Andrew Conway is a Psychology Professor in the Division of Behavioral and Organizational Sciences at Claremont Graduate University in Claremont, California. He has been teaching introduction to statistics for undergraduate students and advanced statistics for graduate students for 20 years, at a variety of institutions, including the University of South Carolina, the University of Illinois in Chicago, and Princeton University.
This course focuses on within-groups comparisons and repeated measures design. With the help of a working memory training experiment, one of Professor Conway’s main areas of research, it will be explained what the pros and cons are of a repeated measures design and how to conduct the calculations in R yourself.
In this chapter, Professor Conway will review the pros and cons of a repeated measures design. There are many benefits to conducting experiments in this way, but there are also some issues that you need to take into consideration. For example, the lower cost and increased statistical power of a repeated measures design are great, but you need to take into account things like order effects, counterbalancing and missing data. This chapter will show you how!
This chapter is very hands-on. You will walk through a full example of a repeated measures ANOVA experiment starting with systematic and unsystematic variances, followed by the F-ratio and p-value, conducting post-hoc tests, and concluding with some final thoughts.