ERIC Number: ED588818
Record Type: Non-Journal
Publication Date: 2018
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
Available Date: N/A
Bayesian Inference under Cluster Sampling with Probability Proportional to Size
Makela, Susanna; Si, Yajuan; Gelman, Andrew
Grantee Submission
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 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. [This is the online version of an article published in "Statistics in Medicine."]
Publication Type: Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); National Institutes of Health (DHHS); Institute of Education Sciences (ED); Office of Naval Research (ONR)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: MMSSES1534400; MMSSES1534414; R21DK110688; R305D140059; N000141512541; N000141712141
Author Affiliations: N/A