Modeling vocabulary size using many-faceted Rasch measurement

Article appearing in Shiken 26.1 (June 2022)Shiken 26.1 (June 2022) pp. 1-19;
Issue DOI: https://doi.org/10.37546/JALTSIG.TEVAL26.1

By Trevor Holster1 and J. W. Lake2

1. Fukuoka University, Fukuoka
2. Fukuoka Jogakuin University, Fukuoka

Article DOI: https://doi.org/10.37546/JALTSIG.TEVAL26.1-1

Abstract

Research into second-language vocabulary size has suffered from inattention to psychometric issues, with ordinal-level raw scores often analyzed as if they represented ratio-level measurement. Additionally, contextual effects have been largely ignored, leading to concern over the interpretation of research findings. This study used many-faceted Rasch measurement to analyze vocabulary data from 1,872 Japanese university students. A test of word synonymy was linked to the Vocabulary Size Test, and the contextual variables of item position and time of administration analyzed as measurement facets. Major findings were that data-model fit was sufficient to allow local linking of different item types and contextual variables, allowing meaningful comparison of results and score gains on a scale of vocabulary size, and that item placement within a test form had a substantive effect on item difficulty.

Keywords: Vocabulary size, many-faceted Rasch measurement, test linking, guessing correction

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