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General Estimators for the Reliability of Qualitative Data

Published online by Cambridge University Press:  01 January 2025

Bruce Cooil  and
Roland T. Rust
Bruce Cooil*
Affiliation:
Owen Graduate School of Management, Vanderbilt University
Roland T. Rust
Affiliation:
Owen Graduate School of Management, Vanderbilt University
*
Requests for reprints should be sent to Bruce Cooil, Owen Graduate School of Management, Vanderbilt University, 401 21st Avenue South, Nashville, TN 37203. E-mail: cooilb@ctruax.vanderbilt.edu
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Abstract

We study a proportional reduction in loss (PRL) measure for the reliability of categorical data and consider the general case in which each of N judges assigns a subject to one of K categories. This measure has been shown to be equivalent to a measure proposed by Perreault and Leigh for a special case when there are two equally competent judges, and the correct category has a uniform prior distribution. We consider a general framework where the correct category is assumed to have an arbitrary prior distribution, and where classification probabilities vary by correct category, judge, and category of classification. In this setting, we consider PRL reliability measures based on two estimators of the correct category—the empirical Bayes estimator and an estimator based on the judges' consensus choice. We also discuss four important special cases of the general model and study several types of lower bounds for PRL reliability.

Keywords

empirical Bayesintercoder reliabilitylatent classproportional reduction in loss

Information

Type
Original Paper
Information
Psychometrika , Volume 60 , Issue 2 , June 1995 , pp. 199 - 220
Copyright
Copyright © 1995 The Psychometric Society

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Footnotes

Bruce Cooil is Associate Professor of Statistics, and Roland T. Rust is Professor and area head for Marketing, Owen Graduate School of Management, Vanderbilt University. The authors thank three anonymous reviewers and an Associate Editor for their helpful comments and suggestions. This work was supported in part by the Dean's Fund for Faculty Research of the Owen Graduate School of Management, Vanderbilt University.

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