Basics of Research Process

What Is a Quasi-Experimental Study: A Detailed Guide on Quasi Experiment & Examples

quasi-experimental design
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A quasi-experimental design is very similar to experimental designs, but unlike the latter, lacks the full control of the experimental method. In a quasi-experimental design, the researcher does not randomly choose participants, as would be done in a true experimental design. Instead, participants are assigned to groups based on existing characteristics, such as age, gender, or medical condition.

You’re in the right place if you need to know what quasi-experimental design is. We got you when it comes to everything academic! For example, we know that quasi-experimental designs create non-random groups. Are you surprised as it is an experiment? But here’s another fact. Such a design has a slightly lower internal validity. But we can still use it. However, you’ll have to read further to understand why. So let’s make a little deal. We promise all answers as for your questions if you promise to read through this guide. Does this sound good? Then carry on as we have lots of exciting things prepared!

What Is a Quasi Experimental Design: Definition

Quasi-experimental design definition is relatively simple, trust us. To make it even easier to remember, here’s a brief list of things you should know about today’s subject:

  • It establishes cause and effect relationships between variables (independent and dependent, to be exact).
  • It might be called an experiment, but researchers assign changed specifications to selected and not random groups.
  • It is usually used to test newly created treatments.
  • Lastly, such experimental studies usually have lower internal validity in research. It is because of the non-random selection and assignments of groups or subjects.

But your results can still be considered credible. So make sure to consider this design for your study!

Advantages of Quasi Experimental Design

Believe it or not, quasi-experimental design has lots of advantages. And, of course, here’s a quick list for you to consider:

  • They have higher external validity threats. Here’s no wonder as such studies are primarily conducted in real-world scenarios. The laboratory ones are overcontrolled and not as close to your real world.
  • They also have higher internal validity. But in this case, we’re comparing the non-experimental studies. Additionally, here the researcher also better controls confounding variables.

P. S. Confounding variables are your third component that influences your study but is not controlled or chosen by the researcher.

Types of Quasi Experimental Design

You have an excellent selection when it comes to the types of quasi-experimental design. So we’re also here to walk you through several of them. Choose wisely:

  • Natural experiments are usually the ones where any researcher does not control the external environment. Instead, we have random or random-like assignments of all the groups. This type, however, is rarely considered a real experiment.
  • Regression discontinuity allows the researcher to decide what threshold will be valid. One group usually follows the threshold, and the second one is commonly below it. Both groups are pretty similar, yet the treatment will act differently in those cases.
  • Nonequivalent groups design involves two very similar groups of people. Your compared groups might be of the same gender, class, ethnicity, and so on. The only thing that differentiates them from one another is treatment. One assigned group will be treated; the other one will not receive the same.

When to Use a Quasi Experimental Study

A quasi-experimental research design can be used for several reasons. It is up to you to choose what type and design fit your purpose the best.

Oftentimes, researchers prefer this type because of ethical reasons. We cannot constantly test our hypothesis on actual people. Or, for example, stop treatments for some individuals and allow others to continue your procedures will not be ethical. In any way, shape, or form, even for studies. 

Practical reasons are relevant as well. Carrying out experiments is not always possible. Maybe there are too many subjects or other reasons. But everything you need, you’ll find in our following sections. Check them out!

Practical Reasons for Quasi Experimental Research

Quasi-experimental design is a vital substitution in case researchers cannot practically complete the experiment. There might be several reasons that stop scientists from going full-on experimental mode. 

Those interested in research can have no funds to complete their study. Therefore, they can easily use the already collected data. If their research is important, there probably exists data that would be enough for them.

In such cases, the government is usually the one that collects information about certain groups. Their data might not always be enough. But not everyone can spend time and funds on academic work.

Ethical Reasons for Quasi Experimental Research Design

Quasi-experimental design can also be chosen due to certain ethical issues. In the majority of instances, it is highly unethical to withhold treatment for individuals, specifically on a non-random basis. Thus, experiment cannot be used to determine causal relationships.

Here’s where our handy quasi comes to mind. You can use it if you’re unsure of ethical repercussions or if you know that there are some.

Quasi Experimental Design Example

We also want to give you an example of a quasi-experimental design. Out of all our options we've proposed, including regression discontinuity, we'll talk about nonequivalent groups design.

Example of nonequivalent groups design

You think that adding an extra class of English will help students receive higher grades. You need to find two groups to test your hypothesis. For example, your first group of eighth-graders will have that class, while your second one won't. You document their grades and consider whether anything has changed. At the end of your experiment, you'll have enough data to conclude whether your newly implemented class has any impact on the children's grades.

Quasi-Experimental Design: Key Takeaways

So there you have it! We know everything about quasi-experimental design and are ready to take over the scholarly world. But we shall prepare a little recap. Here, we’re still determining our favorite relationship between dependent and independent variables, like we would in any standard experiment. 

Yet, there are still several differences we will consider. Most importantly, quasi-experimental design is practical and ethical. Thus, it can be used when other methods fail. We also have another tip for you.

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Frequently Asked Questions About Quasi Experimental Research Design

1. What are the differences between true experiments and quasi-experiments?

Quasi-experiments and true experiments are quite different, especially when it comes to the circumstances of their use. True experiments utilize random subjects and groups. At the same time, quasi-experiments are not only more ethical and practical but not random at all. Here an effective scholar uses randomization for their assigned groups.

2. Does quasi-experimental have a control group?

Quasi-experiment has no control group whatsoever. It is your main difference from other true and valid experiments. The majority of such studies are carried out in natural environments. Besides, your lack of a control group allows researchers to be more ethical and practical in their studies.

3. Is quasi-experimental design quantitative or qualitative?

The quasi-experimental design takes the best of quantitative and qualitative research or studies. Therefore, we cannot choose between one or another. However, it is worth remembering that researchers and scholars have no full control over their environment. Besides, as was previously mentioned, there is no control group to consider. Everything is not random.

4. What is the strongest quasi experimental design?

There are several types you can choose from, but the strongest quasi-experimental design is definitely a randomized experiment. It is one of the types that allow researchers to receive accurate data. Moreover, it gives you an opportunity to measure required variables and achieve high levels of accuracy. That is why this method is preferred.

Article posted on:Nov 18, 2022
Article updated on:Mar 7, 2024


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