Improving and Validating Survey Estimates of Religious Demography Using Bayesian Multilevel Models and Poststratification

Christopher Claassen and Richard Traunmüller

Sociological Methods & Research, 2020, vol. 49, issue 3, 603-636

Abstract: Religious group size, demographic composition, and the dynamics thereof are of interest in many areas of social science including migration, social cohesion, parties and voting, and violent conflict. Existing estimates however are of varying and perhaps poor quality because many countries do not collect official data on religious identity. We propose a method for accurately measuring religious group demographics using existing survey data: Bayesian multilevel regression models with poststratification. We illustrate this method by estimating the demography of Muslims, Hindus, and Jews in Great Britain over a 20-year period and validate it by comparing our estimates to UK census data on religious demography. Our estimates are very accurate, differing from true population proportions by as little as 0.29 (Muslim) to 0.04 (Jewish) percentage points. These findings have implications for the measurement of religious demography as well as small group attributes more generally.

Keywords: religion; United Kingdom; demography; survey methods; multilevel regression with poststratification (MRP); Bayesian methods (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124118769086 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:49:y:2020:i:3:p:603-636

DOI: 10.1177/0049124118769086

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().