The Investigation of Interactional Patterns as Predictors of Pragmatic Competence using Structural Equation Modeling
Subject Areas : Journal of Studies in Learning and Teaching English
Ehsan Hadipour
1
,
Mohammad Bavali
2
,
Mohammad Ali Enshaei Behsaraei
3
1 - Department of English Language, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 - Department of Foreign Languages, Shiraz branch, Islamic Azad University, Shiraz, Iran
3 - Department of English Language, Shiraz Branch, Islamic Azad University, Shiraz, Iran
Keywords: Pragmatic Competence, Interactional patterns, Structural Equation Modeling,
Abstract :
Undoubtedly, pragmatic competence and teacher-learner interaction are crucially important in language teaching, learning, and communication. However, language users at any level experience difficulties in the implementation and understanding of interactional patterns while producing meaningful statements, transforming that meaning, and comprehending the exchange of it. Although pragmatic competence and teacher-learner interaction are both widely highlighted in the field, there are still numerous difficulties in linking theory to practice particularly in the English as a Foreign Language context (EFL). To address this gap, the current study intends to enliven and brighten the relationship between interactional patterns and pragmatic competence of EFL students in the Iranian context; despite previous quantitative approaches to understanding the relationship, for this goal, a model is proposed in Amos (Analysis of moment structures) akin to the Structural Equation Modeling (SEM). The research was conducted on 150 students (male and female) in Islamic Azad University Shiraz branch. The data for the research was gathered via two questionnaires on interactional patterns and speech acts of request and apology. The data analysis procedure benefited from IBM SPSS and Amos via the exploration of model-fit indices. The study's findings demonstrate an excellent fit between the model and the data, as indicated by the fit indices computed through Amos. Specifically, the default model showed a Chi-square value (CMIN/DF) of 0.991, signifying an excellent fit. Furthermore, the Goodness of fit index (GFI) reached 0.968, also reflecting an excellent fit. The Baseline Comparison revealed a comparative fit index (CFI) of 1 surpassing the threshold of 0.95 for an excellent fit.
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