Successfully blends the currently available statistical methodologies for agreement evaluation in a unified, coherent, and lucid manner. This up-to-date and comprehensive book describes the theoretical underpinnings of the methodologies and presents case studies using several real data sets to illustrate the application of the methodologies. Presents statistical methodologies for analyzing common types of data from method comparison experiments and illustrates their applications through detailed case studies Measuring Agreement: Models, Methods, and Applications features statistical evaluation of agreement between two or more methods of measurement of a variable with a primary focus on continuous data. The authors view the analysis of method comparison data as a two-step procedure where an adequate model for the data is found, and then inferential techniques are applied for appropriate functions of parameters of the model. The presentation is accessible to a wide audience and provides the necessary technical details and references. In addition, the authors present chapter-length explorations of data from paired measurements designs, repeated measurements designs, and multiple methods; data with covariates; and heteroscedastic, longitudinal, and categorical data. The book also: Strikes a balance between theory and applications Presents parametric as well as nonparametric methodologies Provides a concise introduction to Cohen's kappa coefficient and other measures of agreement for binary and categorical data Discusses sample size determination for trials on measuring agreement Contains real-world case studies and exercises throughout Provides a supplemental website containing the related datasets and R code Measuring Agreement: Models, Methods, and Applications is a resource for statisticians and biostatisticians engaged in data analysis, consultancy, and methodological research. It is a reference for clinical chemists, ecologists, and biomedical and other scientists who deal with development and validation of measurement methods. This book can also serve as a graduate-level text for students in statistics and biostatistics.