Eckes Thomas Eckes

Introduction to Many-Facet Rasch Measurement (eBook, PDF)

• Format: PDF

Produktbeschreibung

Human ratings are subject to various forms of error and bias. Since the early days of performance assessment, this problem has been sizeable and persistent. For example, expert raters evaluating the quality of an essay, an oral communication, or a work sample, often come up with different ratings for the very same performance. In cases like this, assessment outcomes largely depend upon which raters happen to provide the rating, posing a threat to the validity and fairness of the assessment. This book provides an introduction to a psychometric approach that establishes a coherent framework for drawing reliable, valid, and fair inferences from rater-mediated assessments, thus answering the problem of inevitably fallible human ratings: many-facet Rasch measurement (MFRM). Throughout the book, sample data taken from a writing performance assessment are used to illustrate key concepts, theoretical foundations, and analytic procedures, stimulating the readers to adopt the MFRM approach in their current or future professional context.

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Produktdetails

• Verlag: Peter Lang GmbH, Internationaler Verlag der Wissenschaften
• Erscheinungstermin: 16. Mai 2023
• Englisch
• ISBN-13: 9783631903056
• Artikelnr.: 69609066

Autorenporträt

Thomas Eckes is Head of the Psychometrics and Research Methodology Department at the TestDaF Institute, University of Bochum. He has taught and published widely in the field of language testing, educational and psychological measurement, and multivariate data analysis. His research interests include rater effects in large-scale assessments, standard setting, and web-based testing.

Inhaltsangabe

Conceptual-psychometric framework for rater-mediated performance assessments - Foundations of many-facet Rasch measurement - Measurement of rater severity/leniency - Correcting examinee proficiency estimates for rater severity differences - Examining rater consistency and rating scale effectiveness - Increasing validity and fairness of performance assessments.