Assessments of objective and subjective resilience are the main methods for understanding disaster preparedness and developing policy responses. However, the two assessment outcomes are not directly comparable. A model combining objective and subjective resilience assessments provides a better approximation of the underlying resilience. Building on previous comparisons, we have developed a robust Bayesian hierarchical model for comparing and linking these assessments. Three modeling scenarios provide the opportunity to explore the complex relationship between resilience data and explanatory variables under different conditions and prior knowledge about variables. Our strategy reasonably shares the uncertainty with the posterior distribution of all model parameters, instead of leaving all uncertainties to the variance parameter estimation, thus reducing methodological bias and providing a robust uncertainty estimation. The approach allows connecting and comparing objective and subjective resilience assessments, laying a strong foundation for developing a fully integrated resilience assessment.