We investigated three different approaches for quantifying individual change and reporting it back to persons: (a) the common change score, which is obtained by first computing scale scores from two consecutive measurements and then subtract these scores from one another, (b) the ad-hoc approach, which is similar to the former approach but uses regression scores instead of scale scores, and (c) Kelley's approach, which computes the change score directly from a regression. Specifically, we compared these approaches with one another with regard to the mean squared error (MSE), a measure of the accuracy of an estimator. Our findings indicated that the ad-hoc approach and Kelley's approach provide change scores that can be more accurate in terms of the MSE than the common change score and that Kelley's approach can be more accurate than the ad-hoc approach under certain conditions. Moreover, we present an example and provide a step by step guide to implement the approaches.