Validating Translation Test Items via the Many-Facet Rasch Model.

Abstract

This study applied the many-facet Rasch model to assess learners' translation ability in an English as a foreign language context. Few attempts have been made in extant research to detect and calibrate rater severity in the domain of translation testing. To fill the research gap, this study documented the process of validating a test of Chinese-to-English sentence translation and modeled raters' scoring propensity defined by harshness or leniency, expert/novice effects on severity, and concomitant effects on item difficulty. Two hundred twenty-five, third-year senior high school Taiwanese students and six educators from tertiary and secondary educational institutions served as participants. The students' mean age was 17.80 years ( SD = 1.20, range 17-19). The exam consisted of 10 translation items adapted from two entrance exam tests. The results showed that this subjectively scored performance assessment exhibited robust unidimensionality, thus reliably measuring translation ability free from unmodeled disturbances. Furthermore, discrepancies in ratings between novice and expert raters were also identified and modeled by the many-facet Rasch model. The implications for applying the many-facet Rasch model in translation tests at the tertiary level were discussed.