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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

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