Assessing Rasch measurement estimation methods across R packages with yes/no vocabulary test data. (October 2022)

• Record Type: Journal Article
• Authors: Nicklin, Christopher; Vitta, Joseph P.
• Is Part Of: Language testing. Volume 39:Number 4(2022)
• Page Start: 513
• Page End: 540
• Publication Date: 2022-10
• Subjects: R -- Rasch analysis -- simulation -- vocabulary -- yes/no test; Language and languages -- Ability testing -- Periodicals; Language and languages -- Examinations -- Periodicals; 407.6
• DOI: 10.1177/02655322211066822
• Languages: English
• ISSNs: 0265-5322

Abstract

Instrument measurement conducted with Rasch analysis is a common process in language assessment research. A recent systematic review of 215 studies involving Rasch analysis in language testing and applied linguistics research reported that 23 different software packages had been utilized. However, none of the analyses were conducted with one of the numerous R-based Rasch analysis software packages, which generally employ one of the three estimation methods: conditional maximum likelihood estimation (CMLE), joint maximum likelihood estimation (JMLE), or marginal maximum likelihood estimation (MMLE). For this study, eRm, a CMLE-based R package, was utilized to conduct a dichotomous Rasch analysis of a Yes/No vocabulary test based on the academic word list. The resulting parameters and diagnostic statistics were compared with the equivalent results from four other R-based Rasch measurement software packages and Winsteps. Finally, all of the packages were utilized in the analysis of 1000 simulated datasets to investigate the extent to which results generated from the contrasting estimation methods converged or diverged. Overall, the differences between the results produced with the three estimation methods were negligible, and the discrepancies observed between datasets were attributable to the software choice as opposed to the estimation method.