Shvedova Irina Viktorovna

Multiple-choice (MC) item format is commonly used in educational assessments due to its economy and effectiveness across a variety of content domains. However, numerous studies have examined the quality of MC items in high-stakes and higher education assessments and found many flawed items, especially in terms of distractors. These faulty items lead to misleading insights about the performance of students and the final decisions. The analysis of distractors is typically conducted in educational assessments with multiple-choice items to ensure high quality items are used as the basis of inference. Item response theory (IRT) and Rasch models have received little attention for analyzing distractors. For that reason, the purpose of the present study was to apply the Rasch model, to a grammar test to analyze items’ distractors of the test. To achieve this, the current study investigated the quality of 10 instructor-written MC grammar items used in an undergraduate final exam, using the items responses of 310 English as a foreign language (EFL) students who had taken part in an advanced grammar course. The results showed the acceptable fit to the Rasch model and high reliability. Malfunctioning distractors were identified.

Unidimensionality is an important assumption of measurement but it is violated very often. Most of the time, tests are deliberately constructed to be multidimensional to cover all aspects of the intended construct. In such situations, the application of unidimensional item response theory (IRT) models is not justified due to poor model fit and misleading results. Multidimensional IRT (MIRT) models can handle several dimensions simultaneously and yield person ability parameters on several dimensions which is helpful for diagnostic purposes too. Furthermore, MIRT models use the correlation between the dimensions to enhance the precision of the measurement. In this study a reading comprehension test is modelled with the multidimensional Rasch model. The findings showed that a correlated 2-dimensional model has the best fit to the data. The bifactor model revealed some interesting information about the structure of reading comprehension and the reading curriculum. Implications of the study for the testing and teaching of reading comprehension are discussed.