JSM 2005 Online Program

Abstract #304192

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	73
Type:	Contributed
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	Section on Survey Research Methods
Abstract - #304192
Title:	Application of Confidence Interval Methods for Small Proportions in the Health Care Survey of DoD Beneficiaries
Author(s):	Amang Sukasih*+ and Donsig Jang and Michael Hartzell
Companies:	Mathematica Policy Research, Inc. and Mathematica Policy Research, Inc. and U.S. Department of Defense
Address:	600 Maryland Ave SW Suite 550, Washington, DC, 20024, United States
Keywords:	complex survey ; coverage probability ; simulation
Abstract:	Parameter estimation often is presented in the form of a confidence interval. When data is gathered from a complex survey, confidence interval usually is computed under a normality assumption. However, when the parameter of interest is a proportion and the estimate of proportion is extremely small or large (closed to zero or to one), this approach shows lack of coverage. Alternatively, different approaches have been suggested (Korn and Graubard 1998; Kott, Anderson, and Nerman 2001), such as binomial approach, exact confidence interval, Poisson approach, Logit transformation approach, and Wilson methods. Our paper will evaluate the performance of these methods under a complex survey setting. Application of these methods will be demonstrated with data from the quarterly Health Care Survey of DoD Beneficiaries. Comparison will be done and a simulation will be performed to investigate the performance of each method in terms of coverage probability.

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