Detecting and measuring rater effects using many-facet Rasch measurement: part I.

The purpose of this two-part paper is to introduce researchers to the many-facet Rasch measurement (MFRM) approach for detecting and measuring rater effects. The researcher will learn how to use the Facets (Linacre, 2001) computer program to study five effects: leniency/severity, central tendency, randomness, halo, and differential leniency/severity. Part 1 of the paper provides critical background and context for studying MFRM. We present a catalog of rater effects, introducing effects that researchers have studied over the last three-quarters of a century in order to help readers gain a historical perspective on how those effects have been conceptualized. We define each effect and describe various ways the effect has been portrayed in the research literature. We then explain how researchers theorize that the effect impacts the quality of ratings, pinpoint various indices they have used to measure it, and describe various strategies that have been proposed to try to minimize its impact on the measurement of ratees. The second half of Part 1 provides conceptual and mathematical explanations of many-facet Rasch measurement, focusing on how researchers can use MFRM to study rater effects. First, we present the many-facet version of Andrich's (1978) rating scale model and identify questions about a rating operation that researchers can address using this model. We then introduce three hybrid MFRM models, explain the conceptual distinctions among them, describe how they differ from the rating scale model, and identify questions about a rating operation that researchers can address using these hybrid models.