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What is Quasi-Experimental Design? Definition, Types, and Examples

An experimental design is the process of planning and organizing scientific experiments to obtain useful results from collected data. Experimental design is an important aspect of the scientific method because it ensures the validity and reliability of the information extracted from available data. In several experimental designs, objects or participants are randomly assigned to an experimental group to avoid any type of bias; these are called true experiments.  

A quasi-experimental design is a study design in which participants cannot be randomly assigned to an experimental or control group for practical or ethical reasons. However, like a true experiment, it is used to evaluate the effects of an intervention, or in other words, to establish a cause-and effect relationship between independent and dependent variables. The intervention could be a training program, a policy change, a medical treatment, etc. In such quasi-experimental designs , the assignment of participants is usually based on self-selection or selection by an administrator or researcher.   

This article will provide the quasi-experimental design definition and will describe in detail the types and uses of such experimental designs and their similarities and differences with true experiments.  

Table of Contents

What is Quasi-Experimental Research ?  

The prefix “quasi” means “resembling to a certain degree.” Accordingly, the definition of quasi-experimental research 1,2 is that it is a type of research that resembles true experimental research but is not the exact same concept. A true experimental design has three characteristics—manipulation of the independent variable, presence of a control group, and random assignment of participants to experimental groups.  

In quasi-experimental research , although the independent variable is manipulated, either the participants are not randomly assigned to groups or there is no control group. This is the main difference between quasi and true experiments.  

In quasi-experimental research , the directionality problem (that is, the relationship between two variables is known but the cause and effect is not known) doesn’t exist because of the ability to manipulate independent variables. However, this type of research doesn’t eliminate the problem of confounding variables (an extraneous variable that is not controlled in a study and could affect other variables, resulting in distorted associations between the variables) because quasi-experimental research doesn’t involve random assignment. Consequently, quasi-experimental research is lower in internal validity (the extent to which a study can establish a cause-and-effect relationship between variables) than true experiments.  

Quasi-experimental research is most common in field studies where random assignment is either difficult or impossible. This type of research is often conducted to analyze the effectiveness of a specific treatment. Some of the important types of quasi-experimental research designs are—non-equivalent group design, pretest–posttest design, interrupted time series design, combination design, and regression discontinuity design. After understanding the quasi experimental design meaning , let’s look at when to use this.  

When to Use Quasi-Experimental Research ?   

In some situations, using randomization to assign participants to groups may be unethical (for example, providing a specific health treatment to one group and withholding it from the other group). In such cases, quasi-experimental research can be used to identify a causal relationship without any ethical or practical challenges.   

The following list describes some instances, with examples, where quasi-experimental research designs would be more appropriate. 3,4  

• When being in one group could be harmful to the participants either because the intervention is harmful (e.g., randomizing people to smoking), or the intervention has questionable efficacy, or it is so beneficial that it wouldn’t be appropriate to withhold the intervention from the control group (e.g., randomizing people to receive a minor surgery).  
• When interventions act on a group of people in a specific location , it becomes difficult to adequately randomize participants (e.g., an intervention that reduces pollution in a specific area).  
• When working with small sample sizes, because randomized controlled trials require a large sample size to ensure even distribution of confounding variables between the treatment and control groups.  

Differences Between Quasi Experiments and True Experiments   

Unsure about choosing between experimental and quasi-experimental design for your research? Take a look at the main differences between quasi and true experiments as depicted in the following table. 4,5  

Types of Quasi-Experimental Designs    

Here are some common types of quasi-experimental designs .[ 1,5]  

Non-equivalent Groups Design  

In a between-subjects experimental design, participants are randomly divided into two or more groups and each group is assigned a treatment condition, the outcomes of which are then compared. In such designs, the resulting groups are quite similar and are considered equivalent. However, when participants are not randomly assigned, the groups will be dissimilar and are therefore considered non-equivalent. The groups may have preexisting differences that could affect the outcome of the study because it becomes difficult to attribute any observed changes solely to the intervention being studied. This type of research design is one of the most common quasi-experimental designs .   

Pretest–Posttest Design  

In this type of research design, the dependent variable is measured once before the treatment begins and once after the treatment. For example, a researcher interested in studying the effectiveness of a public-speaking short seminar on elementary school students’ speaking skills could analyze the skills of the children before the course and then after the course to identify any changes. This type of quasi-experimental design is similar to a within-subjects experiment in which each participant is tested first under the control condition and then under the treatment condition. If the average posttest score is better than the average pretest score, it implies that the treatment may be responsible for the improvement. This research design may or may not include control groups and may be prone to internal validity risk (this is the main difference of this design with the non-equivalent groups design). One advantage of this quasi-experimental design is that it provides directionality because the dependent variable is tested both before and after the intervention.  

Combination Design  

This type of quasi-experimental design combines both pretest–posttest and non-equivalent groups designs. In this research design, a treatment group is given a pretest, receives a treatment, and then is given a posttest. The control group is also given a pretest, but does not receive the treatment, and then is given a posttest.  

Interrupted Time Series Design  

A time series is a set of measurements taken at multiple and equally spaced intervals over a period of time before and after an intervention. The main objective of this type of quasi-experimental design is to assess whether the observations before and after the intervention are different. For example, a company wants to measure its employees’ productivity per week for a year. In this type of research design, a time series is “interrupted” by a treatment. In this same example, the company reduced work hours from 10 hours to 8 hours. This measure was found to increase the productivity quickly and it remained elevated for several months. This would help researchers conclude that reduced work hours increased productivity.  

Regression Discontinuity Design  

This quasi-experimental design assesses the influence of a treatment or intervention by using a mechanism that assigns the treatment based on eligibility, or a “cut-off point” of some known variable, such as age and income. This specific threshold value or cut-off score is used to assign participants to treatment groups and helps researchers compare the effectiveness of an intervention on participants immediately above and below the cut-off point.  

Advantages and Disadvantages of Quasi-Experimental Designs  

Here are a few important advantages and disadvantages of quasi-experimental designs . 3,6  

Advantages of Quasi-Experimental Designs  

• Higher external validity : Quasi-experimental designs are more practical with more real-world applications and therefore may be more generalizable.  
• Higher control over targeted hypotheses : Because there is no randomization of participants, the dependent variables can be more controlled, targeted, and efficient.  
• Can be combined with other methodologies : Quasi-experimental design can be combined with statistical analyses and results of other true experiments, which can significantly reduce research time.  
• Less expensive and time consuming than randomized controlled trials and require fewer resources.  

Disadvantages of Quasi-Experimental Designs    

• Randomization is not used , so the study is not useful for concluding a causal relationship between an intervention and an outcome.  
• Lower internal validity : Because the variables can be controlled, it’s difficult to know if researchers have used all confounding variables.  
• Risk of inaccurate data : Quasi-experimental design often borrows data from other research so the data may not necessarily be complete or accurate.  
• Risk of bias : Researchers choose baseline elements and eligibility so there’s a risk of researcher bias. The types of selection bias that can occur in quasi-experimental design include maturation bias, historical bias, instrumentation bias, and Hawthorne effect.  

Key Takeaways  

• Both experimental and quasi-experimental design s are used to evaluate the effectiveness of a treatment.  
• Quasi experiments differ from true experiments in several aspects:  
• Participants cannot be randomly selected or assigned to treatment groups for practical or ethical reasons.  
• A control group may or may not be necessary  
• Quasi experiments have high external validity, are useful for smaller sample sizes, are less expensive, and they require fewer resources.  
• Quasi-experimental designs have low internal validity, and the absence of randomization leads to a risk of bias and confounding.  
• Some of the important types of quasi-experimental designs are—non-equivalent group design, pretest–posttest design, interrupted time series design, combination design, and regression discontinuity design.  

Frequently Asked Questions   

Q1. What is the main purpose of quasi-experimental research ?  

A1. The main purpose of quasi-experimental research is to establish a cause-and-effect relationship between variables and assess the impact of an intervention on the outcome, in the absence of randomization.  

Q2. What are some applications of quasi-experimental design in research?  

A2. Quasi-experimental designs can be used in different disciplines, some of which are mentioned below. 7  

• Education : Quasi-experimental design can be used in education to assess the effectiveness of diverse interventions, such as new teaching measures and tools, curriculum changes, teacher training, policy changes, etc.  
• Healthcare: To analyze the effectiveness of new medicines, dosages, medical equipment, etc.  
• Psychology: To study the influence of factors such as trauma, geographic changes, social media, etc., on behavior.  
• Public policy: To examine the influence of policy changes and government reforms on the public, such as the effect of new tax reforms on spending power.  
• Business and marketing: To analyze the effects of new products, advertisements through different media, product design, etc., on consumer behavior and the purchasing trends of consumers.  

Q3. What are a few real-world quasi-experimental research examples ?  

A3. Here are a few quasi-experimental research examples in the real-world setting. 7  

Education  

To study the effectiveness of a new or updated educational program or curriculum, you could use the non-equivalent groups design method to select two schools with comparable features within the same district, introduce the new program in one, and retain the existing programs in the other. Comparison between the schools after a specific period can help ascertain whether the new program was effective or not. Random assignment of the schools or student groups would not have been appropriate because it could have been rendered advantageous or disadvantageous to either group.  

Healthcare  

Quasi-experimental research can be helpful in analyzing the effectiveness of public health interventions such as vaccination campaigns. A time-series quasi-experimental design would be useful in understanding the effect of vaccination over a prolonged period by studying disease incidence rates before and after vaccination.  

Workplace  

A company can evaluate the effectiveness of a technical skills training program for its employees using the pretest-posttest quasi-experimental design . The technical skills of the employees can be tested both before and after they participate in the training. This research design focuses on immediate impact unlike the time series design, which focuses on the impact over a long period.  

A quasi-experimental design is thus very similar to true experiments, with the same objective of assessing the effectiveness of interventions in various fields. However, it has certain characteristically important differences, which render such designs useful in situations where conducting a true experiment may not be ethical or practical. We hope this article would have expanded your knowledge of experimental and quasi-experimental designs and will help you in selecting the appropriate design for your research.  

References  

• Price PC, Jhangiani R, Chiang I-C A. Quasi-experimental research. Research Methods in Psychology – 2 nd Canadian edition. BCCampus website. Accessed November 12, 2024. https://opentextbc.ca/researchmethods/chapter/quasi-experimental-research/  
• Price PC et al. Chapter 8: Quasi-experimental research. Research Methods in Psychology. Accessed November 13, 2024. https://opentext.wsu.edu/carriecuttler/part/chapter-8-quasi-experimental-research/  
• Quasi-experimental design: Types, examples, pros, and cons. MasterClass website. Published June 16, 2022. Accessed November 14, 2024. https://www.masterclass.com/articles/quasi-experimental  
• Choueiry G. Experimental vs. quasi-experimental design: Which to choose? Quantifying Health. Accessed November 14, 2024. https://quantifyinghealth.com/experimental-vs-quasi-experimental-design/  
• Explaining quasi-experimental design and its various methods. Voxco. Published September 17, 2021. Accessed November 15, 2024. https://www.voxco.com/blog/quasi-experimental-design-explanation-methods-and-faqs/  
• Schweizer ML, Braun BI, Milstone AM. Research methods in healthcare epidemiology and antimicrobial stewardship-quasi experimental designs. Infect Control Hosp Epidemiol . 2016;37(10):1135-40. doi:10.1017/ice.2016.117. Accessed November 16, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC5036994/  
• Quasi-experimental design: Rigor meets real-world conditions. Servicescape. Published October 4, 2023. Accessed November 18, 2024. https://www.servicescape.com/blog/quasi-experimental-design-rigor-meets-real-world-conditions  

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Home » Quasi-Experimental Research Design – Types, Methods

Quasi-Experimental Research Design – Types, Methods

Table of Contents

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

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

Quasi-Experimental Design

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

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

Key Characteristics of Quasi-Experimental Research

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

Purpose of Quasi-Experimental Research

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

Types of Quasi-Experimental Research Design

1. non-equivalent groups design (negd).

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

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

2. Time-Series Design

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

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

3. Regression Discontinuity Design (RDD)

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

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

4. Pretest-Posttest Design

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

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

5. Propensity Score Matching (PSM)

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

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

Methods of Quasi-Experimental Research

1. data collection.

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

2. Statistical Analysis

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

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

3. Control for Confounding Variables

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

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

4. Use of Mixed Methods

Combining quantitative and qualitative methods enhances the depth of analysis.

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

Applications of Quasi-Experimental Research

1. education.

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

2. Healthcare

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

3. Policy Analysis

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

4. Social Sciences

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

Strengths of Quasi-Experimental Research

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

Limitations of Quasi-Experimental Research

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

Steps to Conduct Quasi-Experimental Research

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

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

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

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quasi experimental method

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

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tagalog ng quasi experimental study

tagalog ng quasi eksperimentong pag-aaral

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experimental unit

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power parang panghukuman

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baybayin ng salitang eksperimentong

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hiram na salita sa eksperimento

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experimental

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research design of non experimental research

in this kind of design

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which of them is/are the experimental set ups

which of them is are the experimental set-ups

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used to refer several different types of non experimental studies

hindi pang - eksperimento

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experimental research is use to determine to the natureuse of the relationship between two dependent and independent variable

eksperimental na pananaliksik ay ginagamit upang matukoy ang paggamit ng relasyon sa pagitan ng dalawang umaasa at independiyenteng variable

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the practicalities and cost implications of refurbishing hotels in order to accommodate patients and provide medical or quasi medical services.

ang mga praktikal at implikasyon ng gastos ng mga refurbishing hotel upang mapaunlakan ang mga pasyente at magbigay ng mga serbisyong medikal o quasi medikal.

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a type of randomized clinical trial in which patients are assigned to experimental conditions before they have consented to participate in the research.

isang uri ng pinasadahang klinikal na pagsubok kung saan ang mga pasyente ay nakatalaga sa mga pang-eksperimentong kondisyon bago sila pumayag na lumahok sa pagsasaliksik.

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five experimental plots are conducted in the experiment. multiple trials were done on different times and days to ascertain accuracy, make data reliable and provide enough evidence to prove the claim.

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the global solidarity and european discovery trials of hospitalized people with severe covid-19 infection apply adaptive design to rapidly alter trial parameters as results from the four experimental therapeutic strategies emerge.

ang pandaigdigang solididad at pagtuklas ng taga-europa na mga pagsubok sa naospital na mga taong may malubhang impeksyon sa covid-19 ay naglapat ng adaptibong disenyo upang mabilis na baguhin ang mga parametro ng pagsubok bilang mga resulta mula sa paglabas ng apat na mga terapeutikang mga diskarte na pang-eksperimento.

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Quasi-Experimental Design – Everything You Need to Know

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Despite lacking the random assignment found in true experiments, the quasi-experimental design can still offer valuable insights by comparing groups based on pre-existing variables, such as demographic characteristics or specific behaviors. However, when employing this methodology , the potential for selection bias and confounding variables should be carefully considered, as they may impact the generalizability and causal interpretations of the results.

Inhaltsverzeichnis

• 1 Quasi-Experimental Design – In a Nutshell
• 2 Definition: Quasi-experimental design
• 3 Quasi-experiment vs. true experiment
• 4 3 Types of a quasi-experimental design
• 5 When is a quasi-experimental design relevant?
• 6 Pros and cons of a quasi-experimental design

Quasi-Experimental Design – In a Nutshell

• Due to the manipulation of the independent variable, quasi-experimental research eliminates the problem of directionality.
• A quasi-experimental design manipulates an independent variable without randomly assigning individuals to conditions or ordering conditions.
• A quasi-experimental design is a distinctive research technique because it is distinguished by what it lacks.

Definition: Quasi-experimental design

In contrast to a true experiment, a quasi-experimental design isn’t reliant on random assignment. Non-random criteria are used to assign subjects to groups.

Quasi-experiment vs. true experiment

Example of a quasi-experimental design vs. true experimental design, true experimental design:.

Patients fill out a symptom sheet every few months to determine if the new treatment is considerably more effective (or less effective) than the traditional treatment.

However, due to ethical considerations, the administrators of the mental health center may deny you permission to randomly allocate their patients to treatments. In this situation, a true experiment cannot be conducted.

Instead, you might utilize a quasi-experimental design.

Quasi-experimental design:

You can utilize these pre-existing groups to compare the symptom development of patients getting the new therapy to those receiving the usual treatment.

Even if the groups were not assigned randomly, if you correctly account for any systematic variations between them, you could be pretty confident that any differences must be attributable to the treatment and not to other confounding variables .

3 Types of a quasi-experimental design

Nonequivalent group design.

Researchers ensure that any third or confounding variables do not impact them throughout the grouping process. Consequently, the groupings are as comparable as possible.

• You believe that an after-school program may lead to better grades.
• You select two groups of children, one attending a school that implements the new program and the other does not.
• You can determine if the program affects grades by comparing the students attending it to those who don’t.

Regression discontinuity

Therefore, participants above the cutoff are assigned to a treatment group, while those below the cutoff are not. The distinction between these two divisions is negligible.

• Some high schools in the U. S. are reserved for high-achieving pupils, who must get a minimum score on an entrance exam to attend. Those who pass the assessment differ systematically from those that do not.
• However, because the exact cutoff score is arbitrary, students who are close to the cutoff, who just barely pass the tests, and those who fail by a small margin—tend to have scores that are quite similar with small discrepancies between them being mainly attributable to random chance. Therefore, you can assume that any outcomes disparities are due to the schools the students attended.
• You may examine the long-term outcomes of these two groups of kids to determine the effect of attending a selective school.

Natural experiments

Because natural experiments are observational, they are not regarded as true experiments despite some using random assignments.

Researchers can use the independent variable, even when they have no control over it, to study the treatment’s effect.

• One of the most well-known natural experiments is the Oregon Health Study. In 2008, Oregon agreed to allow additional low-income persons to participate in Medicaid, the United States’ low-income health insurance program.
• As they couldn’t afford to cover all who qualified for the program, they used a random lottery to allocate program slots.
• Researchers evaluated the program’s effect by using those recruited as a randomly assigned treatment group and the individuals who were eligible but did not win the lottery as a control group.

When is a quasi-experimental design relevant?

Ethical reasons.

A noteworthy example is the Oregon Health Study. It’d be unethical to randomly grant health insurance to specific individuals while excluding others from having coverage for the sole purpose of research.

Practical reasons

Pros and cons of a quasi-experimental design, what is a quasi-experiment design.

A quasi-experiment is a research design that aims to prove a cause-and-effect link.

What is a random assignment?

Random assignment is used in experimental research to divide your subjects into distinct groups randomly.

This strategy ensures that every sample member is randomly assigned to either a control or an experimental group.

When should I use a quasi-experimental design?

A quasi-experimental design is most beneficial in instances where conducting a true experiment would be either unethical or impractical.

The internal validity of a study based on quasi-experimental design is lower than actual experiments, but their external validity is frequently greater since they employ real-world interventions rather than contrived laboratory conditions.

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