Developing a rating scale to assess phraseological competence

The study set out to design, develop, and validate a rating scale for assessing second language test-takers' phraseological competence. It also aimed to examine the relationship between human ratings of phraseological competence and assessed levels of proficiency in the Test of English for Educational Purposes (TEEP). The dataset comprises spoken performances of 127 TEEP candidates placed across six levels of proficiency. Four dimensions of accuracy/appropriacy, diversity, sophistication, and abundance were identified, and a scale of 1-6 was developed. Training was provided to three expert raters and two standardisation meetings were held before they were asked to rate the spoken performances independently. Many-facet Rasch Measurement was used to analyse three facets (i.e., candidates, raters, and criteria) and compute logit scores as indicators of test-takers' phraseological competence. The analysis showed that a 5-level scale worked more effectively, demonstrating adequate overall data fit to the Rasch model. The results suggested that both raters and criteria in the modified 5-level scale worked well and sufficiently contributed to the relatively precise and adequate measurement of phraseological competence for research and proficiency assessment purposes. The ANOVA results revealed a significant main effect of proficiency level on the ratings, implying that phraseological competence, represented by the logit scores, differed significantly across assessed level of proficiency. Areas for improvement in the measurement and implications for language testing are discussed.

Authors

Institutions

Publication Details

Journal
Research Methods in Applied Linguistics
Published
2026-09-09
DOI
https://doi.org/10.1016/j.rmal.2026.100366
Primary Topic
Second Language Acquisition and Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Developing a rating scale to assess phraseological competence

Svetlana Mazhurnaya, Philip Smyth, Parvaneh Tavakoli, Rie Koizumi et al.
Research Methods in Applied Linguistics
Second Language Acquisition and Learning
article

Developing a rating scale to assess phraseological competence

Svetlana Mazhurnaya, Philip Smyth, Parvaneh Tavakoli, Rie Koizumi, Takumi Uchihara
article en

Abstract

The study set out to design, develop, and validate a rating scale for assessing second language test-takers' phraseological competence. It also aimed to examine the relationship between human ratings of phraseological competence and assessed levels of proficiency in the Test of English for Educational Purposes (TEEP). The dataset comprises spoken performances of 127 TEEP candidates placed across six levels of proficiency. Four dimensions of accuracy/appropriacy, diversity, sophistication, and abundance were identified, and a scale of 1-6 was developed. Training was provided to three expert raters and two standardisation meetings were held before they were asked to rate the spoken performances independently. Many-facet Rasch Measurement was used to analyse three facets (i.e., candidates, raters, and criteria) and compute logit scores as indicators of test-takers' phraseological competence. The analysis showed that a 5-level scale worked more effectively, demonstrating adequate overall data fit to the Rasch model. The results suggested that both raters and criteria in the modified 5-level scale worked well and sufficiently contributed to the relatively precise and adequate measurement of phraseological competence for research and proficiency assessment purposes. The ANOVA results revealed a significant main effect of proficiency level on the ratings, implying that phraseological competence, represented by the logit scores, differed significantly across assessed level of proficiency. Areas for improvement in the measurement and implications for language testing are discussed.

Research Methods in Applied LinguisticsVol. 5(3)
University of Tsukuba (JP), Tohoku University (JP), University of Reading (GB)
Quality Education
Openalex Percentile: Top 4%
Second Language Acquisition and Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.