Introduction to Many-Facet Rasch Measurement
By T. Eckes. 2011
208 citations recorded by the source corpus.
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.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/1366632d21c34901aabc2c695534c5d9cc3c9f90
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In Management Science and Operations Research
Citations in the corpus
- Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application (2020, unknown): “…for all hate speech components can be jointly modeled with maximum likelihood using the following multilevel equation (Linacre and Wright, 2002; Eckes, 2015), which yields the interval hate speech score where: • p nijk is the probability of comment n being rated as label k by rater j on…”
- Operational Rater Types in Writing Assessment: Linking Rater Cognition to Rater Behavior (2012, methodology): “Use of an MFRM modeling approach implies the assumption that raters acted as independent experts (e.g., Eckes, 2011; Linacre, 1997).”
- Validity argument for assessing L2 pragmatics in interaction using mixed methods (2015, methodology): “FACETS analysis Figure 3 presents a FACETS summary that places the four facets (examinee, rater, roleplay task, and rating criteria) on the same logit scale, which transforms the probability of a particular response to represent a true difference among the facets (Eckes, 2011; McNamara, 1996).”