Abstract
The present article reviews SEM as a statistical tool and its current use in language
re... more Abstract The present article reviews SEM as a statistical tool and its current use in language research, and especially in language assessment. First an introduction is given about the general features of this statistical tool. Its history and distinguishing characteristics are explored in the next sections. The steps of applying SEM are explained. Then, the application of SEM in language assessment is presented in time order. Keywords: Structural Equation Modeling (SEM), Language learning, Language assessment
The present study examined the factor structure of the University of Tehran English Proficiency T... more The present study examined the factor structure of the University of Tehran English Proficiency Test (UTEPT) that aims to examine test takers' knowledge of grammar, vocabulary, and reading comprehension. A Structural Equation Modelling (SEM) approach was used to analyse the responses of participants (N= 850) to a 2010 version of the test. A higher-order model was postulated to test if the underlying factor structure, obtained in a data-driven manner, corresponds with the proposed structure of the test. The results revealed an appropriate model fit with the data, pointing to the fact that the three sections of UTEPT, i.e., structure, vocabulary, and reading, and their sub-components, except for the restatement section of reading, are good indicators of written language proficiency as assessed by the UTEPT. It was also found that the three sections assess distinctive constructs. The findings suggest that UTEPT is a valid measure of the written language proficiency of Ph.D. applicants to University of Tehran.
The language learners' errors can be categorized into two categories; interlingual and intralingu... more The language learners' errors can be categorized into two categories; interlingual and intralingual errors. While the former results from the interference of the learners' mother tongue, the latter should be traced in the target language system itself. The purpose of the study is to find out what proportion of the learners' errors are intralingual errors and whether the native language plays a major role in learners' difficulties in learning the target language. 30 erroneous sentences of some Iranian EFL learners with Persian as their mother tongue were analyzed to find the errors pattern. Only 16.7 percent of the errors were interlingual errors. This shows that most of the difficulties a language learner is faced with can be traced to the target language system and contrastive study of the two languages to predict the learners' learning problems is not without problems.
Abstract
The present article reviews SEM as a statistical tool and its current use in language
re... more Abstract The present article reviews SEM as a statistical tool and its current use in language research, and especially in language assessment. First an introduction is given about the general features of this statistical tool. Its history and distinguishing characteristics are explored in the next sections. The steps of applying SEM are explained. Then, the application of SEM in language assessment is presented in time order. Keywords: Structural Equation Modeling (SEM), Language learning, Language assessment
The present study examined the factor structure of the University of Tehran English Proficiency T... more The present study examined the factor structure of the University of Tehran English Proficiency Test (UTEPT) that aims to examine test takers' knowledge of grammar, vocabulary, and reading comprehension. A Structural Equation Modelling (SEM) approach was used to analyse the responses of participants (N= 850) to a 2010 version of the test. A higher-order model was postulated to test if the underlying factor structure, obtained in a data-driven manner, corresponds with the proposed structure of the test. The results revealed an appropriate model fit with the data, pointing to the fact that the three sections of UTEPT, i.e., structure, vocabulary, and reading, and their sub-components, except for the restatement section of reading, are good indicators of written language proficiency as assessed by the UTEPT. It was also found that the three sections assess distinctive constructs. The findings suggest that UTEPT is a valid measure of the written language proficiency of Ph.D. applicants to University of Tehran.
The language learners' errors can be categorized into two categories; interlingual and intralingu... more The language learners' errors can be categorized into two categories; interlingual and intralingual errors. While the former results from the interference of the learners' mother tongue, the latter should be traced in the target language system itself. The purpose of the study is to find out what proportion of the learners' errors are intralingual errors and whether the native language plays a major role in learners' difficulties in learning the target language. 30 erroneous sentences of some Iranian EFL learners with Persian as their mother tongue were analyzed to find the errors pattern. Only 16.7 percent of the errors were interlingual errors. This shows that most of the difficulties a language learner is faced with can be traced to the target language system and contrastive study of the two languages to predict the learners' learning problems is not without problems.
Uploads
Papers by Akram Nayernia
The present article reviews SEM as a statistical tool and its current use in language
research, and especially in language assessment. First an introduction is given about the
general features of this statistical tool. Its history and distinguishing characteristics are
explored in the next sections. The steps of applying SEM are explained. Then, the
application of SEM in language assessment is presented in time order.
Keywords: Structural Equation Modeling (SEM), Language learning, Language
assessment
The present article reviews SEM as a statistical tool and its current use in language
research, and especially in language assessment. First an introduction is given about the
general features of this statistical tool. Its history and distinguishing characteristics are
explored in the next sections. The steps of applying SEM are explained. Then, the
application of SEM in language assessment is presented in time order.
Keywords: Structural Equation Modeling (SEM), Language learning, Language
assessment