Reliability in Content Analysis

Reliability in Content Analysis In a recent article in this journal, Lombard, Snyder‐Duch, and Bracken (2002) surveyed 200 content analyses for their reporting of reliability tests, compared the virtues and drawbacks of five popular reliability measures, and proposed guidelines and standards for their use. Their discussion revealed that numerous misconceptions circulate in the content analysis literature regarding how these measures behave and can aid or deceive content analysts in their effort to ensure the reliability of their data. This article proposes three conditions for statistical measures to serve as indices of the reliability of data and examines the mathematical structure and the behavior of the five coefficients discussed by the authors, as well as two others. It compares common beliefs about these coefficients with what they actually do and concludes with alternative recommendations for testing reliability in content analysis and similar data‐making efforts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Human Communication Research Oxford University Press

Reliability in Content Analysis

Human Communication Research, Volume 30 (3) – Jul 1, 2004

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Publisher
Oxford University Press
Copyright
Copyright © 2004 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0360-3989
eISSN
1468-2958
DOI
10.1111/j.1468-2958.2004.tb00738.x
Publisher site
See Article on Publisher Site

Abstract

In a recent article in this journal, Lombard, Snyder‐Duch, and Bracken (2002) surveyed 200 content analyses for their reporting of reliability tests, compared the virtues and drawbacks of five popular reliability measures, and proposed guidelines and standards for their use. Their discussion revealed that numerous misconceptions circulate in the content analysis literature regarding how these measures behave and can aid or deceive content analysts in their effort to ensure the reliability of their data. This article proposes three conditions for statistical measures to serve as indices of the reliability of data and examines the mathematical structure and the behavior of the five coefficients discussed by the authors, as well as two others. It compares common beliefs about these coefficients with what they actually do and concludes with alternative recommendations for testing reliability in content analysis and similar data‐making efforts.

Journal

Human Communication ResearchOxford University Press

Published: Jul 1, 2004

References

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