Abstract Details
Activity Number:
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540
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Survey Research Methods Section
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Abstract - #309794 |
Title:
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Using Imputation Procedures to Enhance the DSF Frame
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Author(s):
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Ashley Amaya*+ and Katie Dekker and Felicia LeClere
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Companies:
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NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago
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Keywords:
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Delivery Sequence File ;
address-based sampling ;
drop point ;
imputation ;
frame coverage
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Abstract:
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Previous research on the United States Postal Service's Delivery Sequence File (DSF) has found it to have high national population coverage rates (95-97%) (see Fahini 2009). This coverage varies, however, by survey mode. Some addresses are not complete enough to mail to or match to a telephone number.
This presentation focuses on how to impute missing address information for incomplete addresses on the frame. Specifically, we target drop point addresses since they are known to be clustered in minority neighborhoods within older cities (e.g., Chicago, New York, Boston) (Dekker, English, & Amaya, 2012) and, therefore, prone to coverage bias. Drop points are addresses with street number and name but are missing a unit number (e.g., 123 Main St., instead of 123 Main St. #1). We applied a number of imputation techniques to assign unit numbers to the otherwise complete address. We validate the imputed results against field listings to assess whether the models produce the correct unit number or if the resulting numbering scheme was 'close enough' to make mail delivery likely.
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Authors who are presenting talks have a * after their name.
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