). The benefits of psychotherapy . Baltimore, MD: Johns Hopkins University Press.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.