A matched group design, or matched subjects design, is when an experiment is designed to control for individual differences by matching similar subjects with each other.
For example, if researchers needed two groups to compare how different animals reacted to the flavor of the new Taco Pumpkin Spice Supreme Burrito, they’d likely make each group consist of the same number of cats, dogs, rats, cockroaches, and any other animal that would dare eat such a monstrosity. In this way, they could more accurately compare like-for-like and be more confident in their study’s results.
What is Matched Group Design?
When researchers conduct experiments, they want to make sure their results are as accurate as possible. And one way to achieve this is by using a matched group design.
A matched group design is a type of research design that has two types of groups: an experimental group and a control group.
The experimental group is exposed to a particular treatment or intervention while the control group is not. The idea here is to match the two groups as closely as possible on important variables, so that any differences between the groups can be attributed to the treatment being tested.
Let’s say we’re conducting a study on the effectiveness of a new diet. In this case, we want to match the groups on important variables like age, gender, starting weight, and eating habits. We want to make sure that the only difference between the two groups is the intervention – the new eating program.
The important variable being measured in this study is the amount of weight lost or another important health indicator from the new eating program. This variable is called the dependent variable. And by matching the groups on important variables, we can be more confident that any differences in the dependent variable are a result of the intervention and not just chance.
But not all variables are important to match. For example, researchers would not need to match subjects on variables like hair length or eye color as they have no effect on the study.
The matched group design is important in research studies because it allows researchers to rely on the results with fewer participants. When the subjects in the group are all matched on variables, the researchers can have more confidence in the accuracy of their results.
Matched Group Design Pros and Cons
Why is it useful? The matched group design is a handy research design as it allows researchers to create two equivalent groups to measure the impact of a specific intervention or treatment on a dependent variable.
By matching the groups on important variables, researchers can increase the accuracy of their results and reduce the likelihood of extraneous variables affecting the outcome. This design also allows for a comparison between the treatment group and the control group, which is essential in determining the effectiveness of the intervention.
One limitation of the matched group design is that it can be time-consuming and expensive to find participants that are closely matched on important variables. This can limit the number of participants in the study, potentially reducing the statistical power of the results.
Additionally, the matching process may not be possible for all variables, which may lead to differences between the groups that cannot be controlled for. Finally, the matched group design may not be suitable for all types of research questions, as some studies may require a different design to answer the research question more effectively.
Examples of Matched Group Design
To help solidify just what a matched group design is, here’s a few examples of where you might find it in the wild.
- A study of effectiveness for a new diabetes medication: The researchers would match participants based on variables such as age, gender, and severity of their diabetes symptoms. Then, one group would receive the medication and the other group would receive a placebo, and the researchers would compare the effects of the medication on the two groups.
- A study comparing the cognitive abilities of children who attend preschool vs those who don’t: The researchers would match participants based on variables such as age, socioeconomic status, and home environment. Then, one group of children would attend preschool while the other group would not, and the researchers would compare the cognitive abilities of the two groups.
- A study examining the effectiveness of a new educational program for students with learning disabilities: The researchers would match participants based on variables such as age, grade level, and severity of their learning disability. Then, one group would participate in the new educational program while the other group would receive the standard educational program, and the researchers would compare the academic performance of the two groups.
These are just a few ideas but hopefully they get the main point across.