Cognitive diagnostic models (CDMs) have recently received a surge of interest in the field of second language assessment due to their promise for providing fine-grained information about strengths and weaknesses of test takers. For the same reason, the present study used the additive CDM (ACDM) as a compensatory and additive model to diagnose Iranian English as a foreign language (EFL) university students' L2 writing ability. To this end, the performance of 500 university students on a writing task was marked by four EFL teachers using the Empirically derived Descriptor-based Diagnostic (EDD) checklist. Teachers, as content experts, also specified the relationships among the checklist items and five writing sub-skills. The initial Q-matrix was empirically refined and validated by the GDINA package. Then, the resultant ratings were analyzed by the ACDM in the CDM package. The estimation of the skill profiles of the test takers showed that vocabulary use and content fulfillment are the most difficult attributes for the students. Finally, the study found that the skills diagnosis approach can provide informative and valid information about the learning status of students.

Cloze-elide tests are overall measures of both first (L1) and second language (L2) reading comprehension and communicative skills. Research has shown that a time constraint is an effective method to understand individual differences and increase the reliability and validity of tests. The purpose of this study is to investigate the psychometric quality of a speeded cloze-elide test using a ploytomous Rasch model, called partial credit model (PCM), by inspecting the fit of four different scoring techniques. To this end, responses of 150 English as a foreign language (EFL) students to a speeded cloze-elide test was analyzed. The comparison of different scoring techniques revealed that scoring based on wrong scores can better explain variability in the data. The results of PCM indicated that the assumptions of unidimensionality holds for the speeded cloze-elide test. However, the results of partial credit analysis of data structure revealed that a number of categories do not increase with category values. Finally, suggestions for further research, to better take advantage of the flexibilities of item response theory and Rasch models for explaining count data, will be presented.