Researchers can get significant information from twin research studies when investigating a topic. But what about if we match participants based on specific characteristics? Would this also be helpful in psychology research? A matched pairs design is an experimental technique that investigates phenomena using this strategy.
- We are going to explore matched pair designs in psychological research.
- We will start by highlighting the matched pairs design definition.
- Then we will delve into how the experimental design is used in psychology and matched pairs design statistics.
- After, we will look at a matched pairs design example in the context of a psychological research scenario.
- Finally, the strengths and weaknesses of matched pair designs will be discussed.
Matched Pairs Design: Definition
The matched pairs design is where participants are paired based on a specific characteristic or variable (e.g., age) and then divided into different conditions. A matched pairs design is one of three main experimental designs. Researchers use experimental designs to determine how participants are assigned to experimental conditions.
In research, researchers aim to assign participants to experimental conditions in the most efficient and maximal effective way to test a hypothesis. It's also important to note that this design should have little involvement of the researcher so that bias does not affect the study's validity.
Fig. 1 - In a matched pairs design, participants are matched based on matching characteristics.
Matched Pairs Design: Psychology
Now that we know what a matched pairs design is let's look at the process typically used when conducting psychological research.
There are usually two groups in experimental research: the experimental and the control group. The goal of the two groups is to compare how changes in the independent variable (variable manipulated) affect the dependent variable (variable measured).
The experimental group is the group in which the independent variable is manipulated, and the control group is when the independent variable is controlled to ensure that it doesn't change.
In a matched pairs design, a pair is matched. Before the researchers begin recruiting participants, the characteristics that participants will be matched on should be pre-determined.
Some examples of characteristics that participants are matched with include age, gender, IQ, social class, location, and many other potential characteristics.
Each matched pair is randomly assigned to either the experimental or control group. As we mentioned earlier, the random element is essential; it prevents bias from hindering the study's validity.
The protocol used in matched pairs design is very similar to the one used in an independent measures design.
Matched Pairs Design: Statistics
Now that we've discussed the experimental design method, let's explore the matched pairs design statistics procedures.
As we've learned, there are typically two groups: experimental and control. You can probably guess that the data of the two groups between each pair is compared.
A standard method used in research is to compare the average results of the control and experimental group; most commonly, the mean is used as a comparison tool when possible.
The mean is a statistical measure of central tendency which generates a single value that summarises the average of results. The mean is calculated by adding each value and dividing them by the number of values within a dataset.
Matched Pairs Design: Example
Let's look at a hypothetical psychology research scenario of a matched-pairs design example.
A group of researchers were interested in investigating if students with a revision guide performed better in a test than those who did not have one. However, they wanted to control IQ variability as they identified this as a potential extraneous variable.
An extraneous variable is an external factor that affects the dependent variable.
Remember, in experimental research, the only factor in theory that should influence the dependent variable is the independent variable.
In the study, the IV and DV are:
- The IV: Whether the participant received a revision guide or not.
- The DV: Test scores achieved.
Before the study began, participants completed an IQ test; each was allocated into a pair based on matching IQ scores.
Despite the name, matched pairs design participants can be allocated into groups if they each share the same characteristic.
Each pair was randomly assigned to either the control (no revision guide) or experimental (given revision guide) group.
After the experiment, the average of the pairs was compared to identify if participants who received a revision guide performed better than those who didn't.
The Strengths and Weaknesses of Matched Pairs Design
Let's discuss the strengths and weaknesses of a matched pairs design.
Strengths of Matched Pairs Design
An advantage of matched pairs over repeated measures is that there are no order effects.
Order effects mean that the tasks completed in one condition may influence how the participant performs the task in the following condition.
Since participants experience one condition, there are no practice or boredom effects. Thus, by controlling the order effects, researchers control the potential, improving the study's validity.
Another advantage of matched pairs is their reduced influence on demand characteristics. As in the experimental design, each participant is tested once, and participants are less likely to guess the experiment's hypothesis.
When participants guess the hypothesis, they may change their behaviour to act accordingly, known as the Hawthorne effect. Therefore, reducing demand characteristics may increase the validity of the research.
Participant variables are controlled by selecting participants according to the experiment's relevant variables. Participant variables are the external variables related to the individual characteristics of each participant and can affect their response.
Extraneous variables in participants, such as individual differences, cannot be eliminated but can be reduced. 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
The matched pairs design can take up more financial resources than the other experimental designs because it requires more participants. Additionally, a matched pairs design has a lower economic benefit because it requires additional procedures, e.g. for matching participants. This is an economic disadvantage for researchers because more time and resources are spent collecting additional data or conducting an additional pretest.
Issues also arise in matched pairs designs when participant drops out of the study. Since participants are matched in pairs, the data for both pairs can't be used if one drops out.
Research with a smaller sample is less likely to find statistically significant findings that are generalisable. If this occurs, even if statistical findings are found, they still have limited use, as inferences can't be made when results are not generalisable in scientific research.
Finding pairs can be a time-consuming process. Participants need to be matched on certain variables. For example, if you want to match participants by age and weight, it might not be easy to find pairs of participants with the same age and weight.
Matched Pairs Design - Key takeaways
The matched pairs design definition is an experimental design where participants are paired based on a specific characteristic or variable (e.g., age) and then divided into different conditions.
In matched pairs design, pairs are randomly assigned to a control or experimental group.
Matched pairs design statistics often involve comparing the averages of pairs; most commonly, the mean is used.
The strengths of matched pairs designs are that there are no order effects, and 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 that it can be time-consuming and costly.
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