Repeated measures design
Repeated measures design is a valuable experimental method often used in scientific research where the same participants are tested under different conditions multiple times. This design typically involves dividing participants into two groups: a control group, which does not receive the experimental treatment, and an experimental group, which does. A key advantage of this approach is that it allows researchers to control for individual differences among participants, as the same individuals are involved in both conditions. This can lead to more reliable results, especially when participant availability is limited. However, challenges such as order effects—where participants may perform differently due to practice or fatigue—can arise. To mitigate these issues, researchers often use a technique called counterbalancing, where the order of conditions is varied among participants. Repeated measures design has been instrumental in uncovering significant links in fields like epidemiology, exemplified by studies such as the Framingham Heart Study. Overall, this design is a critical tool for conducting efficient and effective scientific experiments.
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Repeated measures design
Repeated measures design is a method of setting up scientific experiments. Experiments are vital to scientific discovery, so choosing the best design is essential. Repeated measures design relates to experimenters using the same participants multiple times in an experiment. Commonly, the participants will serve in a control (not affected by the experiment) group before or after serving in an experimental (affected by the experiment) group. This method can simplify experiments, especially if participants are limited. Some problems may arise with this method of experimentation, mainly participant practice and fatigue variations, but the experimental organization may help alleviate them.
Background
Repeated measures design refers to a particular type of scientific experiment. Experiments are crucial to scientific learning and progress. They allow scientists to learn more definite information about the world than could be gained through observation or reasoning alone. Experiments have yielded some of humanity’s most important scientific breakthroughs.
Experiments may come in many forms, but all valid experiments should follow some guidelines. Experiments should observe the scientific method, a process for reaching the most accurate and useful conclusions possible. Experimenters begin by making observations of the world, which leads them to form a hypothesis—a possible explanation for some phenomenon that prompts further examination. Constructing the hypothesis is an important step that affects how the hypothesis will be tested. A scientist next designs an experiment to test the hypothesis. Experiments may occur in many ways and take several forms.
One form is the natural experiment, which is based on very careful observation of occurrences that take place in nature. Most experiments are controlled experiments, meaning they do not occur in nature but in an artificial setting like a laboratory. Experimenters can closely control the factors of the experiment in this environment. These experiments generally yield the most concise and accurate results and address their hypotheses most specifically. A third main type of experiment is a field experiment, which occurs in a real-world environment and may include natural or controlled elements.
Controlled experiments generally contain multiple variables, or factors that may be changed. Some variables are the same, while others are different, or independent. Independent variables are chosen specifically to test the hypothesis. Scientists compare the results of these independent variables against the results of the regular variables to see what differences they make.
Once the experiment is complete, scientists must evaluate their results. If the experiment was designed and conducted as intended, the results should prove or disprove the original hypothesis. If they prove the hypothesis, the scientist may conclude the study and publish the findings, or perform new tests to gain further insights into that or related hypotheses. If the results disprove the hypothesis, the scientist may start again and test the hypothesis in a new way, or test an entirely different hypothesis.
Overview
Experiments are a crucial means of gathering scientific information and bringing progress. For that reason, designing experiments in the best way possible is extremely important. Scientists may spend days, weeks, months, or years designing their experiments to get the most accurate results most effectively. Generally, designing experiments deals mainly with how the experimenter chooses and allocates variables. Typical designs fall into three main categories—repeated measures, independent groups, and matched pairs.
Repeated measures design (sometimes called “within-groups” or “within-subjects” design) refers to a type of experimental design in which a scientist seeks to study multiple conditions and uses the same variables to test these conditions. In many cases, this occurs with human participants. Scientists often divide participants into two groups. One is the control group, which will not receive any experimental treatment. The other is the experimental group, which receives the experimental treatment. For example, in medicine and drug testing, the control group may receive a placebo, whereas the experimental group will receive the actual drug being tested.
For example, the experiment may test the hypothesis that bright lights will help student performance on math tests. The control group would likely be math students not subjected to bright light. The experimental group would then be math students subjected to bright light. The control group result demonstrates how people function under normal conditions. The experimental group highlights changes that may occur due to the bright lights. Researchers compare the control results to the experimental results to show the effects of the hypothesis.
In repeated measures experiments, researchers use the same participants in both groups, effectively recycling participants. In this example, the researchers may have ten math students. They may place one through five in the control group and six through ten in the experimental group. After conducting the bright-light experiment, they may reverse the groups and conduct the experiment again. This time, participants one through five will be in the experimental group and six through ten in the control group.
Repeated measures experiments often take place when researchers have access to a limited number of experimental subjects and must employ them as much as possible. Repeated measures experiments save the time and money required to find and recruit twice as many participants. Another potential benefit of repeated measures experiments is that they reduce variables among the participants. The same people are involved in both halves of the experiment. If one math student is better educated than another and likely to score higher on tests, that student will be tested in both situations. The natural differences between the test subjects will, therefore, be less likely to skew the results.
The major downside of repeated measures design is known as order effects. Order effects relate to how experimental participants may change during an experiment. Two main order effects may affect repeated measures experiments—practice and fatigue. The practice effect involves participants performing better in the second part of an experiment because of what they learned while doing the first part. The fatigue effect is the opposite. The fatigue effect relates to participants doing worse in the second part of an experiment simply because they are tired after completing the first part.
Scientists who use repeated measures design have developed a technique called counterbalancing to offset the problems of order effects. In counterbalancing, the experimenter splits participants into groups. One group performs the control component first and the experimental component second. The other group performs the experimental component first and the control component second. That way, practice and fatigue will play out more or less equally between the groups, and the overall results of the experiment will likely be more accurate.
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