This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 685
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #307469
Title: Using Call-Back Data to Adjust for Nonignorable Nonresponse: Results of an Empirical Study
Author(s): Paul Biemer*+ and Kevin Wang and Patrick Chen
Companies: RTI International and RTI International and RTI International
Address: , Research Triangle Park, NC, ,
Keywords: selectivity bias ; drug use ; EM algorithm ; ML estimation ; paradata

The number of call attempts can be used in response propensity models to reduce nonignorable nonresponse bias in sample surveys. In applying these models, a number of issues arise from the definition of process variables like "call attempt," "contact/noncontact,", and "nonresponse" to callback censoring, unrecorded call attempts, and other data errors. As will be demonstrated, these problems can reduce the fit and utility of the models. Despite these problems, callback models can substantially reduce the nonignorable nonresponse bias, particularly when data on nonrespondents are quite limited. This presentation will review some approaches for employing callback models for nonresponse bias adjustment and analysis. Some of the problems in using call-back data in nonresponse adjustments will be described and the potential of the approach will be demonstrated for a large field survey.

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