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Introduction to Many-Facet Rasch Measurement

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

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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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8. Advanced Many-Facet Rasch Measurement

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8.   Advanced Many-Facet Rasch Measurement

The many-facet Rasch model discussed so far has been concerned with basic issues of evaluating the quality of rater-mediated assessments. Each of the facets involved was analyzed in detail, including a first look at the structure of the rating scale. Yet, there is much more to a MFRM modeling approach than described in the preceding chapters. In particular, MFRM models can be tailored to fit a variety of assessment situations. In order to provide a brief overview of more advanced models, different types of scoring or scoring formats are presented first, followed by issues of measurement dimensionality. Then the focus is on the specific ways in which the rating scale was put to use, distinguishing between rating scale, partial credit, and hybrid models. Another section addresses the study of interactions between facets and introduces methods for the analysis of rater bias. The chapter concludes with a summary of MFRM models frequently encountered in the field of rater-mediated assessment.

8.1  Scoring formats

The data used to estimate the parameters of a particular facets model most often follow a polytomous format, as when raters score examinee performance based on a rating scale with three or four ordered categories. There are a number of other types of scoring that also yield data suitable for a MFRM analysis; first and foremost dichotomous scoring, as when examinees take a multiple-choice vocabulary test and their responses to each test item are scored either correct or...

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