A statistical procedure for the estimation of accuracy parameters in interlaboratory studies

Abstract

Interlaboratory studies are conducted to estimate the accuracy of methods of laboratory measurements. The standard parameters used to describe this accuracy are the repeatability and the reproducibility. Usually variance components models are used to estimate these parameters. If the model assumptions are violated the resulting estimates for reproducibility and repeatability may, however, be biased. A new method of residual analysis in variance components models--developed by the author--may be used to detect violations of the model assumptions. If the residual analysis indicates that the model assumptions are violated, a simple robust method--which makes fewer assumptions--may be used for the estimation of accuracy parameters. The application of this residual analysis is demonstrated using data of an interlaboratory study. Graphical methods play an important role in the evaluation of the residuals. The analysis of the residuals uses methods similar to those used for the analysis of Studentized residuals in the linear model. The estimates obtained by the variance components model and the simple robust method are compared. The results of the residual analysis may be used to decide which of the two estimates can be considered more appropriate. The necessity of residual analysis in the analysis of interlaboratory studies by variance components models is pointed out. Potential hazards inherent to residual analysis in variance components models are discussed. Conclusions for the analysis of interlaboratory studies are drawn.