When we think of the field of psychology, we often think of experimentation in a lab. Research and investigation are one of the most exciting parts of the psychology profession. Researchers put a lot of time and effort into their experiments. That's why it is important to use the proper research design. If you are interested in psychology, chances are you will read about or conduct an experiment with a repeated measures design.
- First, we will look at the repeated measures design in psychology.
- Then, we will review the repeated measures design definition.
- Next, we will look at a few repeated measures design examples.
- We will examine the repeated measures design psychology advantages and disadvantages.
- Finally, we will address the repeated measures design psychology uses.
Repeated Measures Design: Psychology
The psychology field utilises various research designs to conduct research and experiments. Before experimenting, it's important to consider many variables. Who will the experiment involve? What is the sample or demographic? Will you need one group of participants or multiple groups? These questions are critical to the planning process of experimentation.
If you are conducting an experiment with multiple variables, but only one group of participants, you will need a repeated measures design.
Repeated Measures Design: Definition
What is the repeated measures design in psychology? Let's start by looking at the definition.
In a repeated measures design, all participants experience all levels of the independent variables (IVs).
In other words, participants are one group and participate in all study conditions. Typically, researchers compare the average results of the conditions before and after exposure to the IVt.
All clear? If not, a repeated measures design example will help you better understand how it operates.
Repeated Measures Definition Psychology
In short, a repeated measures design is the experimental design in which the same participants partake in each experimental condition.
Repeated Measures Design Example in Psychology
Suppose a study investigates whether Vaia helps A-level psychology students better than traditional textbooks, assessing learning with tests. If the researchers conduct a repeated measures experiment, all participants will use Vaia and standard textbooks.
This process differs from an independent group design, where researchers divide participants into two groups, one using Vaia and the other using traditional textbooks.
Let's take a look at another example:
A researcher is testing three drugs that help fight nicotine cravings in five people trying to stop smoking. Each day, the participants received one of the drugs and reported their cravings, irritability, and headache over the course of the day.
This procedure is repeated, with the same participants, for three days, and thus a repeated measures design.
The independent variables in the above example are the three drugs. The participants are the same in all three conditions and receive one of the drugs each day. Results are compared and averaged after analysis of daily reports.
Repeated Measures Design Example
Feder et al. (2014) conducted a similar experiment involving the effectiveness of the drug ketamine on symptoms of PTSD.
The study involved 41 patients who had been diagnosed with PTSD. All patients received ketamine on one visit to the lab and a different anxiety drug (midazolam) two weeks later.
Feder et al. randomised the order of administration of the drugs so participants would not know which drug they were receiving. Participants were given tests to measure PTSD symptoms and depression.
All participants received each drug, and tests were taken to measure the results. Researchers found that ketamine aided in reducing PTSD symptoms significantly better than midazolam.
Repeated Measures Design Psychology: Advantages and Disadvantages
As always, one of the important aspects to consider is the advantages and disadvantages of repeated measures design.
Repeated Measures Design Advantages
Participant variables are controlled because the same participants participate in both conditions. Participant variables are extraneous variables related to the individual characteristics of each participant and may influence their response.
In a repeated measures design, the same participants participate in each condition, so extraneous participant variables such as individual differences can be eliminated. By reducing the influence of participant variables, the repeated measures design has good internal validity.
Repeated measures design has a tremendous economic advantage because it requires fewer participants. Repeated measures designs require only half the participants in independent groups and matched pairs designs. This is a tremendous economic advantage for researchers because they spend less time and resources recruiting participants.
Repeated measures can thus be considered a more cost-effective and efficient experimental design than independent groups and matched pairs.
Repeated Measures Design Disadvantages
One of the major limitations of repeated measures is order effects. Order effects mean that tasks completed in one condition may affect task performance in another. For example, participants may perform better in the second condition either because of the practice effect or worse because of boredom or fatigue. Thus, if all participants complete the tasks in the same order, order effects are a serious problem that affects the study's validity.
Another limitation in repeated measures is demand characteristics. The first test could induce demand characteristics because it allows participants to guess the survey's target when it is repeated in the second test. There is a risk that participants will change some aspect of their behaviour in response to knowing the research hypothesis. In this way, demand characteristics may reduce research validity.
There are several ways to deal with the limitations of repeated measures design. These involve counterbalancing techniques to deal with order effects and cover stories to deal with demand characteristics.
Counterbalancing is an experimental technique used to overcome order effects. Counterbalancing ensures each condition is tested equally first or second. For example, participants are divided in half, with one half completing the two conditions in one order and the other half completing the conditions in reverse order. In this way, a researcher can control the order of the conditions and ensure better validity.
A cover story about the test's purpose can prevent participants from guessing the research hypothesis. The cover story should be plausible but false. Researchers communicate this statement to participants to prevent the true hypothesis from being revealed.
Such deception can be practised when the knowledge of the experiment's true purpose can influence the participant's behaviour in the study. In this way, deception can allow the researcher to control demand characteristics and ensure better validity.
Repeated Measures Design: Uses
Repeated measures designs are often used in longitudinal studies. These studies are often interested in measuring the effects of a variable over time.
Researchers are examining the effects of a drug on a group of people with major depressive disorder.
In the study, all participants take the drug regularly over the course of three years; each attends regular therapy sessions and keeps a history of mood fluctuations. Researchers then measure serotonin and dopamine levels in all participants throughout the study.
Participant variability is low since the same subjects are used throughout the entire experiment. A study like this gives us a better understanding of certain drugs' effectiveness in treating specific conditions. They also give us information about the brain's and body's reactions to specific drugs.
Repeated Measures Design - Key takeaways
- In the repeated measures design, all participants experience all levels of independent variables.
- Repeated measures designs have significant economic advantages and lower participant variability.
- However, repeated measures designs are limited by order effects and demand characteristics.
- Dealing with the limitations of repeated measures design involves counterbalancing techniques to deal with order effects and cover stories to deal with demand characteristics.
- Repeated measures designs are useful in longitudinal studies.
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