Modelling Local Item Dependence in Cloze Tests with the Rasch Model: Applying a New Strategy

Document Type : Original Research Article


1 Professor of Pedagogical Sciences, Vice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, Uzbekistan.

2 Department of Scientific Affairs, Innovations and Training of Scientific Pedagogical Personnel, Urganch State Pedagogical Institute, Urganch, Uzbekistan,

3 Doctor of Philosophy (Ph.D) in Philological Sciences, Department of Pedagogy and Philology, Renaissance University of Education, Tashkent, Uzbekistan

4 Assistant Professor, Department of Teaching the Uzbek Language in Foreign Language Groups, Alisher Navo'i Tashkent State University of the Uzbek Language and Literature, Tashkent, Uzbekistan

5 Ph.D, Philosophy Sciences, Department of Innovation and Sciences, New Uzbekistan University, Tashkent, Uzbekistan. ) Department of Science and Innovation, Tashkent State Pedagogical University named after Nizami, Tashkent, Uzbekistan.


Cloze tests are commonly used in language testing as a quick measure of overall language ability or reading comprehension. A problem for the analysis of cloze tests with item response theory models is that cloze test items are locally dependent. This leads to the violation of the conditional or local independence assumption of IRT models. In this study, a new modeling strategy is suggested to circumvent the problem of local item dependence in cloze tests. This strategy involves identifying locally dependent items in the first step and combining them into polytomous items in the second step. Finally, partial credit model is applied to the combination of dichotomous and polytomous items. Our findings showed that the new strategy results in a better model-data fit than the dichotomous model where dependence is ignored but with a lower reliability. Results also indicated that the person and item parameters from the two models highly correlate. The findings are discussed in light of the literature on managing local dependence in educational tests.