Methods: Following content validation by a panel of experts, a cross-sectional study was conducted to test the psychometric properties of DARS (43 items) with a sample of 625 U.S. participants who had experienced a disaster in the last three years. Data analysis examined sample characteristics, factor structure, reliability, and construct validity using R software and R packages (‘lavaan’ and ‘psych’). A confirmatory factor analysis (CFA) of the measurement structure tested the relationships between latent variables and indicators using a maximum likelihood estimation with robust standard errors. To obtain standardized, unit-free estimates that reflect the indicator reliabilities, the scale was set by the fixed factor method which fixes the latent variance to one (e.g., ψ= 1.0). After the overall structure of DARS was determined from the CFA, reliability was estimated for the global scale and subscales using coefficient omega (ω).
Results: Descriptive statistics reported 53% of the sample was female, 63% was White, and mean age for participants was 32.49. For disaster type, 68% of the sample experienced hurricanes, followed by tornadoes, wildfires, and floods at 9%. CFA results supported a five-factor model (43 items): χ2(849) = 1953.194, p < .01, CFI= .91, TLI = .91, RMSEA= .05 [CI .050-0.056], SRMR= .05 and all item loadings were found to be within good ranges. Omega coefficient reliability estimates were .97 for the global scale (43 items) and each of the subscales ranged from .91 to .95.
Conclusion: This study provides evidence of a robust, reliable, and empirically derived disaster resilience measure that informs further understanding of the mechanisms and resources assisting individuals in adaptation following disaster events. To date, measures of disaster resilience have not included individual-level assessment and DARS will be able to help policy-makers and stakeholders make informed decisions and invest in strengthening responses to the human impacts of environmental change. Findings from this study can also assist social workers in identifying protective factors that guide a framework for practice models to build resilience in disaster settings and provide evaluation of the efficacy of such practice models.