METODOLOGÍA

A correlation matrix was performed to identify any relationships between the predictor variables; gender, age, ethnicity, GPA and course progression (refer to Table 5-3 for details), GPA and course progression were the only variables related (r = 0.445, p = 0.01). In this sample, 16.7% (n = 12) nursing students left the program before completion; this is lower than the national average of 34% reported by Health Workforce Australia (2014).

Table 5-3 Correlation table for Predictor Variables

Sex Age Ethnicity

Grade Point Average

Course Progress Gender Pearson Correlation

Sig. (2-tailed)

N 82

Age Pearson Correlation -0.076 Sig. (2-tailed) 0.497

N 81 81

Ethnicity Pearson Correlation 0.145 -0.149 . Sig. (2-tailed) 0.195 0.185 . N 82 81 82 Grade Point Average Pearson Correlation -0.053 -0.133 -0.098 Sig. (2-tailed) 0.651 0.254 0.401 N 76 75 76 76 Course Progress Pearson Correlation -0.158 0.051 0.068 0.445** Sig. (2-tailed) 0.156 0.651 0.547 .000 N 82 81 82 76 82 **. Correlation is significant at the 0.01 level (2-tailed).

5.5 Results

RQ.1. What is the range of emotional intelligence scores across a sample of nursing students entering the first year of a nurse education program?

The reported emotional intelligence scores include the total emotional intelligence score, the area scores and the four sub-branch scores. Of the total sample, 83 students

attempted the MSCEIT, one participant did not fully complete the branch 2 component, and therefore, a total MSCEIT score was not computed. The branches are indicative of an individuals’ ability to perceive, interpret, understand and manage emotion (Mayer, Salovey, et al., 2008). The area score; experiential emotional intelligence, reflects the composite score of the perception and interpretation branches and assesses ability to identify and

reason about emotion. Branch score analysis allowed examination of individual characteristics concerning emotion perception, interpretation, understanding and management ability.

The relationships between the total MSCEIT score and the branch scores were examined to test the inter-correlation of each level as discussed by Mayer et al. (2003). The

Reasoning area score was most highly correlated with the understanding branch (r = .749) and the managing branch (r = .839). In respect to the individual branches, correlations were low. These results provide some evidence that participants in this sample responded similarly to the sample tested by Mayer et al. (2003). The mean total emotional intelligence score for participants (n= 83) was 99.46 (sd = 12.77) with a range of 59 to122 indicating an average level of emotional intelligence when compared with the normative sample. An average level, i.e., a MSCEIT score of 100, when compared with the normative sample, in each branch would be expected for nursing students who care for patients and their families. The standard deviations for the total score and branch scores found in the sample are similar to those reported in other nursing education research (Beauvais, Stewart, DeNisco, et al., 2014; Codier & Odell, 2014; Nosek, 2015). See Table 5-4 for the inter-item correlations. The mean branch 1 score, which relates to emotion perception was 105.69 (sd = 16.83) indicating an above-average level in 67.5% (n = 56) of respondents. The branch 2 score were in the average range when compared to the normative sample (M= 97.92, sd = 14.61) and the numbers of participants who fell below average was also higher (66.3%) in branch 3: understanding emotions (m = 96.69, sd = 9.22). The mean branch 4 score, which relates to managing own and others’ emotions was 96.57 (sd = 9.98) and was observed in 61.4% of participants.

Table 5-4 Inter-Item Correlation Matrix Between MSCEIT Components

1 2 3 4 5 6 7 1. Total EI 0.772 0.814 0.585 0.678 0.697 0.783 2. Perceiving 0.534 0.178 0.376 0.884 0.350 3. Facilitating 0.437 0.362 0.844 0.475 4. Understanding 0.268 0.319 0.729 5. Managing 0.415 0.837 6. Experential 0.455 7. Reasoning

Table 5-5 describes the results of the analysis of mean emotional intelligence scores for total, area and branches. Most students (n = 53 or 63.9%) demonstrated total emotional intelligence scores in the average range. A further 13 (15.7%) of students achieved scores that indicate a below-average level of emotional intelligence. The remaining 20.5% (n = 18) scored in the above average range. Most students (n = 46 or 55.4%) scored in the competent range (100–128) for the experiential area score. Of the remainder 37 (44.6%) scored below average or Consider Developing with a range of 37–99.

The mean branch 1 score was 105.69 (sd = 16.83) indicating an above-average level in 67.5% (n = 56) of respondents and for branch 2 the mean was 97.92 (sd = 14.61). The numbers of participants who fell below average was also higher (66.3%) in branch 3: (m = 96.69, sd = 9.22). The mean branch 4 score was 96.57 (sd = 9.98) and was observed in 61.4% of participants.

Table 5-5 EI score mean ± SD and range for the total sample.

Range

Score Score M SD Minimum Maximum Total EI Total EI MSCEIT 99.46 12.77 59.02 122.08

Area 1 Experiential 101.71 16.64 37 128 t = 2.88* Branch 1 Perceiving Emotions 105.45 16.84 56 136 t = - 4.20*** Task Faces 117.20 26.14 48 143 Task Pictures 98.28 10.18 70 113 Branch 2 Facilitating Thought 97.97 14.69 60 123 t = 3.80*** Task Sensations 95.90 11.83 61 114 Task Facilitation 101.79 15.44 59 133 Area 2 Reasoning 97 8.84 69 116 Branch 3 Understanding Emotion 96.73 9.27 75 117 Task Blends 95.49 9.34 71 113 Task Changes 8.82 10.15 79 122 Branch 4 Managing Emotions 96.72 9.95 62 114 Task Emotional Management 97.76 9.39 70 120 Task Emotional/Social Relations 95.4 10.71 59 122

Note: Sample scores in Areas 1 and 2; scores in Branch 1 were compared to scores in Branch 2; scores in Branch 3 were compared to scores in Branch 4 *p < .05; ***p < 0.001

After testing for violation of the assumptions of normality, linearity and homoscedasticity I examined the relationship between total emotional intelligence score, age in years and Grade point average (GPA), using Pearson product-moment correlation coefficient. GPA as measured on a seven-point scale, (with higher scores indicating greater academic achievement) ranged from 4.33 to 7.00 with a mean of 5.78 and a standard deviation of .865. I found a positive correlation, (r = .279, n = 82, p = 0.05) between age and total emotional intelligence scores. A medium correlation between three of the variables, GPA, r

= .41, n = 76, p = .001, and age r = .367, n = 81, p = .001, indicated that higher GPA and increasing age was associated with higher levels of emotional intelligence. Refer to Table 5-6 for details.

Table 5-6 Pearson Product-moment correlations between Total EI, GPA and Age

Scale 1 2 3 Total EI - GPA .41** Age .37** **. p < 0.01 (2-tailed).

RQ2. Are there differences in attrition rates between groups of nursing students, identified by emotional intelligence competency?

Participants were grouped by total MSCEIT score to enable comparison between groups (see Table 5-7). At the lowest level (MSCEIT score > = 55 to <90), persons completing the scale are advised to “Consider Developing” (n = 16) their levels of emotional intelligence. The next group (score > = 90 to <110), were characterised as “Competent” (n = 49). The highest-scoring group (> 110 to <130) was represented as “Skilled” (n = 18). Higher scores indicate increased emotional intelligence ability. A chi–square test for independence

indicated no significant association between groups and course progression, c2 _{(2, n = 82)}

= .064, p = .846, phi = 064. The Consider developing, Competent and Skilled ranges of the MSCEIT reflect the normative sampling of a general sample n = 5000). The means and standard deviations are presented in Table 5.7. The frequency distribution for the recoded variable was: 19.2% (n = 16) in the Consider Developing group. A further 59% (n = 49)

demonstrated a competent level of emotional intelligence and 21.6% (n = 18) a skilled level.

Table 5-7 Descriptive statistics for the between group differences in MSCEIT Branch scores

Branch Group N Mean Standard

deviation 1 Perceiving Consider Developing 16 83.31 14.692 Competent 49 108.02 12.104 Skilled 17 118.88 9.614 Total 8 105.45 16.843 2

Facilitating Consider Developing 16 79.60 10.403 Competent 49 98.91 11.129 Skilled 17 112.58 7.212 Total 82 97.97 14.687

3

Understanding Consider Developing 16 89.33 9.702 Competent 49 96.26 7.160 Skilled 17 105.06 7.995 Total 82 96.73 9.270

4

Managing Consider Developing 16 85.63 11.098 Competent 49 97.91 7.505 Skilled 17 103.68 6.161 Total 82 96.71 9.949

A one-way between-groups analysis of variance (ANOVA) was conducted to explore the differences between branch scores (perceiving, facilitating, understanding and managing emotions) measured in the MSCEIT. There were statistically significant differences at p <

0.05 level in all branch scores. Post hoc comparisons indicated that the mean scores for Consider Developing were different from Competent and the Competent group were different from the Consider Developing and Skilled groups. The effect sizes were in the

small to medium range, calculated using eta squared; perceiving emotions = .40,

facilitating emotions = .50, understanding emotions = .30 and managing emotions = .36. Post hoc comparisons using a Bonferroni correction indicated that Consider Developing was significantly different to Competent was different to Skilled in all the branches. To allow visual representation of where the differences lay, an error bar chart was constructed to show the mean, 95% CI, and SD = 1, for all the three groups (refer to Figure 5-1).

Although the groups were different to each other there were no statistically significances between the groups and attrition.

Note: SStot=<90: group Consider Developing; SStot=90-109.99: group Competent; SStot=>110: group Skilled

SS-B1: Branch 1; SS-B2: Branch 2; SS-B3: Branch 3; SS-B4; Branch 4

RQ. 3. Are there differences between MSCEIT scores, demographic characteristics and attrition rates?

Binary logistic regression was performed to assess the impact of several variables on predicting whether students would continue in their nursing program after year one. (Refer to Table 5-8 for details). The model contained four independent variables; total emotional intelligence score, positive/negative bias, age in years and grade point average (GPA). The full model containing all predictors was statistically significant, c2 _{(4, N = 76) = 12.38, p}

< 0.015, indicating that the model was able to distinguish between participants who

completed at least to the end of the first year and those who did not progress to the end of year one. The whole model explained 15% (Cox and Snell R Square) and 32.7%

(Nagelkerke R squared) of the variance in course progression and correctly classified 94% of cases. Only one of the independent variables, however, made a statistically significant contribution to the model (GPA), recording an odds ratio of 6.30.

Table 5-8 Logistic Regression Predicting the Likelihood of Continuing after Year One.

The model found no significant connection between emotional intelligence scores and course progression or age and course progression. The model predicted that 92% of participants would remain in their nursing program to the end of year one. However, there were only 12 participants out of a total of 83 who did not complete. After removing

variables in the regression analysis, the model was only able to predict continuation rather than discontinuation. Thus, emotional intelligence score and age do not predict attrition in this sample. This analysis revealed no statistically significant differences (p=0.05) between groups based on competency (see Table 5-9). The percentage likelihood for each group

B S.E. Wald df p Odds Ratio 95% C.I. Odds Ratio Lower Upper Total EI -.29 .05 .39 1 .53 .98 .89 1.06 Pos/Neg Bias .28 .052 .29 1 .59 1.02 .93 1.14 Age .04 .08 .27 1 .60 1.04 .90 1.22 GPA 1.84 .82 4.99 1 .03 6.3 1.25 31.48 Constant -8.57 5.26 2.65 1 .10 .001

was very close to the computed likelihood. The overall percentage contribution of each group to attrition identified that Consider Developing was responsible for 25% (n = 3) of the attrition, while Competent was responsible for a further 58.3% (n = 7) and Skilled the remaining 16.7% (n = 2). When the percentage contribution within groups was reviewed, 18.8% was attributable to Consider Developing, the Competent group was lower at 14.3%, and Skilled shared the final 11.8%.

Table 5-9 Cross-tabulation table: MSCEIT Competency Groups and Course Progression

Competency Groups Course Progress Total No Yes Consider Developing =<90 Count 3 13 16 Expected Count 2.3 13.7 16.0 % within Competency Groups 18.8% 81.3% 100.0% % within Course Progress 25.0% 18.6% 19.5% % of Total 3.7% 15.9% 19.5% Competent

=90-109.99

Count 7 42 49

Expected Count 7.2 41.8 49.0 % within Competency Groups 14.3% 85.7% 100.0% % within Course Progress 58.3% 60.0% 59.8% % of Total 8.5% 51.2% 59.8% Skilled

=>110

Count 2 15 17

Expected Count 2.5 14.5 17.0 % within Competency Groups 11.8% 88.2% 100.0% % within Course Progress 16.7% 21.4% 20.7% % of Total 2.4% 18.3% 20.7% Total Count 12 70 82 Expected Count 12.0 70.0 82.0 % within Competency Groups 14.6% 85.4% 100.0% % within Course Progress 100.0% 100.0% 100.0% % of Total 14.6% 85.4% 100.0%