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Minimizing Duplication of Samples Drawn from Overlapping Frames of Multiple Surveys

By Peter K. Kwok, Hee-choon Shin, Whitney Murphy, Angela Debello and Kari Carris

Abstract

REACH U.S. (Racial and Ethnic Approaches to Community Health Across the United States) is a CDC’s community-based initiative to eliminate health disparities among various racial and ethnic groups. Five of the 28 participating communities are located within Los Angeles and Orange Counties, California with complicated overlapping among their geographies, and have different scientific interests and eligibility requirements. Although all respondents are asked the same questions, those communities did not intend to share any completed interview at the time of this writing. Therefore, this is a multiple-frame, multiple-survey problem that requires samples to be independently drawn from overlapping areas. We will discuss how the address-based sampling design can meet this demand and what challenges it faces. In particular, we present an estimation algorithm that aims to minimize the impact of sample de-duplication on the independence assumption. Keywords. Address-based sampling, REACH U.S., sample overlap problem, independent sampling, sample de-duplication Introduction and Problem Statement This paper will discuss a new type of optimization problem, called the Sample De-duplication Problem, with the objective of searching for an optimal order of drawing samples from overlapping frames in order to address a certain sampling issue that arises from multiple surveys. It is a variation of the traditional sample overlap problem, but is restricted to situations when sample overlap is a result of random sampling and is open to minimization or even elimination. This excludes the cases that ar

Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.178.5770
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