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Abstract Details
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Activity Number:
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477
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #304343 |
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Title:
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Calibration Adjustment for Nonresponse in Cross-Classified Data
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Author(s):
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Gretchen Falk*+
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Companies:
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Ernst & Young
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Address:
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1325 1/2 T Street NW, Washington, DC, 20009, United States
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Keywords:
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Quasi-randomization ;
Benchmark ;
Poststratification
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Abstract:
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In the interest of accurately estimating a parameter of interest, generally a population total, calibration is a method that adjusts the sampling weights of each selected element such that the adjusted estimates of the totals of auxiliary, or benchmark, variables equal the known population totals. Calibration has been used to adjust for frame undercoverage, nonresponse, and sampling weights. To treat nonresponse, under the quasi-randomization model assumptions, the sample of respondents is treated as an additional phase of sampling, where the probabilities of response are estimated from a set of model variables. Under this model and varying response probability assumptions, we explore a special case of the calibration method to treat doubly cross-classified data that uses characteristics of the classification structure as the benchmark and model variables. The resulting calibration estim
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