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Activity Number:
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70
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #309715 |
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Title:
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Hierarchical Bayes Small-Area Estimates of Adult Literacy Using Unmatched Sampling and Linking Models
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Author(s):
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Leyla Mohadjer*+ and J. N. K. Rao and Benmei Liu and Thomas Krenzke and Wendy VanDeKerckhove
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Companies:
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Westat and Carleton University and Westat and Westat and Westat
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Address:
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1650 Research Blvd, Rockville, MD, 20850,
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Keywords:
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Small area estimation ; Hierarchical Bayes estimates ; Monte Carlo Simulation ; Item Response Theory ; Generalized Variance Functions
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Abstract:
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Funded by the National Center for Education Statistics, the National Assessment of Adult Literacy (NAAL) was designed to measure the English literacy skills of adults in the U.S. based on an assessment containing a series of literacy tasks completed by sampled adults. Sufficiently precise estimates have been produced for the nation and major subdomains of interest using the NAAL data. However, policymakers and researchers/business leaders often need literacy information for states and counties but these areas do not have large enough samples to produce reliable estimates. Therefore, small area estimation techniques are used to produce estimates of literacy levels for all states and counties in the nation. This paper describes the Hierarchical Bayesian estimation techniques used to derive a single area-level linking model to produce both county and state estimates, and credible intervals.
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