Rasch testlet and bifactor models are two measurement models that could deal with local item dependency (LID) in assessing the dimensionality of reading comprehension testlets. This study aimed to apply the measurement models to real item response data of the Iranian EFL reading comprehension tests and compare the validity of the bifactor models and corresponding item parameters with unidimensional and multidimensional Rasch models. The data collected from the EFL reading comprehension section of the Iranian national university entrance examinations from 2016 to 2018. Various advanced packages of the R system were employed to fit the Rasch unidimensional, multidimensional, and testlet models and the exploratory and confirmatory bifactor models. Then, item parameters estimated and testlet effects identified; moreover, goodness of fit indices and the item parameter correlations for the different models were calculated. Results showed that the testlet effects were all small but non-negligible for all of the EFL reading testlets. Moreover, bifactor models were superior in terms of goodness of fit, whereas exploratory bifactor model better explained the factor structure of the EFL reading comprehension tests. However, item difficulty parameters in the Rasch models were more consistent than the bifactor models. This study had substantial implications for methods of dealing with LID and dimensionality in assessing reading comprehension with reference to the EFL testing.
Branch. She is a part-time lecturer and has published some articles and books. She has also attended some national and international conferences. She is a member of the Young Researchers and Elite Club. Her areas of interest mainly lie in Language Testing, Assessment, and Educational Measurement.
According to Ackerman (1994), Hattie (1985), and Reckase (1990), test dimensionality is defined as the interaction of a set of items and underlying constructs of the examinees. According to Traub and Lam (1985, p. 22) "the assumption of unidimensionality seems inappropriate when it refers to an educational achievement." On the other hand, it should be noted that when different dimensions are measured, Masoud Geramipour et al.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.