An Algorithm to Evaluate Methodological Rigor and Risk of Bias in Single-Case Studies

Abstract

Critical appraisal scales play an important role in evaluating methodological rigor (MR) of between-groups and single-case designs (SCDs). For intervention research this forms an essential basis for ascertaining the strength of evidence. Yet, few such scales provide classifications that take into account the differential weighting of items contributing to internal validity. This study aimed to develop an algorithm derived from the Risk of Bias in N-of-1 Trials (RoBiNT) Scale to classify MR and risk of bias magnitude in SCDs. The algorithm was applied to 46 SCD experiments. Two experiments (4%) were classified as Very High MR, 14 (30%) as High, 5 (11%) as Moderate, 2 (4%) as Fair, 2 (4%) as Low, and 21 (46%) as Very Low. These proportions were comparable to the What Works Clearinghouse classifications: 13 (28%) met standards, 8 (17%) met standards with reservations, and 25 (54%) did not meet standards. There was strong association between the two classification systems.