Many-Facet Rasch Model in language testing research:A brief introduction and literature review
Ji Zhang · 2014
Language performance assessment has been an indispensible part of many large-scale language tests. However,the reliability and validity of such assessment tasks are challenged due to the subjective rater judgment,different levels of task difficulty and the setting and use of rating scales introduced into the test context. Many-Facet Rasch Model(MFRM) is the multi-Faceted extension of the classic Rasch model in Item Response Theory. The major advantage of MFRM is to incorporate multiple factors which may influence the final scores of candidates into one unified mathematical model,simultaneously to analyze them and then to estimate the extent to which each factor determines the final scores. The present paper intends to give a brief introduction to the fundamental principles and mathematical models of MFRM and make a comprehensive literature review of using MFRM in the field of language testing research,with the aim to provide the readers with a practical guide to MFRM in language testing research.