Correcting Measurement Error in Content Analysis *Top Faculty Paper

• In Event: Methodological Advances in Communication Research

Abstract

Conducting and reporting reliability tests has become a standard practice in content analytical research. However, the consequences of measurement error in coding data are rarely discussed or taken into consideration in subsequent analyses. In this article, we demonstrate how misclassification in content analysis leads to biased results and introduce matrix back-calculation as a simple remedy. Using Monte Carlo simulation, we investigate how different ways of collecting information on the misclassification process influence the effectiveness of error correction under varying conditions. The results show that error correction with an adequate set-up can often substantially reduce bias. We conclude with an illustrative example, extensions of the procedure, and some recommendations.

Authors

• Marko Bachl, U of Hohenheim

• Michael Scharkow, Zeppelin U