Introduction to Many-Facet Rasch Measurement : Analyzing and Evaluating Rater-Mediated Assessments- 2 Nd Revised and Updated Edition

Since the early days of performance assessment, human ratings have been subject to various forms of error and bias. Expert raters often come up with different ratings for the very same performance and it seems that assessment outcomes largely depend upon which raters happen to assign the rating. This book provides an introduction to __many-facet Rasch measurement__ (MFRM), a psychometric approach that establishes a coherent framework for drawing reliable, valid, and fair inferences from rater-mediated assessments, thus answering the problem of fallible human ratings. Revised and updated throughout, the Second Edition includes a stronger focus on the __Facets__ computer program, emphasizing the pivotal role that MFRM plays for validating the interpretations and uses of assessment outcomes. Presentación del editor: "Since the early days of performance assessment, human ratings have been subject to various forms of error and bias. Expert raters often come up with different ratings for the very same performance and it seems that assessment outcomes largely depend upon which raters happen to assign the rating. This book provides an introduction to many-facet Rasch measurement (MFRM), a psychometric approach that establishes a coherent framework for drawing reliable, valid, and fair inferences from rater-mediated assessments, thus answering the problem of fallible human ratings. Revised and updated throughout, the Second Edition includes a stronger focus on the Facets computer program, emphasizing the pivotal role that MFRM plays for validating the interpretations and uses of assessment outcomes." This revised and updated Second Edition provides an introduction to the psychometric analysis and evaluation of rater-mediated assessments using many-facet Rasch measurement (MFRM). Essay rating data are used to illustrate the study of rater errors and biases and their implications for drawing reliable, valid, and fair inferences from assessment outcomes.