Relationship between perceived emotional intelligence

relationship between perceived emotional intelligence

Exploring the relationship between perceived emotional intelligence and coping skills of undergraduate students. Atılgan Erözkan1. Abstract. This study was. Relationships between perceived emotional intelligence, aggression, and impulsivity in a population-based adult sample. Coccaro EF(1). In this study, we examined possible connections between perceived emotional intelligence (PEI) and effectiveness during the negotiation.

Instruments The instruments used to evaluate the variables under study were self-report questionnaires with Likert-type multiple-choice scales. This questionnaire has 22 items covering cyberbullying in the 2 months prior to participation in the survey, with one subscale for cybervictimization 11 items and another for cyber-aggression 11 items. Answers are entered on a scale of 1 to 5: The included forms of cyberconduct are: Insults said to me; Insults about me said to others; Threats; Identity theft; Use of personal identity without permission; Private information theft; Display of private information; Embarrassing videos or pictures; Manipulation of pictures; Social exclusion; and Spreading of rumors.

This scale has displayed good psychometric properties in studies carried out to date Ortega-Ruiz et al. However, since this study employed only the cybervictimization CV subscale, a confirmatory factor analysis CFA was used in order to test its appropriateness. The results indicated a good-fit for the measurement model, except for the Chi-square due to its sensitivity to sample size: This scale is an adaptation of the PANAS scale specifically designed to analyze cyberbullying situations.

It lists a series of emotions and asks subjects to grade the extent to which they would feel those emotions if they were a cybervictim on a scale of 1 to 5 Not at all [1] to A lot [5]. The scale has three subscales for different types of impact: Since this scale had not been validated beforehand, a measurement model was estimated to test whether the observed items reliably reflected the latent variables.

Statistical Analysis The proposed models were tested using structural equation methods. Taking into account the ordinal nature of the variables involved, robust methods were employed Flora and Curran, Specifically, in those analyses which included the cybervictimization variable — the CFA of the cybervictimization scale and Models 1 and 3 — the unweighted least squares ULSs method was used to take into account deviations due to non-normally distributed variables.

This was necessary because neither the normality nor the kurtosis conditions were satisfied see Table 1. This method has proved to be one of the most accurate and reliable methods for estimating models with ordinal variables that do not fulfill normality conditions Forero et al.

In the other analyses — the CFA of the emotional impact and EI scales and Model 2, in which the included variables PEI and emotional impact did not significantly deviate from normality conditions — robust maximum likelihood RML was adopted as the most appropriate method Hu and Bentler, Descriptive statistics and Spearman correlation for the variables included in the study.

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To compare the suitability of the proposed models, we adhered to the recommendations by Hu and Bentler, and combined different fit indices with the recommended cutoff values: The most frequently experienced forms were insults about me said to others via internet or SMS messages, followed by direct personal insults via email or SMS messages one out of every four pupils. Just over one in ten reported having been excluded or ignored in a social network or chat site and having been the subject of rumors spread via internet.

Percentage of students who reported having experienced the different type of cybervictimization.

relationship between perceived emotional intelligence

With regard to the distribution of the latent variables included in the study, Table 1 shows the main univariate descriptive statistics and the correlation between the variables. It is interesting to note the existence of significant correlation between all the EI factors and the different emotional impact factors. Attention is positively related to the Dejection and Annoyance Impacts, while Repair is inversely related to those factors and positively related to Invigoration.

Clarity was found to have a significant positive link with Invigoration and an inverse link with Dejection.

Structural Models The correlations between the different constructs were analyzed using structural equation models.

relationship between perceived emotional intelligence

A third model was then designed, incorporating both variables simultaneously. The fit indices of these models are shown in Table 2. Figures 23and 4 show the models themselves, including their standardized regression coefficients. For ease of viewing, observed items of the latent variables and error terms have both been omitted from the figures. Model of the direct link between CV and emotional impact.

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The discontinuous arrows indicate non-significant correlations. Model of the direct link between perceived emotional intelligence and emotional impact. Model of the links between cybervictimization, perceived emotional intelligence and emotional impact. As can be seen in Table 2the fit of the first model Figure 2which describes the relationship between CV and emotional impact, is not satisfactory; most of the index values lie outside the proposed cut-off points.

In contrast, all fit indicators of Model 2 Figure 3 lie within the commonly accepted cut-off points. Whereas Attention has a significant positive correlation with the three impact factors, especially with Annoyance and Dejection, Repair correlates positively with Invigoration and inversely with Dejection and Annoyance. Clarity was found to have no significant correlations with emotional impact. The third model see Figure 4 produced better fit indices than the first two models.

Analysis of the beta coefficients showed that when the two constructs were included in the same model, significant correlation appears between all variables.

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Specifically, in the case of Attention, the simultaneous inclusion of CV considerably altered the correlations with emotional impact, leaving a significant positive link only with Invigoration and changing the earlier positive links with Annoyance and Dejection into inverse ones. Correlations between Clarity and emotional impact became significant, showing an inverse link with Invigoration that is to say, the greater the clarity the lower the impact and positive links with Annoyance and Dejection.

Repair displayed the same correlations as in the earlier model — a positive link with Invigoration and inverse links with Annoyance and Dejection — although the magnitude of those correlations increased considerably. CV was found to correlate significantly with all three types of impact, inversely in the case of Invigoration and positively in the case of the other two.

Discussion The results obtained in this study show that cyberbullying is a problem, albeit not an excessively serious one, among university students. Over half the subjects in the sample reported to have experienced some type of cybervictimization in the 2 months prior to the survey.

For two reasons, this prevalence rate is difficult to compare to those found in other studies: In fact, of the studies in the meta-analysis conducted by Kowalski et al. Moreover, these three types of behavior — insults, defamation and social exclusion — also constitute the most frequent forms of conduct found in studies into traditional bullying e.

relationship between perceived emotional intelligence

Several studies have shown how cyberbullying, and more specifically cybervictimization, occur as the result of, and can be predicted by, traditional victimization, although this relationship is not seen in the other direction Del Rey et al. The principal objective of this study was to analyze the role of PEI with regard to the emotional impact of cybervictimization. The first interesting discovery was that CV and PEI have no clear, significant link with emotional impact when the two variables are analyzed separately.

Models created to explore these links resulted in a poor model fit in the case of CV and in low proportions of explained variance in the case of PEI. However, including both variables together improved the model fit, and led to a considerably higher proportion of variance explained for each emotional impact factor.

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These results appear to confirm our starting hypothesis, that PEI acts as a moderator variable between cybervictimization and emotional impact, attenuating or increasing the different dimensions of emotional impact — Invigoration, Annoyance, and Dejection — depending on the factor being considered. Contrary to what might have been expected, CV was found to have no direct relationship with emotional impact.

This was indicated both by the absence of any significant correlations and by the poor fit of model 1. These results seem to contradict studies that have identified links between frequency of harassment and emotional impact of traditional bullying Dyer and Teggart, ; Aluede et al.

The explanation may lie in their use of repetition as a defining criterion of cyberbullying. Therefore, in the case of CV, the emotional impact may not depend on frequency.

However, when PEI is included in the analysis, the results become very different and reveal that CV and emotional impacts are linked. This suggests the existence not of a direct link but of an indirect one, which is moderated by PEI. As Dredge et al. Without these variables it is impossible to gain a full understanding of the relationship. The same applies to the link between PEI and emotional impact. This highlights the importance of meeting the challenge to understand the true weight of the emotional variables.

That is to say, it is only when the needs or the specific problem at hand — in this case CV — are taken into account that emotional skills take on importance as an aid in understanding the impact. When considered in an abstract manner they do not produce the same results. This appears to concur with findings in certain coping strategy analyses, which suggest it is not possible to evaluate the effectiveness of coping responses abstractly, because they are only effective when linked to a specific result Somerfield and McCrae, With regard to the relationship between the specific dimensions of PEI and emotional impact, the results in part support our hypotheses and in part contradict what we expected.

The results of the second model confirm the proposed hypothesis that there exists an inverse relationship between Repair and negative emotional impact — Annoyance and Dejection — and a positive relationship between Attention and these two responses. Life Satifaction Scale SWLS Diener, Emmons, Larsen and Griffin, On a 7- point scale from totally desagree to totally agreeparticipants completed 5 items to assess life satisfaction considered as an affective component of subjective well being.

On a 5-point scale, participants rated 22 items related to their satisfaction about their job. This instrument is compound by six factors: Values range from 22 tothe higher the score the higher the work satisfaction. The global Cronbach alpha coefficient is 0. Procedure Firstly, each participant filled out the whole SWLS to avoid the influence of their responses to this scale on their responses on the other scales.

Results Table 1 shows means, standard deviations and internal consistency index alpha coeficient for all the instruments. Means Pearson correlations are shown on table 2. Emotional Clarity and Emotional Repair. Emotional Clarity and Emotional repair TMMS subscales correlate significantly with positive affect and Attention and Emotional repair with negative affect.

The difficulty to identify, difficulty to describe emotions and external oriented thinking TAS subscales yield significant correlations with negative affect but only the difficulty to describe feelings subscale correlates with positive affect. These analyses show that: The third step introduces the three TAS- 20 subscales as predictors: On the fourth step the three TMMS subscales are introduced as predictors.

We have to take into account that positive affect is shown by positive feelings toward oneself and the world around. Work Satisfaction appears as a second predictor for Life Satisfaction. This fact points out that Emotional Intelligence makes a little contribution to life satisfaction further than mood states contribution.

These results allow us to make two important assertions. Firstly, if we take into account that positive affect is strongly related both to absence of anxiety and absence of depression whereas negative affect is strongly related to anxiety and depression, people with high scores on positive affect would have high scores on life satisfaction whereas people with high scores on negative affect would inform about a lower life satisfaction.

Secondly, our results confirm the validity of our hypothesis: Emotional Intelligence measured by TMMS makes a little contribution to life satisfaction, further than mood states. The twenty-item Toronto Alexithymia Sacale-I.


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relationship between perceived emotional intelligence

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