Bolathan Yessimov - Institute of Natural Sciences and Geography, Abai Kazakh National Pedagogical University, Almaty, Kazakhstan.
Rasha Abed Hussein - Al-Manara College For Medical Sciences, Misan, Iraq
Aisha Mohammed - College of Pharmacy / Al-Farahidi University, Baghdad , Iraq
Aalaa Yaseen Hassan - Al-Nisour University College, Baghdad, Iraq
Ahmed Hashim - Department of English Language, Mazaya University College, Iraq
Salma Saad Najeeb - Al-Esraa University College, Baghdad, Iraq
Yusra Mohammed Ali - Department of Medical Laboratory Techniques, Al-Zahrawi University College, Karbala, Iraq
Ahmed Abdullah - English Department, Al-Noor University College, Nineveh, Iraq
Nashaat Sultan Afif - People’s Friendship University of Russia, Moscow, Russia

Measurement disturbances refer to any conditions that affect the measurement of some psychological latent variables, which result in an inaccurate interpretation of item or person estimates derived from a measurement model. Measurement disturbances are mainly attributed to the characteristics of the person, the properties of the items, and the interaction between the characteristics of the person and the features of the items. Although numerous researchers have detected measurement disturbances in different contexts, too little attention has been devoted to exploring measurement disturbances within the context of language testing and assessment, especially using graphical displays. This study aimed to show the utility of graphical displays, which surpass numeric values of infit and outfit statistics given by the Rasch model, to explore measurement disturbances in a listening comprehension test. Results of the study showed two types of outcomes for examining graphical displays and their corresponding numeric fit values: congruent and incongruent associations. It turned out that graphical displays can provide diagnostic information about the performance of test items which might not be captured through numeric values.

Graphical displays, item characteristic curves, measurement disturbances, model-data fit