• 3-minute read
• 9th January 2022

An Introduction to Quasi-Experimental Design

If you’re a researcher or student in the sciences, you’ll probably come across the term “quasi-experimental design” at some point. But what exactly does it mean?

In this post, we’ll guide you through the different forms of quasi-experimental design and how it compares to true experiments.

What is Quasi-Experimental Design?

Quasi-experimental design (QED) is a research design method that’s useful when regular experimental conditions are impractical or unethical.

Both quasi-experimental designs and true experiments show a cause-and-effect relationship between a dependent and independent variable. Participants in a true experiment are randomly assigned to different treatment groups. The quasi-experimental design, on the other hand, assigns groups based on criteria instead of randomly.

Quasi-Experimental Design Vs. True Experimental Design

The main difference between a quasi-experimental and true experimental design is that in the former, groups aren’t randomly assigned. There are also some other key differences between these research methods.

True experimental design involves:

●     Having control as a researcher over the design of the treatment or program that participants receive (i.e., the independent variable)

●     Control variables as a necessary component

In contrast, a quasi-experimental design involves:

●     The researcher studying groups after they’ve received a treatment or program

●     Control variables as a common element but they aren’t necessary for the experiment to work

Examples of Experimental Design

Perhaps the easiest way to understand quasi-experimental design is to look at how it might be used in practice.

Let’s say you hypothesize that having access to free art lessons will improve the mental health of children from low-income families.

In a true experiment, you’d randomly assign participants to two groups: one that receives free art lessons and another that doesn’t.

However, it’s ethically questionable to deny one group of children access to something that might benefit them.

Instead, you might decide to compare the data from a community that’s already offered free art classes to these children with that of a community that’s not yet done so.

This second example would be a quasi-experimental design.

Advantages and Disadvantages of Quasi-Experimental Design

Quasi-experimental design has some advantages and disadvantages you’ll need to consider when designing your research.

On the plus side, quasi-experimental design:

●     Has a higher external validity than true experimental design, as it usually involves real-world scenarios

●     Allows you to control for unexpected, confounding variables, resulting in a higher internal validity than other non-experimental methods of research

●     Enables the study of cause-and-effect relationships without the ethical issue of denying a treatment to those who may benefit from it

●     Does not require access to large-scale funding and other practical concerns, as the treatment has already been issued by others

The disadvantages of quasi-experimental design, however, include:

●     Lower internal validity than found in true experiments, as it’s more difficult to account for all confounding variables without using random assignment

●     The necessary data required for research potentially being inaccurate, outdated, or difficult to access

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