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Activity Number: 534
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #307487
Title: Confidence Interval Coverage in Stratified Sample Design
Author(s): Jinhee Yang*+ and Wendy Rotz
Companies: Ernst & Young LLP and Ernst & Young LLP
Address: 1225 Connecticut Ave., Washington, DC, 20036,
Keywords: confidence interval ; model-based estimation ; deep stratification ; goodness-of-fit
Abstract:

In some business settings, the cost of reviewing a sample is extremely high, giving rise to the need for an accurate estimate and narrow confidence interval using a very small sample size. When there is a strongly related auxiliary variable, model-based estimation with deep stratification may be used. Generally, deep stratification tends to reduce the sampling error and is expected to produce conservative confidence intervals. This paper explores whether there are situations when the confidence interval coverage is too conservative, or not as robust as believed. We will study the effect of population distribution, model, model fit, and degrees of freedom on the performance of confidence interval coverage.

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