When we want to compare two groups, we would use the t-test for independent samples (in Basic Statistics and Tables); when we want to compare two variables given the same subjects (observations), we would use the t-test for dependent samples. This distinction (dependent and independent samples) is important for ANOVA as well. Basically, if we have repeated measurements of the same variable (under different conditions or at different points in time) on the same subjects, then the factor is a repeated measures factor (also called a within-subjects factor, because to estimate its significance we compute the within-subjects SS). If we compare different groups of subjects (e.g., males and females; three strains of bacteria, etc.) then we refer to the factor as a between-groups factor. The computations of significance tests are different for these different types of factors; however, the logic of computations and interpretations is the same.

Between-within designs. In many instances, experiments call for the inclusion of between-groups and repeated measures factors. For example, we may measure math skills in male and female students (Gender, a between-groups factor) at the beginning and the end of the semester. The two measurements on each student would constitute a within-subjects (repeated measures) factor. The interpretation of main effects and interactions is not affected by whether a factor is between-groups or repeated measures, and both factors may obviously interact with each other (e.g., females improve over the semester while males deteriorate).

GLM. Note that the General ANOVA/MANOVA module only allows one within-subject (repeated measures) factor. If your design has multiple within-subject (repeated measures) factors, you need to use the General Linear Models module.