Is Emotional Intelligence Correlated With Values Among Primary Schoolers?

Purpose of the Research

In the Turkish primary school system, values are addressed under religious education, social studies curriculum, and life science courses which are the equivalent of PHSE in the United Kingdom. There is no specific instructional emotional literacy program which fosters the emotional intelligence skills in the Turkish primary education system (Coskun, 2015). However, a large body of research proved that emotional intelligence is highly correlated with academic achievement in math, reading comprehension, literacy skills, reasoning skills, and physical education (Garg et al., 2016; Jones et al., 2011; Lu & Buchanan, 2014; Mancini et al., 2017; Matthews, 2006; Qualter et al., 2007; Zeidner et al., 2002). Although values and emotional intelligence skills can be clustered under the affective dimension of learning, the relationship between them needs to be proven in terms of organizing a complete and coherent instructional environment in the context of the Turkish primary education system. However, there is no specific instrument to reliably and validly measure common and shared values embedded in the Turkish primary curriculum. In the present research, a new instrument was developed to measure values and reveal the correlation between them.

Values allow primary school children to choose good behavior. However, emotional intelligence helps primary school children to recognize their emotions, and to regulate them. As a result, values focus on choice of good behavior, while emotional intelligence prevents emotions from deteriorating behavior. On a theoretical basis, both of these variables can be considered complementary to each other. Discovery of the correlation between values as convictions about good and bad behavior and emotional intelligence as a construct of emotion recognition and regulation to establish healthy relationship with others enables understanding of the coherency of the learning environment in the Turkish primary school system in the context of value teaching and emotional intelligence. Furthermore, this discovery responds to questions about whether fostering values through curricular activities leads to increases in emotional intelligence or vice versa.

Study 1

The study aimed to provide initial validation of the Value Adoption Scale (VAS). The VAS has two item responses of “I agree” and “I disagree.” Item responses were constructed with two options because the participants are primary school children who are unable to evaluate themselves in terms of latent variables written in items and their response choices.

VAS was developed in four stages. First, the relevant literature was reviewed (De Klerk & Rens, 2003; Halstead & Pike, 2006; Halstead & Taylor, 2000; Rokeach, 1973; Splitter, 2011). Furthermore, curricula in the Turkish primary education system were investigated to find out which values were included. Based on the literature review and investigation of the primary curricula, 30 items were written. These items were examined by researchers with expertise about value education. Moreover, these items were examined by three primary school teachers to determine whether the items were appropriate for primary school children. As a result of expert review, eight items were discarded from the item pool and the number of items was reduced from 30 to 22.

After the initial draft of the VAS was completed, ethical considerations were addressed to gather data. Research protocol and proposal were prepared. In the protocol, the aim of the research was explained, voluntary participation and concealing the identity of the participants were emphasized, and it was submitted to the local education authority. After ethical approval was obtained from the local education authority, primary schools were visited and the aim of the research was explained to primary school teachers and primary school students. Six primary school teachers agreed that their students could respond the items on the VAS. The VAS was given to volunteer primary school teachers and primary school students. A total of 116 children participated in exploratory factor analysis (EFA) and 104 took part in confirmatory factor analysis (CFA). In total, 210 children, aged between 7 and 10 years, participated in the research and responded to the VAS. The participant primary school children took the VAS and completed it within an average of 15 min. After they had responded to the items on the VAS, the VAS forms were collected from them.

After data collection finished, the data were analyzed, structural analysis was carried out to identify which items should be included in the test and to reveal the latent structure of the VAS through item analysis with item-total correlation, EFA, reliability analysis, and CFA (Clark & Watson, 2016). Item analysis and item-total correlation results are indicated in Table 1

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Table 1.

Item Analysis With Corrected Item Correlation Results.

Item	Scale mean if item deleted	Corrected item-total correlation
Item 1	25.40	.23
Item 2	25.55	.55
Item 3	25.59	.61
Item 4	25.52	.44
Item 5	25.46	.22
Item 6	25.54	.49
Item 7	25.30	.01
Item 8	25.19	.10
Item 9	25.59	.41
Item 10	25.58	.46
Item 11	25.55	.38
Item 12	25.52	.33
Item 13	25.52	.56
Item 14	25.50	.31
Item 15	25.91	−.20
Item 16	25.57	.42
Item 17	25.58	.53
Item 18	25.54	.27
Item 19	25.31	.03
Item 20	25.55	.52
Item 21	25.34	.10
Item 22	25.47	.30

Item-total correlation directly influences reliability. Items whose corrected item-total correlation is more than .30 measure similar traits and dispositions, while items with item-total correlation less than .30 assess different dispositions or traits from overall items. Consequently, items with a total score below .30 were discarded, while items with total scores higher than .30 were included in the test analysis (Nunnally & Bernstein, 1994). As a result of the analysis, it was concluded that Item 1, Item 5, Item 7, Item 8, Item 15, Item 18, Item 19, and Item 21 should be discarded from the rest of the analysis due to low corrected item-total correlation. In addition, Item 3, Item 9, Item 10, Item 15, Item 18, and Item 22 were removed from the analysis because of the fact that they reduced internal consistency.

After item analysis was carried out, EFA was computed. EFA enables determination of interrelated items that can be clustered under the same construct. EFA, in turn, is able to identify the latent construct which indicates the broader construct consisting of interrelated items (Field, 2009; Harrington, 2008; Rummel, 1967). Item 2, Item 4, Item 6, Item 11, Item 13, Item 14, Item 16, and Item 20 were included in EFA. The Kaiser–Meyer–Olkin (KMO) coefficient and Bartlett Test are significant indicators about whether the data are large enough to conduct EFA. Findings indicated that the KMO coefficient was .83 and Bartlett test was significant (χ² = 449.02, p< .05). As a result, the KMO coefficient and Bartlett Test proved that the data are large enough to conduct EFA (Field, 2009; Henson & Roberts, 2006). Results of EFA was demonsrated in Table 2.

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Table 2.

Results of Exploratory Factor Analysis.

Factors	Item	Factor loadings	M	SD	Alpha if item deleted
Respect for others	Item 14	.730	1.17	.39	.76
	Item 13	.667	1.14	.36	.72
	Item 2	.655	1.09	.32	.73
	Item 6	.649	1.14	.38	.72
	Item 4	.625	1.12	.34	.75
Motivational factors	Item 20	.897	1.12	.32	.74
	Item 16	.609	1.10	.30	.75
	Item 11	.609	1.12	.32	.75
Eigenvalues factor 1:3.13 Eigenvalues factor 2: 1.13			Total variance explained: 53%Bartlett test: χ2 = 449.02, p < .05, KMO: .83

EFA was carried out with eight items. Eigenvalues were used to decide the number of factors and factors. Findings revealed that there were two factors with eigenvalues over 1.

Findings of EFA indicate that the VAS consists of two constructs and eight items. Items that were clustered in the first factor were examined in terms of their common and shared characteristics. They had common characteristics and based on this common ground, the first factor was named Respect for Others (RFO). Similarly, the items included in the second factor were examined, and the second factor was named Motivational Factors. Furthermore, the two-factor solution of the VAS explains 53% of the total variance. According to Merenda (1997), the total number of factors should explain at least 50% of total variance. Therefore, the construct with two factors was sufficient to explain total variance.

Reliability analysis was conducted using internal consistency. Internal consistency is an indicator of the overall degree to which items constituting a test are intercorrelated (Briggs & Cheek, 1986). Internal consistency also represents reliability of an assessment among items (Cronbach, 1951; Murphy & Davidshofer, 2005; Nunnally & Bernstein, 1994). As a result of reliability analysis, the reliability coefficient was found to be .77.

VAS, with two-factor structure and eight items revised as a result of EFA, was given to 150 primary school students for CFA. CFA was computed by reporting χ²/df, root mean square error of approximation (RMSEA), standardized root mean residual (SRMR), comparative fit indices (CFI), incremental fit index (IFI), Tucker–Lewis index (TLI), goodness of fit index (GFI), and adjusted goodness of fit index (AGFI). As a consequence of CFA, it was found that RMSEA = .45, SRMR = .042, CFI = .97, IFI = .95, TLI = .96, GFI = .97, and AGFI = .95. Based on these findings, it was concluded that VAS has good model fit and construct validity (Harrington, 2008; Shi et al., 2018).

Study 2

Results

Before regression analysis, assumptions for regression were tested. First, the presence of multicollinearity was tested, and to this end correlation analysis was conducted with the Pearson correlation coefficient (PCC). Results of correlation analysis are presented in Table 3.

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Table 3.

Results of PCC.

Variables	N	r	p
Total score of TYEIS	315	.50*	.00*
Total score of VAS

Note. PCC = Pearson correlation coefficient; VAS = Value Adoption Scale; TYEIS = Ten Years Emotional Intelligence Scale.*p < .05.

Correlation coefficients vary from 0 to 1. Coefficients ranging between 0 and .30 indicate weak association. If weak correlation is found, there is no use for regression analysis. A correlation varying between .30 and .50 represents moderate correlation and a coefficient over .50 is evaluated as strong correlation. In collinearity analysis, a correlation coefficient higher than .80 is a sign of collinearity (Cohen, 1988; Field, 2009). As a result of correlation analysis, there was a strong correlation (r = .50, p < .05). Therefore, there is no collinearity between TYEIS and VAS and the correlation coefficient is sufficient to conduct regression analysis.

Collinearity analysis was also tested through variance of inflation factor (VIF). As a result of collinearity analysis, VIF was found to be 1.89. VIF higher than 10 is an indicator of problems related to collinearity (García et al., 2015).

Casewise diagnostics were carried out using Mahalanobis Distance (MD) and Cook Distance (CD) in order to diagnose whether any residuals influencing the correlation exist in the data. Results of the casewise diagnostics are shown in Table 4.

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Table 4.

Results of Casewise Diagnostics.

Diagnostic	Minimum	Maximum	M
Cook distance	0.00	0.09	0.04
Mahalanobis distance	0.03	9.26	1.32

Results of casewise diagnostics using CD indicated that there is no CD value which is higher than 1.00, while casewise diagnostics with MD detected no value of MD exceeding 10.00. Based on the casewise diagnostics, it was concluded that there is no residual which influences the regression model (Barnett & Lewis, 1978; Cook & Weisberg, 1982). Furthermore, analysis of autocorrelation between residuals was conducted by using Durbin-Watson. Durbin-Watson was 1.98 and therefore it was concluded that there is no autocorrelation among residuals (Durbin & Watson, 1950).

Results of regression analysis, which were shown in Table 5, revealed that the regression model built upon the correlation between the outcome variable and predictive variable does not fit the data (R² = .08) (Cohen, 1988; Muijs, 2010). Power of prediction for the total score of the TYEIS as the predictive variable is weak and it is not able to predict the total score of the VAS, the outcome variable.

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Table 5.

Regression Analysis.

Variables	B	SE	β	t	p	r	R 2	ΔR2	F
VAS	7.05	.36	.31	19.48	.00	.31	.08	.08	27.89
TYEIS	.13	.02	.31	5.28	.00

Note. VAS = Value Adoption Scale; TYEIS = Ten Years Emotional Intelligence Scale.

Overall results of the study prove that total score of the TYEIS is not predictive for the total score of the VAS, even though there is a moderate correlation between the two variables.

Theoretical Implications of the Research

Overall findings of the study indicate that the VAS reliably and validly assesses values embedded in the Turkish primary school curriculum among primary school children. Correlation and regression analysis discovered that although emotional intelligence is moderately correlated with values taught in the Turkish primary school curriculum, it does not predict value adoption of participant primary school children who are 10 years old.

On a theoretical basis, emotional intelligence was considered to be a latent trait which contributes to acquisition of values because it is a construct which can enable primary school children to recognize emotions in themselves and others, motivate them, help to establish positive relationships with others, and sustain those relationships. The skills related to emotional intelligence were thought to support the values adoption of the participant primary school children (Faupel, 2003). However, emotional intelligence did not contribute to value adoption as much as considered.

There are three types of learning: cognitive, affective, and motor learning. Definition of emotional intelligence and values has several overlaps so they can be clustered together under affective learning. However, this assumption was not confirmed. Based on these results, it was concluded that emotional intelligence and values seem to be isolated even though they are included in the same dimension of learning.

Both of the variables are outcomes of learning. Learning is the process of gaining new knowledge, skills, understanding, behavior, values, and preferences (Pritchard, 2009). Both of the variables in the regression model can be developed through learning because learning enables transfer of knowledge, skills, behaviors, values, and preferences related to them. The predictive variable emotional intelligence includes self-awareness, self-regulation, motivation, empathy, and social skills, while values can be viewed as convictions guiding behavior as the outcome variable (Goleman, 1995; Halstead & Taylor, 2000; Zeidner et al., 2002). As can be seen from their components, there is coherence between them. However, in the present study it was observed that increases in emotional intelligence do not lead to substantial or significant increases in value adoption among participant primary school children who are 10 years old.

Values are a teaching subject on the primary school social studies curriculum, and life sciences which are counterparts to the PHSE. Findings of previously conducted research contradict the results of the study. Brouzos et al. (2014) and Hen and Goroshit (2014) reported that emotional intelligence predicts academic achievement and school adjustment among university students. Ferrando et al. (2011) noted that emotional intelligence is a moderate predictor of academic achievement for preadolescents whose ages range from 11 to 12. Qualter et al. (2012) concluded that emotional intelligence moderates the effect of cognitive ability among adolescents in their longitudinal research. Mavroveli and Sánchez-Ruiz (2011) found that higher emotional intelligence predicts prosocial behavior, anti-bullying, and academic achievement of primary school children aged from 7 to 12. Maree and Ebersöhn (2002) reached the conclusion that emotional intelligence is a one of the significant predictors of academic achievement among adolescents. Vidal Rodeiro et al. (2012) observed that the more emotional intelligence rises, the more academic achievement emerges in a sample of British secondary school students. Consequently, a large body of research concluded that emotional intelligence is a good predictor of academic achievement even though its learning domain differs from academic achievement.

Affective learning can be conceptualized with different phenomenon such as social competence, social skills, and adjustment, and in the relevant literature emotional intelligence predicts social and emotional aspects of learning. Humphrey et al. (2007) found that emotional intelligence is closely correlated with well-being and emotional health and predicts them in their meta-analytic review. Poulou et al. (2018) investigated preschool teachers’ perceptions concerning emotional intelligence of their students and reported that higher emotional intelligence means fewer behavioral difficulties among preschool children based on the opinions of preschool teachers. Cejudo (2017) concluded that higher emotional intelligence leads to better psychosocial adjustment among primary school children through experimental research. Cejudo et al. (2019) noted that developing emotional intelligence of adolescents contributes to psychosocial adjustment. Lopes et al. (2004) noted that emotional intelligence helps American college students establish better social interactions. Martínez-Marín and Martínez (2019) found emotional intelligence moderately predicts subjective well-being of adolescents. Similarly, Davis et al. (2019) reported that improving children’s emotional intelligence skills reduces behavioral problems. Furthermore, Pozo-Rico and Sandoval (2020) noted that teaching primary school teachers how to instill emotional intelligence in their students led to significant increases in academic achievement. Puertas Molero et al. (2020) noted that interventional programs for emotional intelligence produce more beneficial outcomes for primary school children rather than secondary school or high school students. As a result, the findings of research in the relevant literature report that emotional intelligence is an indicator and predictor of the affective learning domain. Therefore, the results of the present study are discrepant with the results of the abovementioned studies.

When the results are discussed in terms of teaching values and emotional intelligence skills and knowledge, it should be noted that there is no interventional or systematic instructional approach for both of them in the Turkish primary education system. The values are included in social studies, Turkish, and life sciences curricula. However, the curricula consist of just a list of observable behaviors without any theoretical psychological background. Therefore, values are taught to primary school children as values are a product of collective human life because it regulates behavior, enables individuals to receive acceptance and approval from others so it leads to social adjustment and socialization. A social psychological mechanism exists behind values. Values can be acquired through obedience without questioning. On the contrary, values can be learnt by transformation into parts of identity and morality. Each method of value acquisition includes social influence and social interactional processes such as self-concept, symbolic interactionism, and sense of self (Beckley et al., 2018; Festinger, 1954; Mead, 1972; Milgram, 1974). Consequently, values teaching requires well-defined learning programs which are aligned with social psychological processes. Values teaching without social psychological processes may have caused values to become isolated from emotional intelligence and other affective learning outcomes. On the other hand, emotional intelligence is not systematically included in the Turkish primary curriculum. There is no systematic and well-defined Social Emotional Learning (SEL) programs such as Social Emotional Aspect of Learning (SEAL), Promoting Alternative Thinking Strategies, and Collaborative for Academic Social and Emotional Learning (CASEL). SEAL, PATHS, and CASEL are known as emotional literacy programs. Their purpose is to instill knowledge and skills related to emotional intelligence. It was proven that emotional intelligence can be improved through specific interventional and curricular programs (Gilar-Corbi et al., 2019; Gilar-Corbí et al., 2018; Hodzic et al., 2018). Moreover, Kotsou et al. (2019) revealed that specific interventional programs can improve emotional intelligence. However, no SEL programs and Emotional Literacy Programs have been devised yet, so emotional intelligence skills and knowledge are not taught systematically in the Turkish primary school curriculum. Of course, there are very few learning standards for emotional intelligence but they are not integrated within any SEL programs. Integration of SEL with the curriculum produced positive results (Herrera et al., 2017). Therefore, SEL must be included in the primary school curriculum. Moderate correlation but weak power of emotional intelligence for values adoption can be ascribed to the lack of social emotional learning programs. Because very few knowledge and skills related to emotional intelligence are taught, they, in turn, fail to support values adoption and other learning outcomes. Findings of the study are proof of this.

When we discuss the results of the study in the context of learning, it is necessary to mention necessities of learning. According to Kahneman (2011), acquisition of skills and knowledge depends on adequate opportunity to practice, unequivocal feedback about a response, and regular environment. Values teaching without social psychological processes and lack of SEL programs could deprive the participants of adequate opportunities to practice knowledge and skills related to values and emotional intelligence, unequivocal feedback for their responses related to values and emotional intelligence, and a regular environment in which they experience values and emotional intelligence. This may have led to the moderate correlation with weak predictive power.

Practical Implications

In the present study, it was concluded that emotional intelligence fails to predict values adoption and both of the variables are not integrated with each other. Therefore, values teaching must be handled in accordance with social psychological processes and concepts in the Turkish primary curriculum, rather than as a list of values that needs to be taught. On the other hand, the Turkish primary curriculum does not have any SEL programs, and SEL programs must be embedded into the Turkish primary curriculum. Therefore, primary school teachers must design instructional activities for self-awareness, self-management, motivation, empathy, and social skills, which are components of emotional intelligence. Not only do primary school teachers perceive values as a list of subjects which need to be taught, they also have to plan instructional activities based on social psychological processes.

As a result of Study 2, a correlation was discovered. Interventional SEL programs and instructional activities for values teaching with social psychological processes can be developed to integrate with emotional intelligence. The impact of the interventional programs can be investigated with experimental research.

Limitations of the Research and Implications for Future Research

The present research was conducted in the Turkish primary education system which lacks a robust and systematic SEL curriculum but has much room for value teaching. Therefore, the results of the research are confined to the Turkish primary education context. As a result, the study needs to be replicated with larger samples and in different countries.

The present research was carried out with the correlational design. Future research can be designed with experimental design to explore whether specific interventions for SEL can increase the correlation between emotional intelligence and values. The study was conducted in a specific time span so results of the research are not able to explain developmental characteristics of the correlation between emotional intelligence and value. The correlations can be investigated in longitudinal research.

Conclusion

The present study aimed to develop VAS and to discover the correlation between values adoption and emotional intelligence among primary school children who are aged 10. The present study consists of two aims so it was completed as Study 1 in which VAS was developed and Study 2 investigating the correlations. Study 1 revealed that VAS produces reliable and valid results to assess primary school children’s value adoption. In Study 2, emotional intelligence was assigned as predictive variable and values adoption was addressed as outcome variable. Correlation analysis indicates that there is a moderate correlation, but regression analysis revealed that the correlation fails to predict sufficiently.

The moderate correlation with weak predictive power was explained as linked to isolation of the two variables due to values teaching without a social psychological basis and the absence of SEL programs striving to instill knowledge and skills related to emotional intelligence.