JSM 2004 - Toronto

Abstract #301658

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Activity Number: 334
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #301658
Title: Editor Judgment Effect: Modeling a Key Component of Nonsampling Error in Administrative Data
Author(s): Kimberly A. Henry*+ and Yahia Z. Ahmed and Ellen Legel
Companies: Internal Revenue Service and Internal Revenue Service and Internal Revenue Service
Address: Statistics of Income, Washington, DC, 20013,
Keywords: data quality ; reconciliation sample ; bias ; variance component estimation
Abstract:

The presence of sampling and nonsampling errors can significantly affect an agency's national estimates. While sampling errors in Statistics of Income's (SOI) Corporation data are reported and published in the form of Coefficients of Variation (CVs), nonsampling errors are discussed, but not quantified. Instead, they are classified into two types: random errors whose effects may cancel out, and systematic errors whose effects tend to remain relatively fixed, resulting in bias. The abstraction of statistical information from administrative records places SOI's editors in a position to make judgments while recording, cleaning, and processing data. Errors created from these judgments are the largest source of nonsampling error in the corporate data. This paper will attempt to quantify the effect of editor judgment on the data using a quality review reconciliation sample.

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