This chapter introduces fundamental statistical methods for analyzing change over time within the same group, emphasizing dependent measures. Researchers often measure repeated outcomes, such as student reading scores across a school year, to determine whether scores change over multiple time points. For comparisons between two time points, the dependent (paired) samples t-test provides a straightforward method, while repeated measures ANOVA extends this approach to three or more time points. Key assumptions (including independence of observations, normality of differences, and scale-level measurement) are discussed, along with strategies for checking these assumptions in R using histograms, Q-Q plots, and Shapiro-Wilk tests. If assumptions are violated, this chapter introduces nonparametric Wilcoxon V for paired samples and Friedman’s test for repeated measures as well as non-parametric post-hoc and effect size tests.