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Introduction to Many-Facet Rasch Measurement

Analyzing and Evaluating Rater-Mediated Assessments. 2nd Revised and Updated Edition


Thomas Eckes

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.
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Preface to the First Edition


This book grew out of times of doubt and disillusionment, times when I realized that our raters, all experienced professionals specifically trained in rating the performance of examinees on writing and speaking tasks of a high-stakes language test, were unable to reach agreement in the final scores they awarded to examinees. What first seemed to be a sporadic intrusion of inevitable human error, soon turned out to follow an undeniable, clear-cut pattern: Interrater agreement and reliability statistics revealed that ratings of the very same performance differed from one another to an extent that was totally unacceptable, considering the consequences for examinees’ study and life plans.

So, what was I to do about it? Studying the relevant literature in the field of language assessment and beyond, I quickly learned two lessons: First, rater variability of the kind observed in the context of our new language test, the TestDaF (Test of German as a Foreign Language), is a notorious problem that has always plagued human ratings. Second, at least part of the problem has a solution, and this solution builds on a Rasch measurement approach.

Having been trained in psychometrics and multivariate statistics, I was drawn to the many-facet Rasch measurement (MFRM) model advanced by Linacre (1989). It appeared to me that this model could provide the answer to the question of how to deal appropriately with the error-proneness of human ratings. Yet, it was not until October 2002, when I attended a workshop on many-facet...

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