Some Statistical Methods in Assessing Linear Relational Agreement: An Empirical Comparative Study

Abstract

Although multiple indices were introduced in the area of agreement measurements, the only documented index for linear relational agreement, which is for interval scale data, is the Pearson product-moment correlation coefficient. Despite its meaningfulness, the Pearson product-moment correlation coefficient does not convey the practical information such as what proportion of observations is within a certain boundary of the target value. To address this need, based on the inverse regression, we proposed the adjusted mean squared deviation (AMSD), adjusted coverage probability (ACP), and adjusted total deviation index (ATDI) for the measurement of the relational agreement. They can serve as reasonable and practically meaningful measurements for relational agreement. Real life data are considered to illustrate the performance of the methods.

Keywords:

• Coverage probability
• Mean squared deviation
• Pearson's correlation
• Relational agreement
• Reverse regression

Mathematics Subject Classification:

• 62F10
• 62F99

Acknowledgments

The authors are grateful to the referees for their constructive comments and valuable suggestions which certainly improved the presentation and quality of the article. The authors are thankful to the SAS macro available for download at http://www.uic.edu/~hedayat/.