Abstract

This chapter examines the challenges testing organizations face when trying to assess growth in speaking proficiency. It is argued that the conventional ILR scale may be too gross to detect granular changes in proficiency when users of the LPI need to demonstrate the impact of interim training programs. The chapter examines methods of diagnosing rater differences using multi-level logistic models and how such differences can be integrated into indicators of proficiency growth otherwise obscured by rater differences. The chapter outlines use of the Many-Facet Rasch Model that can be used to rescale candidate proficiency in a manner that reduces the influence of differential rater severity.

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