{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Virgili-Gervais G"],"funding":["Wellcome Trust"],"pagination":["859-874"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7617442"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["188(3)"],"pubmed_abstract":["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":["Journal of the Royal Statistical Society. Series A, (Statistics in Society)"],"pubmed_title":["Mapping socio-economic status using mixed data: a hierarchical Bayesian approach."],"pmcid":["PMC7617442"],"funding_grant_id":["209376/Z/17/Z"],"pubmed_authors":["Bixby H","Baumgartner J","Schmidt AM","Owusu G","Virgili-Gervais G","Agyei-Mensah S","Cavanaugh A","Robinson B"],"additional_accession":[]},"is_claimable":false,"name":"Mapping socio-economic status using mixed data: a hierarchical Bayesian approach.","description":"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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-03-12T22:08:09.277Z","creation":"2025-04-04T02:05:06.19Z"},"accession":"S-EPMC7617442","cross_references":{"pubmed":["40026445"],"doi":["10.1093/jrsssa/qnae080"]}}