Safa Mohammed Abdulridah Dhyaaldian

A C-Test is a gap-filling test for measuring language competence in the first and second language. C-Tests are usually analyzed with polytomous Rasch models by considering each passage as a super-item or testlet. This strategy helps overcome the local dependence inherent in C-Test gaps. However, there is little research on the best polytomous Rasch model for C-Tests. In this study, the Rating Scale Model (RSM) and the Partial Credit Model (PCM) for analyzing C-Tests were compared. To this end, a C-Test composed of six passages with both RSM and PCM was analyzed. The models were compared in terms of overall fit, individual item fit, dimensionality, test targeting, and reliability. Findings showed that, although the PCM has a better overall fit compared to the RSM, both models produce similar test statistics. In light of the findings of the study, the choice of the best Rasch model for C-Tests will be discussed.

Cloze tests are gap-filling tests designed to measure overall language ability and reading comprehension in a second language. Due to their ease of construction and scoring, cloze tests are widely used in the context of second and foreign language testing. Previous research over the past decades has shown the reliability and validity of cloze tests in different contexts. However, due to the interdependent structure of cloze test items, item response theory models have not been applied to analyze cloze tests. In this research, we apply a method to circumvent the problem of local dependence for analyzing cloze tests with the Rasch model. Using this method, we applied the Rasch model to a cloze test composed of eight passages each containing 8-15 gaps. Findings showed that the Rasch model fits the data and thus it is possible to scale persons and cloze passages on an interval unidimensional scale. The test had a high reliability and was well-targeted to the examinees. Implications of the study are discussed.