Volume 13, Issue 6 (2023)
LRR 2023, 13(6): 571-607 |
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Taherian T, Fazilatfar A M, Mazdaista G. Longitudinal Examination of Iranian L2 Learners' Trait Emotional Intelligence and Foreign Language Classroom Anxiety: Latent Growth Curve Modeling. LRR 2023; 13 (6) :571-607
URL: http://lrr.modares.ac.ir/article-14-49189-en.html
URL: http://lrr.modares.ac.ir/article-14-49189-en.html
1- PhD student of English language, Faculty of Language and Literature, Yazd University, Yazd, Iran
2- Professor, English Dept. Faculty of Foreign Languages and Literature, Yazd University , afazilatfar@yazd.ac.ir
3- Associate Professor, Department of English, Faculty of Language and Literature, Yazd University, Yazd, Iran
2- Professor, English Dept. Faculty of Foreign Languages and Literature, Yazd University , afazilatfar@yazd.ac.ir
3- Associate Professor, Department of English, Faculty of Language and Literature, Yazd University, Yazd, Iran
Abstract: (1157 Views)
One of the important abilities to understand emotions of others and oneself is emotional intelligence (EI). In addition, studies in the realm of psycholinguistics have indicated that EI is a highly relevant variable for managing negative emotions such as foreign language classroom anxiety (FLCA). In this study, we investigated the longitudinal association between adult English as a foreign language (EFL) learners’ trait emotional intelligence (TEI) and foreign language anxiety (FLCA). To this end, we conducted Latent Growth Curve Modeling (LGCM) to analyze data collected from 309 Iranian L2 learners in three measurement occasions during a year of learning English in private institutes. The results showed that while L2 learners' TEI increased, their level of FLCA decreased during the year. Also, at the beginning of the study, the significant negative correlation between L2 learners' TEI and FLCA was low but, during the semester, the negative correlation between the two variables turned out to be high.
1. Introduction
One of the important abilities to understand the emotions of others and oneself is emotional intelligence (EI). EI can be conceptualized based on different theoretical approaches (see Petrides, 2010; Hughes & Evans, 2018). Among these, the trait approach (Petrides et al., 2016) defines EI as an individual’s self-rating of his/her emotional ability. Trait emotional intelligence (TEI) has been categorized into four main subdomains: well-being, emotionality, self-control, and sociability (Petrides& Furnham, 2000, 2001). Despite the fact that research has indicated that TEI is a pivotal antecedent of learning a new language, the basic processes that underpin its effects are yet to be explored (Pekaar et al., 2020). Regarding this, Pekaar et al. (2020) have inspired researchers to take advantage of multilevel designs to capture individual differences in TEI and its fluctuations across time. To do so, the following research questions were raised:
2. Literature Review
A number EI models have been introduced in the field of psychology. Two more dominant models are ability EI model (Salovey & Mayer, 1990) and the TEI model (Petrides, 2017). Using a deductive approach, Salovey and Mayer (1990) introduced the model of ability EI with four branches: (1) the ability to detect emotions precisely, (2) the ability to apply emotions to further thought, (3) the ability to comprehend emotions, and (4) the ability to manage emotions. On the other hand, Petrides and Furnham (2000) proposed TEI model, which captures individuals' self-perceived or subjective emotional abilities, which is estimated with self-rated questionnaire. TEI entails fifteen facets categorized into four main subdomains or dimensions: well-being, emotionality, self-control, and sociability (Petrides & Furnham, 2003).
With this in mind, considering the advantages of parallel process model (PPM) and factor of curve model (FCM) in this study, we aimed to investigate both the primary growth factors of TEI and their covariations in order to explore the co-development of the different subdomains of TEI and the contribution of each subdomain to the global factor of TEI. Our model was extended by including emotion perception (EP) as a distal outcome.
3. Methodology
In the current study, a convenience sampling approach was applied according to our access to language learners in the private institutes of four cities in Iran. The sampling setting included learners who were acquiring English as a foreign language in these institutes. The data were collected from 28 classes with a range of 8 to 14 students per class. We gathered data from 309 (217 females, 92 males). The data collection occurred from February 2020 to February 2021. The proficiency level of this sample ranged between lower-intermediate to upper-intermediate.
This study aimed to investigate both the primary growth factors of TEI and their covariations in an effort to clarify the co-development of the subdomains of TEI. Given these, the following four hypotheses were developed for the present study.
The following instruments were used to collect the data in this study:
The Trait Emotional Intelligence Questionnaire–Short Form (TEIQue-SF) (Petrides, 2009). Firstly, the participants filled out the Persian translated short version of the TEI Questionnaire (Petrides, 2009), with 30 items. The TEI questionnaire also allowed us to estimate scores on the four TEI subdomains: well-being, emotionality, self-control, and sociability. Emotion Perception Task which (EPT) consists of 6 short audiovisual clips representing examples of four negative emotions (anger, fear, sadness, and disgust) and two positive emotions (surprise and happiness). We asked the participants to complete several sets of questions. The first set included questions on the participants’ semibiographical background, their age, their gender, and their language learning history, such as the languages known, and their self-perceived proficiency of these languages Additionally, the Persian version of TEIQue-SF (Petrides, 2009) was given to the participants in three measurement occasions with six-month intervals. The software program used to analyze the data in this study was Mplus 8.4 with a robust maximum likelihood estimator (MLR). The analysis followed the incremental steps for FCM recommended by Wickrama, et al. (2016).
4. Results
Corresponding to the incremental steps of conducting an FCM procedure, based on which each research question was developed, the results of the analysis are presented here in four steps. With respect to the longitudinal correlation patterns among repeated measures of each subdomain, correlation matrix revealed that the correlation coefficients between the two adjacent occasions for each subdomain were higher than the correlations between non-adjacent occasions. For Parallel process growth curve model (PPM), the intercept and slope variance of each model is also correlated. The model results showed that all between-subdomain auto-correlated errors were statistically significant and were within the acceptable bounds. In Estimating an unconditional Factor-of-Curves Model (FCM) and achieving empirical proof of the successful estimation of second-order growth factors for an FCM of TEI subdomains, the covariances (or correlations) among primary growth factors of these subdomains, as the indicators of the second-order growth curve, were supposed to be checked first. Concerning the association of the initial level of each subdomain with its slope, the PPM results showed moderately high correlations between intercept and slope growth factors within these subdomains.
5. Discussion
The current study investigated the trajectories of global factor of TEI as well as parallel development of the TEI's subdomains (e.g., well-being, emotionality, self-control, and sociability) over one year in the context of a foreign language classroom using PPM and FCM. With regard to the first research question addressing the direction and amount of change in four subdomains of TEI, the PPM results showed a statically significant increase over one year in these subdomains. We can conjecture that both the situational cues and the contextual factors during a foreign language course might have contributed to the increase in the students’ subdomains of TEI. As for the second research question, the association of the initial level of each subdomain of TEI with its slope, the PPM results revealed moderately high and positive associations between intercept and slope growth factors within the subdomains of TEI. The FCM results, with regard to the third research question, showed that the factor loadings for four primary growth factors on the global factors were high and statistically significant, which indicates that each of the primary growth factors contributed significantly to defining the global factor of TEI. To answer the fourth research question, regarding the direct effects of the global growth factors on the distal outcome after controlling for the effects of the primary growth factors, the results indicated that intercept and slope of the global TEI were associated with EP.
The overall findings of this research showed that the FCM procedure was a privileged and comprehensive analytical approach for the exploration of the co-development of L2 learners’ well-being, self-control, emotionality, and sociability subdomains of TEI in the dynamic context of a language class.
6. Conclusion
There is a shift in SLA from traditional one-time survey methods to more dynamic process-based approaches that allow for a distinction in causes, mechanisms, and consequences. Our model, as an inspiring and comprehensive model, intended to clarify the dynamics of TEI. It goes beyond them, however, in four important respects. First, the multilevel format of our model allows examining individual differences in TEI (which is characteristic of the TEI literature) and within-person emotion processes (which is characteristic of the dynamic perspective literature (Pekaar, et. al., 2020)), in tandem. Second, TEI- FCM also includes combinations of TEI dimensions. Research has suggested that not all individuals use all their TEI dimensions to the same extent, but that a unique mixture of TEI dimensions better resembles reality (Dave, et al., 2021; Pekaar, et al., 2020). Third, our model focuses on the role of time, which is an often-disregarded factor in psycholinguistics research (Hiver & Al-Hoorie, 2019). Incorporating time allows the investigation of the interplay of different subdomains of TEI and their amount of contribution to the global construct of TEI. Finally, our model also incorporated EI as a distal outcome of TEI. The inclusion of EP in investigating the developmental process of TEI over time could shed light on the interplay between different TEI dimensions when individuals are processing the emotions of others. This framework can serve as a starting point for the empirical investigation of more detailed processes that may play a role in the enactment of TEI.
1. Introduction
One of the important abilities to understand the emotions of others and oneself is emotional intelligence (EI). EI can be conceptualized based on different theoretical approaches (see Petrides, 2010; Hughes & Evans, 2018). Among these, the trait approach (Petrides et al., 2016) defines EI as an individual’s self-rating of his/her emotional ability. Trait emotional intelligence (TEI) has been categorized into four main subdomains: well-being, emotionality, self-control, and sociability (Petrides& Furnham, 2000, 2001). Despite the fact that research has indicated that TEI is a pivotal antecedent of learning a new language, the basic processes that underpin its effects are yet to be explored (Pekaar et al., 2020). Regarding this, Pekaar et al. (2020) have inspired researchers to take advantage of multilevel designs to capture individual differences in TEI and its fluctuations across time. To do so, the following research questions were raised:
- To what extent and in what direction do TEI's subdomains change over 12 months?
- To what extent are changes in well-being, emotionality, self-control, and sociability related over 12 months?
- How does global factor growth curve of TEI influence language learners’ emotion perception?
- To what extend do the growth parameters the subdomains of TEI predict language learners’ emotion perception?
2. Literature Review
A number EI models have been introduced in the field of psychology. Two more dominant models are ability EI model (Salovey & Mayer, 1990) and the TEI model (Petrides, 2017). Using a deductive approach, Salovey and Mayer (1990) introduced the model of ability EI with four branches: (1) the ability to detect emotions precisely, (2) the ability to apply emotions to further thought, (3) the ability to comprehend emotions, and (4) the ability to manage emotions. On the other hand, Petrides and Furnham (2000) proposed TEI model, which captures individuals' self-perceived or subjective emotional abilities, which is estimated with self-rated questionnaire. TEI entails fifteen facets categorized into four main subdomains or dimensions: well-being, emotionality, self-control, and sociability (Petrides & Furnham, 2003).
With this in mind, considering the advantages of parallel process model (PPM) and factor of curve model (FCM) in this study, we aimed to investigate both the primary growth factors of TEI and their covariations in order to explore the co-development of the different subdomains of TEI and the contribution of each subdomain to the global factor of TEI. Our model was extended by including emotion perception (EP) as a distal outcome.
3. Methodology
In the current study, a convenience sampling approach was applied according to our access to language learners in the private institutes of four cities in Iran. The sampling setting included learners who were acquiring English as a foreign language in these institutes. The data were collected from 28 classes with a range of 8 to 14 students per class. We gathered data from 309 (217 females, 92 males). The data collection occurred from February 2020 to February 2021. The proficiency level of this sample ranged between lower-intermediate to upper-intermediate.
This study aimed to investigate both the primary growth factors of TEI and their covariations in an effort to clarify the co-development of the subdomains of TEI. Given these, the following four hypotheses were developed for the present study.
The following instruments were used to collect the data in this study:
The Trait Emotional Intelligence Questionnaire–Short Form (TEIQue-SF) (Petrides, 2009). Firstly, the participants filled out the Persian translated short version of the TEI Questionnaire (Petrides, 2009), with 30 items. The TEI questionnaire also allowed us to estimate scores on the four TEI subdomains: well-being, emotionality, self-control, and sociability. Emotion Perception Task which (EPT) consists of 6 short audiovisual clips representing examples of four negative emotions (anger, fear, sadness, and disgust) and two positive emotions (surprise and happiness). We asked the participants to complete several sets of questions. The first set included questions on the participants’ semibiographical background, their age, their gender, and their language learning history, such as the languages known, and their self-perceived proficiency of these languages Additionally, the Persian version of TEIQue-SF (Petrides, 2009) was given to the participants in three measurement occasions with six-month intervals. The software program used to analyze the data in this study was Mplus 8.4 with a robust maximum likelihood estimator (MLR). The analysis followed the incremental steps for FCM recommended by Wickrama, et al. (2016).
4. Results
Corresponding to the incremental steps of conducting an FCM procedure, based on which each research question was developed, the results of the analysis are presented here in four steps. With respect to the longitudinal correlation patterns among repeated measures of each subdomain, correlation matrix revealed that the correlation coefficients between the two adjacent occasions for each subdomain were higher than the correlations between non-adjacent occasions. For Parallel process growth curve model (PPM), the intercept and slope variance of each model is also correlated. The model results showed that all between-subdomain auto-correlated errors were statistically significant and were within the acceptable bounds. In Estimating an unconditional Factor-of-Curves Model (FCM) and achieving empirical proof of the successful estimation of second-order growth factors for an FCM of TEI subdomains, the covariances (or correlations) among primary growth factors of these subdomains, as the indicators of the second-order growth curve, were supposed to be checked first. Concerning the association of the initial level of each subdomain with its slope, the PPM results showed moderately high correlations between intercept and slope growth factors within these subdomains.
5. Discussion
The current study investigated the trajectories of global factor of TEI as well as parallel development of the TEI's subdomains (e.g., well-being, emotionality, self-control, and sociability) over one year in the context of a foreign language classroom using PPM and FCM. With regard to the first research question addressing the direction and amount of change in four subdomains of TEI, the PPM results showed a statically significant increase over one year in these subdomains. We can conjecture that both the situational cues and the contextual factors during a foreign language course might have contributed to the increase in the students’ subdomains of TEI. As for the second research question, the association of the initial level of each subdomain of TEI with its slope, the PPM results revealed moderately high and positive associations between intercept and slope growth factors within the subdomains of TEI. The FCM results, with regard to the third research question, showed that the factor loadings for four primary growth factors on the global factors were high and statistically significant, which indicates that each of the primary growth factors contributed significantly to defining the global factor of TEI. To answer the fourth research question, regarding the direct effects of the global growth factors on the distal outcome after controlling for the effects of the primary growth factors, the results indicated that intercept and slope of the global TEI were associated with EP.
The overall findings of this research showed that the FCM procedure was a privileged and comprehensive analytical approach for the exploration of the co-development of L2 learners’ well-being, self-control, emotionality, and sociability subdomains of TEI in the dynamic context of a language class.
6. Conclusion
There is a shift in SLA from traditional one-time survey methods to more dynamic process-based approaches that allow for a distinction in causes, mechanisms, and consequences. Our model, as an inspiring and comprehensive model, intended to clarify the dynamics of TEI. It goes beyond them, however, in four important respects. First, the multilevel format of our model allows examining individual differences in TEI (which is characteristic of the TEI literature) and within-person emotion processes (which is characteristic of the dynamic perspective literature (Pekaar, et. al., 2020)), in tandem. Second, TEI- FCM also includes combinations of TEI dimensions. Research has suggested that not all individuals use all their TEI dimensions to the same extent, but that a unique mixture of TEI dimensions better resembles reality (Dave, et al., 2021; Pekaar, et al., 2020). Third, our model focuses on the role of time, which is an often-disregarded factor in psycholinguistics research (Hiver & Al-Hoorie, 2019). Incorporating time allows the investigation of the interplay of different subdomains of TEI and their amount of contribution to the global construct of TEI. Finally, our model also incorporated EI as a distal outcome of TEI. The inclusion of EP in investigating the developmental process of TEI over time could shed light on the interplay between different TEI dimensions when individuals are processing the emotions of others. This framework can serve as a starting point for the empirical investigation of more detailed processes that may play a role in the enactment of TEI.
Keywords: Trait emotional intelligence, foreign language classroom anxiety, longitudinal study, Latent Growth Curve Modeling.
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