Chapter Summary

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


Flashcards

Test your knowledge of the keywords and definitions in the chapter.

 


Interactive Quiz for Chapter 10

Instructions: For each question, click on the radio button beside your answer. When you have completed the entire quiz, click the “Submit my answers” button at the bottom of the page to receive your results.

Question 1:


a) between subjects design
b) independent samples design
c) consistent samples design
d) repeated measures design

Question 2:


a) low levels of attrition
b) carry-over effects
c) the absence of demand characteristics
d) issues with group consistency

Question 3:


a) requires fewer participants
b) allows for better cross-group comparisons
c) is less time-consuming
d) has lower levels of attrition

Question 4:


a) time of day effects
b) learning effects
c) history effects
d) real-world effects

Question 5:


a) regression to the mean
b) maturation effects
c) history effects
d) real-world effects

Question 6:


a) the order the measures are presented in has been varied for different participants
b) the measures have been tested to ensure that they are in the best order to obtain effects
c) half of the participants will receive half of the measures, the other half will receive the rest of the measures
d) more difficult questions are spread evenly throughout the questionnaire

Question 7:


a) ANOVA
b) f-test
c) t-test
d) null hypothesis test

Question 8:


a) factorial design
b) repeated measures design
c) multi-method design
d) mixed-measures design