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A matched pairs design is a type of experimental design wherein study participants are matched based on key variables, or shared characteristics, relevant to the topic of the study. Then, one member of each pair is placed into the control group while the other is placed in the experimental group. Participants are assigned to each group using random criteria, so as to avoid any potential bias.
The matched pairs experimental design is most beneficial for studies that have small sample sizes. This is because it is harder to obtain balanced groups when using small sample sizes, even with the use of random assignment.
Studies that employ smaller sample sizes generally have financial constraints or time constraints, making it unfeasible to have a larger sample size. With the use of the matched pairs design, researchers can improve the comparability of their study participants despite their smaller sample size, increasing the validity of the cause-and-effect relationship identified in the experiment.
Additionally, matched pairs design can only be used when there are two treatment conditions so that one person from each pair can be assigned the first treatment and the other can be assigned the second treatment.
In this design, members are brought together because of a particular attribute or factors applicable to the concentrate and afterward split into various circumstances. A member will then be allotted to the control group in each pair, and the other member will be assigned to the trial group. The strategies are then equivalent to the free groups’ plan. Each group just encounters one degree of IV. The mean consequences of the matches would be analyzed after the trial.
Let’s take a look at the following example of matched pairs design in order to understand this experimental design better:
Researchers want to find out how a new diet affects weight gain among underweight subjects. This experiment only has two treatment conditions, the new diet and the standard diet, hence the matched pairs design can be used. For this study, the researchers recruited 200 subjects which will be grouped into 100 pairs based on shared characteristics such as age, gender, weight, height, lifestyle, and so on. For example:
Once all 100 pairs are made, a subject from each pair will be randomly assigned into the treatment group (will be administered the new diet for 2 months) while the other subject from the pair will be assigned to the control group (will be assigned to follow the standard diet for two months). At the end of the time time period of 2 months, researchers will measure the total weight gain for each subject.
There are a few outstanding benefits and a few expected disadvantages of utilizing a matched-pairs design.
A hiding variable is a variable that isn’t represented in an examination that might influence the results of the investigation.
In the past model, both age and orientation can altogether affect weight reduction. By matching subjects in light of these two factors, we are wiping out the impact that these two factors could have on weight reduction since we’re just looking at the weight reduction between subjects who are indistinguishable in age and orientation.
In this manner, any distinction in weight reduction that we notice can be credited to the eating routine, instead of old enough or orientation.
Order impact alludes to contrasts in results because of the order where trial materials are introduced to subjects. By utilizing a matched pair design, you don’t need to stress over order impact since each subject just gets one treatment.
In our past model, each subject in the examination was just put on one eating regimen. If we made one subject utilize the standard eating regimen for 30 days, then, at that point, the new eating regimen for 30 days, there could be a request impact because of the way that the subject utilized one specific eating routine before the other.
]Another benefit of matched pairs is their diminished demand attributes. Because we test all members just a single time, members are more averse to figure the analysis’ objective. This might lessen the gamble that members will change a part of their way of behaving because of information on the examination speculation. Therefore, lessening demand attributes might expand the legitimacy of the research.
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On the off chance that one subject chooses to exit the review, you lose two subjects since you never again have a total pair.
It may very well be very tedious to observe subjects who match specific factors, especially assuming you utilize at least two factors. For instance, it probably won’t be difficult to come by 50 females to use as matches, yet it very well may be very elusive for 50 female matches in which each pair matches precisely on age.
Regardless of how diligently analysts attempt, there will generally be some variety inside the subjects in each pair. The best way to match impeccably is to observe indistinguishable twins who share a similar hereditary code, which is really why indistinguishable twins are much of the time utilized in paired match studies.
A matched pairs design is an experimental design where participants are matched in pairs based on shared characteristics before they are assigned to groups; one participant from the pair is randomly assigned to the treatment group while the other is assigned to the control group.
The matched pairs design is best suited to studies that have small sample sizes where it is harder to obtain balanced groups by using random allocation alone. Additionally, this research design can only be used in studies with two treatment conditions.
Some advantages of the matched pairs design are:
Some limitations of the matched pairs design are:
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