In repeated measures ANOVA, the independent variable has categories called levels or related groups.
The important point with these two study designs is that the same people are being measured more than once on the same dependent variable (i.e., why it is called repeated measures). For (2), you might get the same subjects to eat different types of cake (chocolate, caramel and lemon) and rate each one for taste, rather than having different people taste each different cake.
For example, for (1), you might be investigating the effect of a 6-month exercise training programme on blood pressure and want to measure blood pressure at 3 separate time points (pre-, midway and post-exercise intervention), which would allow you to develop a time-course for any exercise effect. Studies that investigate either (1) changes in mean scores over three or more time points, or (2) differences in mean scores under three or more different conditions. We can analyse data using a repeated measures ANOVA for two types of study design. The dependent variable needs to be continuous (interval or ratio) and the independent variable categorical (either nominal or ordinal). This particular test requires one independent variable and one dependent variable. There are many complex designs that can make use of repeated measures, but throughout this guide, we will be referring to the most simple case, that of a one-way repeated measures ANOVA. All these names imply the nature of the repeated measures ANOVA, that of a test to detect any overall differences between related means. A repeated measures ANOVA is also referred to as a within-subjects ANOVA or ANOVA for correlated samples. Repeated measures ANOVA is the equivalent of the one-way ANOVA, but for related, not independent groups, and is the extension of the dependent t-test.