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Methodological challenges to confirmatory latent variable models of social vulnerability
Zachary T Goodman1, Caitlin A Stamatis1, Justin Stoler2,3
1Department of Psychology, University of Miami, 5665 Ponce de Leon Blvd, Office 446, Coral Gables, FL 33146-0751 USA.
Natural Hazards (Dordrecht, Netherlands)
|February 22, 2021
Summary
Social vulnerability measures, like the Social Vulnerability Index (SoVI), often fail to generalize across different regions due to data limitations and structural issues. This study found SoVI
Area of Science:
- Disaster Management
- Public Health
- Sociology
Background:
- Socially vulnerable communities face greater risks from natural disasters.
- Existing social vulnerability measures lack consistent validation and generalizability.
- Data-driven approaches can limit the applicability of these measures across diverse contexts.
Purpose of the Study:
- To validate previously published factor structures of the Social Vulnerability Index (SoVI).
- To assess the generalizability of SoVI using confirmatory factor analysis (CFA) in Florida.
- To identify limitations in current social vulnerability measurement.
Main Methods:
- Confirmatory factor analysis (CFA) was employed.
- 28 sociodemographic variables from the American Community Survey were analyzed.
- Data from 4162 census tracts in Florida were used to model SoVI structures.
Main Results:
- Confirmatory models did not support existing theory-driven pillars of SoVI.
- Modified and alternative SoVI factor structures also showed poor data fit.
- Many input variables lacked sufficient variability, impacting their utility.
Conclusions:
- The Social Vulnerability Index (SoVI) demonstrates poor generalizability across different geographical contexts.
- Findings highlight critical issues with the reliability, validity, and source data of social vulnerability measures.
- Improved theory-driven approaches are needed for accurate social vulnerability measurement.
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