Coordinated analysis is a powerful form of integrative analysis, and is well suited in its capacity to promote cumulative scientific knowledge, particularly in subfields of psychology that focus on the processes of lifespan development and aging. Coordinated analysis uses raw data from individual studies to create similar hypothesis tests for a given research question across multiple datasets, thereby making it less vulnerable to common criticisms of meta-analysis such as file drawer effects or publication bias. Coordinated analysis can sometimes use random effects meta-analysis to summarize results, which does not assume a single true effect size for a given statistical test. By fitting parallel models in separate datasets, coordinated analysis preserves the heterogeneity among studies, and provides a window into the generalizability and external validity of a set of results. The current article achieves three goals: First, it describes the phases of a coordinated analysis so that interested researchers can more easily adopt these methods in their labs. Second, it discusses the importance of coordinated analysis within the context of the credibility revolution in psychology. Third, it encourages the use of existing data networks and repositories for conducting coordinated analysis, in order to enhance accessibility and inclusivity. Subfields of research that require time- or resource- intensive data collection, such as longitudinal aging research, would benefit by adopting these methods. (PsycInfo Database Record (c) 2022 APA, all rights reserved).