Introduction to Many-Facet Rasch Measurement
Regular price
€58.99
14 days return policy
Shipping & Delivery
A01=Thomas Eckes
Age Group_Uncategorized
Age Group_Uncategorized
Analyzing
Author_Thomas Eckes
automatic-update
B09=Günther Sigott
Category1=Non-Fiction
Category=CF
COP=Switzerland
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_dictionaries-language-reference
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Language_English
PA=Available
Price_€50 to €100
PS=Active
softlaunch
Product details
- ISBN 9783631903049
- Weight: 387g
- Dimensions: 148 x 210mm
- Publication Date: 28 Apr 2023
- Publisher: Peter Lang AG
- Publication City/Country: CH
- Product Form: Hardback
- Language: English
Secure
checkout
Fast Shipping
Easy returns
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.
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.
