structural equation modeling (SEM)

The generalizability aspect of Construct validity, as proposed by Messick (1989), requires that a test measures the same trait across different samples from the same population. Differential Item functioning (DIF) analysis is a key component in the fairness evaluation of educational tests. University entrance exam for the candidates who seek admission into master's English programs (MEUEE) at Iranian state universities is a very high stakes test whose fairness is a promising line of research. The current study explored gender and major DIF in the general English (GE) section of the MEUEE using multiple-indicators multiple-causes (MIMIC) structural equation modelling. The data of all the test takers (n=21,642) who took the GE section of the MEUEE in 2012 were analyzed with Mplus. To determine whether an item is flagged for DIF both practical and statistical significance were considered. The results indicated that 12 items were flagged for DIF in terms of statistical significance. However, only 5 items showed practical significance. The items flagged for DIF alert the test developers and users to potential sources of construct-irrelevant variance in the test scores which may call into question comparison of the test takers’ performance, especially when the tests are used for selection purposes.

The present quantitative study examined the role of a set of socially-mediated personal attributes that might intervene in the speaking performance of Iranian EFL learners. The possible relationship between three factors (namely, Willingness to communicate (WTC) in L2, L2 communication confidence, and Beliefs about L2 group work in L2 speaking), which are hypothesized to influence learners’ oral performance, was investigated. In fact, a model reflecting the hypothesis that these three variables would influence the learners’ speaking performance was constructed and tested by collecting data from a convenient sample of 100 B.A. university students in Iran. In order to collect the required data on the above-mentioned variables, a comprehensive questionnaire developed by Fushino (2010) and IELTS speaking test module 1 were utilized. The structural equation modeling confirmed that these three variables were very good predictors of performance of Iranian EFL learners on speaking skill. In other words, the measurement model of this study was approved and the conceptual model of research had an acceptable level of fit index. In addition, the result of multiple regressions indicated that L2 communicative confidence made a higher level of contribution in explaining the L2 speaking performance of the learners, which corroborates the association of these two constructs. The findings of present study also implied that increasing EFL learners’ WTC, communication confidence and beliefs about L2 group work is likely to help to improve their speaking ability and learners who have higher levels of L2 WTC and confidence are likely to achieve higher scores on their speaking performance.

This study was an endeavor to develop a model of identity among Iranian EFL learners. To achieve this end, a multiphase design was implemented. Initially, it attempted to investigate different factors of identity to propose and validate a model. Thus, 120 EFL learners studying in different English language institutes in Iran were randomly selected, and 36 learners were interviewed about their views of their identity in the qualitative phase. After extracting six factors of identity, including: second language acquisition and social status, cultural attachment, Persian language adhesion, pronunciation posture, technology involvement, and language identity, and second language knowledge, a questionnaire was constructed which reflected these factors. Then, in the quantitative phase, the questionnaire went through an exploratory factor analysis for the sake of validity. After its validity and reliability were corroborated through a pilot study with 20 learners, it was distributed among 120 EFL learners. Besides, Structural Equation Modeling (SEM) analysis was run to confirm that the final proposed model enjoyed validity for future research. To do so, the confirmatory factor analysis was run, and the model of identity was developed. Eventually, the possible relationship between 120 EFL learners’ identity and their English language achievement scores were examined, and the results of this phase indicated that there was a significant and positive relationship between learners’ identity and their English language achievement scores. The findings of this study can enhance awareness among English teachers, materials developers, and syllabus designers to equip themselves with the updated techniques to handle the possible challenges that may occur in EFL learning contexts.

Much of what educators address is the overt curriculum; however, there is a hidden curriculum that affects education in a very profound manner. In view of that, the purpose of the present study is to determine the relationship of EFL teachers’ perspectives on hidden curriculum components in the Iranian institutional context with their students’ self-efficacy and national identity. More specifically, the present study surveyed the probable existence of any significant correlation between EFL teachers’ perspectives on the EFL hidden curriculum components, their students’ attitudes towards their own national identity, and self-efficacy. For this purpose, a model was suggested and tested using partial least squares variance-based structural equation modeling (PLS-SEM), to examine EFL teachers’ perspectives on the EFL hidden curriculum components contributing to their students’ national identity and self-efficacy. A total of 164 institutional EFL teachers in Iran completed the EFL hidden curriculum questionnaire. Besides, 987 students (about eighty percent of their learners) were asked to fill in national identity and self-efficacy questionnaires. Based on this model, all the correlations between the latent variables were significant except for three latent variables including the relationships among EFL teachers’ perspectives on the EFL hidden curriculum components (social atmosphere, organizational structure, and interaction between teachers and learners) and their learners’ self-efficacy. In addition, the results depicted all the relationships between latent variables was positive relations; while, the relationship between EFL learners’ national identity and self-efficacy was proved to be negative.

This research aimed at the prediction of social adjustment based on early maladaptive schemas and social skills. In this research, 133 subjects, all were inhabitants of Tehran and were selected by the convenient method, responded to online questionnaires including Bell Adjustment Questionnaire (BAI), Young Early Maladaptive Schemas Questionnaire (YEMSQ), and Matson Evaluation Social Skill with Youngsters (MESSY). The LISREL and SPSS-22 softwares and the methods of Pearson correlation and Structural equation modeling were used for data analysis. Results showed that early maladaptive schemas in five areas (disconnection & rejection, impaired autonomy & performance, impaired limits, other-direction, and overvigilance/ inhibition) were correlated reversely with social adjustment. Social adjustment also was correlated positively with social skills. Applying the structural equation modeling showed that social adjustment is predictable based on early maladaptive schemas and social skills. Out of the schemas, two areas of impaired limits (-0.69) and impaired autonomy & performance (-0.53) have the most negative impact on social adjustment. Social skills, as the second strongest variable, have the highest positive impact on social skills, after impaired autonomy & performance. The results were explained in the context of the theory of early maladaptive schemas, and some points were suggested regarding an increase in social adjustment.

Since the outbreak of the COVID-19 pandemic in 2020 up to at least the beginning of 2022, e-learning has largely replaced the face-to-face teaching method in Iran. Accepting web-based learning could be effective in the continuity of this method, at least in a hybrid one, even in normal circumstances. As such, the role of teachers’ perspectives in this regard should not be neglected. Due to the importance of this kind of technology in teaching a second language and the effect of teacher acceptance on the decision to use it, in this study, we examine 63 Persian as a Second language (PSL) teachers' acceptance of Web-based e-learning technology to explore the various factors that impact their intentions to use it. This study uses the Technology Acceptance Model (TAM) as the theoretical foundation. The survey data obtained from 63 PSL teachers through previously tested and validated questionnaires are analyzed using Structural Equation Modeling with AMOS. The results suggest that the perceived usefulness (PU) directly impacts behavioral intention (BI). Then, there is the motivation to use (MU) construct and the perceived ease of use (PEU), which could indirectly affect BI. The Internet self-efficacy (ISE) construct directly affects BI. Finally, the factor of computer anxiety has a negative effect on behavioral intentions to use web-based E-learning technologies through the factor of perceived ease of use. The research results show that perceived usefulness is the most influential factor in PSL teachers’ intention to use technology. It implies that PSL teachers would be more likely to continue to use Web-based E-learning technologies if they consider them useful.

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences.

Purpose- Climate change has negative effects on the economic, social, and environmental aspects of rural households. Given the importance of the impact of climate change on the livelihoods of rural people, this study was conducted with the aim of reducing vulnerability and increasing resilience and adaptation to these conditions in Mashhad Township.Design/methodology/approach- The statistical population of the study is 11,706 rural households in Mashhad Township, out of which 372 households were selected proportionally by multistage stratified random sampling based on Cochran’s formula. The main tool of the research was a questionnaire whose validity was examined through content validity, structural validity, and reliability by composite reliability and Cronbach’s alpha (a= 0.9). The data were analyzed using SPSS and LISREL software. To examine the fit of the measurement model of the effects of climate change on sustainable livelihoods, the collected data were analyzed using second-order confirmatory factor analysis with LISREL software.Findings- The results of the study showed that the greatest impact of climate change was on financial capital, including income reduction, increased costs and increased product prices, reduced productivity and employment. In addition, the greatest effects of climate change on social capital include were on reduced sense of belonging and increased dependence on government support; on human capital include a were on reduced health levels and quality of life; and on natural capital include a were on reduced land resources and pressure and occurrence of hazards; and on physical capital were on reduced services and facilities for people. The research findings also showed that the goodness-of-fit indices (AGFI=0.91, GFI=0.91), (NNFI=0.91, CFI= 0.92), and (RMSEA= 0.073, X2/df= 2.97) confirmed an excellent fit of the measurement model of the effects of climate change on sustainable livelihoods with observed data. In addition, the results of structural equation modeling showed that the greatest impacts of climate change among livelihood capitals were respectively related to physical, financial, natural, social and human capitals.