In contrast to between subjects designs, studies that deploy within subjects designs rely upon repeated measures, such that all participants are presented with all levels of the independent variable. This is beneficial in that it requires fewer participants than between subjects designs, as we obtain more data from each participant. Rather than having a separate control group, in repeated measures studies, participants serve as their own control, looking at changes across a dependent variable after presentation of the different levels of the independent variable.
However, repeated measures designs are subject to a number of concerns or shortcomings. One of these is demand characteristics, in which, having figured out the purpose of the study, participants try to perform the role of “good participant.” Additionally, attrition is a concern for within subjects designs, as they require more commitment from the participant. Moreover, one condition might impact participants’ responses in other conditions, resulting in carry-over effects that are not present when participants are exposed to a single level of the independent variable. Finally, if the different parts of the study take place over an extended period of time or at different times of day, this can introduce other confounding variables into the design of the study, creating validity issues.
To interpret the results from a within subjects perspective, researchers would consider the change in the dependent variable following presentation of the different levels of the independent variable. If the independent variable has only two levels, they would conduct a t-test, which tells them if any differences in the dependent variable are reliable. Similarly to between subjects designs, when the study has more than two levels of the independent variable, one would look at the ratio of variability between groups with the variability within groups. The greater this value, the more likely they have a significant condition effect.
Additionally, researchers often use mixed measures designs, which combine within subjects measures and between subjects measures. Although this is a more complicated statistical analysis, this strategy often provides researchers with a better picture of the psychological processes that they are hoping to understand.
Additional Online Resources
Should within-subjects or between-subjects designs be employed? http://www.yorku.ca/mack/RN-Counterbalancing.html
Article challenging the criticism that within-subjects designs are transparent: http://journal.sjdm.org/9921/jdm9921.pdf
Video about carryover effects, and how counterbalancing can help: http://education-portal.com/academy/lesson/carryover-effects-how-they-can-be-controlled-through-counterbalancing.html#lesson
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Interactive Quiz for Chapter 10
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