Methodological challenges to confirmatory latent variable models of social vulnerability

Socially vulnerable communities experience disproportionately negative outcomes following natural disasters and underscoring a need for well-validated measures to identify those at risk. However, questions have surfaced regarding the factor structure, internal consistency, and generalizability of social vulnerability measures. A reliance on data-driven techniques, which are susceptible to sample-specific characteristics, has likely exacerbated the difficulty generalizing social vulnerability measures across contexts. This study sought to validate previously published structures of SoVI using confirmatory factor analysis (CFA). We fit CFA models of 28 sociodemographic variables frequently used to calculate a commonly used measure, the social vulnerability index (SoVI), drawn from the American Community Survey across 4162 census tracts in Florida. Confirmatory models generally did not support theory-driven pillars of SoVI that were previously used to study vulnerability in the New York metropolitan area. Modified models and alternative SoVI factor structures also failed to fit the data. Many of the input variables displayed little to no variability, limiting their utility and explanatory power. Taken together, our results highlight the poor generalizability of SoVI across contexts, but raise several important considerations for reliability and validity, as well as issues related to source data and scale. We discuss the implications of these findings for improved theory-driven measurement of social vulnerability.