Abstract
To make transparent individuals' responses to intervention over time in the systematic review of single-case experimental designs, we developed a method of estimating and graphing fine-grained effect sizes. Fine-grained effect sizes are both case- and time-specific and thus provide more nuanced information than effect size estimates that average effects across time, across cases, or both. We demonstrate the method for estimating fine-grained effect sizes under three different baseline stability assumptions: outcome stability, level stability, and trend stability. We then use the method to graph individual effect trajectories from three single-case experimental design studies that examined the impact of self-management interventions on students identified with autism. We conclude by discussing limitations associated with estimating and graphing fine-grained effect sizes and directions for further development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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