JSM 2004 - Toronto

Abstract #300145

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Activity Number: 391
Type: Invited
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #300145
Title: Design-based Methods for Survey Data: Alternative Uses of Estimating Functions
Author(s): David A. Binder*+ and Milorad S. Kovacevic and Georgia R. Roberts
Companies: Statistics Canada and Statistics Canada and Statistics Canada
Address: Methodology Branch, Ottawa, ON, K1A 0T6, Canada
Keywords: design-based variances ; confidence intervals ; linearization methods
Abstract:

The appropriateness of using design-based methods to analyze survey data is now well recognized. Recent research has shown that design-based methods offer some protection against model misspecification and informative sample designs. The popularity of design-based methods has grown, now that software has been developed to make such methods more accessible to data analysts. Choices for estimating the design-based variances of estimated model parameters include linearization, balanced repeated replication, jackknifing, and bootstrapping. However, the use of survey bootstraps suffers from some of the same deficiencies as the standard bootstrap; in particular, the estimated variances can be unstable in certain circumstances. Recently, methods have been developed for making inferences using the estimating function bootstrap in a model-based setting. This approach seems provide more stable results. We adapt these methods to create a design-based estimating function survey bootstrap (EFSB). We study the EFSB and a linearized version of the EFSB. Results using real survey data collected by Statistics Canada are given.

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