biostudies-literatureVirgili-Gervais GWellcome Trust859-874https://www.ebi.ac.uk/biostudies/studies/S-EPMC7617442biostudies-literatureUnknown188(3)We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn's ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. <i>Political Analysis, 12</i>(4), 338-353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting's ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. <i>Journal of Agricultural, Biological, and Environmental Statistics, 18</i>(4), 492-513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the Journal of the Royal Statistical Society. Series A, (Statistics in Society)Mapping socio-economic status using mixed data: a hierarchical Bayesian approach.PMC7617442209376/Z/17/ZBixby HBaumgartner JSchmidt AMOwusu GVirgili-Gervais GAgyei-Mensah SCavanaugh ARobinson BfalseMapping socio-economic status using mixed data: a hierarchical Bayesian approach.We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn's ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. <i>Political Analysis, 12</i>(4), 338-353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting's ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. <i>Journal of Agricultural, Biological, and Environmental Statistics, 18</i>(4), 492-513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the 2025-01-01T00:00:00Z2025 Jul2026-03-12T22:08:09.277Z2025-04-04T02:05:06.19ZS-EPMC76174424002644510.1093/jrsssa/qnae080