Integrating five linear trend techniques into performance‐criteria‐based effect size measurements: Impressions and recommendations

Abstract Effect size measurements in single‐case experimental designs are essential for identifying evidence‐based practices; however, each effect size index has notable limitations. To address constraints of nonoverlap‐based indices, Aydin and Tanious (2022) introduced the performance‐criteria‐based effect size (PCES), which quantifies socially meaningful behavioral change. However, the original PCES for trend correction (PCES trend ) uses a basic split‐middle method. This study sought to enhance PCES trend measurements by incorporating five advanced linear trend‐fitting techniques. A total of 87 published graphs with therapeutic baseline trends were analyzed, comparing techniques using performance indicators such as mean absolute scaled error, coefficient of variation, and R 2 , along with visual judgments and ancillary outcomes (e.g., out‐of‐bounds forecasts and missing data imputation). Results revealed that ordinary least squares (OLS) provided the most accurate and reliable adjustments. Based on these findings, PCES calculations with OLS were incorporated into a user‐friendly Google Sheet tool to increase accessibility and facilitate applied use.