title: Examining the reliability of interval level data using root mean square differences and concordance correlation coefficients.
abstract: This article introduces new statistics for evaluating score consistency. Psychologists usually use correlations to measure the degree of linear relationship between 2 sets of scores, ignoring differences in means and standard deviations. In medicine, biology, chemistry, and physics, a more stringent criterion is often used: the extent to which scores are identically equal. For each test taker (or other unit of measurement), the difference between the 2 scores is calculated. The root mean square difference (RMSD) represents the average change from 1 set of scores to the other, and the concordance correlation coefficient (CCC) rescales this coefficient to have a maximum value of 1. This article shows the relationship of the RMSD and CCC to the intraclass correlation coefficients, product-moment correlation, and standard error of measurement. Finally, this article adapts the RMSD and the CCC for linear, consistency, and absolute definitions of agreement.
authors.affiliation: Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Parkway, P.O. Box 455030, Las Vegas, NV 89154-5030, USA. barchard@unlv.nevada.edu
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Barchard
mesh_terms: D002648:Child; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D011584:Psychology / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*