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# Matched Pairs Design

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The last experimental design you need to know about is matched pairs design. Let's first provide a matched pairs design definition and then have a look at an example.

In matched pairs design, participants are paired based on a specific characteristic or variable (e.g., age) and then divided into different conditions. Researchers assign one member to the control group and the other to the experimental group in each pair. The procedures are then the same as in the independent group design. Each group experiences only one level of IV. Researchers compare the average results of the groups after the experiment.

Pairs of people, Kindpng

## Matched pairs design example

Suppose we conduct a research project to investigate whether StudySmarter helps A-level psychology students better than traditional textbooks and test participants’ learning outcomes using a post-test after graduation.

• We would pair participants based on their prior exam performance in psychology, as academic ability can be a confounding variable that affects post-test scores.
• Thus, we would pair the student with the best performance with the second-best, the third-best with the fourth-best, and so on.
• Then we would divide the pairs into two groups: the experimental group (high school graduates studying with StudySmarter) and the control group (high school graduates studying with traditional textbooks), following the same procedure as the independent group design.
• We would then compare the mean score of each group on the post-test.
• Most importantly, we would compare the mean score of the post-test between the two groups, which provides us with the results to interpret the research question.

## What are the strengths and weaknesses of matched pairs design?

We will now discuss the strengths and weaknesses of matched pairs design.

### Strengths of matched pairs design

In the following, we will present the strengths of the matched pairs design. We will discuss order effects, demand characteristics, and participant variables. The evaluation points are shown in PEEL format: Point/Evidence/Explanation/Link.

#### No order effects

P: An advantage of matched pairs over repeated measures is that there are no order effects. E: Order effects mean that the tasks completed in one condition may influence how the participant performs the task in the following condition. E: Since participants are only tested on matched pairs, there is no practise or boredom effect. L: Thus, by controlling the order effects, which can be a serious confounding variable, matched pairs improve the study’s validity.

#### Reduced demand characteristics

P: Another advantage of matched pairs is their reduced demand characteristics. E: Because we test all participants only once, participants are less likely to guess the experiment’s target. E: This may reduce the risk that participants will change some aspect of their behaviour in response to knowledge of the research hypothesis. L: Therefore, reducing demand characteristics may increase the validity of the research.

#### Control of participant variables

P: Participant variables can be controlled by selecting participants according to the experiment’s relevant variables. E: Participant variables are the external variables related to the individual characteristics of each participant and can affect their response. E: Extraneous variables in participants, such as individual differences, cannot be eliminated, but they can be reduced. L: By matching participants to relevant variables, we can reduce the confounding influence of participant variables to some extent, improving internal validity.

### Weaknesses of matched pairs design

In the following, we will highlight the weaknesses of the matched pairs design. We will discuss the data needed, the number of participants required, the time required to find participants, and the elimination of participants.

#### More participants are needed

P: The matched pairs design has a lower economic benefit because it requires more participants. E: The matched pairs design requires twice as many participants as the repeated measures design. E: This is an economic disadvantage for researchers because they must spend more time and resources recruiting participants. L: Therefore, independent groups can be considered a less cost-effective and efficient experimental design than a repeated measures design.

#### Requires extra data

P: A matched pairs design has a lower economic benefit because it requires additional procedures. E: A matched pairs design requires data specifying each participant’s particular characteristic or variable. E: This is an economic disadvantage for researchers because more time and resources must be spent collecting additional data or conducting an additional pretest. L: Matched pairs can thus be considered a less cost-effective and efficient experimental design than repeated measures design and independent groups.

#### Disadvantage if one participant drops out

P: There is a disadvantage if a participant drops out of the study. E: Since participants are matched in pairs, the pair data would be useless if one dropped out. E: The data from two participants would be lost, which could set the study back and lead to the need to recruit replacement participants. L: For this reason, the matched pairs design may be less efficient.

#### Time-consuming

P: Finding pairs can be a time-consuming process. E: Participants need to be matched on certain variables. For example, if you want to match participants by age and weight, it might be difficult to find pairs of participants who are the same age and weight. E: This is a disadvantage because finding matching pairs could take a lot of time. L: This also leads to matched pairs perhaps being a less efficient design.

## Matched Pairs Design - Key takeaways

• In a matched pairs design, participants are paired based on a specific characteristic or variable relevant to the study and then split across different conditions.

• The strengths of matched pairs designs are that there are no order effects and that demand is lower because all participants are tested only once. We can control participants’ variables to reduce extraneous participant variables, such as individual differences between participants.

• The weakness of the matched-pairs design is lower economic benefits because it requires more participants. If one participant drops out, the data for the pair is lost and it is time-consuming to find matching pairs.

• The matched pairs design requires data specifying a particular characteristic or variable of each participant.

• If one participant drops out, the data for the pair is lost.

Matched pairs designs allow us to better compare the data than if participants were randomised entirely into groups. For example, say you were testing a new drug and found that it seemed to work better compared to another one. However, your participants are grouped all randomly in age and gender, so there were many young females in the group that responded well to the new drug. You couldn't say for sure if it were the drug that worked, or maybe they improved because of their young age, or females have better recovery abilities.

A study is conducted to examine whether StudySmarter helps A-level psychology students better than traditional textbooks and test participants' learning outcomes using a post-test after studying. Participants may be paired based on their previous exam performance in psychology because academic ability can be a confounding variable that alters post-test results. Then the pairs will be split into two groups: the experimental group (A-level students who study with StudySmarter) and the control group (A-level students who study with traditional textbooks). They would then take the test. Researchers will compare the mean score of each pair in the post-test.

In this design, participants are paired up based on a specific trait or variables relevant to the study and then split into different conditions. A member will then be allocated to the control group in each pair, and the other member will be allocated to the experimental group. The procedures are then the same as for the independent groups' design. Each group only experiences one level of IV. The mean results of the pairs would be compared after the experiment.

## Final Matched Pairs Design Quiz

Question

What is a matched pairs design?

Participants are paired up based on a specific trait or variables relevant to the study and then split into different conditions in the matched pairs design.

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Question

Why don't matched pairs experiments have any issues with order effects?

Matched pairs experiments don't have issues with order effects because all participants are only tested once in matched pairs. No practice effect or boredom effect will occur.

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Question

Why is matched pairs design criticised as having low cost-effectiveness?

Matched pairs design has a lower economic benefit as it requires more participants. Also, extra data must check the participants in pairs according to the relevant variables.

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Question

Why is matched pairs design praised for having reduced demand characteristics?

Matched pairs experiments don't have issues with demand characteristics. This is because participants are only tested once in matched pairs. They are less likely to guess the aim of the experiment.

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Question

How can researchers deal with confounding participant variables?

Through matched pairs design, researchers can manipulate the participant variables. Extraneous participant variables like individual differences cannot be eliminated but can be reduced, improving internal validity.

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Question

How are participants allocated in the matched pairs design?

In the matched pairs design, one participant goes to one condition, and the other goes to a different one.

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Question

How would participants be matched in pairs?

Participants are paired up in matched pairs based on a specific trait or variables relevant to the study. For example, by gender, IQ, age, ethnicity, socioeconomic status.

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Question

What are confounding variables?

Confounding variables are uncontrolled variables that can influence the dependent and independent variables.

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Question

What are the strengths of matched pairs designs?

• No order effects.
• Reduced demand characteristics.
• Better control of participant variables.

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Question

What are the weaknesses of matched pairs designs?

• Lower economic benefit as it requires more participants.
• It needs extra data to match the participants in pairs.
• If one participant drops out, the data for the pair is lost.
• It is time-consuming to find matching pairs.

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