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• Quasi-Experimental Design | Definition, Types & Examples

Quasi-Experimental Design | Definition, Types & Examples

Published on July 31, 2020 by Lauren Thomas . Revised on January 22, 2024.

Like a true experiment , a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable .

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.

Table of contents

Differences between quasi-experiments and true experiments, types of quasi-experimental designs, when to use quasi-experimental design, advantages and disadvantages, other interesting articles, frequently asked questions about quasi-experimental designs.

There are several common differences between true and quasi-experimental designs.

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.

You can use these pre-existing groups to study the symptom progression of the patients treated with the new therapy versus those receiving the standard course of treatment.

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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.

Regression discontinuity

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.

However, since the exact cutoff score is arbitrary, the students near the threshold—those who just barely pass the exam and those who fail by a very small margin—tend to be very similar, with the small differences in their scores mostly due to random chance. You can therefore conclude that any outcome differences must come from the school they attended.

Natural experiments

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.

However, as they could not afford to cover everyone who they deemed eligible for the program, they instead allocated spots in the program based on a random lottery.

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).

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.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

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Quasi Experimental Design Overview & Examples

By Jim Frost Leave a Comment

What is a Quasi Experimental Design?

A quasi experimental design is a method for identifying causal relationships that does not randomly assign participants to the experimental groups. Instead, researchers use a non-random process. For example, they might use an eligibility cutoff score or preexisting groups to determine who receives the treatment.

Quasi-experimental research is a design that closely resembles experimental research but is different. The term “quasi” means “resembling,” so you can think of it as a cousin to actual experiments. In these studies, researchers can manipulate an independent variable — that is, they change one factor to see what effect it has. However, unlike true experimental research, participants are not randomly assigned to different groups.

Learn more about Experimental Designs: Definition & Types .

When to Use Quasi-Experimental Design

Researchers typically use a quasi-experimental design because they can’t randomize due to practical or ethical concerns. For example:

• Practical Constraints : A school interested in testing a new teaching method can only implement it in preexisting classes and cannot randomly assign students.
• Ethical Concerns : A medical study might not be able to randomly assign participants to a treatment group for an experimental medication when they are already taking a proven drug.

Quasi-experimental designs also come in handy when researchers want to study the effects of naturally occurring events, like policy changes or environmental shifts, where they can’t control who is exposed to the treatment.

Quasi-experimental designs occupy a unique position in the spectrum of research methodologies, sitting between observational studies and true experiments. This middle ground offers a blend of both worlds, addressing some limitations of purely observational studies while navigating the constraints often accompanying true experiments.

A significant advantage of quasi-experimental research over purely observational studies and correlational research is that it addresses the issue of directionality, determining which variable is the cause and which is the effect. In quasi-experiments, an intervention typically occurs during the investigation, and the researchers record outcomes before and after it, increasing the confidence that it causes the observed changes.

However, it’s crucial to recognize its limitations as well. Controlling confounding variables is a larger concern for a quasi-experimental design than a true experiment because it lacks random assignment.

In sum, quasi-experimental designs offer a valuable research approach when random assignment is not feasible, providing a more structured and controlled framework than observational studies while acknowledging and attempting to address potential confounders.

Types of Quasi-Experimental Designs and Examples

Quasi-experimental studies use various methods, depending on the scenario.

Natural Experiments

This design uses naturally occurring events or changes to create the treatment and control groups. Researchers compare outcomes between those whom the event affected and those it did not affect. Analysts use statistical controls to account for confounders that the researchers must also measure.

Natural experiments are related to observational studies, but they allow for a clearer causality inference because the external event or policy change provides both a form of quasi-random group assignment and a definite start date for the intervention.

For example, in a natural experiment utilizing a quasi-experimental design, researchers study the impact of a significant economic policy change on small business growth. The policy is implemented in one state but not in neighboring states. This scenario creates an unplanned experimental setup, where the state with the new policy serves as the treatment group, and the neighboring states act as the control group.

Researchers are primarily interested in small business growth rates but need to record various confounders that can impact growth rates. Hence, they record state economic indicators, investment levels, and employment figures. By recording these metrics across the states, they can include them in the model as covariates and control them statistically. This method allows researchers to estimate differences in small business growth due to the policy itself, separate from the various confounders.

Nonequivalent Groups Design

This method involves matching existing groups that are similar but not identical. Researchers attempt to find groups that are as equivalent as possible, particularly for factors likely to affect the outcome.

For instance, researchers use a nonequivalent groups quasi-experimental design to evaluate the effectiveness of a new teaching method in improving students’ mathematics performance. A school district considering the teaching method is planning the study. Students are already divided into schools, preventing random assignment.

The researchers matched two schools with similar demographics, baseline academic performance, and resources. The school using the traditional methodology is the control, while the other uses the new approach. Researchers are evaluating differences in educational outcomes between the two methods.

They perform a pretest to identify differences between the schools that might affect the outcome and include them as covariates to control for confounding. They also record outcomes before and after the intervention to have a larger context for the changes they observe.

Regression Discontinuity

This process assigns subjects to a treatment or control group based on a predetermined cutoff point (e.g., a test score). The analysis primarily focuses on participants near the cutoff point, as they are likely similar except for the treatment received. By comparing participants just above and below the cutoff, the design controls for confounders that vary smoothly around the cutoff.

For example, in a regression discontinuity quasi-experimental design focusing on a new medical treatment for depression, researchers use depression scores as the cutoff point. Individuals with depression scores just above a certain threshold are assigned to receive the latest treatment, while those just below the threshold do not receive it. This method creates two closely matched groups: one that barely qualifies for treatment and one that barely misses out.

By comparing the mental health outcomes of these two groups over time, researchers can assess the effectiveness of the new treatment. The assumption is that the only significant difference between the groups is whether they received the treatment, thereby isolating its impact on depression outcomes.

Controlling Confounders in a Quasi-Experimental Design

Accounting for confounding variables is a challenging but essential task for a quasi-experimental design.

In a true experiment, the random assignment process equalizes confounders across the groups to nullify their overall effect. It’s the gold standard because it works on all confounders, known and unknown.

Unfortunately, the lack of random assignment can allow differences between the groups to exist before the intervention. These confounding factors might ultimately explain the results rather than the intervention.

Consequently, researchers must use other methods to equalize the groups roughly using matching and cutoff values or statistically adjust for preexisting differences they measure to reduce the impact of confounders.

A key strength of quasi-experiments is their frequent use of “pre-post testing.” This approach involves conducting initial tests before collecting data to check for preexisting differences between groups that could impact the study’s outcome. By identifying these variables early on and including them as covariates, researchers can more effectively control potential confounders in their statistical analysis.

Additionally, researchers frequently track outcomes before and after the intervention to better understand the context for changes they observe.

Statisticians consider these methods to be less effective than randomization. Hence, quasi-experiments fall somewhere in the middle when it comes to internal validity , or how well the study can identify causal relationships versus mere correlation . They’re more conclusive than correlational studies but not as solid as true experiments.

In conclusion, quasi-experimental designs offer researchers a versatile and practical approach when random assignment is not feasible. This methodology bridges the gap between controlled experiments and observational studies, providing a valuable tool for investigating cause-and-effect relationships in real-world settings. Researchers can address ethical and logistical constraints by understanding and leveraging the different types of quasi-experimental designs while still obtaining insightful and meaningful results.

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

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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.

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Quasi-experimental Research: What It Is, Types & Examples

Much like an actual experiment, quasi-experimental research tries to demonstrate a cause-and-effect link between a dependent and an independent variable. A quasi-experiment, on the other hand, does not depend on random assignment, unlike an actual experiment. The subjects are sorted into groups based on non-random variables.

What is Quasi-Experimental Research?

“Resemblance” is the definition of “quasi.” Individuals are not randomly allocated to conditions or orders of conditions, even though the regression analysis is changed. As a result, quasi-experimental research is research that appears to be experimental but is not.

The directionality problem is avoided in quasi-experimental research since the regression analysis is altered before the multiple regression is assessed. However, because individuals are not randomized at random, there are likely to be additional disparities across conditions in quasi-experimental research.

As a result, in terms of internal consistency, quasi-experiments fall somewhere between correlational research and actual experiments.

The key component of a true experiment is randomly allocated groups. This means that each person has an equivalent chance of being assigned to the experimental group or the control group, depending on whether they are manipulated or not.

Simply put, a quasi-experiment is not a real experiment. A quasi-experiment does not feature randomly allocated groups since the main component of a real experiment is randomly assigned groups. Why is it so crucial to have randomly allocated groups, given that they constitute the only distinction between quasi-experimental and actual  experimental research ?

Let’s use an example to illustrate our point. Let’s assume we want to discover how new psychological therapy affects depressed patients. In a genuine trial, you’d split half of the psych ward into treatment groups, With half getting the new psychotherapy therapy and the other half receiving standard  depression treatment .

And the physicians compare the outcomes of this treatment to the results of standard treatments to see if this treatment is more effective. Doctors, on the other hand, are unlikely to agree with this genuine experiment since they believe it is unethical to treat one group while leaving another untreated.

A quasi-experimental study will be useful in this case. Instead of allocating these patients at random, you uncover pre-existing psychotherapist groups in the hospitals. Clearly, there’ll be counselors who are eager to undertake these trials as well as others who prefer to stick to the old ways.

These pre-existing groups can be used to compare the symptom development of individuals who received the novel therapy with those who received the normal course of treatment, even though the groups weren’t chosen at random.

If any substantial variations between them can be well explained, you may be very assured that any differences are attributable to the treatment but not to other extraneous variables.

As we mentioned before, quasi-experimental research entails manipulating an independent variable by randomly assigning people to conditions or sequences of conditions. Non-equivalent group designs, pretest-posttest designs, and regression discontinuity designs are only a few of the essential types.

What are quasi-experimental research designs?

Quasi-experimental research designs are a type of research design that is similar to experimental designs but doesn’t give full control over the independent variable(s) like true experimental designs do.

In a quasi-experimental design, the researcher changes or watches an independent variable, but the participants are not put into groups at random. Instead, people are put into groups based on things they already have in common, like their age, gender, or how many times they have seen a certain stimulus.

Because the assignments are not random, it is harder to draw conclusions about cause and effect than in a real experiment. However, quasi-experimental designs are still useful when randomization is not possible or ethical.

The true experimental design may be impossible to accomplish or just too expensive, especially for researchers with few resources. Quasi-experimental designs enable you to investigate an issue by utilizing data that has already been paid for or gathered by others (often the government). 

Because they allow better control for confounding variables than other forms of studies, they have higher external validity than most genuine experiments and higher  internal validity  (less than true experiments) than other non-experimental research.

Is quasi-experimental research quantitative or qualitative?

Quasi-experimental research is a quantitative research method. It involves numerical data collection and statistical analysis. Quasi-experimental research compares groups with different circumstances or treatments to find cause-and-effect links. 

It draws statistical conclusions from quantitative data. Qualitative data can enhance quasi-experimental research by revealing participants’ experiences and opinions, but quantitative data is the method’s foundation.

Quasi-experimental research types

There are many different sorts of quasi-experimental designs. Three of the most popular varieties are described below: Design of non-equivalent groups, Discontinuity in regression, and Natural experiments.

Design of Non-equivalent Groups

Example: design of non-equivalent groups, discontinuity in regression, example: discontinuity in regression, natural experiments, example: natural experiments.

However, because they couldn’t afford to pay everyone who qualified for the program, they had to use a random lottery to distribute slots.

Experts were able to investigate the program’s impact by utilizing enrolled people as a treatment group and those who were qualified but did not play the jackpot as an experimental group.

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A Modern Guide to Understanding and Conducting Research in Psychology

Chapter 7 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 et al., 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, focusing first on nonequivalent groups, pretest-posttest, interrupted time series, and combination designs before turning to single subject designs (including reversal and multiple-baseline designs).

7.1 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.

7.2 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 STEM education program on elementary school students’ attitudes toward science, technology, engineering and math. The researcher could measure the attitudes of students at a particular elementary school during one week, implement the STEM 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 science program aired on television and many of the students watched it, or perhaps a major scientific discover occured 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 more exposed to STEM subjects in class 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.

Finally, it is possible that the act of taking a pretest can sensitize participants to the measurement process or heighten their awareness of the variable under investigation. This heightened sensitivity, called a testing effect , can subsequently lead to changes in their posttest responses, even in the absence of any external intervention effect.

7.3 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 a recent COVID-19 study, the intervention involved the implementation of state-issued mask mandates and restrictions on on-premises restaurant dining. The researchers examined the impact of these measures on COVID-19 cases and deaths ( Guy Jr et al., 2021 ) . Since there was a rapid reduction in daily case and death growth rates following the implementation of mask mandates, and this effect persisted for an extended period, the researchers concluded that the implementation of mask mandates was the cause of the decrease in COVID-19 transmission. This study employed an interrupted time series design, similar to a pretest-posttest design, as it involved measuring the outcomes before and after the intervention. However, unlike the pretest-posttest design, it incorporated multiple measurements before and after the intervention, providing a more comprehensive analysis of the policy impacts.

Figure 7.1 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 7.1 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 7.1 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 7.1: 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.

7.4 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 current level of engagement in pro-environmental behaviors (i.e., recycling, eating less red meat, abstaining for single-use plastics, etc.), then are exposed to an pro-environmental program in which they learn about the effects of human caused climate change on the planet, and finally are given a posttest. Students in a similar school are given the pretest, not exposed to an pro-environmental program, and finally are given a posttest. Again, if students in the treatment condition become more involved in pro-environmental behaviors, 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 engage in more pro-environmental behaviors than students in the control condition. But if it is a matter of history (e.g., news of a forest fire or drought) or maturation (e.g., improved reasoning or sense of responsibility), 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 local heat wave with record high temperatures), 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, this kind of design 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.

regression to the mean

Spontaneous remission, 7.5 single-subject research.

• Explain what single-subject research is, including how it differs from other types of psychological research and who uses single-subject research and why.
• Design simple single-subject studies using reversal and multiple-baseline designs.
• Explain how single-subject research designs address the issue of internal validity.
• Interpret the results of simple single-subject studies based on the visual inspection of graphed data.
• Explain some of the points of disagreement between advocates of single-subject research and advocates of group research.

Researcher Vance Hall and his colleagues were faced with the challenge of increasing the extent to which six disruptive elementary school students stayed focused on their schoolwork ( Hall et al., 1968 ) . For each of several days, the researchers carefully recorded whether or not each student was doing schoolwork every 10 seconds during a 30-minute period. Once they had established this baseline, they introduced a treatment. The treatment was that when the student was doing schoolwork, the teacher gave him or her positive attention in the form of a comment like “good work” or a pat on the shoulder. The result was that all of the students dramatically increased their time spent on schoolwork and decreased their disruptive behavior during this treatment phase. For example, a student named Robbie originally spent 25% of his time on schoolwork and the other 75% “snapping rubber bands, playing with toys from his pocket, and talking and laughing with peers” (p. 3). During the treatment phase, however, he spent 71% of his time on schoolwork and only 29% on other activities. Finally, when the researchers had the teacher stop giving positive attention, the students all decreased their studying and increased their disruptive behavior. This was consistent with the claim that it was, in fact, the positive attention that was responsible for the increase in studying. This was one of the first studies to show that attending to positive behavior—and ignoring negative behavior—could be a quick and effective way to deal with problem behavior in an applied setting.

Figure 7.2: Single-subject research has shown that positive attention from a teacher for studying can increase studying and decrease disruptive behavior. Photo by Jerry Wang on Unsplash.

Most of this book is about what can be called group research, which typically involves studying a large number of participants and combining their data to draw general conclusions about human behavior. The study by Hall and his colleagues, in contrast, is an example of single-subject research, which typically involves studying a small number of participants and focusing closely on each individual. In this section, we consider this alternative approach. We begin with an overview of single-subject research, including some assumptions on which it is based, who conducts it, and why they do. We then look at some basic single-subject research designs and how the data from those designs are analyzed. Finally, we consider some of the strengths and weaknesses of single-subject research as compared with group research and see how these two approaches can complement each other.

Overview of Single-Subject Research

What is single-subject research.

Single-subject research is a type of quantitative, quasi-experimental research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small-n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this book is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques (see. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see box “The Case of ‘Anna O.’”) and John Watson and Rosalie Rayner’s description of Little Albert ( Watson & Rayner, 1920 ) who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis ( Freud, 1957 ) . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst (p. 9).

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 7.3: “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: Wikimedia Commons

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behavior of each one. But why take this approach instead of the group approach? There are two important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behavior of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behavior of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

Another assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity ( Wolf, 1978 ) . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often chaotic elementary school classrooms.

Who Uses Single-Subject Research?

Single-subject research has been around as long as the field of psychology itself. In the late 1800s, one of psychology’s founders, Wilhelm Wundt, studied sensation and consciousness by focusing intensively on each of a small number of research participants. Herman Ebbinghaus’s research on memory and Ivan Pavlov’s research on classical conditioning are other early examples, both of which are still described in almost every introductory psychology textbook.

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques ( Skinner, 1938 ) . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis ( Baer et al., 1968 ) . Applied behavior analysis plays a significant role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis . The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioral, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement ( Kazdin, 2019 ) .

Single-Subject Research Designs

General features of single-subject designs.

Before looking at any specific single-subject research designs, it will be helpful to consider some features that are common to most of them. Many of these features are illustrated in Figure 7.4 , which shows the results of a generic single-subject study. First, the dependent variable (represented on the y-axis of the graph) is measured repeatedly over time (represented by the x-axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is tested under one condition per phase. The conditions are often designated by capital letters: A, B, C, and so on. Thus Figure 7.4 represents a design in which the participant was tested first in one condition (A), then tested in another condition (B), and finally retested in the original condition (A). (This is called a reversal design and will be discussed in more detail shortly.)

Figure 7.4: Results of a generic single-subject study illustrating several principles of single-subject research.

Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Instead, it depends on the participant’s behavior. Specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions. This is sometimes referred to as the steady state strategy ( Sidman, 1960 ) . The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally. The effect of an independent variable is easier to detect when the “noise” in the data is minimized.

Reversal Designs

The most basic single-subject research design is the reversal design , also called the ABA design . During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on. The study by Hall and his colleagues was an ABAB reversal design (Figure 7.5 ).

Figure 7.5: An approximation of the results for Hall and colleagues’ participant Robbie in their ABAB reversal design. The percentage of time he spent studying (the dependent variable) was low during the first baseline phase, increased during the first treatment phase until it leveled off, decreased during the second baseline phase, and again increased during the second treatment phase.

Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? If the dependent variable changes after the treatment is introduced, it is not always clear that the treatment was responsible for the change. It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment, it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study.

Multiple-Baseline Designs

There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. One is that if a treatment is working, it may be unethical to remove it. For example, if a treatment seemed to reduce the incidence of self-injury in a developmentally disabled child, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good, but it could also mean that the positive attention was not really the cause of the increased studying in the first place.

One solution to these problems is to use a multiple-baseline design , which is represented in Figure 7.6 . In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different time for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is less likely to be a coincidence.

Figure 7.6: Results of a generic multiple-baseline study. The multiple baselines can be for different participants, dependent variables, or settings. The treatment is introduced at a different time on each baseline.

As an example, consider a study by Scott Ross and Robert Horner ( Ross et al., 2009 ) . They were interested in how a school-wide bullying prevention program affected the bullying behavior of particular problem students. At each of three different schools, the researchers studied two students who had regularly engaged in bullying. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviors they exhibited toward their peers. (The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. After 2 more weeks, they implemented it at the second school. And after 2 more weeks, they implemented it at the third school. They found that the number of aggressive behaviors exhibited by each student dropped shortly after the program was implemented at his or her school. Notice that if the researchers had only studied one school or if they had introduced the treatment at the same time at all three schools, then it would be unclear whether the reduction in aggressive behaviors was due to the bullying program or something else that happened at about the same time it was introduced (e.g., a holiday, a television program, a change in the weather). But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—an unlikely occurrence—to explain their results.

Data Analysis in Single-Subject Research

In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed. As we have seen throughout the book, group research involves combining data across participants. Inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. Single-subject research, by contrast, relies heavily on a very different approach called visual inspection . This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable. Inferential statistics are typically not used.

In visually inspecting their data, single-subject researchers take several factors into account. One of them is changes in the level of the dependent variable from condition to condition. If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trend , which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behavior is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is latency , which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible.

In the top panel of Figure 7.7 , there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 7.7 , however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see.

Figure 7.7: Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel.

The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. One approach parallels what is typically done in group research. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied ( Fisch, 2001 ) . (Note that averaging across participants is less common.) Another approach is to compute the percentage of nonoverlapping data (PND) for each participant ( Scruggs & Mastropieri, 2021 ) . This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%. The greater the percentage of nonoverlapping data, the stronger the treatment effect. Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it.

The Single-Subject Versus Group “Debate”

Single-subject research is similar to group research—especially experimental group research—in many ways. They are both quantitative approaches that try to establish causal relationships by manipulating an independent variable, measuring a dependent variable, and controlling extraneous variables. As we will see, single-subject research and group research are probably best conceptualized as complementary approaches.

Data Analysis

One set of disagreements revolves around the issue of data analysis. Some advocates of group research worry that visual inspection is inadequate for deciding whether and to what extent a treatment has affected a dependent variable. One specific concern is that visual inspection is not sensitive enough to detect weak effects. A second is that visual inspection can be unreliable, with different researchers reaching different conclusions about the same set of data ( Danov & Symons, 2008 ) . A third is that the results of visual inspection—an overall judgment of whether or not a treatment was effective—cannot be clearly and efficiently summarized or compared across studies (unlike the measures of relationship strength typically used in group research).

In general, single-subject researchers share these concerns. However, they also argue that their use of the steady state strategy, combined with their focus on strong and consistent effects, minimizes most of them. If the effect of a treatment is difficult to detect by visual inspection because the effect is weak or the data are noisy, then single-subject researchers look for ways to increase the strength of the effect or reduce the noise in the data by controlling extraneous variables (e.g., by administering the treatment more consistently). If the effect is still difficult to detect, then they are likely to consider it neither strong enough nor consistent enough to be of further interest. Many single-subject researchers also point out that statistical analysis is becoming increasingly common and that many of them are using it as a supplement to visual inspection—especially for the purpose of comparing results across studies ( Scruggs & Mastropieri, 2021 ) .

Turning the tables, some advocates of single-subject research worry about the way that group researchers analyze their data. Specifically, they point out that focusing on group means can be highly misleading. Again, imagine that a treatment has a strong positive effect on half the people exposed to it and an equally strong negative effect on the other half. In a traditional between-subjects experiment, the positive effect on half the participants in the treatment condition would be statistically cancelled out by the negative effect on the other half. The mean for the treatment group would then be the same as the mean for the control group, making it seem as though the treatment had no effect when in fact it had a strong effect on every single participant!

But again, group researchers share this concern. Although they do focus on group statistics, they also emphasize the importance of examining distributions of individual scores. For example, if some participants were positively affected by a treatment and others negatively affected by it, this would produce a bimodal distribution of scores and could be detected by looking at a histogram of the data. The use of within-subjects designs is another strategy that allows group researchers to observe effects at the individual level and even to specify what percentage of individuals exhibit strong, medium, weak, and even negative effects.

External Validity

The second issue about which single-subject and group researchers sometimes disagree has to do with external validity—the ability to generalize the results of a study beyond the people and situation actually studied. In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population. Imagine, for example, that in a single-subject study, a treatment has been shown to reduce self-injury for each of two developmentally disabled children. Even if the effect is strong for these two children, how can one know whether this treatment is likely to work for other developmentally disabled children?

Again, single-subject researchers share this concern. In response, they note that the strong and consistent effects they are typically interested in—even when observed in small samples—are likely to generalize to others in the population. Single-subject researchers also note that they place a strong emphasis on replicating their research results. When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions. Each time they observe similar results, they rightfully become more confident in the generality of those results. Single-subject researchers can also point to the fact that the principles of classical and operant conditioning—most of which were discovered using the single-subject approach—have been successfully generalized across an incredibly wide range of species and situations.

And again turning the tables, single-subject researchers have concerns of their own about the external validity of group research. One extremely important point they make is that studying large groups of participants does not entirely solve the problem of generalizing to other individuals. Imagine, for example, a treatment that has been shown to have a small positive effect on average in a large group study. It is likely that although many participants exhibited a small positive effect, others exhibited a large positive effect, and still others exhibited a small negative effect. When it comes to applying this treatment to another large group , we can be fairly sure that it will have a small effect on average. But when it comes to applying this treatment to another individual , we cannot be sure whether it will have a small, a large, or even a negative effect. Another point that single-subject researchers make is that group researchers also face a similar problem when they study a single situation and then generalize their results to other situations. For example, researchers who conduct a study on the effect of cell phone use on drivers on a closed oval track probably want to apply their results to drivers in many other real-world driving situations. But notice that this requires generalizing from a single situation to a population of situations. Thus the ability to generalize is based on much more than just the sheer number of participants one has studied. It requires a careful consideration of the similarity of the participants and situations studied to the population of participants and situations that one wants to generalize to ( Shadish et al., 2002 ) .

Single-Subject and Group Research as Complementary Methods

As with quantitative and qualitative research, it is probably best to conceptualize single-subject research and group research as complementary methods that have different strengths and weaknesses and that are appropriate for answering different kinds of research questions ( Kazdin, 2019 ) . Single-subject research is particularly good for testing the effectiveness of treatments on individuals when the focus is on strong, consistent, and biologically or socially important effects. It is especially useful when the behavior of particular individuals is of interest. Clinicians who work with only one individual at a time may find that it is their only option for doing systematic quantitative research.

Group research, on the other hand, is good for testing the effectiveness of treatments at the group level. Among the advantages of this approach is that it allows researchers to detect weak effects, which can be of interest for many reasons. For example, finding a weak treatment effect might lead to refinements of the treatment that eventually produce a larger and more meaningful effect. Group research is also good for studying interactions between treatments and participant characteristics. For example, if a treatment is effective for those who are high in motivation to change and ineffective for those who are low in motivation to change, then a group design can detect this much more efficiently than a single-subject design. Group research is also necessary to answer questions that cannot be addressed using the single-subject approach, including questions about independent variables that cannot be manipulated (e.g., number of siblings, extroversion, culture).

• Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
• Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
• Single-subject research designs typically involve measuring the dependent variable repeatedly over time and changing conditions (e.g., from baseline to treatment) when the dependent variable has reached a steady state. This approach allows the researcher to see whether changes in the independent variable are causing changes in the dependent variable.
• Single-subject researchers typically analyze their data by graphing them and making judgments about whether the independent variable is affecting the dependent variable based on level, trend, and latency.
• Differences between single-subject research and group research sometimes lead to disagreements between single-subject and group researchers. These disagreements center on the issues of data analysis and external validity (especially generalization to other people). Single-subject research and group research are probably best seen as complementary methods, with different strengths and weaknesses, that are appropriate for answering different kinds of research questions.
• Does positive attention from a parent increase a child’s toothbrushing behavior?
• Does self-testing while studying improve a student’s performance on weekly spelling tests?
• Does regular exercise help relieve depression?
• Practice: Create a graph that displays the hypothetical results for the study you designed in Exercise 1. Write a paragraph in which you describe what the results show. Be sure to comment on level, trend, and latency.
• Discussion: Imagine you have conducted a single-subject study showing a positive effect of a treatment on the behavior of a man with social anxiety disorder. Your research has been criticized on the grounds that it cannot be generalized to others. How could you respond to this criticism?
• Discussion: Imagine you have conducted a group study showing a positive effect of a treatment on the behavior of a group of people with social anxiety disorder, but your research has been criticized on the grounds that “average” effects cannot be generalized to individuals. How could you respond to this criticism?

7.6 Glossary

The simplest reversal design, in which there is a baseline condition (A), followed by a treatment condition (B), followed by a return to baseline (A).

applied behavior analysis

A subfield of psychology that uses single-subject research and applies the principles of behavior analysis to real-world problems in areas that include education, developmental disabilities, organizational behavior, and health behavior.

A condition in a single-subject research design in which the dependent variable is measured repeatedly in the absence of any treatment. Most designs begin with a baseline condition, and many return to the baseline condition at least once.

A detailed description of an individual case.

experimental analysis of behavior

A subfield of psychology founded by B. F. Skinner that uses single-subject research—often with nonhuman animals—to study relationships primarily between environmental conditions and objectively observable behaviors.

group research

A type of quantitative research that involves studying a large number of participants and examining their behavior in terms of means, standard deviations, and other group-level statistics.

interrupted time-series design

A research design in which a series of measurements of the dependent variable are taken both before and after a treatment.

item-order effect

The effect of responding to one survey item on responses to a later survey item.

Refers collectively to extraneous developmental changes in participants that can occur between a pretest and posttest or between the first and last measurements in a time series. It can provide an alternative explanation for an observed change in the dependent variable.

multiple-baseline design

A single-subject research design in which multiple baselines are established for different participants, different dependent variables, or different contexts and the treatment is introduced at a different time for each baseline.

naturalistic observation

An approach to data collection in which the behavior of interest is observed in the environment in which it typically occurs.

nonequivalent groups design

A between-subjects research design in which participants are not randomly assigned to conditions, usually because participants are in preexisting groups (e.g., students at different schools).

nonexperimental research

Research that lacks the manipulation of an independent variable or the random assignment of participants to conditions or orders of conditions.

open-ended item

A questionnaire item that asks a question and allows respondents to respond in whatever way they want.

percentage of nonoverlapping data

A statistic sometimes used in single-subject research. The percentage of observations in a treatment condition that are more extreme than the most extreme observation in a relevant baseline condition.

pretest-posttest design

A research design in which the dependent variable is measured (the pretest), a treatment is given, and the dependent variable is measured again (the posttest) to see if there is a change in the dependent variable from pretest to posttest.

quasi-experimental research

Research that involves the manipulation of an independent variable but lacks the random assignment of participants to conditions or orders of conditions. It is generally used in field settings to test the effectiveness of a treatment.

rating scale

An ordered set of response options to a closed-ended questionnaire item.

The statistical fact that an individual who scores extremely on one occasion will tend to score less extremely on the next occasion.

A term often used to refer to a participant in survey research.

reversal design

A single-subject research design that begins with a baseline condition with no treatment, followed by the introduction of a treatment, and after that a return to the baseline condition. It can include additional treatment conditions and returns to baseline.

single-subject research

A type of quantitative research that involves examining in detail the behavior of each of a small number of participants.

single-variable research

Research that focuses on a single variable rather than on a statistical relationship between variables.

social validity

The extent to which a single-subject study focuses on an intervention that has a substantial effect on an important behavior and can be implemented reliably in the real-world contexts (e.g., by teachers in a classroom) in which that behavior occurs.

Improvement in a psychological or medical problem over time without any treatment.

steady state strategy

In single-subject research, allowing behavior to become fairly consistent from one observation to the next before changing conditions. This makes any effect of the treatment easier to detect.

survey research

A quantitative research approach that uses self-report measures and large, carefully selected samples.

testing effect

A bias in participants’ responses in which scores on the posttest are influenced by simple exposure to the pretest

visual inspection

The primary approach to data analysis in single-subject research, which involves graphing the data and making a judgment as to whether and to what extent the independent variable affected the dependent variable.

The use and interpretation of quasi-experimental design

Last updated

6 February 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

• What is a quasi-experimental design?

Commonly used in medical informatics (a field that uses digital information to ensure better patient care), researchers generally use this design to evaluate the effectiveness of a treatment – perhaps a type of antibiotic or psychotherapy, or an educational or policy intervention.

Even though quasi-experimental design has been used for some time, relatively little is known about it. Read on to learn the ins and outs of this research design.

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• When to use a quasi-experimental design

A quasi-experimental design is used when it's not logistically feasible or ethical to conduct randomized, controlled trials. As its name suggests, a quasi-experimental design is almost a true experiment. However, researchers don't randomly select elements or participants in this type of research.

Researchers prefer to apply quasi-experimental design when there are ethical or practical concerns. Let's look at these two reasons more closely.

Ethical reasons

In some situations, the use of randomly assigned elements can be unethical. For instance, providing public healthcare to one group and withholding it to another in research is unethical. A quasi-experimental design would examine the relationship between these two groups to avoid physical danger.

Practical reasons

Randomized controlled trials may not be the best approach in research. For instance, it's impractical to trawl through large sample sizes of participants without using a particular attribute to guide your data collection .

Recruiting participants and properly designing a data-collection attribute to make the research a true experiment requires a lot of time and effort, and can be expensive if you don’t have a large funding stream.

A quasi-experimental design allows researchers to take advantage of previously collected data and use it in their study.

• Examples of quasi-experimental designs

Quasi-experimental research design is common in medical research, but any researcher can use it for research that raises practical and ethical concerns. Here are a few examples of quasi-experimental designs used by different researchers:

Example 1: Determining the effectiveness of math apps in supplementing math classes

A school wanted to supplement its math classes with a math app. To select the best app, the school decided to conduct demo tests on two apps before selecting the one they will purchase.

Scope of the research

Since every grade had two math teachers, each teacher used one of the two apps for three months. They then gave the students the same math exams and compared the results to determine which app was most effective.

Reasons why this is a quasi-experimental study

This simple study is a quasi-experiment since the school didn't randomly assign its students to the applications. They used a pre-existing class structure to conduct the study since it was impractical to randomly assign the students to each app.

Example 2: Determining the effectiveness of teaching modern leadership techniques in start-up businesses

A hypothetical quasi-experimental study was conducted in an economically developing country in a mid-sized city.

Five start-ups in the textile industry and five in the tech industry participated in the study. The leaders attended a six-week workshop on leadership style, team management, and employee motivation.

After a year, the researchers assessed the performance of each start-up company to determine growth. The results indicated that the tech start-ups were further along in their growth than the textile companies.

The basis of quasi-experimental research is a non-randomized subject-selection process. This study didn't use specific aspects to determine which start-up companies should participate. Therefore, the results may seem straightforward, but several aspects may determine the growth of a specific company, apart from the variables used by the researchers.

Example 3: A study to determine the effects of policy reforms and of luring foreign investment on small businesses in two mid-size cities

In a study to determine the economic impact of government reforms in an economically developing country, the government decided to test whether creating reforms directed at small businesses or luring foreign investments would spur the most economic development.

The government selected two cities with similar population demographics and sizes. In one of the cities, they implemented specific policies that would directly impact small businesses, and in the other, they implemented policies to attract foreign investment.

After five years, they collected end-of-year economic growth data from both cities. They looked at elements like local GDP growth, unemployment rates, and housing sales.

The study used a non-randomized selection process to determine which city would participate in the research. Researchers left out certain variables that would play a crucial role in determining the growth of each city. They used pre-existing groups of people based on research conducted in each city, rather than random groups.

• Advantages of a quasi-experimental design

Some advantages of quasi-experimental designs are:

Researchers can manipulate variables to help them meet their study objectives.

It offers high external validity, making it suitable for real-world applications, specifically in social science experiments.

Integrating this methodology into other research designs is easier, especially in true experimental research. This cuts down on the time needed to determine your outcomes.

• Disadvantages of a quasi-experimental design

Despite the pros that come with a quasi-experimental design, there are several disadvantages associated with it, including the following:

It has a lower internal validity since researchers do not have full control over the comparison and intervention groups or between time periods because of differences in characteristics in people, places, or time involved. It may be challenging to determine whether all variables have been used or whether those used in the research impacted the results.

There is the risk of inaccurate data since the research design borrows information from other studies.

There is the possibility of bias since researchers select baseline elements and eligibility.

• What are the different quasi-experimental study designs?

There are three distinct types of quasi-experimental designs:

Nonequivalent

Regression discontinuity, natural experiment.

This is a hybrid of experimental and quasi-experimental methods and is used to leverage the best qualities of the two. Like the true experiment design, nonequivalent group design uses pre-existing groups believed to be comparable. However, it doesn't use randomization, the lack of which is a crucial element for quasi-experimental design.

Researchers usually ensure that no confounding variables impact them throughout the grouping process. This makes the groupings more comparable.

Example of a nonequivalent group design

A small study was conducted to determine whether after-school programs result in better grades. Researchers randomly selected two groups of students: one to implement the new program, the other not to. They then compared the results of the two groups.

This type of quasi-experimental research design calculates the impact of a specific treatment or intervention. It uses a criterion known as "cutoff" that assigns treatment according to eligibility.

Researchers often assign participants above the cutoff to the treatment group. This puts a negligible distinction between the two groups (treatment group and control group).

Example of regression discontinuity

Students must achieve a minimum score to be enrolled in specific US high schools. Since the cutoff score used to determine eligibility for enrollment is arbitrary, researchers can assume that the disparity between students who only just fail to achieve the cutoff point and those who barely pass is a small margin and is due to the difference in the schools that these students attend.

Researchers can then examine the long-term effects of these two groups of kids to determine the effect of attending certain schools. This information can be applied to increase the chances of students being enrolled in these high schools.

This research design is common in laboratory and field experiments where researchers control target subjects by assigning them to different groups. Researchers randomly assign subjects to a treatment group using nature or an external event or situation.

However, even with random assignment, this research design cannot be called a true experiment since nature aspects are observational. Researchers can also exploit these aspects despite having no control over the independent variables.

Example of the natural experiment approach

An example of a natural experiment is the 2008 Oregon Health Study.

Oregon intended to allow more low-income people to participate in Medicaid.

Since they couldn't afford to cover every person who qualified for the program, the state used a random lottery to allocate program slots.

Researchers assessed the program's effectiveness by assigning the selected subjects to a randomly assigned treatment group, while those that didn't win the lottery were considered the control group.

• Differences between quasi-experiments and true experiments

There are several differences between a quasi-experiment and a true experiment:

Participants in true experiments are randomly assigned to the treatment or control group, while participants in a quasi-experiment are not assigned randomly.

In a quasi-experimental design, the control and treatment groups differ in unknown or unknowable ways, apart from the experimental treatments that are carried out. Therefore, the researcher should try as much as possible to control these differences.

Quasi-experimental designs have several "competing hypotheses," which compete with experimental manipulation to explain the observed results.

Quasi-experiments tend to have lower internal validity (the degree of confidence in the research outcomes) than true experiments, but they may offer higher external validity (whether findings can be extended to other contexts) as they involve real-world interventions instead of controlled interventions in artificial laboratory settings.

Despite the distinct difference between true and quasi-experimental research designs, these two research methodologies share the following aspects:

Both study methods subject participants to some form of treatment or conditions.

Researchers have the freedom to measure some of the outcomes of interest.

Researchers can test whether the differences in the outcomes are associated with the treatment.

• An example comparing a true experiment and quasi-experiment

Imagine you wanted to study the effects of junk food on obese people. Here's how you would do this as a true experiment and a quasi-experiment:

How to carry out a true experiment

In a true experiment, some participants would eat junk foods, while the rest would be in the control group, adhering to a regular diet. At the end of the study, you would record the health and discomfort of each group.

This kind of experiment would raise ethical concerns since the participants assigned to the treatment group are required to eat junk food against their will throughout the experiment. This calls for a quasi-experimental design.

How to carry out a quasi-experiment

In quasi-experimental research, you would start by finding out which participants want to try junk food and which prefer to stick to a regular diet. This allows you to assign these two groups based on subject choice.

In this case, you didn't assign participants to a particular group, so you can confidently use the results from the study.

When is a quasi-experimental design used?

Quasi-experimental designs are used when researchers don’t want to use randomization when evaluating their intervention.

What are the characteristics of quasi-experimental designs?

Some of the characteristics of a quasi-experimental design are:

Researchers don't randomly assign participants into groups, but study their existing characteristics and assign them accordingly.

Researchers study the participants in pre- and post-testing to determine the progress of the groups.

Quasi-experimental design is ethical since it doesn’t involve offering or withholding treatment at random.

Quasi-experimental design encompasses a broad range of non-randomized intervention studies. This design is employed when it is not ethical or logistically feasible to conduct randomized controlled trials. Researchers typically employ it when evaluating policy or educational interventions, or in medical or therapy scenarios.

How do you analyze data in a quasi-experimental design?

You can use two-group tests, time-series analysis, and regression analysis to analyze data in a quasi-experiment design. Each option has specific assumptions, strengths, limitations, and data requirements.

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5 Quasi-Experimental Design Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Quasi-experimental design refers to a type of experimental design that uses pre-existing groups of people rather than random groups.

Because the groups of research participants already exist, they cannot be randomly assigned to a cohort . This makes inferring a causal relationship between the treatment and observed/criterion variable difficult.

Quasi-experimental designs are generally considered inferior to true experimental designs.

Limitations of Quasi-Experimental Design

Since participants cannot be randomly assigned to the grouping variable (male/female; high education/low education), the internal validity of the study is questionable.

Extraneous variables may exist that explain the results. For example, with quasi-experimental studies involving gender, there are numerous cultural and biological variables that distinguish males and females other than gender alone.

Each one of those variables may be able to explain the results without the need to refer to gender.

See More Research Limitations Here

Quasi-Experimental Design Examples

1. smartboard apps and math.

A school has decided to supplement their math resources with smartboard applications. The math teachers research the apps available and then choose two apps for each grade level. Before deciding on which apps to purchase, the school contacts the seller and asks for permission to demo/test the apps before purchasing the licenses.

The study involves having different teachers use the apps with their classes. Since there are two math teachers at each grade level, each teacher will use one of the apps in their classroom for three months. At the end of three months, all students will take the same math exams. Then the school can simply compare which app improved the students’ math scores the most.

The reason this is called a quasi-experiment is because the school did not randomly assign students to one app or the other. The students were already in pre-existing groups/classes.

Although it was impractical to randomly assign students to use one version or the other of the apps, it creates difficulty interpreting the results.

For instance, if students in teacher A’s class did better than the students in teacher B’s class, then can we really say the difference was due to the app? There may be other differences between the two teachers that account for the results. This poses a serious threat to the study’s internal validity.

2. Leadership Training

There is reason to believe that teaching entrepreneurs modern leadership techniques will improve their performance and shorten how long it takes for them to reach profitability. Team members will feel better appreciated and work harder, which should translate to increased productivity and innovation.

This hypothetical study took place in a third-world country in a mid-sized city. The researchers marketed the training throughout the city and received interest from 5 start-ups in the tech sector and 5 in the textile industry. The leaders of each company then attended six weeks of workshops on employee motivation, leadership styles, and effective team management.

At the end of one year, the researchers returned. They conducted a standard assessment of each start-up’s growth trajectory and administered various surveys to employees.

The results indicated that tech start-ups were further along in their growth paths than textile start-ups. The data also showed that tech work teams reported greater job satisfaction and company loyalty than textile work teams.

Although the results appear straightforward, because the researchers used a quasi-experimental design, they cannot say that the training caused the results.

The two groups may differ in ways that could explain the results. For instance, perhaps there is less growth potential in the textile industry in that city, or perhaps tech leaders are more progressive and willing to accept new leadership strategies.

3. Parenting Styles and Academic Performance   

Psychologists are very interested in factors that affect children’s academic performance. Since parenting styles affect a variety of children’s social and emotional profiles, it stands to reason that it may affect academic performance as well. The four parenting styles under study are: authoritarian, authoritative, permissive, and neglectful/uninvolved.

To examine this possible relationship, researchers assessed the parenting style of 120 families with third graders in a large metropolitan city. Trained raters made two-hour home visits to conduct observations of parent/child interactions. That data was later compared with the children’s grades.

The results revealed that children raised in authoritative households had the highest grades of all the groups.

However, because the researchers were not able to randomly assign children to one of the four parenting styles, the internal validity is called into question.

There may be other explanations for the results other than parenting style. For instance, maybe parents that practice authoritative parenting also come from a higher SES demographic than the other parents.

Because they have higher income and education levels, they may put more emphasis on their child’s academic performance. Or, because they have greater financial resources, their children attend STEM camps, co-curricular and other extracurricular academic-orientated classes.

4. Government Reforms and Economic Impact

Government policies can have a tremendous impact on economic development. Making it easier for small businesses to open and reducing bank loans are examples of policies that can have immediate results. So, a third-world country decides to test policy reforms in two mid-sized cities. One city receives reforms directed at small businesses, while the other receives reforms directed at luring foreign investment.  

The government was careful to choose two cities that were similar in terms of size and population demographics.

Over the next five years, economic growth data were collected at the end of each fiscal year. The measures consisted of housing sells, local GDP, and unemployment rates.

At the end of five years the results indicated that small business reforms had a much larger impact on economic growth than foreign investment. The city which received small business reforms saw an increase in housing sells and GDP, but a drop in unemployment. The other city saw stagnant sells and GDP, and a slight increase in unemployment.

On the surface, it appears that small business reform is the better way to go. However, a more careful analysis revealed that the economic improvement observed in the one city was actually the result of two multinational real estate firms entering the market. The two firms specialize in converting dilapidated warehouses into shopping centers and residential properties.

5. Gender and Meditation

Meditation can help relieve stress and reduce symptoms of depression and anxiety. It is a simple and easy to use technique that just about anyone can try. However, are the benefits real or is it just that people believe it can help? To find out, a team of counselors designed a study to put it to a test.

Since they believe that women are more likely to benefit than men, they recruit both males and females to be in their study.

Both groups were trained in meditation by a licensed professional. The training took place over three weekends. Participants were instructed to practice at home at least four times a week for the next three months and keep a journal each time they meditate.

At the end of the three months, physical and psychological health data were collected on all participants. For physical health, participants’ blood pressure was measured. For psychological health, participants filled out a happiness scale and the emotional tone of their diaries were examined.

The results showed that meditation worked better for women than men. Women had lower blood pressure, scored higher on the happiness scale, and wrote more positive statements in their diaries.

Unfortunately, the researchers noticed that men apparently did not actually practice meditation as much as they should. They had very few journal entries and in post-study interviews, a vast majority of men admitted that they only practiced meditation about half the time.

The lack of practice is an extraneous variable. Perhaps if men had adhered to the study instructions, their scores on the physical and psychological measures would have been higher than women’s measures.

The quasi-experiment is used when researchers want to study the effects of a variable/treatment on different groups of people. Groups can be defined based on gender, parenting style, SES demographics, or any number of other variables.

The problem is that when interpreting the results, even clear differences between the groups cannot be attributed to the treatment.

The groups may differ in ways other than the grouping variables. For example, leadership training in the study above may have improved the textile start-ups’ performance if the techniques had been applied at all. Similarly, men may have benefited from meditation as much as women, if they had just tried.

Baumrind, D. (1991). Parenting styles and adolescent development. In R. M. Lerner, A. C. Peterson, & J. Brooks-Gunn (Eds.), Encyclopedia of Adolescence (pp. 746–758). New York: Garland Publishing, Inc.

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

Matthew L. Maciejewski (2020) Quasi-experimental design. Biostatistics & Epidemiology, 4 (1), 38-47. https://doi.org/10.1080/24709360.2018.1477468

Thyer, Bruce. (2012). Quasi-Experimental Research Designs . Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195387384.001.0001

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Theory of Planned Behavior Examples

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Quasi-Experimental Design

• Usability Studies

Quasi-Experimental Design is a unique research methodology because it is characterized by what is lacks. For example, Abraham & MacDonald (2011) state:

" Quasi-experimental research is similar to experimental research in that there is manipulation of an independent variable. It differs from experimental research because either there is no control group, no random selection, no random assignment, and/or no active manipulation. "

This type of research is often performed in cases where a control group cannot be created or random selection cannot be performed. This is often the case in certain medical and psychological studies. 

For more information on quasi-experimental design, review the resources below: 

Where to Start

Below are listed a few tools and online guides that can help you start your Quasi-experimental research. These include free online resources and resources available only through ISU Library.

• Quasi-Experimental Research Designs by Bruce A. Thyer This pocket guide describes the logic, design, and conduct of the range of quasi-experimental designs, encompassing pre-experiments, quasi-experiments making use of a control or comparison group, and time-series designs. An introductory chapter describes the valuable role these types of studies have played in social work, from the 1930s to the present. Subsequent chapters delve into each design type's major features, the kinds of questions it is capable of answering, and its strengths and limitations.
• Experimental and Quasi-Experimental Designs for Research by Donald T. Campbell; Julian C. Stanley. Call Number: Q175 C152e Written 1967 but still used heavily today, this book examines research designs for experimental and quasi-experimental research, with examples and judgments about each design's validity.

Online Resources

• Quasi-Experimental Design From the Web Center for Social Research Methods, this is a very good overview of quasi-experimental design.
• Experimental and Quasi-Experimental Research From Colorado State University.
• Quasi-experimental design--Wikipedia, the free encyclopedia Wikipedia can be a useful place to start your research- check the citations at the bottom of the article for more information.
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Home » Quasi-Experimental Research Design – Types, Methods

Quasi-Experimental Research Design – Types, Methods

Table of Contents

Quasi-Experimental Design

Quasi-experimental design is a research method that seeks to evaluate the causal relationships between variables, but without the full control over the independent variable(s) that is available in a true experimental design.

In a quasi-experimental design, the researcher uses an existing group of participants that is not randomly assigned to the experimental and control groups. Instead, the groups are selected based on pre-existing characteristics or conditions, such as age, gender, or the presence of a certain medical condition.

Types of Quasi-Experimental Design

There are several types of quasi-experimental designs that researchers use to study causal relationships between variables. Here are some of the most common types:

Non-Equivalent Control Group Design

This design involves selecting two groups of participants that are similar in every way except for the independent variable(s) that the researcher is testing. One group receives the treatment or intervention being studied, while the other group does not. The two groups are then compared to see if there are any significant differences in the outcomes.

Interrupted Time-Series Design

This design involves collecting data on the dependent variable(s) over a period of time, both before and after an intervention or event. The researcher can then determine whether there was a significant change in the dependent variable(s) following the intervention or event.

Pretest-Posttest Design

This design involves measuring the dependent variable(s) before and after an intervention or event, but without a control group. This design can be useful for determining whether the intervention or event had an effect, but it does not allow for control over other factors that may have influenced the outcomes.

Regression Discontinuity Design

This design involves selecting participants based on a specific cutoff point on a continuous variable, such as a test score. Participants on either side of the cutoff point are then compared to determine whether the intervention or event had an effect.

Natural Experiments

This design involves studying the effects of an intervention or event that occurs naturally, without the researcher’s intervention. For example, a researcher might study the effects of a new law or policy that affects certain groups of people. This design is useful when true experiments are not feasible or ethical.

Data Analysis Methods

Here are some data analysis methods that are commonly used in quasi-experimental designs:

Descriptive Statistics

This method involves summarizing the data collected during a study using measures such as mean, median, mode, range, and standard deviation. Descriptive statistics can help researchers identify trends or patterns in the data, and can also be useful for identifying outliers or anomalies.

Inferential Statistics

This method involves using statistical tests to determine whether the results of a study are statistically significant. Inferential statistics can help researchers make generalizations about a population based on the sample data collected during the study. Common statistical tests used in quasi-experimental designs include t-tests, ANOVA, and regression analysis.

Propensity Score Matching

This method is used to reduce bias in quasi-experimental designs by matching participants in the intervention group with participants in the control group who have similar characteristics. This can help to reduce the impact of confounding variables that may affect the study’s results.

Difference-in-differences Analysis

This method is used to compare the difference in outcomes between two groups over time. Researchers can use this method to determine whether a particular intervention has had an impact on the target population over time.

Interrupted Time Series Analysis

This method is used to examine the impact of an intervention or treatment over time by comparing data collected before and after the intervention or treatment. This method can help researchers determine whether an intervention had a significant impact on the target population.

Regression Discontinuity Analysis

This method is used to compare the outcomes of participants who fall on either side of a predetermined cutoff point. This method can help researchers determine whether an intervention had a significant impact on the target population.

Steps in Quasi-Experimental Design

Here are the general steps involved in conducting a quasi-experimental design:

• Identify the research question: Determine the research question and the variables that will be investigated.
• Choose the design: Choose the appropriate quasi-experimental design to address the research question. Examples include the pretest-posttest design, non-equivalent control group design, regression discontinuity design, and interrupted time series design.
• Select the participants: Select the participants who will be included in the study. Participants should be selected based on specific criteria relevant to the research question.
• Measure the variables: Measure the variables that are relevant to the research question. This may involve using surveys, questionnaires, tests, or other measures.
• Implement the intervention or treatment: Implement the intervention or treatment to the participants in the intervention group. This may involve training, education, counseling, or other interventions.
• Collect data: Collect data on the dependent variable(s) before and after the intervention. Data collection may also include collecting data on other variables that may impact the dependent variable(s).
• Analyze the data: Analyze the data collected to determine whether the intervention had a significant impact on the dependent variable(s).
• Draw conclusions: Draw conclusions about the relationship between the independent and dependent variables. If the results suggest a causal relationship, then appropriate recommendations may be made based on the findings.

Quasi-Experimental Design Examples

Here are some examples of real-time quasi-experimental designs:

• Evaluating the impact of a new teaching method: In this study, a group of students are taught using a new teaching method, while another group is taught using the traditional method. The test scores of both groups are compared before and after the intervention to determine whether the new teaching method had a significant impact on student performance.
• Assessing the effectiveness of a public health campaign: In this study, a public health campaign is launched to promote healthy eating habits among a targeted population. The behavior of the population is compared before and after the campaign to determine whether the intervention had a significant impact on the target behavior.
• Examining the impact of a new medication: In this study, a group of patients is given a new medication, while another group is given a placebo. The outcomes of both groups are compared to determine whether the new medication had a significant impact on the targeted health condition.
• Evaluating the effectiveness of a job training program : In this study, a group of unemployed individuals is enrolled in a job training program, while another group is not enrolled in any program. The employment rates of both groups are compared before and after the intervention to determine whether the training program had a significant impact on the employment rates of the participants.
• Assessing the impact of a new policy : In this study, a new policy is implemented in a particular area, while another area does not have the new policy. The outcomes of both areas are compared before and after the intervention to determine whether the new policy had a significant impact on the targeted behavior or outcome.

Applications of Quasi-Experimental Design

Here are some applications of quasi-experimental design:

• Educational research: Quasi-experimental designs are used to evaluate the effectiveness of educational interventions, such as new teaching methods, technology-based learning, or educational policies.
• Health research: Quasi-experimental designs are used to evaluate the effectiveness of health interventions, such as new medications, public health campaigns, or health policies.
• Social science research: Quasi-experimental designs are used to investigate the impact of social interventions, such as job training programs, welfare policies, or criminal justice programs.
• Business research: Quasi-experimental designs are used to evaluate the impact of business interventions, such as marketing campaigns, new products, or pricing strategies.
• Environmental research: Quasi-experimental designs are used to evaluate the impact of environmental interventions, such as conservation programs, pollution control policies, or renewable energy initiatives.

When to use Quasi-Experimental Design

Here are some situations where quasi-experimental designs may be appropriate:

• When the research question involves investigating the effectiveness of an intervention, policy, or program : In situations where it is not feasible or ethical to randomly assign participants to intervention and control groups, quasi-experimental designs can be used to evaluate the impact of the intervention on the targeted outcome.
• When the sample size is small: In situations where the sample size is small, it may be difficult to randomly assign participants to intervention and control groups. Quasi-experimental designs can be used to investigate the impact of an intervention without requiring a large sample size.
• When the research question involves investigating a naturally occurring event : In some situations, researchers may be interested in investigating the impact of a naturally occurring event, such as a natural disaster or a major policy change. Quasi-experimental designs can be used to evaluate the impact of the event on the targeted outcome.
• When the research question involves investigating a long-term intervention: In situations where the intervention or program is long-term, it may be difficult to randomly assign participants to intervention and control groups for the entire duration of the intervention. Quasi-experimental designs can be used to evaluate the impact of the intervention over time.
• When the research question involves investigating the impact of a variable that cannot be manipulated : In some situations, it may not be possible or ethical to manipulate a variable of interest. Quasi-experimental designs can be used to investigate the relationship between the variable and the targeted outcome.

Purpose of Quasi-Experimental Design

The purpose of quasi-experimental design is to investigate the causal relationship between two or more variables when it is not feasible or ethical to conduct a randomized controlled trial (RCT). Quasi-experimental designs attempt to emulate the randomized control trial by mimicking the control group and the intervention group as much as possible.

The key purpose of quasi-experimental design is to evaluate the impact of an intervention, policy, or program on a targeted outcome while controlling for potential confounding factors that may affect the outcome. Quasi-experimental designs aim to answer questions such as: Did the intervention cause the change in the outcome? Would the outcome have changed without the intervention? And was the intervention effective in achieving its intended goals?

Quasi-experimental designs are useful in situations where randomized controlled trials are not feasible or ethical. They provide researchers with an alternative method to evaluate the effectiveness of interventions, policies, and programs in real-life settings. Quasi-experimental designs can also help inform policy and practice by providing valuable insights into the causal relationships between variables.

Overall, the purpose of quasi-experimental design is to provide a rigorous method for evaluating the impact of interventions, policies, and programs while controlling for potential confounding factors that may affect the outcome.

Advantages of Quasi-Experimental Design

Quasi-experimental designs have several advantages over other research designs, such as:

• Greater external validity : Quasi-experimental designs are more likely to have greater external validity than laboratory experiments because they are conducted in naturalistic settings. This means that the results are more likely to generalize to real-world situations.
• Ethical considerations: Quasi-experimental designs often involve naturally occurring events, such as natural disasters or policy changes. This means that researchers do not need to manipulate variables, which can raise ethical concerns.
• More practical: Quasi-experimental designs are often more practical than experimental designs because they are less expensive and easier to conduct. They can also be used to evaluate programs or policies that have already been implemented, which can save time and resources.
• No random assignment: Quasi-experimental designs do not require random assignment, which can be difficult or impossible in some cases, such as when studying the effects of a natural disaster. This means that researchers can still make causal inferences, although they must use statistical techniques to control for potential confounding variables.
• Greater generalizability : Quasi-experimental designs are often more generalizable than experimental designs because they include a wider range of participants and conditions. This can make the results more applicable to different populations and settings.

Limitations of Quasi-Experimental Design

There are several limitations associated with quasi-experimental designs, which include:

• Lack of Randomization: Quasi-experimental designs do not involve randomization of participants into groups, which means that the groups being studied may differ in important ways that could affect the outcome of the study. This can lead to problems with internal validity and limit the ability to make causal inferences.
• Selection Bias: Quasi-experimental designs may suffer from selection bias because participants are not randomly assigned to groups. Participants may self-select into groups or be assigned based on pre-existing characteristics, which may introduce bias into the study.
• History and Maturation: Quasi-experimental designs are susceptible to history and maturation effects, where the passage of time or other events may influence the outcome of the study.
• Lack of Control: Quasi-experimental designs may lack control over extraneous variables that could influence the outcome of the study. This can limit the ability to draw causal inferences from the study.
• Limited Generalizability: Quasi-experimental designs may have limited generalizability because the results may only apply to the specific population and context being studied.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• v.13(1); Jan-Feb 2006

The Use and Interpretation of Quasi-Experimental Studies in Medical Informatics

Associated data.

Quasi-experimental study designs, often described as nonrandomized, pre-post intervention studies, are common in the medical informatics literature. Yet little has been written about the benefits and limitations of the quasi-experimental approach as applied to informatics studies. This paper outlines a relative hierarchy and nomenclature of quasi-experimental study designs that is applicable to medical informatics intervention studies. In addition, the authors performed a systematic review of two medical informatics journals, the Journal of the American Medical Informatics Association (JAMIA) and the International Journal of Medical Informatics (IJMI), to determine the number of quasi-experimental studies published and how the studies are classified on the above-mentioned relative hierarchy. They hope that future medical informatics studies will implement higher level quasi-experimental study designs that yield more convincing evidence for causal links between medical informatics interventions and outcomes.

Quasi-experimental studies encompass a broad range of nonrandomized intervention studies. These designs are frequently used when it is not logistically feasible or ethical to conduct a randomized controlled trial. Examples of quasi-experimental studies follow. As one example of a quasi-experimental study, a hospital introduces a new order-entry system and wishes to study the impact of this intervention on the number of medication-related adverse events before and after the intervention. As another example, an informatics technology group is introducing a pharmacy order-entry system aimed at decreasing pharmacy costs. The intervention is implemented and pharmacy costs before and after the intervention are measured.

In medical informatics, the quasi-experimental, sometimes called the pre-post intervention, design often is used to evaluate the benefits of specific interventions. The increasing capacity of health care institutions to collect routine clinical data has led to the growing use of quasi-experimental study designs in the field of medical informatics as well as in other medical disciplines. However, little is written about these study designs in the medical literature or in traditional epidemiology textbooks. 1 , 2 , 3 In contrast, the social sciences literature is replete with examples of ways to implement and improve quasi-experimental studies. 4 , 5 , 6

In this paper, we review the different pretest-posttest quasi-experimental study designs, their nomenclature, and the relative hierarchy of these designs with respect to their ability to establish causal associations between an intervention and an outcome. The example of a pharmacy order-entry system aimed at decreasing pharmacy costs will be used throughout this article to illustrate the different quasi-experimental designs. We discuss limitations of quasi-experimental designs and offer methods to improve them. We also perform a systematic review of four years of publications from two informatics journals to determine the number of quasi-experimental studies, classify these studies into their application domains, determine whether the potential limitations of quasi-experimental studies were acknowledged by the authors, and place these studies into the above-mentioned relative hierarchy.

The authors reviewed articles and book chapters on the design of quasi-experimental studies. 4 , 5 , 6 , 7 , 8 , 9 , 10 Most of the reviewed articles referenced two textbooks that were then reviewed in depth. 4 , 6

Key advantages and disadvantages of quasi-experimental studies, as they pertain to the study of medical informatics, were identified. The potential methodological flaws of quasi-experimental medical informatics studies, which have the potential to introduce bias, were also identified. In addition, a summary table outlining a relative hierarchy and nomenclature of quasi-experimental study designs is described. In general, the higher the design is in the hierarchy, the greater the internal validity that the study traditionally possesses because the evidence of the potential causation between the intervention and the outcome is strengthened. 4

We then performed a systematic review of four years of publications from two informatics journals. First, we determined the number of quasi-experimental studies. We then classified these studies on the above-mentioned hierarchy. We also classified the quasi-experimental studies according to their application domain. The categories of application domains employed were based on categorization used by Yearbooks of Medical Informatics 1992–2005 and were similar to the categories of application domains employed by Annual Symposiums of the American Medical Informatics Association. 11 The categories were (1) health and clinical management; (2) patient records; (3) health information systems; (4) medical signal processing and biomedical imaging; (5) decision support, knowledge representation, and management; (6) education and consumer informatics; and (7) bioinformatics. Because the quasi-experimental study design has recognized limitations, we sought to determine whether authors acknowledged the potential limitations of this design. Examples of acknowledgment included mention of lack of randomization, the potential for regression to the mean, the presence of temporal confounders and the mention of another design that would have more internal validity.

All original scientific manuscripts published between January 2000 and December 2003 in the Journal of the American Medical Informatics Association (JAMIA) and the International Journal of Medical Informatics (IJMI) were reviewed. One author (ADH) reviewed all the papers to identify the number of quasi-experimental studies. Other authors (ADH, JCM, JF) then independently reviewed all the studies identified as quasi-experimental. The three authors then convened as a group to resolve any disagreements in study classification, application domain, and acknowledgment of limitations.

Results and Discussion

What is a quasi-experiment.

Quasi-experiments are studies that aim to evaluate interventions but that do not use randomization. Similar to randomized trials, quasi-experiments aim to demonstrate causality between an intervention and an outcome. Quasi-experimental studies can use both preintervention and postintervention measurements as well as nonrandomly selected control groups.

Using this basic definition, it is evident that many published studies in medical informatics utilize the quasi-experimental design. Although the randomized controlled trial is generally considered to have the highest level of credibility with regard to assessing causality, in medical informatics, researchers often choose not to randomize the intervention for one or more reasons: (1) ethical considerations, (2) difficulty of randomizing subjects, (3) difficulty to randomize by locations (e.g., by wards), (4) small available sample size. Each of these reasons is discussed below.

Ethical considerations typically will not allow random withholding of an intervention with known efficacy. Thus, if the efficacy of an intervention has not been established, a randomized controlled trial is the design of choice to determine efficacy. But if the intervention under study incorporates an accepted, well-established therapeutic intervention, or if the intervention has either questionable efficacy or safety based on previously conducted studies, then the ethical issues of randomizing patients are sometimes raised. In the area of medical informatics, it is often believed prior to an implementation that an informatics intervention will likely be beneficial and thus medical informaticians and hospital administrators are often reluctant to randomize medical informatics interventions. In addition, there is often pressure to implement the intervention quickly because of its believed efficacy, thus not allowing researchers sufficient time to plan a randomized trial.

For medical informatics interventions, it is often difficult to randomize the intervention to individual patients or to individual informatics users. So while this randomization is technically possible, it is underused and thus compromises the eventual strength of concluding that an informatics intervention resulted in an outcome. For example, randomly allowing only half of medical residents to use pharmacy order-entry software at a tertiary care hospital is a scenario that hospital administrators and informatics users may not agree to for numerous reasons.

Similarly, informatics interventions often cannot be randomized to individual locations. Using the pharmacy order-entry system example, it may be difficult to randomize use of the system to only certain locations in a hospital or portions of certain locations. For example, if the pharmacy order-entry system involves an educational component, then people may apply the knowledge learned to nonintervention wards, thereby potentially masking the true effect of the intervention. When a design using randomized locations is employed successfully, the locations may be different in other respects (confounding variables), and this further complicates the analysis and interpretation.

In situations where it is known that only a small sample size will be available to test the efficacy of an intervention, randomization may not be a viable option. Randomization is beneficial because on average it tends to evenly distribute both known and unknown confounding variables between the intervention and control group. However, when the sample size is small, randomization may not adequately accomplish this balance. Thus, alternative design and analytical methods are often used in place of randomization when only small sample sizes are available.

What Are the Threats to Establishing Causality When Using Quasi-experimental Designs in Medical Informatics?

The lack of random assignment is the major weakness of the quasi-experimental study design. Associations identified in quasi-experiments meet one important requirement of causality since the intervention precedes the measurement of the outcome. Another requirement is that the outcome can be demonstrated to vary statistically with the intervention. Unfortunately, statistical association does not imply causality, especially if the study is poorly designed. Thus, in many quasi-experiments, one is most often left with the question: “Are there alternative explanations for the apparent causal association?” If these alternative explanations are credible, then the evidence of causation is less convincing. These rival hypotheses, or alternative explanations, arise from principles of epidemiologic study design.

Shadish et al. 4 outline nine threats to internal validity that are outlined in ▶ . Internal validity is defined as the degree to which observed changes in outcomes can be correctly inferred to be caused by an exposure or an intervention. In quasi-experimental studies of medical informatics, we believe that the methodological principles that most often result in alternative explanations for the apparent causal effect include (a) difficulty in measuring or controlling for important confounding variables, particularly unmeasured confounding variables, which can be viewed as a subset of the selection threat in ▶ ; (b) results being explained by the statistical principle of regression to the mean . Each of these latter two principles is discussed in turn.

Threats to Internal Validity

Adapted from Shadish et al. 4

An inability to sufficiently control for important confounding variables arises from the lack of randomization. A variable is a confounding variable if it is associated with the exposure of interest and is also associated with the outcome of interest; the confounding variable leads to a situation where a causal association between a given exposure and an outcome is observed as a result of the influence of the confounding variable. For example, in a study aiming to demonstrate that the introduction of a pharmacy order-entry system led to lower pharmacy costs, there are a number of important potential confounding variables (e.g., severity of illness of the patients, knowledge and experience of the software users, other changes in hospital policy) that may have differed in the preintervention and postintervention time periods ( ▶ ). In a multivariable regression, the first confounding variable could be addressed with severity of illness measures, but the second confounding variable would be difficult if not nearly impossible to measure and control. In addition, potential confounding variables that are unmeasured or immeasurable cannot be controlled for in nonrandomized quasi-experimental study designs and can only be properly controlled by the randomization process in randomized controlled trials.

Example of confounding. To get the true effect of the intervention of interest, we need to control for the confounding variable.

Another important threat to establishing causality is regression to the mean. 12 , 13 , 14 This widespread statistical phenomenon can result in wrongly concluding that an effect is due to the intervention when in reality it is due to chance. The phenomenon was first described in 1886 by Francis Galton who measured the adult height of children and their parents. He noted that when the average height of the parents was greater than the mean of the population, the children tended to be shorter than their parents, and conversely, when the average height of the parents was shorter than the population mean, the children tended to be taller than their parents.

In medical informatics, what often triggers the development and implementation of an intervention is a rise in the rate above the mean or norm. For example, increasing pharmacy costs and adverse events may prompt hospital informatics personnel to design and implement pharmacy order-entry systems. If this rise in costs or adverse events is really just an extreme observation that is still within the normal range of the hospital's pharmaceutical costs (i.e., the mean pharmaceutical cost for the hospital has not shifted), then the statistical principle of regression to the mean predicts that these elevated rates will tend to decline even without intervention. However, often informatics personnel and hospital administrators cannot wait passively for this decline to occur. Therefore, hospital personnel often implement one or more interventions, and if a decline in the rate occurs, they may mistakenly conclude that the decline is causally related to the intervention. In fact, an alternative explanation for the finding could be regression to the mean.

What Are the Different Quasi-experimental Study Designs?

In the social sciences literature, quasi-experimental studies are divided into four study design groups 4 , 6 :

• Quasi-experimental designs without control groups
• Quasi-experimental designs that use control groups but no pretest
• Quasi-experimental designs that use control groups and pretests
• Interrupted time-series designs

There is a relative hierarchy within these categories of study designs, with category D studies being sounder than categories C, B, or A in terms of establishing causality. Thus, if feasible from a design and implementation point of view, investigators should aim to design studies that fall in to the higher rated categories. Shadish et al. 4 discuss 17 possible designs, with seven designs falling into category A, three designs in category B, and six designs in category C, and one major design in category D. In our review, we determined that most medical informatics quasi-experiments could be characterized by 11 of 17 designs, with six study designs in category A, one in category B, three designs in category C, and one design in category D because the other study designs were not used or feasible in the medical informatics literature. Thus, for simplicity, we have summarized the 11 study designs most relevant to medical informatics research in ▶ .

Relative Hierarchy of Quasi-experimental Designs

O = Observational Measurement; X = Intervention Under Study. Time moves from left to right.

The nomenclature and relative hierarchy were used in the systematic review of four years of JAMIA and the IJMI. Similar to the relative hierarchy that exists in the evidence-based literature that assigns a hierarchy to randomized controlled trials, cohort studies, case-control studies, and case series, the hierarchy in ▶ is not absolute in that in some cases, it may be infeasible to perform a higher level study. For example, there may be instances where an A6 design established stronger causality than a B1 design. 15 , 16 , 17

Quasi-experimental Designs without Control Groups

Here, X is the intervention and O is the outcome variable (this notation is continued throughout the article). In this study design, an intervention (X) is implemented and a posttest observation (O1) is taken. For example, X could be the introduction of a pharmacy order-entry intervention and O1 could be the pharmacy costs following the intervention. This design is the weakest of the quasi-experimental designs that are discussed in this article. Without any pretest observations or a control group, there are multiple threats to internal validity. Unfortunately, this study design is often used in medical informatics when new software is introduced since it may be difficult to have pretest measurements due to time, technical, or cost constraints.

This is a commonly used study design. A single pretest measurement is taken (O1), an intervention (X) is implemented, and a posttest measurement is taken (O2). In this instance, period O1 frequently serves as the “control” period. For example, O1 could be pharmacy costs prior to the intervention, X could be the introduction of a pharmacy order-entry system, and O2 could be the pharmacy costs following the intervention. Including a pretest provides some information about what the pharmacy costs would have been had the intervention not occurred.

The advantage of this study design over A2 is that adding a second pretest prior to the intervention helps provide evidence that can be used to refute the phenomenon of regression to the mean and confounding as alternative explanations for any observed association between the intervention and the posttest outcome. For example, in a study where a pharmacy order-entry system led to lower pharmacy costs (O3 < O2 and O1), if one had two preintervention measurements of pharmacy costs (O1 and O2) and they were both elevated, this would suggest that there was a decreased likelihood that O3 is lower due to confounding and regression to the mean. Similarly, extending this study design by increasing the number of measurements postintervention could also help to provide evidence against confounding and regression to the mean as alternate explanations for observed associations.

This design involves the inclusion of a nonequivalent dependent variable ( b ) in addition to the primary dependent variable ( a ). Variables a and b should assess similar constructs; that is, the two measures should be affected by similar factors and confounding variables except for the effect of the intervention. Variable a is expected to change because of the intervention X, whereas variable b is not. Taking our example, variable a could be pharmacy costs and variable b could be the length of stay of patients. If our informatics intervention is aimed at decreasing pharmacy costs, we would expect to observe a decrease in pharmacy costs but not in the average length of stay of patients. However, a number of important confounding variables, such as severity of illness and knowledge of software users, might affect both outcome measures. Thus, if the average length of stay did not change following the intervention but pharmacy costs did, then the data are more convincing than if just pharmacy costs were measured.

The Removed-Treatment Design

This design adds a third posttest measurement (O3) to the one-group pretest-posttest design and then removes the intervention before a final measure (O4) is made. The advantage of this design is that it allows one to test hypotheses about the outcome in the presence of the intervention and in the absence of the intervention. Thus, if one predicts a decrease in the outcome between O1 and O2 (after implementation of the intervention), then one would predict an increase in the outcome between O3 and O4 (after removal of the intervention). One caveat is that if the intervention is thought to have persistent effects, then O4 needs to be measured after these effects are likely to have disappeared. For example, a study would be more convincing if it demonstrated that pharmacy costs decreased after pharmacy order-entry system introduction (O2 and O3 less than O1) and that when the order-entry system was removed or disabled, the costs increased (O4 greater than O2 and O3 and closer to O1). In addition, there are often ethical issues in this design in terms of removing an intervention that may be providing benefit.

The Repeated-Treatment Design

The advantage of this design is that it demonstrates reproducibility of the association between the intervention and the outcome. For example, the association is more likely to be causal if one demonstrates that a pharmacy order-entry system results in decreased pharmacy costs when it is first introduced and again when it is reintroduced following an interruption of the intervention. As for design A5, the assumption must be made that the effect of the intervention is transient, which is most often applicable to medical informatics interventions. Because in this design, subjects may serve as their own controls, this may yield greater statistical efficiency with fewer numbers of subjects.

Quasi-experimental Designs That Use a Control Group but No Pretest

An intervention X is implemented for one group and compared to a second group. The use of a comparison group helps prevent certain threats to validity including the ability to statistically adjust for confounding variables. Because in this study design, the two groups may not be equivalent (assignment to the groups is not by randomization), confounding may exist. For example, suppose that a pharmacy order-entry intervention was instituted in the medical intensive care unit (MICU) and not the surgical intensive care unit (SICU). O1 would be pharmacy costs in the MICU after the intervention and O2 would be pharmacy costs in the SICU after the intervention. The absence of a pretest makes it difficult to know whether a change has occurred in the MICU. Also, the absence of pretest measurements comparing the SICU to the MICU makes it difficult to know whether differences in O1 and O2 are due to the intervention or due to other differences in the two units (confounding variables).

Quasi-experimental Designs That Use Control Groups and Pretests

The reader should note that with all the studies in this category, the intervention is not randomized. The control groups chosen are comparison groups. Obtaining pretest measurements on both the intervention and control groups allows one to assess the initial comparability of the groups. The assumption is that if the intervention and the control groups are similar at the pretest, the smaller the likelihood there is of important confounding variables differing between the two groups.

The use of both a pretest and a comparison group makes it easier to avoid certain threats to validity. However, because the two groups are nonequivalent (assignment to the groups is not by randomization), selection bias may exist. Selection bias exists when selection results in differences in unit characteristics between conditions that may be related to outcome differences. For example, suppose that a pharmacy order-entry intervention was instituted in the MICU and not the SICU. If preintervention pharmacy costs in the MICU (O1a) and SICU (O1b) are similar, it suggests that it is less likely that there are differences in the important confounding variables between the two units. If MICU postintervention costs (O2a) are less than preintervention MICU costs (O1a), but SICU costs (O1b) and (O2b) are similar, this suggests that the observed outcome may be causally related to the intervention.

In this design, the pretests are administered at two different times. The main advantage of this design is that it controls for potentially different time-varying confounding effects in the intervention group and the comparison group. In our example, measuring points O1 and O2 would allow for the assessment of time-dependent changes in pharmacy costs, e.g., due to differences in experience of residents, preintervention between the intervention and control group, and whether these changes were similar or different.

With this study design, the researcher administers an intervention at a later time to a group that initially served as a nonintervention control. The advantage of this design over design C2 is that it demonstrates reproducibility in two different settings. This study design is not limited to two groups; in fact, the study results have greater validity if the intervention effect is replicated in different groups at multiple times. In the example of a pharmacy order-entry system, one could implement or intervene in the MICU and then at a later time, intervene in the SICU. This latter design is often very applicable to medical informatics where new technology and new software is often introduced or made available gradually.

Interrupted Time-Series Designs

An interrupted time-series design is one in which a string of consecutive observations equally spaced in time is interrupted by the imposition of a treatment or intervention. The advantage of this design is that with multiple measurements both pre- and postintervention, it is easier to address and control for confounding and regression to the mean. In addition, statistically, there is a more robust analytic capability, and there is the ability to detect changes in the slope or intercept as a result of the intervention in addition to a change in the mean values. 18 A change in intercept could represent an immediate effect while a change in slope could represent a gradual effect of the intervention on the outcome. In the example of a pharmacy order-entry system, O1 through O5 could represent monthly pharmacy costs preintervention and O6 through O10 monthly pharmacy costs post the introduction of the pharmacy order-entry system. Interrupted time-series designs also can be further strengthened by incorporating many of the design features previously mentioned in other categories (such as removal of the treatment, inclusion of a nondependent outcome variable, or the addition of a control group).

Systematic Review Results

The results of the systematic review are in ▶ . In the four-year period of JAMIA publications that the authors reviewed, 25 quasi-experimental studies among 22 articles were published. Of these 25, 15 studies were of category A, five studies were of category B, two studies were of category C, and no studies were of category D. Although there were no studies of category D (interrupted time-series analyses), three of the studies classified as category A had data collected that could have been analyzed as an interrupted time-series analysis. Nine of the 25 studies (36%) mentioned at least one of the potential limitations of the quasi-experimental study design. In the four-year period of IJMI publications reviewed by the authors, nine quasi-experimental studies among eight manuscripts were published. Of these nine, five studies were of category A, one of category B, one of category C, and two of category D. Two of the nine studies (22%) mentioned at least one of the potential limitations of the quasi-experimental study design.

Systematic Review of Four Years of Quasi-designs in JAMIA

JAMIA = Journal of the American Medical Informatics Association; IJMI = International Journal of Medical Informatics.

In addition, three studies from JAMIA were based on a counterbalanced design. A counterbalanced design is a higher order study design than other studies in category A. The counterbalanced design is sometimes referred to as a Latin-square arrangement. In this design, all subjects receive all the different interventions but the order of intervention assignment is not random. 19 This design can only be used when the intervention is compared against some existing standard, for example, if a new PDA-based order entry system is to be compared to a computer terminal–based order entry system. In this design, all subjects receive the new PDA-based order entry system and the old computer terminal-based order entry system. The counterbalanced design is a within-participants design, where the order of the intervention is varied (e.g., one group is given software A followed by software B and another group is given software B followed by software A). The counterbalanced design is typically used when the available sample size is small, thus preventing the use of randomization. This design also allows investigators to study the potential effect of ordering of the informatics intervention.

Although quasi-experimental study designs are ubiquitous in the medical informatics literature, as evidenced by 34 studies in the past four years of the two informatics journals, little has been written about the benefits and limitations of the quasi-experimental approach. As we have outlined in this paper, a relative hierarchy and nomenclature of quasi-experimental study designs exist, with some designs being more likely than others to permit causal interpretations of observed associations. Strengths and limitations of a particular study design should be discussed when presenting data collected in the setting of a quasi-experimental study. Future medical informatics investigators should choose the strongest design that is feasible given the particular circumstances.

Supplementary Material

Dr. Harris was supported by NIH grants K23 AI01752-01A1 and R01 AI60859-01A1. Dr. Perencevich was supported by a VA Health Services Research and Development Service (HSR&D) Research Career Development Award (RCD-02026-1). Dr. Finkelstein was supported by NIH grant RO1 HL71690.

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Chapter 7: Nonexperimental Research

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). [1] 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 design 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) [2] . 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) [3] . 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 parallel 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: Classics in the History of Psychology .

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) [4] . 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.

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 one 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) [5] . 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 7.3 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 7.3 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 7.3 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.

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 change in attitude 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 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.
• regression to the mean
• spontaneous remission

Image Descriptions

Figure 7.3 image description: Two line graphs charting the number of absences per week over 14 weeks. The first 7 weeks are without treatment and the last 7 weeks are with treatment. In the first line graph, there are between 4 to 8 absences each week. After the treatment, the absences drop to 0 to 3 each week, which suggests the treatment worked. In the second line graph, there is no noticeable change in the number of absences per week after the treatment, which suggests the treatment did not work. [Return to Figure 7.3]

• Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin. ↵
• 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. ↵
• Eysenck, H. J. (1952). The effects of psychotherapy: An evaluation. Journal of Consulting Psychology, 16 , 319–324. ↵
• Smith, M. L., Glass, G. V., & Miller, T. I. (1980). The benefits of psychotherapy . Baltimore, MD: Johns Hopkins University Press. ↵

A between-subjects design in which participants have not been randomly assigned to conditions.

The dependent variable is measured once before the treatment is implemented and once after it is implemented.

A category of alternative explanations for differences between scores such as events that happened between the pretest and posttest, unrelated to the study.

An alternative explanation that refers to how the participants might have changed between the pretest and posttest in ways that they were going to anyway because they are growing and learning.

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.

The tendency for many medical and psychological problems to improve over time without any form of treatment.

A set of measurements taken at intervals over a period of time that are interrupted by a treatment.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 6: Data Collection Strategies

6.3 Quasi-Experiments

Under certain conditions, researchers often turn to field experiments, also known as quasi-experiment. These conditions usually occur when it is not possible to randomly assign participants to treatment and control groups (White & Sabarwal, 2014). Rather, selection to a group is by the participants, the researcher, or both the participant and the researcher (White & Sabarwal, 2014).

In a quasi-experiment, the independent variable is manipulated and similar to an experiment, it tests causal hypothesis (Campbell & Stanley, 1963).

Quasi-experiments allow researchers to infer causality by using the logic behind the experiment in a different way; however, there are three criteria that must be satisfied for causality to be inferred:

• The independent variable (X) comes before the dependent variable (Y) in time.
• X and Y are related to each other (i.e., they occur together).
• The relationship between X and Y aren’t explained by other causal agents (Crump, Price, Jhangiani, Chiang, & Leighton, 2017).

In a quasi-experiment the researcher identifies a comparison group that is as similar as is possible to the treatment group, as it relates to baseline (pre-intervention) characteristics. There are techniques for reducing selection bias when creating a comparison group. These techniques are regression discontinuity design and propensity score matching (White & Sabarwal, 2014).

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental vs Quasi-Experimental Design: Which to Choose?

Here’s a table that summarizes the similarities and differences between an experimental and a quasi-experimental study design:

What is a quasi-experimental design?

A quasi-experimental design is a non-randomized study design used to evaluate the effect of an intervention. The intervention can be a training program, a policy change or a medical treatment.

Unlike a true experiment, in a quasi-experimental study the choice of who gets the intervention and who doesn’t is not randomized. Instead, the intervention can be assigned to participants according to their choosing or that of the researcher, or by using any method other than randomness.

Having a control group is not required, but if present, it provides a higher level of evidence for the relationship between the intervention and the outcome.

(for more information, I recommend my other article: Understand Quasi-Experimental Design Through an Example ) .

Examples of quasi-experimental designs include:

• One-Group Posttest Only Design
• Static-Group Comparison Design
• One-Group Pretest-Posttest Design
• Separate-Sample Pretest-Posttest Design

What is an experimental design?

An experimental design is a randomized study design used to evaluate the effect of an intervention. In its simplest form, the participants will be randomly divided into 2 groups:

• A treatment group: where participants receive the new intervention which effect we want to study.
• A control or comparison group: where participants do not receive any intervention at all (or receive some standard intervention).

Randomization ensures that each participant has the same chance of receiving the intervention. Its objective is to equalize the 2 groups, and therefore, any observed difference in the study outcome afterwards will only be attributed to the intervention – i.e. it removes confounding.

(for more information, I recommend my other article: Purpose and Limitations of Random Assignment ).

Examples of experimental designs include:

• Posttest-Only Control Group Design
• Pretest-Posttest Control Group Design
• Solomon Four-Group Design
• Matched Pairs Design
• Randomized Block Design

When to choose an experimental design over a quasi-experimental design?

Although many statistical techniques can be used to deal with confounding in a quasi-experimental study, in practice, randomization is still the best tool we have to study causal relationships.

Another problem with quasi-experiments is the natural progression of the disease or the condition under study — When studying the effect of an intervention over time, one should consider natural changes because these can be mistaken with changes in outcome that are caused by the intervention. Having a well-chosen control group helps dealing with this issue.

So, if losing the element of randomness seems like an unwise step down in the hierarchy of evidence, why would we ever want to do it?

This is what we’re going to discuss next.

When to choose a quasi-experimental design over a true experiment?

The issue with randomness is that it cannot be always achievable.

So here are some cases where using a quasi-experimental design makes more sense than using an experimental one:

• If being in one group is believed to be harmful for the participants , either because the intervention is harmful (ex. randomizing people to smoking), or the intervention has a questionable efficacy, or on the contrary it is believed to be so beneficial that it would be malevolent to put people in the control group (ex. randomizing people to receiving an operation).
• In cases where interventions act on a group of people in a given location , it becomes difficult to adequately randomize subjects (ex. an intervention that reduces pollution in a given area).
• When working with small sample sizes , as randomized controlled trials require a large sample size to account for heterogeneity among subjects (i.e. to evenly distribute confounding variables between the intervention and control groups).

Further reading

• Statistical Software Popularity in 40,582 Research Papers
• Checking the Popularity of 125 Statistical Tests and Models
• Objectives of Epidemiology (With Examples)
• 12 Famous Epidemiologists and Why

Experimental and Quasi-Experimental Research

Guide Title: Experimental and Quasi-Experimental Research Guide ID: 64

You approach a stainless-steel wall, separated vertically along its middle where two halves meet. After looking to the left, you see two buttons on the wall to the right. You press the top button and it lights up. A soft tone sounds and the two halves of the wall slide apart to reveal a small room. You step into the room. Looking to the left, then to the right, you see a panel of more buttons. You know that you seek a room marked with the numbers 1-0-1-2, so you press the button marked "10." The halves slide shut and enclose you within the cubicle, which jolts upward. Soon, the soft tone sounds again. The door opens again. On the far wall, a sign silently proclaims, "10th floor."

You have engaged in a series of experiments. A ride in an elevator may not seem like an experiment, but it, and each step taken towards its ultimate outcome, are common examples of a search for a causal relationship-which is what experimentation is all about.

You started with the hypothesis that this is in fact an elevator. You proved that you were correct. You then hypothesized that the button to summon the elevator was on the left, which was incorrect, so then you hypothesized it was on the right, and you were correct. You hypothesized that pressing the button marked with the up arrow would not only bring an elevator to you, but that it would be an elevator heading in the up direction. You were right.

As this guide explains, the deliberate process of testing hypotheses and reaching conclusions is an extension of commonplace testing of cause and effect relationships.

Basic Concepts of Experimental and Quasi-Experimental Research

Discovering causal relationships is the key to experimental research. In abstract terms, this means the relationship between a certain action, X, which alone creates the effect Y. For example, turning the volume knob on your stereo clockwise causes the sound to get louder. In addition, you could observe that turning the knob clockwise alone, and nothing else, caused the sound level to increase. You could further conclude that a causal relationship exists between turning the knob clockwise and an increase in volume; not simply because one caused the other, but because you are certain that nothing else caused the effect.

Independent and Dependent Variables

Beyond discovering causal relationships, experimental research further seeks out how much cause will produce how much effect; in technical terms, how the independent variable will affect the dependent variable. You know that turning the knob clockwise will produce a louder noise, but by varying how much you turn it, you see how much sound is produced. On the other hand, you might find that although you turn the knob a great deal, sound doesn't increase dramatically. Or, you might find that turning the knob just a little adds more sound than expected. The amount that you turned the knob is the independent variable, the variable that the researcher controls, and the amount of sound that resulted from turning it is the dependent variable, the change that is caused by the independent variable.

Experimental research also looks into the effects of removing something. For example, if you remove a loud noise from the room, will the person next to you be able to hear you? Or how much noise needs to be removed before that person can hear you?

Treatment and Hypothesis

The term treatment refers to either removing or adding a stimulus in order to measure an effect (such as turning the knob a little or a lot, or reducing the noise level a little or a lot). Experimental researchers want to know how varying levels of treatment will affect what they are studying. As such, researchers often have an idea, or hypothesis, about what effect will occur when they cause something. Few experiments are performed where there is no idea of what will happen. From past experiences in life or from the knowledge we possess in our specific field of study, we know how some actions cause other reactions. Experiments confirm or reconfirm this fact.

Experimentation becomes more complex when the causal relationships they seek aren't as clear as in the stereo knob-turning examples. Questions like "Will olestra cause cancer?" or "Will this new fertilizer help this plant grow better?" present more to consider. For example, any number of things could affect the growth rate of a plant-the temperature, how much water or sun it receives, or how much carbon dioxide is in the air. These variables can affect an experiment's results. An experimenter who wants to show that adding a certain fertilizer will help a plant grow better must ensure that it is the fertilizer, and nothing else, affecting the growth patterns of the plant. To do this, as many of these variables as possible must be controlled.

Matching and Randomization

In the example used in this guide (you'll find the example below), we discuss an experiment that focuses on three groups of plants -- one that is treated with a fertilizer named MegaGro, another group treated with a fertilizer named Plant!, and yet another that is not treated with fetilizer (this latter group serves as a "control" group). In this example, even though the designers of the experiment have tried to remove all extraneous variables, results may appear merely coincidental. Since the goal of the experiment is to prove a causal relationship in which a single variable is responsible for the effect produced, the experiment would produce stronger proof if the results were replicated in larger treatment and control groups.

Selecting groups entails assigning subjects in the groups of an experiment in such a way that treatment and control groups are comparable in all respects except the application of the treatment. Groups can be created in two ways: matching and randomization. In the MegaGro experiment discussed below, the plants might be matched according to characteristics such as age, weight and whether they are blooming. This involves distributing these plants so that each plant in one group exactly matches characteristics of plants in the other groups. Matching may be problematic, though, because it "can promote a false sense of security by leading [the experimenter] to believe that [the] experimental and control groups were really equated at the outset, when in fact they were not equated on a host of variables" (Jones, 291). In other words, you may have flowers for your MegaGro experiment that you matched and distributed among groups, but other variables are unaccounted for. It would be difficult to have equal groupings.

Randomization, then, is preferred to matching. This method is based on the statistical principle of normal distribution. Theoretically, any arbitrarily selected group of adequate size will reflect normal distribution. Differences between groups will average out and become more comparable. The principle of normal distribution states that in a population most individuals will fall within the middle range of values for a given characteristic, with increasingly fewer toward either extreme (graphically represented as the ubiquitous "bell curve").

Differences between Quasi-Experimental and Experimental Research

Thus far, we have explained that for experimental research we need:

• a hypothesis for a causal relationship;
• a control group and a treatment group;
• to eliminate confounding variables that might mess up the experiment and prevent displaying the causal relationship; and
• to have larger groups with a carefully sorted constituency; preferably randomized, in order to keep accidental differences from fouling things up.

But what if we don't have all of those? Do we still have an experiment? Not a true experiment in the strictest scientific sense of the term, but we can have a quasi-experiment, an attempt to uncover a causal relationship, even though the researcher cannot control all the factors that might affect the outcome.

A quasi-experimenter treats a given situation as an experiment even though it is not wholly by design. The independent variable may not be manipulated by the researcher, treatment and control groups may not be randomized or matched, or there may be no control group. The researcher is limited in what he or she can say conclusively.

The significant element of both experiments and quasi-experiments is the measure of the dependent variable, which it allows for comparison. Some data is quite straightforward, but other measures, such as level of self-confidence in writing ability, increase in creativity or in reading comprehension are inescapably subjective. In such cases, quasi-experimentation often involves a number of strategies to compare subjectivity, such as rating data, testing, surveying, and content analysis.

Rating essentially is developing a rating scale to evaluate data. In testing, experimenters and quasi-experimenters use ANOVA (Analysis of Variance) and ANCOVA (Analysis of Co-Variance) tests to measure differences between control and experimental groups, as well as different correlations between groups.

Since we're mentioning the subject of statistics, note that experimental or quasi-experimental research cannot state beyond a shadow of a doubt that a single cause will always produce any one effect. They can do no more than show a probability that one thing causes another. The probability that a result is the due to random chance is an important measure of statistical analysis and in experimental research.

Example: Causality

Let's say you want to determine that your new fertilizer, MegaGro, will increase the growth rate of plants. You begin by getting a plant to go with your fertilizer. Since the experiment is concerned with proving that MegaGro works, you need another plant, using no fertilizer at all on it, to compare how much change your fertilized plant displays. This is what is known as a control group.

Set up with a control group, which will receive no treatment, and an experimental group, which will get MegaGro, you must then address those variables that could invalidate your experiment. This can be an extensive and exhaustive process. You must ensure that you use the same plant; that both groups are put in the same kind of soil; that they receive equal amounts of water and sun; that they receive the same amount of exposure to carbon-dioxide-exhaling researchers, and so on. In short, any other variable that might affect the growth of those plants, other than the fertilizer, must be the same for both plants. Otherwise, you can't prove absolutely that MegaGro is the only explanation for the increased growth of one of those plants.

Such an experiment can be done on more than two groups. You may not only want to show that MegaGro is an effective fertilizer, but that it is better than its competitor brand of fertilizer, Plant! All you need to do, then, is have one experimental group receiving MegaGro, one receiving Plant! and the other (the control group) receiving no fertilizer. Those are the only variables that can be different between the three groups; all other variables must be the same for the experiment to be valid.

Controlling variables allows the researcher to identify conditions that may affect the experiment's outcome. This may lead to alternative explanations that the researcher is willing to entertain in order to isolate only variables judged significant. In the MegaGro experiment, you may be concerned with how fertile the soil is, but not with the plants'; relative position in the window, as you don't think that the amount of shade they get will affect their growth rate. But what if it did? You would have to go about eliminating variables in order to determine which is the key factor. What if one receives more shade than the other and the MegaGro plant, which received more shade, died? This might prompt you to formulate a plausible alternative explanation, which is a way of accounting for a result that differs from what you expected. You would then want to redo the study with equal amounts of sunlight.

Methods: Five Steps

Experimental research can be roughly divided into five phases:

Identifying a research problem

The process starts by clearly identifying the problem you want to study and considering what possible methods will affect a solution. Then you choose the method you want to test, and formulate a hypothesis to predict the outcome of the test.

For example, you may want to improve student essays, but you don't believe that teacher feedback is enough. You hypothesize that some possible methods for writing improvement include peer workshopping, or reading more example essays. Favoring the former, your experiment would try to determine if peer workshopping improves writing in high school seniors. You state your hypothesis: peer workshopping prior to turning in a final draft will improve the quality of the student's essay.

Planning an experimental research study

The next step is to devise an experiment to test your hypothesis. In doing so, you must consider several factors. For example, how generalizable do you want your end results to be? Do you want to generalize about the entire population of high school seniors everywhere, or just the particular population of seniors at your specific school? This will determine how simple or complex the experiment will be. The amount of time funding you have will also determine the size of your experiment.

Continuing the example from step one, you may want a small study at one school involving three teachers, each teaching two sections of the same course. The treatment in this experiment is peer workshopping. Each of the three teachers will assign the same essay assignment to both classes; the treatment group will participate in peer workshopping, while the control group will receive only teacher comments on their drafts.

Conducting the experiment

At the start of an experiment, the control and treatment groups must be selected. Whereas the "hard" sciences have the luxury of attempting to create truly equal groups, educators often find themselves forced to conduct their experiments based on self-selected groups, rather than on randomization. As was highlighted in the Basic Concepts section, this makes the study a quasi-experiment, since the researchers cannot control all of the variables.

For the peer workshopping experiment, let's say that it involves six classes and three teachers with a sample of students randomly selected from all the classes. Each teacher will have a class for a control group and a class for a treatment group. The essay assignment is given and the teachers are briefed not to change any of their teaching methods other than the use of peer workshopping. You may see here that this is an effort to control a possible variable: teaching style variance.

Analyzing the data

The fourth step is to collect and analyze the data. This is not solely a step where you collect the papers, read them, and say your methods were a success. You must show how successful. You must devise a scale by which you will evaluate the data you receive, therefore you must decide what indicators will be, and will not be, important.

Continuing our example, the teachers' grades are first recorded, then the essays are evaluated for a change in sentence complexity, syntactical and grammatical errors, and overall length. Any statistical analysis is done at this time if you choose to do any. Notice here that the researcher has made judgments on what signals improved writing. It is not simply a matter of improved teacher grades, but a matter of what the researcher believes constitutes improved use of the language.

Writing the paper/presentation describing the findings

Once you have completed the experiment, you will want to share findings by publishing academic paper (or presentations). These papers usually have the following format, but it is not necessary to follow it strictly. Sections can be combined or not included, depending on the structure of the experiment, and the journal to which you submit your paper.

• Abstract : Summarize the project: its aims, participants, basic methodology, results, and a brief interpretation.
• Introduction : Set the context of the experiment.
• Review of Literature : Provide a review of the literature in the specific area of study to show what work has been done. Should lead directly to the author's purpose for the study.
• Statement of Purpose : Present the problem to be studied.
• Participants : Describe in detail participants involved in the study; e.g., how many, etc. Provide as much information as possible.
• Materials and Procedures : Clearly describe materials and procedures. Provide enough information so that the experiment can be replicated, but not so much information that it becomes unreadable. Include how participants were chosen, the tasks assigned them, how they were conducted, how data were evaluated, etc.
• Results : Present the data in an organized fashion. If it is quantifiable, it is analyzed through statistical means. Avoid interpretation at this time.
• Discussion : After presenting the results, interpret what has happened in the experiment. Base the discussion only on the data collected and as objective an interpretation as possible. Hypothesizing is possible here.
• Limitations : Discuss factors that affect the results. Here, you can speculate how much generalization, or more likely, transferability, is possible based on results. This section is important for quasi-experimentation, since a quasi-experiment cannot control all of the variables that might affect the outcome of a study. You would discuss what variables you could not control.
• Conclusion : Synthesize all of the above sections.
• References : Document works cited in the correct format for the field.

Experimental and Quasi-Experimental Research: Issues and Commentary

Several issues are addressed in this section, including the use of experimental and quasi-experimental research in educational settings, the relevance of the methods to English studies, and ethical concerns regarding the methods.

Using Experimental and Quasi-Experimental Research in Educational Settings

Charting causal relationships in human settings.

Any time a human population is involved, prediction of casual relationships becomes cloudy and, some say, impossible. Many reasons exist for this; for example,

• researchers in classrooms add a disturbing presence, causing students to act abnormally, consciously or unconsciously;
• subjects try to please the researcher, just because of an apparent interest in them (known as the Hawthorne Effect); or, perhaps
• the teacher as researcher is restricted by bias and time pressures.

But such confounding variables don't stop researchers from trying to identify causal relationships in education. Educators naturally experiment anyway, comparing groups, assessing the attributes of each, and making predictions based on an evaluation of alternatives. They look to research to support their intuitive practices, experimenting whenever they try to decide which instruction method will best encourage student improvement.

Combining Theory, Research, and Practice

The goal of educational research lies in combining theory, research, and practice. Educational researchers attempt to establish models of teaching practice, learning styles, curriculum development, and countless other educational issues. The aim is to "try to improve our understanding of education and to strive to find ways to have understanding contribute to the improvement of practice," one writer asserts (Floden 1996, p. 197).

In quasi-experimentation, researchers try to develop models by involving teachers as researchers, employing observational research techniques. Although results of this kind of research are context-dependent and difficult to generalize, they can act as a starting point for further study. The "educational researcher . . . provides guidelines and interpretive material intended to liberate the teacher's intelligence so that whatever artistry in teaching the teacher can achieve will be employed" (Eisner 1992, p. 8).

Bias and Rigor

Critics contend that the educational researcher is inherently biased, sample selection is arbitrary, and replication is impossible. The key to combating such criticism has to do with rigor. Rigor is established through close, proper attention to randomizing groups, time spent on a study, and questioning techniques. This allows more effective application of standards of quantitative research to qualitative research.

Often, teachers cannot wait to for piles of experimentation data to be analyzed before using the teaching methods (Lauer and Asher 1988). They ultimately must assess whether the results of a study in a distant classroom are applicable in their own classrooms. And they must continuously test the effectiveness of their methods by using experimental and qualitative research simultaneously. In addition to statistics (quantitative), researchers may perform case studies or observational research (qualitative) in conjunction with, or prior to, experimentation.

Relevance to English Studies

Situations in english studies that might encourage use of experimental methods.

Whenever a researcher would like to see if a causal relationship exists between groups, experimental and quasi-experimental research can be a viable research tool. Researchers in English Studies might use experimentation when they believe a relationship exists between two variables, and they want to show that these two variables have a significant correlation (or causal relationship).

A benefit of experimentation is the ability to control variables, such as the amount of treatment, when it is given, to whom and so forth. Controlling variables allows researchers to gain insight into the relationships they believe exist. For example, a researcher has an idea that writing under pseudonyms encourages student participation in newsgroups. Researchers can control which students write under pseudonyms and which do not, then measure the outcomes. Researchers can then analyze results and determine if this particular variable alone causes increased participation.

Transferability-Applying Results

Experimentation and quasi-experimentation allow for generating transferable results and accepting those results as being dependent upon experimental rigor. It is an effective alternative to generalizability, which is difficult to rely upon in educational research. English scholars, reading results of experiments with a critical eye, ultimately decide if results will be implemented and how. They may even extend that existing research by replicating experiments in the interest of generating new results and benefiting from multiple perspectives. These results will strengthen the study or discredit findings.

Concerns English Scholars Express about Experiments

Researchers should carefully consider if a particular method is feasible in humanities studies, and whether it will yield the desired information. Some researchers recommend addressing pertinent issues combining several research methods, such as survey, interview, ethnography, case study, content analysis, and experimentation (Lauer and Asher, 1988).

Advantages and Disadvantages of Experimental Research: Discussion

In educational research, experimentation is a way to gain insight into methods of instruction. Although teaching is context specific, results can provide a starting point for further study. Often, a teacher/researcher will have a "gut" feeling about an issue which can be explored through experimentation and looking at causal relationships. Through research intuition can shape practice .

A preconception exists that information obtained through scientific method is free of human inconsistencies. But, since scientific method is a matter of human construction, it is subject to human error . The researcher's personal bias may intrude upon the experiment , as well. For example, certain preconceptions may dictate the course of the research and affect the behavior of the subjects. The issue may be compounded when, although many researchers are aware of the affect that their personal bias exerts on their own research, they are pressured to produce research that is accepted in their field of study as "legitimate" experimental research.

The researcher does bring bias to experimentation, but bias does not limit an ability to be reflective . An ethical researcher thinks critically about results and reports those results after careful reflection. Concerns over bias can be leveled against any research method.

Often, the sample may not be representative of a population, because the researcher does not have an opportunity to ensure a representative sample. For example, subjects could be limited to one location, limited in number, studied under constrained conditions and for too short a time.

Despite such inconsistencies in educational research, the researcher has control over the variables , increasing the possibility of more precisely determining individual effects of each variable. Also, determining interaction between variables is more possible.

Even so, artificial results may result . It can be argued that variables are manipulated so the experiment measures what researchers want to examine; therefore, the results are merely contrived products and have no bearing in material reality. Artificial results are difficult to apply in practical situations, making generalizing from the results of a controlled study questionable. Experimental research essentially first decontextualizes a single question from a "real world" scenario, studies it under controlled conditions, and then tries to recontextualize the results back on the "real world" scenario. Results may be difficult to replicate .

Perhaps, groups in an experiment may not be comparable . Quasi-experimentation in educational research is widespread because not only are many researchers also teachers, but many subjects are also students. With the classroom as laboratory, it is difficult to implement randomizing or matching strategies. Often, students self-select into certain sections of a course on the basis of their own agendas and scheduling needs. Thus when, as often happens, one class is treated and the other used for a control, the groups may not actually be comparable. As one might imagine, people who register for a class which meets three times a week at eleven o'clock in the morning (young, no full-time job, night people) differ significantly from those who register for one on Monday evenings from seven to ten p.m. (older, full-time job, possibly more highly motivated). Each situation presents different variables and your group might be completely different from that in the study. Long-term studies are expensive and hard to reproduce. And although often the same hypotheses are tested by different researchers, various factors complicate attempts to compare or synthesize them. It is nearly impossible to be as rigorous as the natural sciences model dictates.

Even when randomization of students is possible, problems arise. First, depending on the class size and the number of classes, the sample may be too small for the extraneous variables to cancel out. Second, the study population is not strictly a sample, because the population of students registered for a given class at a particular university is obviously not representative of the population of all students at large. For example, students at a suburban private liberal-arts college are typically young, white, and upper-middle class. In contrast, students at an urban community college tend to be older, poorer, and members of a racial minority. The differences can be construed as confounding variables: the first group may have fewer demands on its time, have less self-discipline, and benefit from superior secondary education. The second may have more demands, including a job and/or children, have more self-discipline, but an inferior secondary education. Selecting a population of subjects which is representative of the average of all post-secondary students is also a flawed solution, because the outcome of a treatment involving this group is not necessarily transferable to either the students at a community college or the students at the private college, nor are they universally generalizable.

When a human population is involved, experimental research becomes concerned if behavior can be predicted or studied with validity. Human response can be difficult to measure . Human behavior is dependent on individual responses. Rationalizing behavior through experimentation does not account for the process of thought, making outcomes of that process fallible (Eisenberg, 1996).

Nevertheless, we perform experiments daily anyway . When we brush our teeth every morning, we are experimenting to see if this behavior will result in fewer cavities. We are relying on previous experimentation and we are transferring the experimentation to our daily lives.

Moreover, experimentation can be combined with other research methods to ensure rigor . Other qualitative methods such as case study, ethnography, observational research and interviews can function as preconditions for experimentation or conducted simultaneously to add validity to a study.

We have few alternatives to experimentation. Mere anecdotal research , for example is unscientific, unreplicatable, and easily manipulated. Should we rely on Ed walking into a faculty meeting and telling the story of Sally? Sally screamed, "I love writing!" ten times before she wrote her essay and produced a quality paper. Therefore, all the other faculty members should hear this anecdote and know that all other students should employ this similar technique.

On final disadvantage: frequently, political pressure drives experimentation and forces unreliable results. Specific funding and support may drive the outcomes of experimentation and cause the results to be skewed. The reader of these results may not be aware of these biases and should approach experimentation with a critical eye.

Advantages and Disadvantages of Experimental Research: Quick Reference List

Experimental and quasi-experimental research can be summarized in terms of their advantages and disadvantages. This section combines and elaborates upon many points mentioned previously in this guide.

Ethical Concerns

Experimental research may be manipulated on both ends of the spectrum: by researcher and by reader. Researchers who report on experimental research, faced with naive readers of experimental research, encounter ethical concerns. While they are creating an experiment, certain objectives and intended uses of the results might drive and skew it. Looking for specific results, they may ask questions and look at data that support only desired conclusions. Conflicting research findings are ignored as a result. Similarly, researchers, seeking support for a particular plan, look only at findings which support that goal, dismissing conflicting research.

Editors and journals do not publish only trouble-free material. As readers of experiments members of the press might report selected and isolated parts of a study to the public, essentially transferring that data to the general population which may not have been intended by the researcher. Take, for example, oat bran. A few years ago, the press reported how oat bran reduces high blood pressure by reducing cholesterol. But that bit of information was taken out of context. The actual study found that when people ate more oat bran, they reduced their intake of saturated fats high in cholesterol. People started eating oat bran muffins by the ton, assuming a causal relationship when in actuality a number of confounding variables might influence the causal link.

Ultimately, ethical use and reportage of experimentation should be addressed by researchers, reporters and readers alike.

Reporters of experimental research often seek to recognize their audience's level of knowledge and try not to mislead readers. And readers must rely on the author's skill and integrity to point out errors and limitations. The relationship between researcher and reader may not sound like a problem, but after spending months or years on a project to produce no significant results, it may be tempting to manipulate the data to show significant results in order to jockey for grants and tenure.

Meanwhile, the reader may uncritically accept results that receive validity by being published in a journal. However, research that lacks credibility often is not published; consequentially, researchers who fail to publish run the risk of being denied grants, promotions, jobs, and tenure. While few researchers are anything but earnest in their attempts to conduct well-designed experiments and present the results in good faith, rhetorical considerations often dictate a certain minimization of methodological flaws.

Concerns arise if researchers do not report all, or otherwise alter, results. This phenomenon is counterbalanced, however, in that professionals are also rewarded for publishing critiques of others' work. Because the author of an experimental study is in essence making an argument for the existence of a causal relationship, he or she must be concerned not only with its integrity, but also with its presentation. Achieving persuasiveness in any kind of writing involves several elements: choosing a topic of interest, providing convincing evidence for one's argument, using tone and voice to project credibility, and organizing the material in a way that meets expectations for a logical sequence. Of course, what is regarded as pertinent, accepted as evidence, required for credibility, and understood as logical varies according to context. If the experimental researcher hopes to make an impact on the community of professionals in their field, she must attend to the standards and orthodoxy's of that audience.

Related Links

Contrasts: Traditional and computer-supported writing classrooms. This Web presents a discussion of the Transitions Study, a year-long exploration of teachers and students in computer-supported and traditional writing classrooms. Includes description of study, rationale for conducting the study, results and implications of the study.

http://kairos.technorhetoric.net/2.2/features/reflections/page1.htm

Annotated Bibliography

A cozy world of trivial pursuits? (1996, June 28) The Times Educational Supplement . 4174, pp. 14-15.

A critique discounting the current methods Great Britain employs to fund and disseminate educational research. The belief is that research is performed for fellow researchers not the teaching public and implications for day to day practice are never addressed.

Anderson, J. A. (1979, Nov. 10-13). Research as argument: the experimental form. Paper presented at the annual meeting of the Speech Communication Association, San Antonio, TX.

In this paper, the scientist who uses the experimental form does so in order to explain that which is verified through prediction.

Anderson, Linda M. (1979). Classroom-based experimental studies of teaching effectiveness in elementary schools . (Technical Report UTR&D-R- 4102). Austin: Research and Development Center for Teacher Education, University of Texas.

Three recent large-scale experimental studies have built on a database established through several correlational studies of teaching effectiveness in elementary school.

Asher, J. W. (1976). Educational research and evaluation methods . Boston: Little, Brown.

Abstract unavailable by press time.

Babbie, Earl R. (1979). The Practice of Social Research . Belmont, CA: Wadsworth.

A textbook containing discussions of several research methodologies used in social science research.

Bangert-Drowns, R.L. (1993). The word processor as instructional tool: a meta-analysis of word processing in writing instruction. Review of Educational Research, 63 (1), 69-93.

Beach, R. (1993). The effects of between-draft teacher evaluation versus student self-evaluation on high school students' revising of rough drafts. Research in the Teaching of English, 13 , 111-119.

The question of whether teacher evaluation or guided self-evaluation of rough drafts results in increased revision was addressed in Beach's study. Differences in the effects of teacher evaluations, guided self-evaluation (using prepared guidelines,) and no evaluation of rough drafts were examined. The final drafts of students (10th, 11th, and 12th graders) were compared with their rough drafts and rated by judges according to degree of change.

Beishuizen, J. & Moonen, J. (1992). Research in technology enriched schools: a case for cooperation between teachers and researchers . (ERIC Technical Report ED351006).

This paper describes the research strategies employed in the Dutch Technology Enriched Schools project to encourage extensive and intensive use of computers in a small number of secondary schools, and to study the effects of computer use on the classroom, the curriculum, and school administration and management.

Borg, W. P. (1989). Educational Research: an Introduction . (5th ed.). New York: Longman.

An overview of educational research methodology, including literature review and discussion of approaches to research, experimental design, statistical analysis, ethics, and rhetorical presentation of research findings.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research . Boston: Houghton Mifflin.

A classic overview of research designs.

Campbell, D.T. (1988). Methodology and epistemology for social science: selected papers . ed. E. S. Overman. Chicago: University of Chicago Press.

This is an overview of Campbell's 40-year career and his work. It covers in seven parts measurement, experimental design, applied social experimentation, interpretive social science, epistemology and sociology of science. Includes an extensive bibliography.

Caporaso, J. A., & Roos, Jr., L. L. (Eds.). Quasi-experimental approaches: Testing theory and evaluating policy. Evanston, WA: Northwestern University Press.

A collection of articles concerned with explicating the underlying assumptions of quasi-experimentation and relating these to true experimentation. With an emphasis on design. Includes a glossary of terms.

Collier, R. Writing and the word processor: How wary of the gift-giver should we be? Unpublished manuscript.

Unpublished typescript. Charts the developments to date in computers and composition and speculates about the future within the framework of Willie Sypher's model of the evolution of creative discovery.

Cook, T.D. & Campbell, D.T. (1979). Quasi-experimentation: design and analysis issues for field settings . Boston: Houghton Mifflin Co.

The authors write that this book "presents some quasi-experimental designs and design features that can be used in many social research settings. The designs serve to probe causal hypotheses about a wide variety of substantive issues in both basic and applied research."

Cutler, A. (1970). An experimental method for semantic field study. Linguistic Communication, 2 , N. pag.

This paper emphasizes the need for empirical research and objective discovery procedures in semantics, and illustrates a method by which these goals may be obtained.

Daniels, L. B. (1996, Summer). Eisenberg's Heisenberg: The indeterminancies of rationality. Curriculum Inquiry, 26 , 181-92.

Places Eisenberg's theories in relation to the death of foundationalism by showing that he distorts rational studies into a form of relativism. He looks at Eisenberg's ideas on indeterminacy, methods and evidence, what he is against and what we should think of what he says.

Danziger, K. (1990). Constructing the subject: Historical origins of psychological research. Cambridge: Cambridge University Press.

Danzinger stresses the importance of being aware of the framework in which research operates and of the essentially social nature of scientific activity.

Diener, E., et al. (1972, December). Leakage of experimental information to potential future subjects by debriefed subjects. Journal of Experimental Research in Personality , 264-67.

Research regarding research: an investigation of the effects on the outcome of an experiment in which information about the experiment had been leaked to subjects. The study concludes that such leakage is not a significant problem.

Dudley-Marling, C., & Rhodes, L. K. (1989). Reflecting on a close encounter with experimental research. Canadian Journal of English Language Arts. 12 , 24-28.

Researchers, Dudley-Marling and Rhodes, address some problems they met in their experimental approach to a study of reading comprehension. This article discusses the limitations of experimental research, and presents an alternative to experimental or quantitative research.

Edgington, E. S. (1985). Random assignment and experimental research. Educational Administration Quarterly, 21 , N. pag.

Edgington explores ways on which random assignment can be a part of field studies. The author discusses both non-experimental and experimental research and the need for using random assignment.

Eisenberg, J. (1996, Summer). Response to critiques by R. Floden, J. Zeuli, and L. Daniels. Curriculum Inquiry, 26 , 199-201.

A response to critiques of his argument that rational educational research methods are at best suspect and at worst futile. He believes indeterminacy controls this method and worries that chaotic research is failing students.

Eisner, E. (1992, July). Are all causal claims positivistic? A reply to Francis Schrag. Educational Researcher, 21 (5), 8-9.

Eisner responds to Schrag who claimed that critics like Eisner cannot escape a positivistic paradigm whatever attempts they make to do so. Eisner argues that Schrag essentially misses the point for trying to argue for the paradigm solely on the basis of cause and effect without including the rest of positivistic philosophy. This weakens his argument against multiple modal methods, which Eisner argues provides opportunities to apply the appropriate research design where it is most applicable.

Floden, R.E. (1996, Summer). Educational research: limited, but worthwhile and maybe a bargain. (response to J.A. Eisenberg). Curriculum Inquiry, 26 , 193-7.

Responds to John Eisenberg critique of educational research by asserting the connection between improvement of practice and research results. He places high value of teacher discrepancy and knowledge that research informs practice.

Fortune, J. C., & Hutson, B. A. (1994, March/April). Selecting models for measuring change when true experimental conditions do not exist. Journal of Educational Research, 197-206.

This article reviews methods for minimizing the effects of nonideal experimental conditions by optimally organizing models for the measurement of change.

Fox, R. F. (1980). Treatment of writing apprehension and tts effects on composition. Research in the Teaching of English, 14 , 39-49.

The main purpose of Fox's study was to investigate the effects of two methods of teaching writing on writing apprehension among entry level composition students, A conventional teaching procedure was used with a control group, while a workshop method was employed with the treatment group.

Gadamer, H-G. (1976). Philosophical hermeneutics . (D. E. Linge, Trans.). Berkeley, CA: University of California Press.

A collection of essays with the common themes of the mediation of experience through language, the impossibility of objectivity, and the importance of context in interpretation.

Gaise, S. J. (1981). Experimental vs. non-experimental research on classroom second language learning. Bilingual Education Paper Series, 5 , N. pag.

Aims on classroom-centered research on second language learning and teaching are considered and contrasted with the experimental approach.

Giordano, G. (1983). Commentary: Is experimental research snowing us? Journal of Reading, 27 , 5-7.

Do educational research findings actually benefit teachers and students? Giordano states his opinion that research may be helpful to teaching, but is not essential and often is unnecessary.

Goldenson, D. R. (1978, March). An alternative view about the role of the secondary school in political socialization: A field-experimental study of theory and research in social education. Theory and Research in Social Education , 44-72.

This study concludes that when political discussion among experimental groups of secondary school students is led by a teacher, the degree to which the students' views were impacted is proportional to the credibility of the teacher.

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Grossman and Tierney present evidence to suggest that comparison groups are not the same as nontreatment groups.

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• Published: 24 August 2024

Quasi-one-dimensional hydrogen bonding in nanoconfined ice

• Pavan Ravindra 1 , 2   na1 ,
• Xavier R. Advincula   ORCID: orcid.org/0009-0001-5343-0456 1 , 3 , 4   na1 ,
• Christoph Schran   ORCID: orcid.org/0000-0003-4595-5073 3 , 4 ,
• Angelos Michaelides   ORCID: orcid.org/0000-0002-9169-169X 1 , 4 &
• Venkat Kapil   ORCID: orcid.org/0000-0003-0324-2198 1 , 4 , 5 , 6  

Nature Communications volume  15 , Article number:  7301 ( 2024 ) Cite this article

Metrics details

• Computational chemistry
• Theory and computation
• Two-dimensional materials

The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.

Introduction

Nanoconfined water is prevalent in biological and geological systems, and it has technological applications in tribology 1 , desalination 2 , and clean energy 3 . Experiments have revealed a diverse range of unusual properties in these systems containing confined water 4 , 5 . While a unified theory of the thermodynamics of systems confined to nanoscale dimensions is missing 6 , 7 , atomistic simulations have aided our interpretation of experiments by revealing the microscopic structure and dynamics of nanoscale water 8 , 9 , 10 , 11 . Specifically, first-principles simulations provide a bottom-up format for predicting the physicochemical behavior of water in complex conditions 12 , 13 , 14 , 15 , 16 , 17 where empirical models fitted to bulk data may be insufficient.

Recent first-principles studies, direct 15 or machine-learning-accelerated 16 , 17 , have explored finite-temperature phase behaviors of nanoconfined water in atomistically flat nanocavities – meant to resemble the conditions of water trapped between two graphene sheets. Notably, Kapil et al. 16 calculated the temperature-pressure phase diagram of a monolayer film of water by using machine learning potentials (MLPs) 18 , 19 . They unveiled crystalline monolayer phases comprising hexagonal, pentagonal, and rhombic motifs, an entropically stabilized hexatic phase of water, and a superionic phase at dramatically lower temperatures and pressures than its bulk counterpart 20 , 21 , 22 , 23 . More recently, Lin et al. 17 extended this investigation to larger confinement widths and a wider range of confinement pressures, revealing new bilayer ice phases. An intriguing aspect of these nanoconfined ice phases is their deviation from the Gibbs-Thomson relation 16 , 17 . These phases aren’t simply bulk ice phases with altered phase boundaries; rather, they consist of unique motifs and topologies that are not observed in bulk ice.

Given the significant differences in the structure of bulk and nanoconfined water, it is essential to understand the underlying principles that distinguish their phase behaviors. The behaviors of crystalline ice are well explained using Pauling’s principle of hydrogen bonding 24 and the Bernal-Fowler ice rules, which state that each water molecule should participate in four total hydrogen bonds 25 . However, these ice rules can be broken under extreme conditions and through translational symmetry disruptions at interfaces and in nanoscale confinement. These violations result in a variety of unusual phase behaviors, including a quantum delocalized state of water across nanocapillaries 26 , a square-like ice phase encapsulated between graphene sheets 27 , thin epitaxial films of ice exhibiting ferroelectric nature 28 , and the formation of water chains 29 and nanoclusters resembling cyclic hydrocarbons on metals 30 . Attempts have been made to extend the Bernal-Fowler ice rules to these settings, and such modified ice rules generally focus on incorporating water-surface interactions into the hydrogen bonding picture of the Bernal-Fowler ice rules 12 , 30 , 31 . However, several recently predicted nanoconfined ice phases exhibit fewer than four hydrogen bonds per water molecule 15 , 16 , 17 , which brings into question the extent to which nanoconfined water phases are stabilized by hydrogen bonding and if such modified ice rules should be centered around hydrogen bonding at all.

In this work, we utilize first-principles calculations and MLPs to identify a new stabilization mechanism in nanoconfined ice phases beyond conventional ice rules. Instead of forming four hydrogen bonds per water molecule, nanoconfined ice phases can form zig-zagging quasi-one-dimensional hydrogen-bonded chains stacked together by van der Waals (vdW) interactions. We identify this mechanism across monolayer and bilayer crystalline ice found in refs. 15 , 16 , 17 in confinement slits of varying sizes. Employing MLPs to access long timescales at significantly lower computational costs than traditional first-principles methods, we show that quasi-one-dimensional hydrogen bonding is stable at finite temperatures up to the melting point of nanoconfined ice. The arrangement of strong and weak interactions in nanoconfined water bears similarities to two-dimensional vdW materials consisting of so-called quasi-one-dimensional chains 32 , 33 . Conventionally, these materials comprise strong covalent bonds within the quasi-one-dimensional chains (as opposed to hydrogen bonds), with weak inter-chain vdW attraction holding these chains together. These materials exhibit unusual electronic, vibrational, and optical properties 32 , 33 , potentially exhibiting superconductivity at higher pressures 34 . Analogously, the intrinsic anisotropic bonding in the flat-rhombic phase leads to anomalous rotational dynamics of water molecules, including detectable long-range concerted disorder on the scale of nanometers, which has potential applications for molecular devices.

Violations of conventional ice rules lead to a quasi-one-dimensional hydrogen bonded ice phase

In bulk ice, the Bernal-Fowler ice rules dictate that each water molecule in a stable ice phase should participate in four hydrogen bonds with its neighboring water molecules: 2 donated and 2 accepted hydrogen bonds 25 . Previous work on nanoconfined water at the DFT level has suggested that the thermodynamically stable monolayer phase is a square structure that still exhibits this 4-fold coordination 9 , 12 . However, more recent first-principles-level simulations have suggested a wide variety of stable phases of nanoconfined water spanning a broad range of different temperature and lateral pressure conditions 14 , 15 , 16 , 17 . Kapil et al. 16 report a hexagonal phase stable below 0.1 GPa, a pentagonal phase stable from 0.1 to 0.5 GPa, and a flat-rhombic phase stable above 0.5 GPa for a confinement width of 5 Å. Reference 16 also confirms the greater thermodynamic stabilities of these phases with respect to the square phase using the chemically accurate and robust Quantum Monte Carlo (QMC) calculation setup 35 , 36 . References 15 , 17 explored even higher lateral pressures for a confinement width of 6 Å. These studies found that the stable phase beyond 0.5 GPa is a zigzag monolayer ice (ZZMI) phase, which is topologically equivalent to the flat-rhombic phase, except that the larger confinement width leads to a small buckling of the oxygen atoms. In the 15–20 GPa regime, Lin et al. report a zigzag quasi bilayer ice (ZZ-qBI) structure as the stable phase 17 . This phase also resembles the flat-rhombic phase, although the buckling in this phase results in two nearly distinct layers of water molecules.

As shown in Fig.  1 a and Table  1 , none of the above thermodynamically stable nanoconfined ice phases satisfy conventional ice rules: all of these phases display fewer than four hydrogen bonds per water molecule. However, the water molecules in each of these phases violate the ice rules in different ways. In the low-pressure hexagonal phase, all water molecules exhibit a 3-fold hydrogen bonding coordination: half of the water molecules donate two and accept one hydrogen bond, and the other half donate one and accept two. Water molecules in the intermediate-pressure pentagonal phase exhibit diverse hydrogen bonding motifs, with coordination numbers ranging from 2 to 4. This results in an average hydrogen bond coordination of 3.2 in the pentagonal crystal structure. The high-pressure flat-rhombic (or the ZZMI) phase exhibits a single hydrogen-bonding motif in which each water molecule donates one and accepts one hydrogen bond. The resulting hydrogen bonding network contains just two hydrogen bonds per water molecule, deviating significantly from the conventional ice rules. The ZZ-qBI phase exhibits a similar network, with just two hydrogen bonds per water molecule. Since this phase is stable even at lateral pressures much higher than the flat-rhombic phase, this suggests that such extreme violations of the bulk ice rules are likely to occur in high lateral pressure conditions.

a The four nanoconfined ice phases considered in this work from refs. 16 , 17 , along with the lateral pressures used for simulating each phase. Solid lines between water molecules indicate hydrogen bonds. We highlight chains of hydrogen-bonded water molecules in each phase. For estimating E chain , we use the unique highlighted chains in the case of the flat-rhombic and ZZ-qBI phases. For the hexagonal and pentagonal phases, we report the average value and the standard deviation as the error bar, where the standard deviation is computed across different choices of chains, as described in the text. b For each phase, we compute the stabilization energy of a single molecule in the 0 K crystal structure, E lattice ; the (average) stabilization energy of a water molecule within the hydrogen bonded chain(s), E chain ; and the stabilization energy between chains in the crystal structure, E stack . The hatched bars show the contribution of vdW interactions to the corresponding stabilization energy. Source data are provided as a Source Data file.

The flat-rhombic phase is thermodynamically stable in a sizeable temperature-pressure region of the phase diagram 15 , 16 , 17 despite having just two hydrogen bonds per molecule. Considering that ice in general is primarily stabilized by a dense network of hydrogen bonds 24 , 37 , this stability over a broad range of conditions is highly unusual. To understand this unexpected behavior, we examine the structure of the flat-rhombic phase and look for similarities with other types of materials. As depicted in Fig.  1 a, the flat-rhombic phase is characterized by a zigzagging hydrogen bond network that runs along quasi-one-dimensional chains 15 , 17 . These chains are stacked alongside each other without any hydrogen bonding between chains. This structure is similar to quasi-one-dimensional vdW materials 32 , 33 , which exhibit unusual electronic, optical, and vibrational properties. However, while these materials consist of covalently bonded chains held together by weak inter-chain vdW interactions, the chains in the flat-rhombic phase are stabilized by hydrogen bonding.

To determine whether the flat-rhombic phase can be classified as a quasi-one-dimensional vdW material, we calculate the binding energy of the one-dimensional chains and study the role of vdW interactions in stabilizing these chains. In Fig.  1 b and Table  1 , we report three different types of binding energies of a water molecule in each phase: the stabilization energy of a water molecule in the corresponding crystal structure, E lattice ; the stabilization energy of a water molecule within a continuous chain of hydrogen-bonded water molecules, E chain (see highlighted chains of water molecules in Fig.  1 a); and the remaining stabilization energy from interactions between chains of water molecules in each phase, E stack . This means that the stacking energy is the stabilization energy of the lattice that is not captured by the chains alone: E stack  =  E lattice  −  E chain . In the hexagonal phase, we consider two distinct chain types in the ‘zigzag’ and ‘armchair’ directions and report the average binding energies. As can be seen in the pentagonal phase snapshot in Fig.  1 a, there is no clear choice for a unique hydrogen bond chain in the pentagonal phase. This shows qualitatively that the pentagonal phase cannot clearly be separated into distinct hydrogen bond chains. Nonetheless, to perform the quantitative comparison shown in Fig.  1 b, we averaged our energies over three different hydrogen bond chains in the pentagonal phase. The energies for these different chains were all extremely close to each other, suggesting that this quantitative comparison is robust with respect to the exact choice of hydrogen bond chain in the pentagonal phase. For the flat-rhombic and ZZ-qBI phases, we considered a single unique chain.

In Fig.  1 b, we observe that E chain contributes to approximately 50% or less of the crystal stabilization energy in both hexagonal and pentagonal phases. Therefore, these phases have high stacking energies between chains, stabilized primarily by hydrogen bonds, with vdW interactions (indicated by hatched regions) playing only a secondary role in stabilizing the chains. Conversely, in the flat-rhombic phase, the primary crystal stabilization source is from the chains. Here, the stacking energy is low, with roughly 90% of the stacking energy resulting from vdW interactions. This difference indicates that the interactions stabilizing water molecules in the flat-rhombic phase contrast starkly with those in the hexagonal and pentagonal phases. The flat-rhombic phase appears to form hydrogen bonds in one dimension while being stabilized by weak vdW forces in the other, classifying it as a quasi-one-dimensional vdW crystal. Similarly, the stacking energy of the ZZ-qBI phase is almost entirely driven by vdW interactions, meaning that vdW forces also stabilize the interactions between this phase’s hydrogen-bonded chains. In fact, in the absence of vdW stacking, the lattice energy is positive, meaning that the ZZ-qBI crystal structure would be thermodynamically unstable. This suggests that these vdW interactions between hydrogen-bonded chains play an essential role in stabilizing nanoconfined crystal structures at high lateral pressures. As our MLP isn’t trained for pressures beyond 8 GPa, we are unable to comment on the dynamical stability or metastability of the ZZ-qBI phase. Similarly, in order to comment on the true dynamical stability or metastability of any of these phases in the absence of vdW interactions, we would need to retrain an MLP at the revPBE0 level (in the absence of the D3 dispersion correction).

Anomalous finite-temperature dependence of hydrogen bonding

The characteristic interactions in the flat-rhombic phase lead to anomalous finite-temperature hydrogen bonding structure and dynamics. In bulk or conventionally hydrogen-bonded ice phases, such as the hexagonal and pentagonal monolayer phases, the most probable molecular orientations are the ones that maximize the number of hydrogen bonds 25 , and the protons maximize their residence time in orientations with the maximum possible hydrogen bonds. However, in the flat-rhombic phase, even at finite temperatures (extending past 300 K), the most probable molecular orientations are not those that maximize hydrogen bonds but instead, those that maintain a distinct network of quasi-one-dimensional chains.

To characterize the rotational motion of individual water molecules, we define ϕ and θ angles for molecular dipoles. These are depicted in Fig.  2 a and b, respectively. These figures also indicate the coordinate axes used to define these angles. The \(\phi=\frac{\pi }{2}-\arccos \left(\hat{{{{\boldsymbol{\mu }}}}}\cdot \hat{{{{\bf{z}}}}}\right)\) angle captures the alignment between the molecular dipole vector μ and the z -axis, with hats above vectors indicating normalized unit vectors. A water molecule with ϕ  = ±  π /2 will have its dipole vector aligned with the  ±  z -axis. We also wrap around the ϕ angles computed such that ϕ always lies in the range \([-\frac{\pi }{2},+ \frac{\pi }{2}]\) . The \(\theta=\arctan \left({{{\bf{c}}}}\cdot \hat{{{{\bf{z}}}}}/{{{\bf{c}}}}\cdot \hat{{{{\bf{x}}}}}\right)\) angle captures a molecule’s rotation about its center of mass around the y axis with \({{{\bf{c}}}}={{{{\bf{r}}}}}_{{{{{\rm{OH}}}}}_{1}}\times {{{{\bf{r}}}}}_{{{{{\rm{OH}}}}}_{2}}\) and \({{{{\bf{r}}}}}_{{{{{\rm{OH}}}}}_{1}}\) and \({{{{\bf{r}}}}}_{{{{{\rm{OH}}}}}_{2}}\) being the OH bond vectors of an individual water molecule. θ measures the rotation of c around the y -axis. Since the choice of H 1 and H 2 is arbitrary, we wrap around the θ angles so that they always lie in the range [0,  π ].

a The ϕ angle captures in-plane and out-of-plane fluctuations of water molecules' dipole moments. b The θ angle captures in-plane rotations of water molecules. The mathematical definitions of the ϕ and θ angles are provided in the text. c The left and right images depict the molecular orientations at the probability maxima in Fig.  3 a, while the middle image depicts the molecular orientation at the maximum for the number of putative hydrogen bonds in Fig.  3 b.

To illustrate the flat-rhombic phases’ apparent hesitancy to form additional hydrogen bonds, we computed the probabilities of molecular orientations defined by these θ and ϕ angles. These two-dimensional log probabilities are juxtaposed with the mean number of hydrogen bonds for each two-dimensional bin determined by the θ and ϕ angles, as depicted in Fig.  3 . As shown in Supplementary Note  V , the nanoconfined hexagonal and pentagonal phases exhibit the expected trend: molecular orientations with more hydrogen bonds should be more stable. In contrast, the most probable flat-rhombic molecular orientations (depicted in the left and right plots of Fig.  2 c) correspond to only two hydrogen bonds. This is true even though orientations with three hydrogen bonds (like the one in the middle plot of Fig.  2 c) are possible. We begin to observe these orientations with three hydrogen bonds via the entropic exploration of θ and ϕ angles at high temperatures and in the presence of quantum nuclear fluctuations (see Supplementary Note  VI ). However, these orientations still do not emerge as local probability maxima, suggesting they are not stable or long-lived states. This behavior reinforces the observation that the two-dimensional flat-rhombic ice phase prefers to remain hydrogen bonded only along one dimension, even at finite temperatures.

a The two-dimensional log probabilities along the ϕ and θ parameters for the flat-rhombic phase at 2 GPa and three different temperatures. b The average number of putative hydrogen bonds associated with each ( θ ,  ϕ ) molecular orientation for the flat-rhombic phase under the same conditions. Relevant molecular orientations are depicted in Fig.  2 c . We employ the geometric definition of the hydrogen bond from ref. 68 . Source data are provided as a Source Data file.

The peculiar hydrogen bonding behavior of the flat-rhombic phase hence emphasizes the importance of incorporating dynamical information into the hydrogen bonding definition, as is needed in, e.g., the description of supercritical water 38 . As shown in Fig.  4 a, if we employ a purely geometric definition of a hydrogen bond, counting the number of instantaneous putative hydrogen bonds that exist on average, we observe an apparent increase in the number of hydrogen bonds with temperature. In the same figure, we also observe that nuclear quantum effects apparently exhibit an additional 0.2 putative hydrogen bonds, even at temperatures as low as 20 K. This is the result of the zero-point fluctuations lowering the energy barrier for sampling these additional hydrogen-bonded configurations, as we elaborate upon in Supplementary Note  VI . In these configurations, hydrogen bonds form between different chains in the flat-rhombic crystal structure, as they do classically at high temperatures. The increased disorder due to quantum nuclear effects is not surprising, as the effect of quantum nuclear motion on properties of water has often been mapped to a temperature increase 39 . To characterize the hydrogen bonding motifs in this structure, we introduce the notation N D M A to indicate a water molecule that donates N hydrogen bonds and accepts M hydrogen bonds. Using this notation, we see that this increase in the number of putative hydrogen bonds can be attributed to the increasing prevalence of instantaneous 2D1A motifs, i.e. instantaneous hydrogen bonds between chains.

a The average number of putative/geometric hydrogen bonds across the range of temperatures in which the flat-rhombic phase is stable using classical and quantum simulations. The error bars show the standard error of the mean, as computed by block averaging over 10 blocks. b The proportion of water molecules of each hydrogen bonding motif (defined in the text). The 1D2A proportions are perfectly aligned with the 2D1A proportions. c The 2D1A motif’s lifetime remains extremely short across the range of temperatures in which it is observed. The error bars show the standard error of the mean, computed across all instances of hydrogen bonding. Dashed lines serve as a visual guide for the eye. Source data are provided as a Source Data file.

This apparent increase emerges from the shortcoming of the standard geometric definition of a hydrogen bond, which cannot distinguish fluctuating molecular orientations from long-lived orientations that correspond to true hydrogen bonds. As shown in Fig.  4 c, the lifetime of the 2D1A orientations is extremely short – on the order of femtoseconds. As suggested by Schienbein and Marx 38 , these fleeting hydrogen bonds are best referred to as either putative or geometric, as they do not last long enough to be considered hydrogen bonds from a traditional perspective. In Supplementary Note  II , we compute the number of hydrogen bonds that survive for typical intermolecular oscillations. Within this definition by Schienbein and Marx 38 , a hydrogen bond is not counted if its lifetime is shorter than the period of typical intermolecular oscillations. The resulting dynamical hydrogen bond count indeed exhibits the expected disorder-induced decrease as we raise temperature.

Dielectric nature of the flat-rhombic phase

A consequence of the quasi-one-dimensional hydrogen-bonded structure is that it may possess ferroelectric behavior due to its net dipole moment along the direction of the hydrogen-bonded chains. Ferroelectricity in ice has been conjectured previously in force field simulations 40 , first-principles calculations of nanoconfined water 41 , and experiments on confined 28 and supported 42 films. To investigate the dielectric behavior of flat-rhombic ice, we model the temperature dependence of its dielectric response in Fig.  5 . We analyzed the in-plane ε ∥ and out-of-plane ε ⊥ dielectric constants based on the variance of the system’s polarization. Since our MLP only predicts the potential energy surfaces, a first principles investigation of the dielectric response would be computationally demanding due to numerous single point calculations of the electronic polarization 43 . Therefore, for a semi-quantitative understanding, we use a simple linear polarization model based on TIP4P charges 44 .

The classical dielectric constant of the flat-rhombic phase across the range of temperatures considered in this work. The singularity at 380 K corresponds to the transition to the hexatic phase of nanoconfined water 53 . Source data are provided as a Source Data file.

We select the TIP4P water model due to its low computational cost and a semi-quantitative description of the dielectric response of bulk 44 and confined 45 water. The TIP4P model underpredicts the polarization of aqueous systems as it doesn’t incorporate the electronic polarization of the water molecules. For instance, TIP4P predicts a molecular dipole moment of 2.348 D which is 12.8% lower compared to first principles estimates 46 , and effectively leads to a  ~ 25% lower dielectric response. To incorporate the lack of the electronic polarization in the TIP4P model, we rescale the calculated dielectric response by a factor of 1.25, as has been done previously 45 . We report the classical dielectric response, as quantum nuclear effects are expected to only make a small quantitative difference 44 .

We observe low and near-constant in-plane and out-of-plane dielectric constants for the flat-rhombic phase, in agreement with the experiments on nanoconfined water from ref. 47 . The low dielectric response of this phase is only an indirect outcome of vdW interactions, as they dictate the thermodynamic stability of the flat rhombic phase. The dielectric nature of flat-rhombic ice remains the same from 0 K up to its phase transition into the paraelectric hexatic (disordered) water phase 16 . The temperature independence of the flat-rhombic phase’s dielectric behaviors reflects the resilience of the quasi-one-dimensional structure to thermal and quantum nuclear fluctuations. The singularity observed in Fig.  5 indicates the temperature at which the phase transition to the hexatic phase occurs. The hexatic phase exhibits an increased in-plane dielectric constant but a similarly small out-of-plane dielectric constant.

Long-ranged spatial ordering and coherent proton dynamics

Thus far, the results in this work have considered large simulation boxes with 576 molecules spanning tens of nanometers. However, in simulation boxes containing 144 molecules in a relatively small but experimentally accessible system size on the order of nanometers 48 , 49 , we identify unusual concerted dynamics at intermediate temperatures involving the breaking and forming of hydrogen bonds between equivalent structures. In this concerted motion, we observe an exchange between the individual molecular ϕ  = ±  π /4 states as a collective motion throughout the entire system, in which all the water molecules simultaneously swap the signs of their ϕ angles (see Supplementary Movie  1 ).

To describe this concerted motion, we define a σ order parameter (see Supplementary Note  III ) to distinguish the two states shown in Fig.  6 a. The structural change to the flat-rhombic phase between σ values can be seen in Fig.  6 a. When the sign of σ changes, the directions of the dipoles in each row switch in a concerted fashion. During this motion, we also observe the complete rearrangement of the hydrogen bonding network in the lattice. Figure  6 a shows labeled water molecules in each σ state, illustrating that the hydrogen bonds in these two states are between different pairs of water molecules. At low temperatures, the system remains frozen in a single σ value. The resulting σ free energy profile contains two minima separated by a large free energy barrier, shown in Fig.  6 c. At intermediate temperatures, we begin to see exchanges between σ signs on the order of  ~10 ps, as shown in Fig.  6 b. Compared to the low-temperature setting, the intermediate temperature free energy profile exhibits a clear finite free energy barrier between these states. Eventually, at high enough temperatures, the free energy profile along σ exhibits an extremely small free energy barrier, allowing the system to explore molecular configurations freely. While the highest temperatures explored here are greater than the melting temperature of ice, we believe that our trajectories correspond to a metastable state associated with the solid phase. Since the free energy profiles at the intermediate and high-temperature conditions are qualitatively the same, the near-free exploration of molecular configurations in the high-temperature simulations is a result of thermal activation. To confirm this hypothesis, we compute rotational autocorrelation functions at these different temperatures in Supplementary Note  IV .

a The two symmetry-related structures of the flat-rhombic phase, with alternating rows of aligned dipoles, discerned by the different signs of σ . A zero value of σ indicates that the molecular dipoles are not aligned row-wise. The side views in each panel show the system as viewed from the right side, where the switch in the row-dependent dipole direction between the two states is clear. b For smaller unit cells with 144 water molecules, we observe coherent switching between these two states at intermediate temperatures, as shown in the σ trajectories for three different temperatures. c The free energy profiles for this σ parameter show that raising the temperature lowers the free energy barrier between these states. The smooth, low-barrier free energy profile at high temperatures indicates that the molecular dipoles lose spatial coherence. Source data are provided as a Source Data file.

We also note that quantum nuclear motion makes the system more disordered due to zero-point fluctuations lowering the barrier for the system’s hydrogen bond network to switch between the free energy minima, as shown in Supplementary Fig.  8 . Furthermore, since nuclear quantum effects lead to increased proton disorder, the σ parameter gets pushed closer to 0 on average. This causes the minima in the free energy profiles along σ to move closer to 0 in our PIMD simulations as compared to the free energy profiles from our classical simulations.

As stated earlier, this concerted motion is not observed in the larger simulation cell size that contains 576 molecules for the timescales we consider: it is only seen in simulations with 144 water molecules. This suggests that the free energy barrier for this concerted motion is size-extensive. As a result, we would expect a larger system size to either exhibit an ordered phase or a phase in which smaller domains that exhibit this behavior coexist. However, crystallites of nanoconfined water molecules containing around 100 water molecules are experimentally accessible, so such concerted motion could be observed in experiments as small ice crystallites forming between graphene sheets 48 or high-pressure graphene nanobubbles formed by irradiation 49 . Previous work has suggested that the simulation setup we employ is still reasonable for studying such encapsulated systems, even without explicit confining atoms 48 , 50 . A careful treatment of such encapsulated systems would require a detailed analysis of the water-carbon interactions at the edge of the confined water pockets. In this work, we ignore these edge effects and instead focus on characterizing the behavior of nanoconfined water under idealized atomistically flat nanoconfinement. A careful consideration of the edge effect would be an interesting and relevant topic for future work.

We have analyzed the hydrogen bond structure and dynamics in the flat-rhombic phase of monolayer nanoconfined ice at first-principles-level accuracy using machine learning-driven simulations. Our work not only corroborates previous experimental and theoretical studies showing violations of ice rules at interfaces 15 , 17 , 26 , 28 , 51 but also highlights an extreme scenario where hydrogen bonding plays a limited role in stabilizing the crystalline structure of ice. General ice rules must account carefully for the enthalpic penalty of breaking a hydrogen bond due to interactions with the confining walls and favorable vdW interactions. Instead of maximizing the number of hydrogen bonds, flat-rhombic ice forms one-dimensional chains stabilized by vdW interactions akin to quasi-one-dimensional vdW functional materials 32 , 33 . The ZZ-qBI phase identified in ref. 17 is stable at higher lateral pressures than the flat-rhombic phase, yet it exhibits a similar behavior, indicating that such hydrogen bond topologies may occur across a broad range of nanoconfined conditions. This quasi-one-dimensional ice structure is robust with respect to thermal fluctuations and the rotational dynamics of water molecules, forming two or fewer hydrogen bonds up to its transition to the hexatic phase. We anticipate that direct evidence of the unique structure of the flat-rhombic phase could be experimentally investigated using sum-frequency generation spectroscopy 52 by disentangling the O–H vibrational stretching band 53 into donor-acceptor contributions.

Like quasi-one-dimensional vdW functional materials that exhibit unique electronic, vibrational, and optical properties 32 , 33 , 34 , the flat-rhombic phase exhibits unusual properties with technological prospects. We observe long-ranged ordering of molecular dipoles along with a high spatial coherence in the hydrogen bond dynamics in the flat-rhombic ice phase, which is typically uncommon in systems containing on the order of 100s of molecules. This behavior is consistent with the characteristics of a two-dimensional Ising model system. We anticipate that this concerted motion in flat-rhombic ice patches could be used to guide directional behaviors in nanoscale molecular devices that have already shown success in performing macroscopic-level tasks at surfaces by exploiting directed translational motion on the nanometer scale 54 . Coherent proton dynamics observed experimentally in bulk systems, such as proton tunneling in hexagonal ice 55 and dielectric phase transitions in molecular ferroelectrics 56 , 57 , have already been computationally detected on much smaller scales 58 , 59 , 60 . This suggests that confinement effects might be crucial in phenomena like tunneling-induced or dielectric phase transitions. Our work opens doors to exploring new confined molecular materials with the structure-function relationships of low-dimensional functional materials that exhibit technologically relevant properties.

Confining potential

The nanoconfined system considered in this work is a single layer of water molecules trapped between two parallel sheets – mimicking the experimental setup in ref. 27 . We model the interactions between the sheets and the water molecules using a simple Morse potential fit to water-carbon QMC interaction energies 61 . Such a potential, characterized by perfectly smooth walls, has been widely adopted in previous studies, both in force field 9 , 10 , 11 and first-principles research 12 , 14 , 15 , 16 , 17 , 62 . The uniform confinement potential has demonstrated semi-quantitative accuracy in describing the behavior of water confined within graphene-like cavities 11 , as corroborated by a good agreement of stable phases and melting temperatures with respect to confinement simulations that include explicit carbon atoms 50 . Furthermore, the uniform confinement model’s atomistically flat nature allows for a clean interpretation of topological confinement effects – a phenomenon that extends beyond the specific context of graphene-based confinement.

Machine learning potential

For water-water interactions, we employ a newly-trained MLP 18 , 19 at the revPBE0-D3 level 63 , 64 – an appropriate dispersion-corrected hybrid-functional DFT level for bulk, interfacial and nanoconfined water 16 , 53 , 65 , 66 . We report the training and validation protocols for the MLP in Supplementary Note  I B. Our simulations are run in the N P x y T ensemble, where P x y denotes the lateral pressure, or vdW pressure 48 , that acts in the x and y directions. This lateral pressure emulates the net lateral forces on the water molecules due to the edges of the confining pocket (further details on the origins of this lateral pressure can be found in Supplementary Note  I C). Both simulations 50 and experiments 48 have estimated this vdW pressure to be on the gigapascal scale. Hence, our simulations are run at a lateral pressure of 2.0 GPa across various temperatures within the metastability range of the flat-rhombic phase 16 . We refer the reader to Supplementary Note  I for more details on the simulations.

First-principles calculations

To estimate the (zero temperature) static stabilization energy of a nanoconfined ice crystal or a quasi-one-dimensional chain of water molecules, we perform direct first-principles calculations using the CP2K code 67 employing the revPBE0-D3 functional and the convergence parameters from ref. 16 . We perform these calculations on geometry-optimized structures of the confined ice phases 17 . The geometry-optimized structures of the hexagonal, pentagonal, and flat-rhombic monolayer phases were taken from ref. 16 . In contrast, the structure of the zigzag quasi bilayer ice from ref. 17 was optimized using the CP2K code 67 . We define the stabilization energy of a crystal with respect to an isolated molecule as E lattice  =  E crystal  −  E gas , where E crystal is the single-point energy of the crystalline lattice per water molecule, and E gas is the single-point energy of an isolated water molecule in vacuum. Similarly, we define the stabilization energy of a chain of water molecules with respect to an isolated molecule as \({E}_{{{{\rm{chain}}}}}={E}_{{{{\rm{chain}}}}}^{{{{\rm{struct}}}}.}-{E}_{{{{\rm{gas}}}}}\) , where \({E}_{{{{\rm{chain}}}}}^{{{{\rm{struct}}}}.}\) is the single-point energy of the chain structure, and the stabilization energy of a crystal with respect to a chain of water molecules as E stack  =  E lattice  −  E chain . We also estimate these quantities without the D3 dispersion correction to assess the role of vdW interactions.

Data availability

The data supporting the findings of this study are openly available on GitHub ( https://github.com/water-ice-group/hbond_nanoconfined_water ). This includes all simulation scripts and starting structures. They are also available via this GitHub repository.  Source data are provided with this paper.

Code availability

All of the analysis scripts supporting the findings of this study are openly available on GitHub ( https://github.com/water-ice-group/hbond_nanoconfined_water ).

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Acknowledgements

We thank Jinggang Lan, Dominik Marx, Philipp Schienbein, Michele Ceriotti, and all of A.M.’s research group members for their comments on the manuscript. P.R. would also like to thank David R. Reichman for his academic support during this project. V.K. acknowledges support from the Ernest Oppenheimer Early Career Fellowship and the Sydney Harvey Junior Research Fellowship, Churchill College, University of Cambridge. A.M. and X.R.A. acknowledge support from the European Union under the “n-AQUA” European Research Council project (Grant no. 101071937). C.S. acknowledges partial financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project number 500244608. P.R. would like to thank The Winston Churchill Foundation of the United States for their financial support. P.R. would also like to acknowledge that: “This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under Award Number DE-SC0024386. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.” We are grateful for computational support from the Swiss National Supercomputing Centre under project s1209, the UK national high-performance computing service, ARCHER2, for which access was obtained via the UKCP consortium and the EPSRC grant ref EP/P022561/1, and the Cambridge Service for Data Driven Discovery (CSD3).

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These authors contributed equally: Pavan Ravindra, Xavier R. Advincula.

Authors and Affiliations

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK

Pavan Ravindra, Xavier R. Advincula, Angelos Michaelides & Venkat Kapil

Department of Chemistry, Columbia University, 3000 Broadway, New York, NY, 10027, USA

Pavan Ravindra

Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK

Xavier R. Advincula & Christoph Schran

Lennard-Jones Centre, University of Cambridge, Trinity Ln, Cambridge, CB2 1TN, UK

Xavier R. Advincula, Christoph Schran, Angelos Michaelides & Venkat Kapil

Department of Physics and Astronomy, University College London, 17-19 Gordon St, London, WC1H 0AH, UK

Venkat Kapil

Thomas Young Centre and London Centre for Nanotechnology, 19 Gordon St, London, WC1H 0AH, UK

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P.R., A.M., and V.K. conceived the study. P.R., V.K., and C.S. trained the machine learning potential used in this work. P.R. and X.R.A. performed molecular dynamics simulations using this MLP. All of the authors were involved in discussing results and assembling the manuscript.

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Correspondence to Angelos Michaelides or Venkat Kapil .

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Ravindra, P., Advincula, X.R., Schran, C. et al. Quasi-one-dimensional hydrogen bonding in nanoconfined ice. Nat Commun 15 , 7301 (2024). https://doi.org/10.1038/s41467-024-51124-z

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Received : 31 December 2023

Accepted : 30 July 2024

Published : 24 August 2024

DOI : https://doi.org/10.1038/s41467-024-51124-z

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