Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

6-2016

Journal or Book Title

Survey Methodology

Volume

42

Issue

1

First Page

19

Last Page

40

Abstract

Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent expectation-maximisation (EM) sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.

Comments

This article is published as J.K. Kim, E, Berg, and T. Park. (2016). “Statistical matching using fractional imputation”. Survey Methodology, 42, 19–40. Published with permission.

Rights

Source: Statistics Canada; Survey Methodology; June 22, 2016. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.

Copyright Owner

Minister of Industry

Language

en

File Format

application/pdf

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