Studying development processes, as they unfold over time, involves collecting repeated measures from individuals and modeling the changes over time. One methodological challenge in this type of longitudinal data is separating retest effects, due to the repeated assessments, from developmental processes such as maturation or age. In this article, we describe several specifications of latent change score models using age as the underlying time metric and include parameters to account for retest effects. We illustrate the models with data on fluid reasoning collected from children and adolescents in a cohort-sequential design ranging from 6 to 20 years. Our models include alternative approaches to specify retest effects at the structural or measurement level of the model, and as an observed or a latent covariate. We discuss the benefits and limitations of the different approaches for univariate and multivariate data in the context of studying developmental processes.