Journal of Legal, Ethical and Regulatory Issues (Print ISSN: 1544-0036; Online ISSN: 1544-0044)

Research Article: 2021 Vol: 24 Issue: 6S

Trait Emotional Intelligence and Esl Teacher Effectiveness: Assessing the Moderating Effect of Demographic Variables Using PLS-MGA

Rahila Huma Anwar, NED University of Engineering and Technology

Sajida Zaki, NED University of Engineering and Technology

Ramayah Thurasamy, Universiti Sains Malaysia

Natasha Memon, NED University of Engineering and Technology


This study presents a framework to measure and empirically validate the relationship between trait EI and English as a Second Language teachers’ effectiveness. Moreover, the demographic variables (Gender, Teaching Experience, and Academic Qualification) are incorporated into the framework to assess their moderating effect on the relationship. We adopted a quantitative survey design involving both public and private sector higher education institutes (HEIs) of Pakistan. The participants (N=243 ESL teachers) were selected via convenience sampling. Partial Least Squares – Structural Equation Modeling (PLS-SEM) via SmartPLS3 software was utilized to perform latent variable and multigroup analyses. Findings indicate that trait EI enhances ESL teachers’ effectiveness resulting in a large effect size. While multigroup analyses revealed academic qualifications to moderate the relationship with PhD/Similar qualifications resulting in stronger trait EI – teacher effectiveness relationship. However, gender and teaching experience failed to reveal any moderating effect. This study is of significance as it underscored the importance of trait EI in facilitating teacher effectiveness at the tertiary level. Implications and directions for future research are discussed.


Trait Emotional Intelligence (Trait EI), Teacher Effectiveness (TE), ESL Teachers, Demographic Variables, Higher Education Institutes (HEIs), Multi group Analysis (MGA), PLS-SEM.


Administrators and policymakers in the educational sector are continually striving to devise policy and action plans to cater to the global market’s ever-emerging demands and new challenges (Gleason, 2018). Educational standards in Pakistan are also put to contest with challenging standards, high stakes testing, and raised accountability for administrators and teachers at all levels due to globalization and internationalization (Jibeen & Khan, 2015; Khan, 2017; Zakaria et al., 2016). It has become imperative to incorporate the innovative trends and changes practiced around the developed nations to promote meaningful and substantive educational improvement at all educational levels in Pakistan (Ahmad et al., 2014; Iqbal, 2016). In this pretext, Pakistan has realized the importance of the English language for global upward mobility and socio-economic prosperity.

Relating to this, universities in Pakistan face the challenge of attaining internationally recognized academic quality and reputation (Parveen et al., 2011; Riaz et al., 2017) primarily through the medium of English. The responsibilities and contributions of the ESL teachers are becoming more prominent and complicated. ESL teachers have a significant role in facilitating learners to join successfully in the fast-paced world. Therefore, it is necessary to renew the probe into the factors contributing to higher education teacher effectiveness, particularly for English as Second Language (ESL) teachers. A revived awareness of such links will generate stable, high-quality professional development experiences for teachers through teacher development programs. Su & Wood (2012) emphasize that “teachers in higher education encourage, support, inspire and enable those whom they teach”, similar to the teachers in the school context. ESL teaching is an even more complex process (Burns & Richards, 2009). Effective language teachers are identified as “active, thinking decision makers whose actions are influenced by the unobservable cognitive (and affective) dimension of teaching” (Borg, 2011). Psychological constructs and socio-affective skills are significant attributes of effective language teachers (Williams et al., 2016). Research studies have established the importance of trait emotional intelligence (trait EI) (Dewaele, 2018; Shahivand & Moradkhani, 2019) as an essential psychological variable closely related to teacher effectiveness (Penrose et al., 2007).

Trigwell (2012) asserts that “emotion is ubiquitous”, being a vital component of both students’ learning as well as teachers’ competencies. Teachers’ emotional intelligence skills help them utilize and control emotions in conflicting situations, influence their own motivation and cognition, enhance their students’ learning and perception as well as mental and physical well-being (Sutton & Wheatley, 2003). Thus, emotional intelligence is one of the most significant factors among the various skills and behaviors required of effective higher/tertiary ESL/EFL teachers (Dewaele, 2018; Dewaele et al., 2018) for the promotion of higher education agenda. Demographic factors such as gender, teaching experience, and academic qualification may interact as moderators with trait EI to impact its relationship with ESL teacher effectiveness (Dewaele, 2018; Mikolajczak et al., 2007; Petrides & Furnham, 2000). Regardless of the significance of trait EI in educational contexts and for teacher effectiveness, the association between these variables is an understudied area, and this dearth of research is particularly obvious at the higher education institutes (Hagenauer & Volet, 2014; Trigwell, 2012). Though research centering on English language teacher factors linking with emotional intelligence in the EFL context received considerable interest at the school level (Ashraf et al., 2017; Koçoglu, 2011) and some attention at the tertiary level (Kostic-Bobanovic, 2020). However, the ESL context remains underexplored, specifically at the higher education level.

Therefore, this study is planned to address this gap in knowledge and meet the demands for advancement in English education in Pakistan’s higher education ESL context. The purpose of this study was to examine the influence of trait emotional intelligence (Trait EI) (Petrides, 2009a) on the effectiveness of higher/tertiary English language teachers across Pakistan. The study also intended to probe the role of the demographic variables, i.e., gender, teaching experience, and academic qualification, in the trait EI and teacher effectiveness relationship. Thus, this research attempted to provide a pathway to develop global progress through teacher training, improved language pedagogy, and understanding of the field.

Literature Review and Hypotheses

Teacher Effectiveness

“The term teacher effectiveness is used broadly, to mean the collection of characteristics, competencies, and behaviors of teachers at all educational levels that enable students to reach desired outcomes” (Hunt, 2009). Although numerous definitions are proposed of teacher effectiveness, there is no consensual definition for the concept (Kim et al., 2019). Skelton (2009) proposed that teacher effectiveness “… represents a potent force to drive us forward in our efforts to understand and improve what we do”.

In the higher education context, Gibson (2010) emphasized that teachers must develop and equip themselves with creative and innovative skills to facilitate the latest trends in learning. Supporting this, Zhu et al. (2013) elaborated that the innovative skills of teachers involve four competencies based on learning, social, educational, and technological skills. Fitzmaurice (2010) suggests that teaching is more than simply being an intellectual exercise. Teaching is “relational” and “involves creating and maintaining caring physical, cultural, intellectual, social and moral environments which induce learning”. In an ever-changing context, assigning a finite set of skills for the modern-day teachers in any context is an unrealistic pursuit and remains a topic of debate in the literature.

ESL Teacher Effectiveness

According to Borg (2006), English language teachers deal with a number of challenges as compared to the teachers of other subjects. A language teacher needs to stay updated about the language teaching methodologies and practices (Carmel & Badash, 2019). English language teachers are also expected to maintain relaxed and close relationships with their students owing to the intensity that language learning entails (Borg, 2006). Along with general and cognitive capabilities, language teachers are deemed to possess certain personality traits (Shishavan & Sadeghi, 2009), making them develop a relationship of care and empathy with their students. Although “language teaching is a complex, multi-dimensional and dynamic process that conveys different meanings to different people” (Külekçi, 2018). At the same time, several research studies have associated teacher characteristics (Borg, 2006) with teacher effectiveness at varied educational contexts and levels. The present research study concerns ESL teachers at Pakistani HEIs.

English Language Teacher Self-Assessment

Borg & Edmett (2018) assert that there is no specific “universally accepted list of competencies that teachers generally or English language teachers specifically need” (p. 558). Nevertheless, some specifications of target competencies are essential to evaluate the teachers’ skills and address the teachers’ developmental needs (Borg & Edmett, 2018). Supporting this claim, Borg & Edmett (2018) developed the Self-Assessment Tool (SAT) for English language teachers. Elements in SAT are distributed among nine professional practices attributing it to being a multidimensional construct. Self-assessment is the process through which individuals assess their own competence. It is a crucial component of a teacher’s professional development and contributes to teacher evaluation (Borg, 2018). Goe et al. (2008) advocate teacher self-reports as an effective mode to evaluate teacher effectiveness. They believe that “self-report data can tap into a teacher’s intentions, thought processes, knowledge, and beliefs” better than any other method. Borg (2018) asserts that “teacher self-assessment is thus more likely to generate accurate results where the focus is on using these to inform professional development rather than for accountability”. In the present study, self-assessment is used to measure the teacher effectiveness of ESL teachers.

Trait Emotional Intelligence

Petrides & Furnham (2001) developed the trait EI model and instrument centered on the theory of trait EI, which is operationalized via trait EI Questionnaire (TEIQue). The trait EI theory “describes our perceptions of our emotional world: what our emotional dispositions are and how good we believe we are in terms of perceiving, understanding, managing, and utilizing our own and other people’s emotions” (Petrides et al., 2018). Petrides (2009) formulated TEIQue-SF (short form) based on 30 items which measure four broad factors i.e., well-being, self-control, emotionality, and sociability, whereas the global trait EI that directly feeds into the total score of TEIQue-SF. Well-being is about an individual’s positivity, happiness, and fulfillment that result from past happenings and future expectations. Self-control deals with the regulation of stress and pressure and control of urges, desires, and impulses. Emotionality relates with perceiving and expressing emotions to maintain relationships. Sociability stresses social relationships, stimulus, and self-confidence to communicate with varied groups.

TEIQue-SF assesses emotion related perceptions measured through self-report questionnaires. Higher-order structure of TEIQue-SF is hypothesized as oblique making it a multifaceted construct (Petrides & Mavroveli, 2018). Interrelated dimensions of well-being, emotionality, self-control, and sociability lie at the first-order level (Petrides, 2009), reflective of the higher-order trait EI. TEIQue is literally utilized in research and has been found to be a highly valid and reliable instrument in different research contexts (Parker et al., 2021). Therefore, following the EI literature in EFL/ESL contexts (Dewaele, 2018; Dewaele et al., 2008; Shahivand & Moradkhani, 2019; Shao et al., 2013), this study adopts trait EI theory and model for its purpose.

Trait Emotional Intelligence and Teacher Effectiveness

Although scarce, recent literature has highlighted the significance of emotions in the teaching and learning process at the HEIs (Asrar-ul-Haq et al., 2017; Pekrun, 2019; Postareff & Lindblom-Ylänne, 2011; Trigwell, 2012). Martin & Lueckenhausen (2005) revealed that teachers who have a good understanding of the teaching-learning process are emotionally charged, while teachers who are not emotionally affected face confusion and anxiety. Trigwell (2012) stated that positive emotions are related to a student-focused and conceptual-change approach to teaching, while negative emotions make teachers adopt a teacher-focused, information-transmission approach. Hagenauer & Volet (2014) highlighted the importance of university teachers’ emotions resulting from their teaching practices and student-teacher interactions.

An evolving body of literature maintains that the teachers’ EI influences their effectiveness (Dewaele, 2018; Li et al., 2018; Zhang et al., 2019). Ignat and Clipa (2012) stated that the teachers’ EI contributes in displaying positive attitude in them and contentment in professional and personal domains, which enhances their effectiveness as teachers. Dewaele’s (2018) research involving 513 ESL/EFL teachers from all around the world, with the majority teaching at the university, implied that trait EI enhances TE enabling teachers to deal with their own and their students’ emotions well. Among other studies on the same data, research revealed that high trait EI of EFL/ESL teachers is positively associated with positive attitudes towards their students (Dewaele & Mercer, 2018). On the same data, positive relationships were found between global trait EI and other variables resulting in more creativity, slightly less predictability, improved classroom management, effective pedagogical skills (Dewaele et al., 2018).

Asrar-ul-Haq et al. (2017) involved 166 teachers from universities in central Punjab in Pakistan in their study. They revealed that the teachers’ emotional characteristics are positively associated with their job performance. In the same context, another study conducted with a group of public sector university teachers revealed a positive correlation between EI and teaching success of the university ESL teachers (Anwar et al., 2013). Research studies suggest that EI trainings can help bring about positive EI development and EI related behaviors which in turn may influence teachers’ practice, understanding and relationship with students (Dolev & Leshem 2017; Shahivand & Moradkhani 2019).

Literature presented here suggests the imperative role of EI in teacher factors and effectiveness. Literature also highlights the scarcity of available research in ESL context and HE settings. Thus, it is hypothesized that ESL teachers’ trait EI is positively related to their TE.

H1 Trait EI is positively related to teacher effectiveness.

Gender as a Moderator

Research on emotional intelligence indicates that there are differences in the endorsement of high and low levels of emotional intelligence between males and females. Mikolajczak et al. (2007) conducted the psychometrical analysis of the Trait Emotional Intelligence Questionnaire (TEIQue, Petrides & Furnham, 2003) in a French-speaking population. Among other significant findings, they concluded that the TEIQue scores were influenced by the gender. Regression analysis results of Petrides & Furnham’s (2000) research showed that gender was a significant predictor of self-estimated trait EI. Gkonou & Mercer (2017) explored the predictive power of demographic variables including gender, age, and teaching experience on trait EI. Results showed that gender had the strongest influence on trait EI. Most of the studies in international and local contexts have shown female teachers to have higher EI scores than male teachers (Abbas & Haq, 2011; Ahmed, 2015; Bibi et al., 2016; Fernández-Berrocal et al., 2012; Hekmatzadeh et al., 2016).

Majority of the studies discussed here report females showing higher EI than males. Therefore, this study will examine the moderating role of gender in the relationship between trait EI and ESL teacher effectiveness, hypothesizing that the relationship will be stronger for female ESL teachers than for male teachers.

H2 Gender will moderate the positive relationship between trait EI and ESL teacher effectiveness, such that relationship will be stronger for female’s teachers than for male teachers.

Years of Teaching Experience as a Moderator

Dewaele (2018) revealed significant positive relationship between the teachers’ teaching experience and trait EI. Quantitative findings of Gkonou & Mercer’s (2017) mixed methods study revealed high levels of trait EI among English language teachers, and that gender and years of teaching experience were significant predictors of trait EI. Dewaele (2018) in their study found that the four trait EI facets and years of teaching experience are significantly related showing positive correlations with sociability and self-control. Dewaele (2018) claimed that the results indicate that experience enhances sociability and self-control which are important factors of trait EI. Another encouraging study on the same data conducted by Dewaele & Mercer (2018) revealed that teachers with more experience displayed more positive attitudes towards their students. Studies have shown that the years of teaching experience is significantly associated with EI and influence EI relationships (Ghanizadeh & Moafian, 2009; Shaukat, 2016) Based on the discussed literature this study tends to investigate the moderating role of years of teaching experience in the relationship between trait EI and ESL teacher effectiveness in Pakistan assuming that the relationship may be stronger when experience is more.

H3 Years of teaching experience will moderate the positive relationship between trait EI and ESL teacher effectiveness, such that relationship will be stronger when experience in more.

Academic Qualification as a Moderator

Studies have also shown influence of level of qualification on teachers’ trait EI and their effectiveness. Shaukat (2016) explored the emotional intelligence of 372 in-service and pre-service teachers from two public universities and four public schools. Results confirmed that teachers with advanced professional qualifications had a higher EI level than less qualified teachers. Shaukat (2016) asserted that university teachers’ qualification was an important factor influencing their teaching profession. Ahmed (2015) analyzed the demographic variation among university teachers’ EI and job satisfaction. Using statistical analysis, Ahmed (2015) received study outcomes that indicated that the university teachers holding a PhD qualification were more emotionally intelligent than the teachers with other qualification levels. However, literature reveals a gap in understanding the moderating effect of academic qualification on the relationship between trait EI and ESL teacher effectiveness. Therefore, this study attempts to fill this gap by investigating the moderating role of HE teachers’ academic qualification in the relationship between trait EI and ESL teacher effectiveness.

H4 Academic qualification will moderate the positive relationship between trait EI and ESL teacher effectiveness, such that relationship will be stronger when experience in more.

Figure 1: Conceptual Framework of the Study


Participants and Procedure

The study adopted a quantitative survey design involving Pakistan HEIs, including public and private sector universities and colleges. The participants comprised 243 ESL teachers (Males=81 and Females=162) teaching at HEIs in Pakistan and holding English related academic credentials. Teachers’ teaching experience varied from 1-2 years to more than 10 years, and their ages ranged between 21 and 60 years or above. Teachers’ selection was based on homogeneous convenience sampling (Jager et al., 2017), and participation was completely voluntary (see Table 1 for demographic details). As the present study intends to test the truth about proposed theoretical effects, thus the use of a convenience sample is justified (Hulland et al., 2018). See Table 1 for respondent demographics.

Table 1
Respondent Demographics
Characteristics Frequency Percentage  
Gender Male 81 33.3
Female 162 66.7
Age 21 to 30 72 29.6
31 to 40 97 39.9
41 to 50 58 23.9
51 to 60 15 6.2
Above 60 1 0.4
Education BA/BS or similar 14 5.8
MA/MS/MPhil 188 77.4
PhD 39 16
Other 2 0.8
Position Professor 8 3.3
Associate Professor 14 5.8
Assistant Professor 53 21.8
Lecturer 135 55.6
Other 33 13.6
Sector Public 157 64.6
Private 86 35.4
Experience 1-2 years 36 14.8
3-5 years 51 20.9
6-10 years 46 18.9
More than 10 years 110 45.7


This study adopted multidimensional constructs as multidimensional constructs consist of several diverse, but related dimensions which are taken as one theoretical concept (Law et al., 1998). An open access anonymous online research questionnaire was developed and forwarded to the ESL teachers, teaching at HEIs across the country, through emails. The first section of the questionnaire probed demographic details to gather information such as gender, age, experience, academic qualification of the participants.

Section two of the questionnaire was based on assessing TE through Self-Assessment Tool (SAT) for English language teachers developed by Borg and Edmett (2018). Reliability of individual factors (Professional practices) in all nine sections of the tool ranged between 0.74 and 0.89 (Borg & Edmett, 2018). Seven SAT professional practices were retained for the purpose of the present study: (1) Planning lessons and courses (PLC); (2) Managing the lesson (ML); (3) Assessing learning (AL); (4) Knowing the subject (KS); (5) Managing resources (MR); (6) Using inclusive practices (UIP); and (7) Understanding your learners (UYL). However, two of SAT practices i.e. ‘IICT’ and ‘promoting 21st century skills’ were excluded as being less relevant to the present context and respondents of the study, after approval of the developers. Inclusion of these two practices would have led to unreliable responses. An item stating “I involve parents, learners and any other relevant persons in an inclusive learning environment” was also excluded from the professional practice UIP, as was not relevant to the HE context. Therefore, 36 items were retained in SAT for the present study. However, reliability (Cronbach’s alpha) remained same as for the entire scale (i.e., 0.94). Teachers were supposed to assess themselves on a five-point scale.

Third section of the questionnaire comprised TEIQue-Short Form (Petrides 2009), which is composed of four EI factors: well-being, self-control, emotionality, and sociability. TEIQue-SF is a 7-point Likert scale, where 1=strongly disagree and 7=strongly agree. Research has evidence of good reliability of TEIQue-SF total score of around 0.70 (Petrides 2009), as the internal reliability of the assessment is mostly above 0.80 and has never evidenced below 0.70 in research studies (Shao et al. 2013). In this study TEIQue-SF total revealed reliability (Cronbach’s alpha) of 0.903 with Global items included and 0.796 with latent variable scores generated through SmartPLS 3 (See Table 2).

Data Analysis

Data were analyzed using Partial Least Squares – Structural Equation Modelling (PLS-SEM) (Hair et al., 2017) employing SmartPLS3 software (Ringle et al., 2015). The adoption of multidimensional constructs led to a more advanced model design in PLS-SEM for the present study using higher-order model (Ringle et al., 2018). Higher-order model facilitates modeling construct on an abstract dimension, termed as higher-order components (HOCs), and the same abstract dimension is represented by its more concrete sub-dimensions termed as lower-order components (LOCs) (Sarstedt et al., 2019). HOCs reduce the number of path model relationships making the model more parsimonious (Becker et al., 2012). Reflective-reflective type model is specified for the study as the LOCs are reflectively measured latent variables which are distinct but correlated (Becker et al., 2012). Such a model facilitates the study objective ‘to find the common factor of several related, yet distinct constructs’ (Becker et al., 2012, p. 363). Further, two-stage approach is allocated to the HOCs in the model (Ringle et al., 2012). For the evaluation of the higher-order models similar criteria apply as for usual PLS-SEM analysis. However, two additional measurement models are computed: (1) Measuring LOCs and (2) Measuring HOCs all-over, reflecting relationships between HOCs and its LOCs (Sarstedt et al., 2019). In addition, the moderating effects of Gender, Teaching Experience, and Academic Qualification on the strength of the relationship between the Trait EI teacher effectiveness was tested using MGA technique (Henseler et al., 2009) using SmartPLS 3.3.3 (Ringle et al., 2015). PLS-SEM is best suited for the multi-group analysis. Assessing moderation through PLS-MGA can substantially help to arrive at meaningful differences in multiple relationships across group-specific results (Cheah et al., 2020).


Preliminary Analysis

Prior to assessing the model in SmartPLS3, normality was confirmed with the skewness and kurtosis of all the sub-scales and total scores of trait EI (TEIQue) and TE (SAT) within the approved limits of ±1 (Gravetter & Wallnau, 2014) see Table 2. Since data was collected using a single source, we first tested the issue of Common Method Bias by following the suggestions of Kock & Lynn (2012) and Kock (2015) by testing the full collinearity. In this method, all the variables are regressed against a common variable, and if the VIF ≤ 5, there is no bias from the single source data. The analysis yielded VIF less than 5; thus single source bias is not a serious issue with the study data.

Measurement Model Quality: Reliability and Validity Tests

Standard criteria are applicable to assess reliability and validity for LOCs and HOCs, i.e., trait EI (TEIQue). Following Hair et al. (2017), indicator reliability was established through the standardized loadings, and items loading between 0.4 and 0.7 were considered for deletion. Items with loading less than 0.5 were omitted among LOC dimensions. Internal consistency reliability was ensured through composite reliability (CR) score for each LOC and HOC, which was above the recommended 0.7 (Hair et al. 2019). Convergent validity, through assessing average variance extracted (AVE) scores, for each LOC and HOC, was ensured with AVE observed above 0.5 (Hair et al. 2019). See Table 2 for measurement model quality of the LOCs and HOCs (given in bold), See Table 3 for LOC and Table 4 for HOC (pooled data and grouped data) measurement model loadings and Figure 2 and Figure 3 for the same. Discriminant validity of the LOCs and HOCs were assessed through the HTMT values which should be ≤ 0.85, the stricter criterion and the mode lenient criterion is it should be ≤ 0.90 (Henseler et al., 2015). Values of HTMT were all lower than the stricter criterion of ≤ 0.85 implying that the respondents understood that 11 sub-constructs are distinct, see Table 5 and Table 6 for HTMT values of LOCs and HOCs (pooled data and grouped data) respectively. HOC measurement model was also valid and reliable (as seen in Figure 3). Latent variable scores of the LOCs obtained from the first stage were used to create and estimate the stage two models.

Table 2
Measurement Model Quality: Normality, Internal Consistency Reliability and Convergent Validity
Constructs Mean Std. Deviation Skewness Kurtosis Alpha CR AVE
TE (SAT) 4.326 0.412 -0.438 -0.011 0.894 0.917 0.614
Planning Lessons & Courses (PLC) 4.38 0.508 -0.72 0.658 0.792 0.858 0.547
Managing Lesson (ML) 4.537 0.441 -0.831 0.25 0.783 0.852 0.537
Assessing Learning (AL) 4.286 0.556 -0.513 -0.207 0.821 0.875 0.583
Knowing the Subject (KS) 4.243 0.542 -0.477 0.225 0.854 0.889 0.533
Managing Resources (MR) 4.337 0.608 -0.92 0.536 0.857 0.897 0.637
Using Inclusive Practices (UIP) 4.458 0.507 -0.762 -0.156 0.667 0.804 0.579
Understanding Your Learners (UYL) 4.043 0.587 -0.369 -0.178 0.815 0.872 0.578
TEIQue_Total 5.676 0.779 -0.517 -0.383 0.796 0.867 0.621
Well-being 5.949 0.758 -0.61 -0.016 0.72 0.825 0.542
Self-control 5.372 1.005 -0.551 0.167 0.714 0.824 0.54
Emotionality 5.637 0.912 -0.416 -0.654 0.758 0.838 0.509
Sociability 5.341 0.922 -0.331 -0.385 0.717 0.822 0.54
Table 3
Indicator Reliability Lower-Order Constructs (LOCs)
Indicators Loadings Indicators Loadings Indicators Loadings Indicators Loadings
Well-being Sociability ML MR        
Well_being1 0.702 Sociability1 0.821 SATML5 0.757 SATMR1 0.789
Well_being2 0.72 Sociability2 0.818 AL SATMR2 0.84  
Well_being3 0.77 Sociability5 0.662 SATAL1 0.803 SATMR3 0.844
Well_being4 0.751 Sociability6 0.615 SATAL2 0.723 SATMR4 0.722
Self-control PLC SATAL3 0.762 SATMR5 0.789    
Self-control1 0.72 SATPLC1 0.775 SATAL4 0.755 UIP  
Self_control2 0.8 SATPLC2 0.729 SATAL5 0.772 SATUIP1 0.706
Self_control5 0.745 SATPLC3 0.781 KS SATUIP2 0.781  
Self_control6 0.667 SATPLC4 0.738 SATKS1 0.701 SATUIP4 0.792
Emotionality SATPLC5 0.67 SATKS2 0.777 UYL    
Emotionality1 0.7 ML SATKS3 0.702 SATUYL1 0.688  
Emotionality2 0.732 SATML1 0.755 SATKS4 0.772 SATUYL2 0.82
Emotionality3 0.632 SATML2 0.693 SATKS5 0.715 SATUYL3 0.783
Emotionality5 0.762 SATML3 0.794 SATKS6 0.724 SATUYL4 0.819
Emotionality8 0.735 SATML4 0.656 SATKS7 0.718 SATUYL5 0.677
Table 4
Indicator Reliability of Higher-Order Constructs (HOC)and Grouped Data
Trait EI (TEIQue) Pooled Data Female Males Exp 1 - 10 Years Exp 10 years + Grad/PostGrad PhD/Similar
Outer Loadings
Well-being 0.73 0.73 0.748 0.683 0.799 0.689 0.897
Self-control 0.787 0.816 0.735 0.775 0.781 0.796 0.745
Emotionality 0.828 0.836 0.804 0.811 0.838 0.816 0.91
Sociability 0.804 0.778 0.847 0.837 0.726 0.804 0.812
Teacher Effectiveness (SAT)
Planning Lessons and Courses 0.829 0.807 0.854 0.809 0.845 0.818 0.875
Managing Lesson 0.813 0.81 0.807 0.784 0.845 0.811 0.822
Assessing Learning 0.836 0.823 0.851 0.819 0.843 0.84 0.812
Subject Knowledge 0.821 0.818 0.82 0.791 0.845 0.808 0.873
Managing Resources 0.796 0.795 0.788 0.756 0.836 0.785 0.842
Using Inclusive Practices 0.716 0.726 0.686 0.71 0.735 0.71 0.748
Understanding Learners 0.656 0.663 0.632 0.645 0.692 0.681 0.59
Table 5
Discriminant Validity Assessment of The Lower-Order Constructs (HTMT)
Construct 1 2 3 4 5 6 7 8 9 10 11  
1 Well-being 0                    
2 Self-control 0.622 0                  
3 Emotionality 0.577 0.747 0                
4 Sociability 0.576 0.64 0.776 0              
5 PLC 0.43 0.419 0.445 0.375 0            
6 ML 0.427 0.379 0.435 0.454 0.826 0          
7 AL 0.418 0.403 0.426 0.328 0.878 0.797 0        
8 KS 0.365 0.382 0.327 0.28 0.769 0.741 0.789 0      
9 MR 0.31 0.321 0.417 0.371 0.74 0.659 0.721 0.766 0    
10 UIP 0.318 0.394 0.382 0.424 0.594 0.695 0.552 0.654 0.639 0  
11 UYL 0.281 0.376 0.441 0.514 0.519 0.524 0.561 0.503 0.515 0.472 0
Note: HTMT= Heterotrait-Monotrait Ratio, PLC = Planning Lessons and Courses, ML = Managing Lesson, AL = Assessing Learning, KS = Subject Knowledge, MR = Managing Resources, UIP = Using Inclusive Practices, UYL = Understanding Learners
Table 6 Discriminant Validity (HTMT)HOCs and Grouped Data
    1 2
Pooled Data Trait EI    
Teacher Effectiveness (SAT) 0.579  
Females Trait EI    
Teacher Effectiveness (SAT) 0.584  
Males Trait EI    
Teacher Effectiveness (SAT) 0.597  
Exp 1 - 10 Years Trait EI    
Teacher Effectiveness (SAT) 0.614  
Exp 10 Years + Trait EI    
Teacher Effectiveness (SAT) 0.502  
Grad/PostGrad Trait EI    
Teacher Effectiveness (SAT) 0.561  
Grad/PostGrad Trait EI    
Teacher Effectiveness (SAT) 0.688  

Figure 2: Measurement Model for the Lower-Order Constructs (LOCS)

Figure 3: Measurement Model for the Higher-Order Constructs (HOCS)

Structural Model

Path Coefficients Assessment

A standard PLS-SEM criterion is applied to assess the structural model of the HOCs (Becker et al., 2012). Thus, in order to assess the significance of the path coefficients standard bootstrapping procedure was run with 5,000 samples (Hair et al., 2017) using SmartPLS3 software resulting in direct trait EI → teacher effectiveness path coefficients. H1 of the study found empirical support (β=0.491, t=10.644, p=0.000). Path coefficient results are presented in Table 7.

Table 7
Structural Model Assessment
Path Assessment Predictive Capability of the Model
Hypothesis Relationships Std Beta t-values p-values BCI LL BCI UL Support R2 Q2 f2 Effect Size
H1 Trait EI -> TE 0.491 10.644 0 0.406 0.557 Yes 0.241 0.141 0.317 Large

Predictive Power of the Model

Coefficient of determination (R2) helps to understand the variation explained in the dependent variables of the study (Hair et al., 2017). However, r-squared value could vary across different fields of study. In social sciences research domains, a minimum of 0.10 R2 is agreeable (Falk & Miller, 1992). Study found R2 values of 0.241 for TE, considered moderate but acceptable value (Cohen, 1998). Effect size, f- squared (f2) of trait EI on TE revealed large effect (f2=0.317) (Cohen, 1998). In order to assess the predictive relevance of the study blindfolding procedure was run (Geisser, 1974; Stone, 1974) which suggests that cross validation redundancy measure Q2 value should be greater than zero (Chin, 1998). Consequently, the Q2 values for the endogenous variable (i.e. TE) were found to be greater than zero, see Table 7.

Multigroup Analysis (MGA)

It is recommended to assess the data groups for measurement invariance in PLS path model before performing MGA between two or more groups (Cheah et al., 2020). Measurement invariance or measurement equivalence is a technique that confirms that any differences in survey responses were not because of group-specific interpretation of the survey content (Henseler et al., 2016). The MICOM procedure confirms that “group differences in model estimates do not result from the distinctive content and/or meanings of the latent variables across groups” (Hair et al., 2018).

The MICOM procedure is comprised of three steps which involves assessment of configural invariance (Step I). Configural invariance (Step I) is met before running the MICOM procedure. Configural invariance (Step I) was assumed for all moderation analyses. Each group was presented with the same instrument, an online survey with no deterministic or branching questions, and the resulting data was not segregated when building the initial PLS model. It is worth mentioning when running MICOM, configural invariance (Step I) is automatically confirmed in SmartPLS 3 (Cheah et al., 2020). Compositional invariance (Step II), and the equality of a composite’s mean value and variance across groups (Step III) (Cheah et al., 2020; Hair et al., 2018). Acquiring acceptable results in configural invariance (Step I) and compositional invariance (Step II) that partial measurement invariance is established and that the researchers can run MGA to compare the path coefficients. However, in addition to confirming partial measurement (Step I and Step II), full measurement invariance is established if composites reveal equal means and variances across the groups (Step III). Hence, acceptable results in Step I, Step II, and fulfilling any one of the requirements of Step III (either equality of composite variance or equality of composite mean) leads to the claim that partial measurement invariance is achieved in MICOM and thus, can proceed with MGA (Cheah et al., 2020). Following acceptable results of the MICOM procedure, either partial or full, the group differences can be assessed using PLS-MGA.

For group comparisons, this study adopted Henseler’s bootstrap-based MGA (Henseler et al., 2009) in the SmartPLS 3 software. Henseler’s PLS-MGA test (Henseler et al., 2009) uses a nonparametric assumption (Cheah et al., 2020). Henseler’s PLS-MGA result is significant at the 5% probability level, where p-value for the difference in group-specific path coefficients should be smaller than 0.05 or larger than 0.95 (Cheah et al., 2020). When the sample in groups is unequal, Henseler’s PLS-MGA approach is recommended especially when one-sided hypotheses are being tested (Hair et al., 2018). The results from the multiplegroup moderation analysis (MGA) served to complete the evaluation of the moderation hypotheses (H2 to H4) established in the study. The data groups were then processed with PLS-MGA using the PLS algorithm and bootstrapping to assess differences in path coefficients between the respective groups (see table 8 for MGA groups).

Table 8
Groups For Multigroup Analysis
Variable Groups Frequency Percentage
Gender Females 162 66.66
Males 81 33.33
40 Above 74 30.45
Teaching Experience 1 - 10 Years 133 54.73
More than 10 Years 110 45.26
Academic Qualification Graduate/Post Graduate 202 83.12
PhD/Similar 41 16.87

In accordance with the MICOM procedure, we established the partial measurement invariance of the three groups (Table 9 and Table 10), which is a requirement for comparing and interpreting the MGA group specific differences of PLS-SEM results (Henseler, Ringle, & Sarstedt, 2016). Satisfactory MICOM results led to performing PLS-MGA to observe group differences for Gender, Teaching Experience, and Academic qualification (see Table 11).

As PLS-MGA uses a one-tailed test, the p-values in the report showed whether the path coefficient was significantly larger in the first group (i.e. Females, Exp 1-10 years, Grad/PostGRad) than in the second group (i.e. Males, Exp above 10 years, PhD and similar) indicating the significant difference between the groups. Based on the analysis from PLS-MGA, the result indicated a difference between the groups regarding the endogenous variable teacher effectiveness. Results for Gender and Teaching experience groups revealed that none of the relationships had any significant p-value (p< 0.05), all p-values being larger than 0.05. Hence, there was reported no significant difference in the group-specific parameter estimates for outer weights, outer loadings, and path coefficients. This concludes that the relationship between trait EI and teacher effectiveness is not moderated by Gender differences or teaching experience differences, as no moderating effect was found through PLS-MGA. Thus, rejecting H2 and H3 of the study.

The results for Academic qualification groups (Grad/PostGRad – PhD/similar) indicated that the relationship between trait EI and teacher effectiveness had a significant p-value (p< 0.05), supporting H4 of the study. Therefore, implying that the relationship between trait EI and teacher effectiveness is moderated by Academic Qualification levels (Grad/PostGRad - PhD and similar).

Table 9
Measurement Invariance of Composite Models (MICOM) - Step 2 Correlations
Original Permutation 5.00% p-Values  
On Gender
TE 0.999 0.997 0.993 0.905
Trait EI 0.998 0.996 0.989 0.675
On Teaching Experience
TE 0.995 0.997 0.994 0.07
Trait EI 0.997 0.996 0.99 0.542
On Academic Qualification
TE 0.997 0.995 0.986 0.551
Trait EI 0.995 0.992 0.976 0.452
Table 10
Measurement Invariance of Composite Models (MICOM) Step 3
On Gender
Mean Difference (Females-Males) Variance Difference (Females-Males)
Original Perm LL UL p-Values Original Perm LL UL p-Values  
TE 0.345 -0.001 -0.218 0.223 0.004 -0.203 0.017 -0.306 0.34 0.147
Trait EI 0.001 -0.004 -0.222 0.223 0.512 0.138 0.012 -0.28 0.306 0.239

On Teaching Experience

Mean Difference (Exp 1-10 years - Exp above 10 years) Variance Difference (Exp 1-10 years - Exp above 10 years)
Original Perm LL UL p-Values Original Perm LL UL p-Values  
TE -0.273 -0.006 -0.217 0.209 0.016 -0.059 -0.003 -0.323 0.304 0.367
Trait EI -0.453 0.007 -0.22 0.22 0.398 0.004 -0.285 0.291 0.008  
On Academic Qualification
Mean Difference (Grad/PostGRad – PhD/similar) Variance Difference (Grad/PostGRad – PhD/similar)
Original Perm LL UL p-Values Original Perm LL UL p-Values  
TE -0.039 0.006 -0.268 0.296 0.409 -0.15 0.028 -0.387 0.453 0.241
Trait EI -0.374 0.001 -0.281 0.289 0.014 0.21 0.038 -0.304 0.427 0.208
Note: Step 3 results concluded that not all the composite mean values and variances were equal; only partial measurement invariance was supported.

This supports the study H4. This means that higher trait EI will lead to higher teacher effectiveness if Academic Qualification is more. Further bootstrapping results were compared to assess the difference between the Academic Qualification groups’ path coefficients. Table 11 shows that PhD/Similar (β=0.625) had a stronger path coefficient than Graduate/PostGraduate (β=0.476).

Table 11
Moderation Path Coefficients
Gender PLS MGA Path Coefficients Bootstrap Path Coefficients  
(Females - Males ) (Females vs Males ) Original    
Relationship Path Coefficients-diff p-Value original p-Value new Support Females Males  
H2 Trait EI -> TE -0.013 0.556 0.444 No 0.497 0.51
Teaching Experience PLS MGA Path Coefficients Bootstrap Path Coefficients
(Exp 1-10 years - Exp above 10 years) (Exp 1-10 years - Exp above 10 years ) Original      
Relationship Path Coefficients-diff p-Value original p-Value new Support Exp. 1-10 years Exp. above 10 years
H3 Trait EI -> TE 0.08 0.171 0.171 No 0.522 0.443
Academic Qualification PLS MGA Path Coefficients Bootstrap Path Coefficients
(Grad/PostGRad – PhD/similar) (Grad/PostGRad – PhD/similar ) Original  
Relationship Path Coefficients-diff p-Value original p-Value new Support Grad/PostGRad PhD/ similar
H4 Trait EI -> TE -0.15 0.952 0.048 Yes 0.476 0.625


The study hypothesized that trait EI is positively related to teacher effectiveness (H1). The study has found that trait EI positively influences teacher effectiveness (β=0.491, t=10.644, p<0.000). This asserts that trait EI enhances the effectiveness of the ESL teachers. The relationship between trait EI and TE is of significance, as asserted by the large effect size (f2=0.317) of trait EI on TE, indicating the important role that trait EI plays in establishing the high effectiveness of the teachers. One of the most important findings is that the ESL teachers’ trait EI explains 24% (R2=0.241) of the total variance of their effectiveness. Also, the positive relationship suggests that the more the ESL teachers practice their trait EI, the more it will enhance their effectiveness as teachers. The results advance previous studies that emphasize EI's importance as a pre-requisite for an effective teacher (Trigwell, 2012). This finding is consistent with prior research indicating the positive effect of teachers’ trait EI on their teaching practices, performance, professionalism, and effectiveness (Li et al., 2018) in general, English language teachers (ESL/EFL) specifically (Dewaele, 2018; Dewaele et al., 2018), and higher/tertiary education teachers particularly (Ahmed, 2015; Asrar-ul-Haq et al., 2017). The most important studies in line with the results of this study are the studies conducted by Dewaele (2018) and Dewaele, Gkonou, et al. (2018). Their research related trait EI to teacher effectiveness by revealing relationships with several variables and concluded that emotions are an indispensable part of language education. It is crucial to probe further how teachers might manage their emotions and those of their learners.

Based on the analysis from PLS-MGA, it was revealed that the relationship between trait EI and teacher effectiveness is not moderated by Gender differences, as the relationship had did not find a significant p-value (p<0.05). This result is inconsistent with Petrides and Furnham’s (2003) conclusion that gender influences the TEIQue scores. The finding is also contradictory with several other studies that emphasized the role of gender in influencing trait EI and favoring the female gender (Abbas & Haq, 2011; Bibi et al., 2016; Dewaele et al., 2018; Fernández-Berrocal et al., 2012; Gkonou & Mercer, 2017; Hekmatzadeh et al., 2016; Shaukat, 2016). However, the result is in line with Petrides et al.’s (2018) study, where he explored the role of trait EI in educational settings and found that gender differences did not influence trait EI. They also claimed that gender-specific effects were found in some research studies, but their findings did not favor one gender over another (Petrides et al., 2018). It could be stated that the debate on gender and emotional intelligence influence is inconclusive and needs more exploration.

Years of teaching experience was also not found to moderate the trait EI and teacher effectiveness relationship. Thus, rejecting H3 of the study with resulting insignificant p-values through the PLS-MGA. The results contradict the studies that revealed and supported the role of teaching experience as an important factor in influencing trait EI and teacher effectiveness factors (Dewaele, 2018; Dewaele et al., 2018; Dewaele & Mercer, 2018; Ghanizadeh & Moafian, 2009; Gkonou & Mercer, 2017; Kostic-Bobanovic, 2020; Shaukat, 2016). Although literature supports the imperative role of years of teaching experience in the relationship with trait EI and its development, but contradictory findings are also reported in some research studies (Ahmed, 2015; Rahmat et al., 2014). The result of this study is in line with contradictory findings on the role of experience. Interestingly, as mentioned above, international studies widely confirm ‘experience’ acting as a potential moderator in trait EI and teacher effectiveness relationship directly or indirectly. This opens an interesting avenue to probe the role of teaching experience as moderator, especially in the Pakistani context.

Based on the analysis from PLS-MGA, significant p-value (p< 0.05) revealed that the relationship between trait EI and teacher effectiveness is moderated by academic qualification variable. Thus, H4 of this study found support (p=0.048 i.e., p<0.05) and academic qualification is confirmed as a moderator in trait EI and teacher effectiveness relationship in this study. The bootstrapping results were compared to assess the difference between the academic qualification path coefficients and found that PhD/Similar (β=0.625) had a stronger path coefficient than Grad/PostGrad (β=0.476) (see Table 11). This asserts that the higher/tertiary ESL teachers with higher academic qualification (PhD/similar) will have stronger trait – teacher effectiveness relationship than the ESL teachers with low academic qualification (Graduate/Post Graduate). This result is in line with the literature that supported the role of academic qualification as important in the trait EI and teacher effectiveness relationship (Amirian & Behshad, 2016; Gkonou & Mercer, 2017). In the Pakistani context, Shaukat (2016) revealed that university teachers’ qualification was an important factor influencing their teaching profession. Ahmed (2015) explored that the university teachers holding a Ph.D. qualification were more emotionally intelligent than the teachers with other qualification levels. However, literature reveals a gap in understanding the moderating effect of academic qualification on the relationship between trait EI and ESL teacher effectiveness. This study attempted to fill this gap, assuming that the relationship will strengthen when teachers are more qualified.

Implications and Future Directions

The study results have led to several implications. Results have supported the positive relationship between ESL teachers’ trait EI and teacher effectiveness. The findings are consistent with prior research indicating the positive effect of teachers’ trait EI on their effectiveness (Dewaele, 2018) within the teaching profession at higher education. It is likely that the trait EI positively predicted the teacher effectiveness due to the teaching profession's inherent nature that constantly involves social interactions, challenges, dealing with new and changing tasks, especially in the higher education context (Trigwell, 2012; Yin et al., 2020). In this study, academic qualification moderated the relationship between the trait EI and teacher effectiveness relationship. Previous research remained inconclusive on the role of academic qualification in the trait EI development. Thus, this study adds to the theoretical literature based on the study’s findings that academic qualification moderated the relationship between trait EI and teacher effectiveness of university/tertiary ESL teachers.

The empirical investigation on the moderating role of the other demographic variables (gender and teaching experience) on the trait EI – teacher effectiveness relationship was not supported in this study. This finding stands in contrast to previous research that led to the development of the hypotheses on the moderating role of these variables (e.g., 2009; Gkonou & Mercer, 2017). This possible deviation from the previous research may be due to the cultural, contextual, or sample size differences.

Previous research has established the importance of trait EI application in clinical, physical health, relationships, workplace, and educational contexts (Petrides, 2010). This research adds to the educational domain by revealing the effect of trait EI application on effectiveness of the teachers teaching at the higher education level and, more specifically ESL teachers in the higher education context of Pakistan.

The study outcomes emphasize the importance of incorporating trait EI and trainings as part of teacher education programs in general and in ESL/EFL contexts. Trait EI can be developed through trainings, professional development programs, and self-awareness sessions (Dewaele, 2018; Schutte et al., 2013). Henceforth, it is suggested that educational administrators, stakeholders, and policy makers emphasize these factors to promote teacher effectiveness of the higher/tertiary education ESL teachers in Pakistan.

Though this research contributes to the scholarly literature, but certain limitations were a unavoidable in this research. Therefore, future research should attempt to replicate the findings of this study on a larger scale with a randomly selected sample. In future research, student feedback and classroom observation protocols could be used alongside self-report data (Goe et al., 2008). Moreover, the study model explained 24.1 percent of total variance in TE. Therefore, future studies may consider explaining the remaining percent of variance for with other potential variables.

Previous literature lacks evidence of any study of such a nature in the Asia-Pacific context that investigated the role of trait EI of higher/tertiary ESL teachers in their effectiveness. Study findings have important implications for the success of higher education in Pakistan and the Asia-Pacific region. Implications of this study are equally pertinent at all educational levels (Dewaele, 2018).


This research study significantly contributes to the existing body of knowledge in that it relates ESL teachers’ trait EI directly to teacher effectiveness. The study has also provided insight into the moderating role of academic qualification in the relationship between trait EI and teacher effectiveness. Thus, the study results have provided considerable support to the essential theoretical propositions of this study. Additionally, the study is significant to the administrators, teacher educators, and teachers interested in understanding the underlying psychological mechanism that could facilitate teacher effectiveness, particularly in the HEIs. In conclusion, an understanding that this research has generated on the significance of the trait EI in increasing ESL teacher effectiveness may result in the commitment of teacher educators, administrators, and policymakers to put efforts in this direction for positive student outcomes.


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