Part V: creating the EQ-i 2.0 and EQ 360 2.0

Australian Norms

EQ-i 2.0 Australian Standardization

Standardization

NORMATIVE SAMPLE

Normative data for the Australian General Population Norm sample (N = 1,250) were collected from October to December of 2011.

The demographic composition of the normative sample is shown in Tables D.1-D.3. The General Population Norm sample was collected across five age ranges, evenly proportioned by gender within each age interval. The normative sample included respondents from a variety of geographic regions and education levels. In terms of employment status, 82.0% of the sample were employed or self-employed, 3.8% were unemployed, 12.1% were retired, and 2.2% indicated “other.”

NORMING PROCEDURES

The first step in the preparation of the Australian norms was to determine if any age or gender trends existed in the data. Large differences in scores between men and women, or across various age groups, would suggest a need to create an option for separate gender- or age-based norm groups. Conversely, a lack of such differences may dictate the use of a single norm group with genders and age groups combined.

A series of analyses of covariance (ANCOVA; for the Total EI score) and multivariate analyses of covariance (MANCOVA; for the composites and subscales) were used to examine the relationships between gender and age with EQ-i 2.0 scores. Education level was used as a covariate in order to control for the effect of this demographic variable. To better control for Type I errors that might occur with multiple analyses, a more conservative criterion of p < .01 was used for all F-tests.

The Wilks’ lambda statistic generated from these analyses ranges from 0.00 to 1.00 and conveys the proportion of variance that is not explained by the effect (gender, age, or the interaction between gender and age) in the multivariate analyses. These values were all close to 1.00, suggesting that only a small amount of variance could be explained by the effects of these variables. However, F-tests revealed significant effects of gender, age, and the interaction between gender and age (see Table D.4). Given these results, the univariate effects are described in detail below.

Gender Effects. Results of the gender analyses showed that males and females did not differ significantly on the Total EI score, indicating that overall emotional intelligence as measured by the EQ-i 2.0 is about the same for males and females. However, small to medium effects were seen on a number of scales (see Table D.5 for effect sizes and Table D.6 for descriptive statistics and significance test results). The largest gender difference seen in the Australian sample was on Empathy, with women scoring higher than men with a moderate effect size (d = -0.60). Smaller differences were found with women scoring higher than men on the Interpersonal Composite (d = -0.36), Emotional Self-Awareness (d = -0.35), and Emotional Expression (d = -0.38). Men scored higher than women with small effect sizes on Independence (d = 0.27), Problem Solving (d = 0.31) and Stress Tolerance (d = 0.34).

Age Effects. Significant effects were found across age groups for the Total EI score, as well as all composite scales and subscales, mostly with small to medium effect sizes (Independence being the only scale to reach a large effect size). See Table D.5 for effect sizes and Table D.7 for descriptive statistics and significance test results. Though patterns across the age groups differed across scales, scores were generally lowest for the 18–29 year-old age group and highest for the 60+ age group, showing a clear pattern of increases in EI with age. Differences between the youngest and oldest age groups ranged from 5.7 (Emotional Expression) to 16.2 (Independence) standard score points.

Gender × Age Interaction. None of the scales showed a significant interaction effect between age and gender, and only one scale (the Decision Making Composite) reached the minimum partial η2 criterion for a small effect size. Overall, age effects were largely consistent within males and females, and gender effects were largely consistent across age groups.

Norm Groups and Norm Construction. Overall, the age and gender analyses revealed significant effects that were small to medium in size. Therefore, specific Age and Gender General Population Norms, as well as Overall General Population Norms (i.e., collapsed across ages and genders), were both developed. Results revealed that skewness and kurtosis values were close to 0 (skewness values ranged from -0.76 to -0.07; kurtosis values ranged from -0.32 to 0.80), and an examination of the scale histograms did not reveal any significant departures from a bell-shaped (Gaussian) curve. A histogram for the EQ-i 2.0 Australian Total EI score is provided in Figure D.1. This figure displays the shape of the distribution for the Total EI score. The shapes of the distributions for the other scales are comparable to this one. Since the data closely match a bell-shaped curve, artificial transformation of scores was deemed unnecessary. Actual construction of the norms was conducted in the same manner as the North American Norms, including the use of statistical smoothing (see Standardization, Reliability, and Validity for more information on the construction of the North American General Population Norms).

Comparison of Australian General Population Norms to North American General Population Norms. The Australian sample was compared against the North American normative sample by computing standard scores for the EQ-i 2.0 scales with the North American norms, and comparing these scores against a mean of 100. No difference between the Australian sample and the North American norms was observed on the Total EI score. Although for a number of scales the effects did reach statistical significance because of the large sample size, all differences were within 3 standard score points, and none reached a meaningful effect size (all Cohen’s d values were below 0.20). Results are presented in Table D.8.

Internal Consistency

Internal consistency, a measure of reliability, conveys the degree to which a set of items are associated with one another. High levels of internal consistency suggest that the set of items are measuring a single, cohesive construct. Internal consistency is typically measured using Cronbach’s alpha (Cronbach, 1951). Cronbach’s alpha ranges from 0.0 to 1.0 and is a function of both the interrelatedness of the items in a test or scale and the length of the test (John & Benet-Martinez, 2000). Higher values reflect higher internal consistency.

Cronbach’s alpha values for the EQ-i 2.0 scales for the Australian normative sample are presented in Table D.9. Although there is no universal criterion for a good alpha level, informal cutoffs for evaluating alpha are typically .90 is “excellent,” .80 is “good,” .70 is “acceptable,” and lower than .70 is “questionable.” Most of the values found in Table D.9 demonstrate excellent or good reliability, and these values are particularly favorable given the small number of items included in most subscales. For the overall sample, the alpha value of the Total EI scale was .97, values for the composite scales ranged from .87 to .92, and values for the subscales ranged from .77 to .91. Similar patterns were seen across the age and gender normative groups, including a Total EI alpha value of .96 or higher for each normative group. The high level of internal consistency found in the Total EI score supports the idea that the EQ-i 2.0 items are measuring a single cohesive construct, namely emotional intelligence. The same can be said of the individual components of emotional intelligence that make up the EQ-i 2.0 (i.e., the composite scales and subscales).

Factor Validity

EXPLORATORY FACTOR ANALYSIS

Exploratory factor analysis (EFA) was used to determine whether the subscales established with the North American EQ-i 2.0 normative data empirically emerge from the Australian normative dataset. Five EFAs were conducted, analyzing the items within each composite scale separately. In each EFA, a three-factor solution was forced to examine whether the items corresponding to each subscale within the composite loaded together in the Australian normative data. As with the North American normative data, principal axis factoring extraction was used, with direct oblimin (i.e., oblique) rotation, as the factors within each composite are expected to correlate with each other. Reverse scoring was applied to relevant items prior to the analysis. Factor loadings were considered significant if they reached at least ± .30, and an item was defined as cross-loading if it was significant on more than one factor and had loadings within .10 of each other on these factors.

For the Self-Perception Composite EFA, items for the Self-Regard, Emotional Self-Awareness, and Self-Actualization subscales loaded together as expected by the established factor structure (i.e., all items loaded significantly onto their respective factors, with no cross-loadings).

For the Self-Expression Composite EFA, all but one item loaded onto their respective factors for the Independence, Emotional Expression, and Assertiveness subscales. The factor loading for one Assertiveness item was .25, just below the cutoff.

For the Interpersonal Composite EFA, items for the Interpersonal Relationships, Empathy, and Social Responsibility subscales all loaded onto their respective factors, with only one Interpersonal Relationships item cross-loading with Empathy.

For the Decision Making Composite EFA, Problem Solving, Impulse Control, and Reality Testing items loaded onto their respective factors with no cross-loadings, with the exception of one Impulse Control item that loaded onto Problem Solving.

For the Stress Management Composite EFA, Optimism, Flexibility, and Stress Tolerance items loaded onto their respective factors with no cross-loadings, except for a single Optimism item that cross-loaded with Flexibility.

To summarize, the EFAs generated solutions that strongly correspond to the established EQ-i 2.0 factor structure, with the items for each subscale empirically grouping together onto the expected factors. Of the 126 items entered in the EFAs, only two items showed cross-loadings, one item fell slightly below the .30 criteria, and one item loaded onto a factor other than the expected one.

CORRELATIONS AMONG EQ-i 2.0 COMPOSITE SCALES AND SUBSCALES

Correlations among the EQ-i 2.0 composite scales and subscales were examined to determine the degree of cohesiveness among them. It is expected that these correlations will generally be high, given that they are all measuring the same underlying construct of emotional intelligence, but they should not be so high as to indicate redundancy between the scales. Correlations observed in the Australian normative sample are presented in Tables D.10 (composite scales) and D.11 (subscales). These results are similar to what is seen with the North American normative sample.

The composite scale correlations ranged from r = .46 (Interpersonal/Decision Making) to r = .76 (Self-Perception/Stress Management), with an average correlation of r = .66. For the subscales, correlations ranged from r = .10 (Assertiveness/Impulse Control) to r = .80 (Happiness with both Self-Regard and Optimism), with an average correlation of r = .45. These results support the notion that a single, underlying dimension is being represented in the EQ-i 2.0, yet the values are not overly high and there is enough variation in the correlations to provide clear evidence for the multidimensional nature of the assessment, and support the existence of composite scales and subscales.