JSM 2004 - Toronto

Abstract #300559

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Activity Number: 278
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #300559
Title: Optimal Sample Allocation for Design-consistent Regression
Author(s): Hui Zheng*+ and Alan Zaslavsky and John L. Adams
Companies: Harvard Medical School and Harvard Medical School and RAND Corporation
Address: 180 Longwood Ave., Boston, MA, 02115,
Keywords: descriptive population quantity ; survey ; measurement error
Abstract:

We consider optimal sampling rates when the anticipated analysis is survey-weighted linear regression and the estimands of interest are combinations of regression coefficients from one or more models. Methods are first developed assuming that exact design information is available in the sampling frame and then generalized to situations in which some design variables are available only as aggregates for groups of potential subjects, or from inaccurate or old data. We also consider design for estimation of combinations of coefficients from more than one model. A further generalization allows for flexible combinations of coefficients chosen to improve estimation of one effect while controlling for another. We illustrate the potential gains from using these methods with simulated continuous variables. Potential applications include estimation of means for several sets of overlapping domains, or improving estimates for subpopulations such as minority races by disproportionate sampling of geographic areas.

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Revised March 2004