Rater Severity/Leniency and Bias in EFL Students' Composition Using Many-Facet Rasch Measurement (MFRM) - Yenni Arif Rahman; Fitri Apriyanti; Rahmi Aulia Nurdini (Scope : Journal of English Language Teaching, 2023)
The study aims to investigate the extent to which raters exhibit tendencies towards being overly severe, lenient, or even bias when evaluating students' writing compositions in Indonesia. Data were collected from 15 student essays and four raters with master's degrees in English education. The Many-facet Rasch measurement (MFRM), automatized by Minifac software, a program created for the Many-facet Rasch measurement, was used for data analysis. This was done by meticulously dissecting the assessment process into its distinct components—raters, essay items, and the specific traits or criteria being evaluated in the writing rubric. Each rater's level of severity or leniency, essentially how strict or lenient they are in assigning scores, is scrutinized. Likewise, the potential biases that raters might introduce into the grading process are carefully examined. The findings revealed that, while the raters used the rubric consistently when scoring all test takers, they varied in how lenient or severe they were. Scores of 70 were given more frequently than the other score. Based on the findings, composition raters may differ in how they rate students which potentially leading to student dissatisfaction, particularly when raters adopt severe scoring. The bias in scoring has highlighted that certain raters consistently tend to inaccurately score items, deviating from the established criteria (traits). Furthermore, the study also found that having more than four items/criteria (content, diction, structure, and mechanic) is essential to achieve a more diverse distribution of item difficulty and effectively measure students' writing abilities. These results are valuable for writing departments to improve the oversight of inter-rater reliability and rating consistency. To address this issue, implementing rater training is suggested as the most feasible method to ensure more dependable and consistent evaluations.