A new approach to measuring Overall Liking with the Many-Facet Rasch Model

Many-Facet Rasch Measurement

This chapter provides an introductory overview of many-facet Rasch measurement (MFRM). Broadly speaking, MFRM refers to a class of measurement models that extend the basic Rasch model by incorporating more variables (or facets) than the two that are typically included in a test (i.e., examinees and items), such as raters, scoring criteria, and tasks. Throughout the chapter, a sample of rating d...

Rater Errors among Peer-Assessors: Applying the Many-Facet Rasch Measurement Model

In this study, the researcher used the many-facet Rasch measurement model (MFRM) to detect two pervasive rater errors among peer-assessors rating EFL essays. The researcher also compared the ratings of peer-assessors to those of teacher assessors to gain a clearer understanding of the ratings of peer-assessors. To that end, the researcher used a fully crossed design in which all peer-assessors ...

Introduction to Many-Facet Rasch Measurement - Preamble

A Many-facet Rasch Model to Detect Halo Effect in Three Types of Raters

Raters play a central role in rater-mediated assessment, and rater variability manifested in various forms including rater errors contributes to construct-irrelevant variance which can adversely affect an examinee’s test score. Halo effect as a subcomponent of rater errors is one of the most pervasive errors which, if not detected, can result in obscuring an examinee’s score and threatening val...

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 backgr...