Subprocesos, Áreas de procesos, prácticas y actividades

The following hypotheses were investigated to determine which of the EI dimensions predicts the greatest variance in the dimensions of EL.

Hypothesis 28: The different dimensions of EI (emotional recognition and expression, understanding emotions external, emotions direct cognition, emotional management and emotional control) can be used to predict frequency of emotional displays (a dimension of EL).

Hypothesis 29: The different dimensions of EI (emotional recognition and expression, understanding emotions external, emotions direct cognition, emotional management and emotional control) can be used to predict intensity of emotional displays (a dimension of EL).

Hypothesis 30: The different dimensions of EI (emotional recognition and expression, understanding emotions external, emotions direct cognition, emotional management and emotional control) can be used to predict variety of emotional displays (a dimension of EL).

Hypothesis 31: The different dimensions of EI (emotional recognition and expression, understanding emotions external, emotions direct cognition, emotional management and emotional control) can be used to predict surface acting (a dimension of EL).

Hypothesis 32: The different dimensions of EI (emotional recognition and expression, understanding emotions external, emotions direct cognition, emotional management and emotional control) can be used to predict deep acting (a dimension of EL).

Five regression models were tested to investigate these hypotheses. The first model includes emotional recognition and expression, understanding emotions (external), emotions direct cognition, emotional management and emotional control as predictors (independent variables) and frequency of emotional displays as the criterion (dependent variable). The results are presented in table 4.9 and 4.10 below. The standard regression results indicate that the model was significant (p < .05) and that it explained 9% of the variance in frequency of emotional displays. Only one of the independent variables entered into the regression model, i.e. understanding emotions (external) ( = .284, p < .05) made a unique significant contribution to the equation. The R for regression was significantly different from zero, F (5, 210) = 3.965, p < .05. Hypothesis 28 is therefore supported.

Table 4.9: Model summary: EI and Frequency of Emotional Displays

Model Multiple R Multiple R Adjusted R Std Error of the F Sig.

square square estimate

1 .298 .089 .066 2.033 3.965 .002

a. Predictors: (Constant), Emotional Recognition and Expression, Understanding Emotions (external), Emotions Direct Cognition, Emotional Management, Emotional Control

b. Dependent Variable: Frequency of Emotional Displays

Table 4.10: Coefficients obtained from the regression between the dimensions of EI and Frequency of Emotional Displays

Standardised Coefficients

Model Beta t Sig.

EI (Constant) 3.365 .001

Emotional Recognition

and Expression -.007 -.093 .926 Understanding Emotions

(external) .284 3.562 .000

Emotions Direct Cognition .063 .897 .371 Emotional Management -.059 -.654 .514 Emotional Control .074 .832 .407

a. Dependent Variable: Frequency of Emotional Displays

The second model includes emotional recognition and expression, understanding emotions (external), emotions direct cognition, emotional management and emotional control as predictors (independent variables) and intensity of emotional displays as the criterion (dependent variable). The results are presented in table 4.11 and 4.12 below.

The standard regression results indicate that the model was significant (p < .01) and that it explained 12% of the variance in intensity of emotional displays. Only one of the independent variables entered into the regression model made a significant unique contribution to explaining the variance in intensity of emotional display scores.

Emotional control ( = -.324, p < .05) made a unique significant contribution to the equation. The R for regression was significantly different from zero, F (5, 210) = 5.792, p < .01. Hypothesis 29 is therefore supported.

Table 4.11: Model summary: EI and Intensity of Emotional Displays

Model Multiple R Multiple R Adjusted R Std Error of the F Sig.

square square estimate

2 .353 .124 .103 1.560 5.792 .000 a. Predictors: (Constant), Emotional Recognition and Expression, Understanding Emotions (external), Emotions Direct Cognition, Emotional Management, Emotional Control

b. Dependent Variable: Intensity of Emotional Displays

Table 4.12: Coefficients obtained from the regression between the dimensions of EI and Intensity of Emotional Displays

Standardised Coefficients

Model Beta t Sig.

EI (Constant) 4.588 .000

Emotional Recognition

and Expression .110 1.544 .124

Understanding Emotions

(external) -.016 -.209 .835

Emotions Direct Cognition .123 1.781 .076 Emotional Management .127 1.432 .154

Emotional Control -.324 - 3.730 .000

a. Dependent Variable: Intensity of Emotional Displays

The third model includes emotional recognition and expression, understanding emotions (external), emotions direct cognition, emotional management and emotional control as predictors (independent variables) and variety of emotional displays as the criterion (dependent variable). The results are presented in table 4.13 and 4.14 below. The standard regression results indicate that the model was significant (p < .01) and that it explained 14% of the variance in variety of emotional displays. Only two of the independent variables entered into the regression model made a significant unique contribution to explaining the variance in variety of emotional display scores.

Understanding emotions (external) ( = .223, p < .05) made the strongest, unique significant contribution to the equation, followed by a significant unique contribution by emotions direct cognition ( = .159, p < .05). The R for regression was significantly different from zero, F (5, 210) = 6.648, p < .01. Hypothesis 30 is therefore supported.

Table 4.13: Model summary: EI and Variety of Emotional Displays

Model Multiple R Multiple R Adjusted R Std Error of the F Sig.

square square estimate

3 .374 .140 .119 2.712 6.648 .000 a. Predictors: (Constant), Emotional Recognition and Expression, Understanding Emotions (external), Emotions Direct Cognition, Emotional Management, Emotional Control

a. Dependent Variable: Variety of Emotional Displays

Table 4.14: Coefficients obtained from the regression between the dimensions of EI and Variety of Emotional Displays

The fourth model includes emotional recognition and expression, understanding emotions (external), emotions direct cognition, emotional management and emotional control as predictors (independent variables) and surface acting as the criterion (dependent variable). The results are presented in table 4.15 and 4.16 below. The standard regression results indicate that the model was significant (p < .05) and that it explained 9% of the variance in surface acting. Only two of the independent variables entered into the regression model made significant unique contributions to explaining the variance in surface acting scores. Emotional management ( = -.212, p < .05) made the strongest, unique significant contribution to the equation, followed by a significant unique contribution by emotional recognition and expression ( = -.191, p < .05). The R for regression was significantly different from zero, F (5, 210) = 3.798, p < .05.

Hypothesis 31 is therefore supported.

Table 4.15: Model summary: EI and Surface Acting

Model Multiple R Multiple R Adjusted R Std Error of the F Sig.

square square estimate

4 .292 .085 .063 2.373 3.798 .003 a. Predictors: (Constant), Emotional Recognition and Expression, Understanding Emotions (external), Emotions Direct Cognition, Emotional Management, Emotional Control

a. Dependent Variable: Surface Acting

Table 4.16: Coefficients obtained from the regression between the dimensions of EI and Surface Acting

The fifth model includes emotional recognition and expression, understanding emotions (external), emotions direct cognition, emotional management and emotional control as predictors (independent variables) and deep acting as the criterion (dependent variable).

The results are presented in table 4.17 and 4.18 below. The standard regression results indicate that the model was significant (p < .01) and that it explained 20% of the variance in deep acting. Two of the independent variables entered into the regression model made significant unique contributions to explaining the variance in deep acting scores. Emotional recognition and expression ( = .216, p < .05) made the strongest, unique significant contribution to the equation, followed by a significant unique contribution by emotions direct cognition ( = .206, p < .05). The R for regression was significantly different from zero, F (5, 210) = 10.486, p < .01. Hypothesis 32 is therefore supported.

Table 4.17: Model summary: EI and Deep Acting

Model Multiple R Multiple R Adjusted R Std Error of the F Sig.

square square estimate

5 .452 .204 .185 2.004 10.486 .000 a. Predictors: (Constant), Emotional Recognition and Expression, Understanding Emotions (external), Emotions Direct Cognition, Emotional Management, Emotional Control

b. Dependent Variable: Deep Acting

Table 4.18: Coefficients obtained from the regression between the dimensions of EI and Deep Acting

Standardised Coefficients

Model Beta t Sig.

EI (Constant) -.698 .486

Emotional Recognition

and Expression .216 -3.187 .002

Understanding Emotions

(external) .129 1.735 .084

Emotions Direct Cognition .206 3.115 .002 Emotional Management .139 1.649 .101 Emotional Control .070 .844 .400

a. Dependent Variable: Deep Acting