|
Activity Number:
|
191
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, July 30, 2007 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Survey Research Methods
|
| Abstract - #308545 |
|
Title:
|
Multiple Imputation Alternatives to Top-Coding for Statistical Disclosure Control
|
|
Author(s):
|
Di An*+ and Roderick J. Little
|
|
Companies:
|
University of Michigan and University of Michigan
|
|
Address:
|
Department of Biostatistics, Ann Arbor, MI, 48109,
|
|
Keywords:
|
confidentiality ; disclosure protection ; multiple imputation
|
|
Abstract:
|
Top-coding of extreme values of variables like income is a common method of statistical disclosure control, but it creates problems for the data analyst. This article proposes two alternative methods to top-coding for SDC based on multiple imputation (MI). We show in simulation studies that the MI methods provide better inferences of the publicly-released data than top-coding, using straightforward MI methods of analysis, while maintaining good SDC properties. We illustrate the methods on data from the 1995 Chinese household income project.
|
