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The repeated measures design is also known as a within-subject design. It is a frequently used ANOVA design in which all subjects participate in all conditions of the research experiment . In the repeated measure design all the participants serve as their own control because they are involved in the experiment and control groups . The repeated measure design is only to be used when the two sets of scores represent measures of exactly the same thing. Therefore exactly the same test needs to be given at both times or under both conditions to all participants .
Uses of a repeated measures design
- Conduct an experiment when few participants are available: The repeated measure design is ideal for maximizing the amount of data that can be gathered from a small number of subjects, as all experimental subjects participate in every part of the experiment.
- Conduct experiment more efficiently: Repeated measures designs allow many experiments to be completed more quickly, as only a few groups need to be trained to complete an entire experiment. For example, there are many experiments where each condition takes only a few minutes, whereas the training to complete the tasks take as much, if not more time.
- Increases the sensitivity of the experiment: Decreasing the number of participants helps to increase the sensitivity of an experiment. Because increasing the sensitivity of an experiment helps to decrease the variability of the results, a repeated measures design experiment typically yields less variable results than other designs, such as an independent groups design. This is because people typically vary less within themselves (that is, when compared to themselves) then when compared to others. This is especially useful when studying an independent variable with otherwise small effects.
- Study changes in participants’ behavior over time: Repeated measures designs allow researchers to monitor how the participants change over the passage of time, both in the case of long-term situations like longitudinal studies and in the much shorter-term case of practice effects.
Practice effects occur when a subject in an experiment is able to perform a task and then perform it again at some later time. Generally, they either have a positive (subjects become better at performing the task) or negative (subjects become worse at performing the task) effect. Repeated measures designs are almost always affected by practice effects; the primary exception to this rule is in the case of a longitudinal study. How well these are measured is controlled by the exact type of repeated measure design that is used.
Two types of repeated measures designs
- Complete: A complete repeated measures design balances the practice effects that participants undergo against each other. This is accomplished by having the individual participants go through each of the conditions several times, changing the order of conditions with each administration.
- Incomplete: An incomplete repeated measures design does not have the participants repeat the conditions; instead, each participant completes each task only one time. Instead, the overall order is varied for each participant, which allows the practice effects to balance out over all of the participants.
Both types, however, have the goal of controlling for practice effects.
Advantages and disadvantages
The primary strengths of the repeated measures design is that it makes an experiment more efficient and helps keep the variability low. This helps to keep the validity of the results higher, while still allowing for smaller than usual subject groups. 
A disadvantage to the repeated measure design is that it may not be possible for each participant to be in all conditions of the experiment (i.e. time constraints, location of experiment, etc.). The second disadvantage is the findings may not generalize to the public because of the amount of participants. Lastly analysis is more difficult because participants are in the control and experimental groups, therefore the participants’ observations might be altered .
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- Shaughnessy, J. J. (2006). Research Methods in Psychology. New York: McGraw-Hill.
- Wendorf, C. A. (1997). Part V: Analysis of Variance (ANOVA). Retrieved February 18, 2008, from MANUALS FOR UNIVARIATE AND : http://www.uwsp.edu/psych/cw/statmanual/rmdanova.html
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