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Activity Number: 304
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #311185
Title: Designing the National Immunization Survey to Account for Geographic Misclassification of the Cell Phone Sample
Author(s): Xian Tao*+ and Nadarajasundaram Ganesh and Kirk Wolter and Laurie Elam-Evans and Meena Khare and Jenny Jeyarajah and David Yankey
Companies: NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago and National Center for Immunization and Respiratory Diseases and NCHS/CDC/DHHS and CDC and CDC
Keywords: geographic inaccuracy ; geographic misclassification ; variance ; design effect ; weight
Abstract:

Samples in cell phone surveys are subject to substantial geographic misclassification that can add bias or variance to survey estimators. The National Immunization Survey (NIS) uses a dual-frame sample to monitor vaccination coverage rates among children. Because NIS selects samples in all areas of the country, geographic misclassification does not create bias in an overall estimate for any given Estimation Area (EA). However, completed interviews from respondents located within a given EA may actually have been sampled in two or more EAs due to geographic misclassification, and therefore can have different probabilities of selection, which in turn can increase the design effect. Using data from the 2012-2013 NIS, we examine a two-step strategy to control the variance of the estimator of the vaccination rate. First, we determine a minimum number of completes that must come from the sampling frame of the target geographical EA and actually be located in the EA. Second, we calibrate the weights to the known numbers of children actually living in the given EA. We demonstrate the extent to which this method can reduce the increase in variance due to geographic misclassification.


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