Structural Equation Modeling of the Individual, Linguistic, and Social Variables in Predicting Iranian Young Adult Immigrants’ Speaking Fluency, Accuracy, Complexity, and Pronunciation in Canada - Abstract

Aug 29, 2026

The study investigated the role of age of arrival, length of resistance, amount and types of input, language-richness, and parents’ educational background in predicting Iranian immigrants’ speaking fluency, accuracy, complexity, and pronunciation. Using 108 Iranian intermediate EFL learners living in Canada, performances were collected via the Alberta Language Environment Questionnaire and a speaking test. Fluency, accuracy, and complexity were evaluated using established frameworks, while pronunciation was measured using Jenkins’ Lingua Franca Core, and structural equation modeling tested the proposed relationships.

LanguageEnglish

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ABSTRACT

1. INTRODUCTION

# Structural Equation Modeling of the Individual, Linguistic, and Social Variables in Predicting Iranian Young Adult Immigrants’ Speaking Fluency, Accuracy, Complexity, and Pronunciation in Canada Najma Amir Soleimani 􀀍 Department ofTEFL, Payame Noor University, Tehran, Iran.Email:amirsoleimani.tefl@gmail.com Hoda Divsar􀀍􀀍􀀍0000- 0003-3978- 9283 Department ofTEFL, Payame Noor University, Tehran, Iran.Email:h.divsar@pnu.ac.ir Manoochehr Jafari Gohar 􀀍􀀍􀀍0000- 0001- 6892-3248 Department ofTEFL, Payame Noor University, Tehran, Iran.Email:jafari@pnu.ac.ir Maryam Farnia 􀀍􀀍􀀍􀀍0000-0001-7046-6533 Department ofTEFL, Payame Noor University, Tehran, Iran.Email:mfarnia@pnu.ac.ir ## ABSTRACT The study investigated the role of age of arrival, length of resistance, amount and types of input,language-richness, and parents’ educational background in predicting Iranian immigrants’speaking fluency, accuracy, complexity, and pronunciation. To attain the goal, 108 Iranianintermediate EFL learners living in Canada, who were homogenized through the CELPIP-GeneralTest, were selected based on the availability sampling to complete the Alberta LanguageEnvironment Questionnaire (ALEQ) and participated in a speaking test. The performances wereassessed based on Wigglesworth and Storch’s (2009) fluency, Storch and Wigglesworth’s (2007)accuracy, and Skehan’s (2009) complexity. Pronunciation was measured according to Jenkins’(2000) Lingua Franca Core (LFC) which focuses on phonetic features crucial to intelligibility.The structural Equation Modeling (SEM) and the schematic illustration confirmed thehypothesized model (x^2⁄df= .037 RMSEA=.043; RMR =.01; GFI = .95; AGFI =.74; NFI =.70;CFI =.79; IFI =.88; TLI=.89) revealing that age of arrival (AoA), schooling in L2, and language -rich activities could predict fluency, accuracy, complexity, and pronunciation, however, LoR wasa significant predictor of neither complexity nor pronunciation. While siblings' input/output couldpredict only speaking accuracy, maternal education significantly predicted speaking complexity.The findings supported the Critical Period Hypothesis, the significant role of AoA in thedevelopment of bilingualism, and the effectiveness of L2 instruction. The results confirmed thedirection of the path model exposing the inefficacy of paternal and maternal education and parents'input/output in predicting the variances in immigrants' speaking ability. The findings suggestedthat parents maximize the number of activities in the second language and, if possible, migrate tosecond-language countries before their children pass the critical age to learn the second language. ARTICLE INFO Article history: Received: 17 August 2022Received in revised form 11 October 2022 Accepted: 11 October 2022 Available online: Spring2023 Keywords: AgeofArrival, AmountandTypes of input, language-rich Activities, Immigrants,Length of Resistance,Pronunciation, SpeakingFluency, Accuracy,Complexity, andPronunciation. Divsar, H., Jafari Gohar, M., Farnia, M., & Amir Soleimani, N. (2023) . Structural Equation Modeling of the Individual, Linguistic, and SocialVariables in Predicting Iranian Young Adult Immigrants’ Speaking Fluency, Accuracy, Complexity, and Pronunciation in Canada. Journal of ForeignLanguage Research, 13(1), 79-92 © The Author(s) . Publisher: University of Tehran Press. DOI: http//doi.org/ 10.22059/jflr.2022.346572.974 􀀍 Najma Amir Soleimani is a Ph.D. candidate in TEFL at Payame Noor University (PNU) . 􀀍􀀍 Hoda Divsaris Assistant Professor ofTEFL Department at Payame Noor University. Her areas of interest cover critical thinking, language learning strategies, and multiple intelligences theory.􀀍􀀍􀀍 Manoochehr Jafari Gohar isa full professor inthe TEFL Department of Payame Noor University (PNU) . His areas of interest include TEFL in distance education, learner variables, and SLA.􀀍􀀍􀀍􀀍Maryam Farnia is Assistant Professor of Applied Linguistics at PNU. Her areas of resea

FAQs

Which learner and social variables were tested for predicting immigrants’ speaking performance?

The study tested age of arrival, length of resistance, amount and types of input, language-richness, and parents’ educational background as predictors of speaking fluency, accuracy, complexity, and pronunciation.

How were fluency, accuracy, complexity, and pronunciation measured?

Speaking fluency was assessed using Wigglesworth and Storch’s framework, accuracy using Storch and Wigglesworth’s framework, and complexity using Skehan’s model. Pronunciation was measured using Jenkins’ Lingua Franca Core focusing on intelligibility-relevant phonetic features.

What did the structural equation modeling results show about the predictive factors?

The model supported the hypothesized relationships, indicating that age of arrival, schooling in L2, and language-rich activities could predict fluency, accuracy, complexity, and pronunciation. However, length of resistance did not significantly predict complexity or pronunciation.

Aug 29, 2026

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