Home > Research . Search . Country . Browse . Small Grants

PSC In The News

RSS Feed icon

Bailey and Danziger's War on Poverty book reviewed in NY Review of Books

Bloomberg cites MTF data in story on CDC's anti-smoking ads for e-cigarettes

Bound says notion that foreign college students are displacing U.S. students "isn't right"

Highlights

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

PSC Fall 2014 Newsletter now available

Next Brown Bag

Mon, April 6
Jinkook Lee, Wellbeing of the Elderly in East Asia

Michael R. Elliott photo

Hierarchical Bayesian Analysis of Complex Sample Survey Data

a PSC Research Project

Investigators:   Michael R. Elliott, Trivellore Raghunathan

The proposed research will use hierarchical Bayesian modeling to tackle three interrelated problems in the analysis of population-based survey data: accounting for unequal probabilities of inclusion due to sample design or post-sampling non-response; accounting for non-ignorable missingness in item-level data; and combining information from multiple complex survey data sets to obtain more accurate and efficient estimates of the population quantities. We intend to develop robust models that can provide “data-driven” weight trimming procedures for a general class of population statistics under a variety of sample designs; develop selection models that accommodate non-ignorable missingness mechanisms in the context of complex survey designs; and combine data from multiple surveys by creating synthetic populations from each survey and then combining these populations across surveys to develop combined estimates. While our methods will be applicable to a wide variety of analytic procedures, we will focus on small area or small domain estimation in particular, since the issues that this proposal intends to address are often most acute in the setting.

Funding Period: 07/17/2009 to 08/31/2013

Search . Browse