Bayesian inference under cluster sampling with probability proportional to size

Publication Abstract

Makela, Susanna, Yajuan Si, and Andrew Gelman. 2018. "Bayesian inference under cluster sampling with probability proportional to size." Statistics in Medicine, 37(26): 3849-3868.

Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to cluster size, and then units are randomly sampled inside selected clusters. Challenges arise when the sizes of the nonsampled cluster are unknown. We propose nonparametric and parametric Bayesian approaches for predicting the unknown cluster sizes, with this inference performed simultaneously with the model for survey outcome, with computation performed in the open-source Bayesian inference engine Stan. Simulation studies show that the integrated Bayesian approach outperforms classical methods with efficiency gains, especially under informative cluster sampling design with small number of selected clusters. We apply the method to the Fragile Families and Child Wellbeing study as an illustration of inference for complex health surveys.



Browse | Search | Next

PSC In The News

RSS Feed icon

Shaefer comments on the Cares Act impact in negating hardship during COVID-19 pandemic

Heller comments on lasting safety benefit of youth employment programs

More News


Dean Yang's Combatting COVID-19 in Mozambique study releases Round 1 summary report

Help Establish Standard Data Collection Protocols for COVID-19 Research

More Highlights

Connect with PSC follow PSC on Twitter Like PSC on Facebook