Dimensionality, Reliability, and Validity of Academic Resilience Scales: A Systematic Review and Meta-analysis

Abstract

Abstract Academic resilience refers to students’ capacity to maintain or regain adaptive academic functioning when confronted with academic stressors, setbacks, failure, or threat. Although the construct is often considered central to educational psychology, its psychometric foundations remain fragmented. This systematic review and meta-analysis synthesized evidence from 46 studies evaluating academic resilience and closely related academic buoyancy measures. Evidence on dimensionality was mixed. Unidimensional models showed higher pass rates for CFI, TLI, and SRMR, whereas multidimensional models showed a higher RMSEA pass rate; however, exploratory Welch comparisons showed significant differences for CFI and TLI, but not for RMSEA or SRMR, indicating that the available global-fit evidence does not clearly favor either model family. Pooled internal consistency was high, α/ $$\omega$$ = 0.89, 95% CI [0.86, 0.90], 95% PI [0.62, 0.97], although heterogeneity was substantial. Construct validity evidence showed a moderate pooled association with theoretically relevant external criteria, r = .55, 95% CI [0.46, 0.63], 95% PI [-0.19, 0.89], with substantial variability across outcome domains. After direction alignment, associations were strongest for distress and vulnerability, learning strategy/self-regulation, and resilience-aligned constructs, and weakest for academic achievement. Overall, the findings suggest that current academic resilience measures are reliable and show theoretically coherent validity patterns. Dimensionality evidence indicates that many measures may be dominated by a broad general resilience factor, but the available global-fit comparisons do not establish whether proposed subdimensions provide differentiated explanatory, diagnostic, or predictive value beyond composite scores. Future research should further clarify construct boundaries, strengthen content-validity evidence, and directly compare competing measurement models.