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

North American Professional Norms

EQ-i 2.0 North American Professional Norms – Standardization

This section describes the psychometric properties of the EQ-i 2.0 North American Professional Norms, including standardization, reliability, and validity information.

All tables and figures representing detailed depictions of the analyses described in this chapter are available in Appendix B.

Standardization is an important part of test development, because it involves the collection of normative data. This section describes the method of data collection and the breakdown of the normative samples, including the effects of age and gender on the EQ-i 2.0 results.

Data Collection for the Professional Norm Sample

Data collection for the Professional Norm sample took place over two phases. The first phase (Phase 1) of data collection took place from March, 2010 to December, 2010, as part of the full standardization process for the EQ-i 2.0. Data from Phase 1 (N = 700) comprises a subset of 571 professionals from the General Population Norm sample, as well as 129 leaders from a leadership validity study. The sample was selected from all regions in the United States and Canada, and also had good representation from various race/ethnicities (64.0% White, 15.7% Hispanic/Latino, 10.4% Black, 6.7% Asian, and 3.3% Other). This sample included only employed/self-employed professionals who had at least some post-secondary education (i.e., 15.0% had some college or university completed, 11.3% had a college diploma, 42.5% graduated from university with a bachelor’s degree, and 31.3% had a post-graduate or professional degree). The second phase (Phase 2) of data collection took place between July, 2011 and December, 2011. This sample (N = 700) included data from a randomly selected set of 700 employed/self-employed EQ-i 2.0 customers.

The total Professional Norm sample includes 1,400 individuals (N = 1,260 [90%] from the United States, and N = 140 [10%] from Canada). The sample includes an equal number of men and women, with a good spread across the age groups (see Table B.1 for the age x gender distribution of the sample; note that the 60+ group is smaller due to a higher proportion of retired individuals in this age group). The individuals in this sample were employed in a variety of professional occupations (see Table B.2 for a breakdown of employment areas).

Normative Phase

NORMING PROCEDURES

The first step in norm preparation was to determine if any trends existed in the data. For instance, large differences in scores between men and women, or across various age groups, could provide an argument for creating separate gender- or age-based norm groups. Conversely, a lack of such differences may dictate the use of a single norm group with gender and age groups combined. A series of analyses of covariance (ANCOVA; for Total EI) and multivariate analyses of covariance (MANCOVA; for the composites and subscales) was used to examine the relationships between gender and age with EQ-i 2.0 scores. Five age groups were used for this analysis: 18–29 years, 30–39 years, 40–49 years, 50–59 years, and 60+ years. In an attempt to 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 Wilk’s 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 (in this case, 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. However, F-tests revealed significant effects of gender and age for both the composite scale and subscale analyses, as well as a significant gender by age interaction for the subscales (see Table B.3). Given these results, the univariate effects are described in detail next.

AGE AND GENDER EFFECTS

Gender Effects. Results from the gender analyses showed that men and women did not differ significantly on the EQ-i 2.0 Total EI score, indicating that overall emotional intelligence (as measured by the EQ-i 2.0) is the same for men and women. However, small to medium gender effects were found for some composite scales and subscales (see Table B.4 for effect sizes and Table B.5 for descriptive statistics and significance test results). The largest difference was on Empathy; women scored higher than men with a moderate effect size (d = -0.47). Women also scored higher than men on the Interpersonal Composite (d = -0.37), Emotional Expression (d = -0.34), Emotional Self-Awareness (d = -0.28), and Interpersonal Relationships (d = -0.23). Finally, men scored higher than women with small effects on both Problem Solving (d = 0.25) and Stress Tolerance (d = 0.24). While there were several gender effects, it is important to note that overall gender effects were relatively small and represent only a few absolute standard score points.

Age Effects. Results of the age analyses revealed several small effects of age on EQ-i 2.0 scores (see Table B.4 for the effect sizes and Table B.6 for descriptive statistics and significance test results). Although the exact pattern of effects changes from scale to scale, there was a general tendency for scores to increase with age. More specifically, the lowest scores for the majority of the scales were found in the 18–29 or 30–39 year-old groups. Scores increased somewhat in the 40–49 and 50–59 year-old groups, and tended to increase again slightly in the 60+ group.

Gender × Age Interaction.There were no interactions between age and gender, and partial η² values were all between 0.00 and 0.01 (see Table B.4); this indicates that age effects were consistent across males and females, and any gender effects were consistent across age groups.

NORM GROUPS AND NORM CONSTRUCTION

Overall, similar to what was found in the General Population Norm sample, the age and gender analyses revealed significant, but relatively small effects. Therefore, specific Age and Gender Professional Norms, as well as an Overall Professional Norm (i.e., collapsed across ages and genders) were both developed. Similar to results found in the General Population Norm sample, results from the Professional Norm sample revealed that skewness and kurtosis values were close to 0 (skewness values ranged from -0.86 to -0.24; kurtosis values ranged from -0.19 to 0.78) and did not reveal any significant departures from a bell-shaped (Gaussian) curve. Therefore, following the procedures used with the General Population Norm sample, artificial transformation of scores was deemed unnecessary. Actual construction of the norms was conducted in the same manner as the General Population Norms (see Standardization, Reliability, and Validity for more information on the construction of the General Population Norm).

Internal Consistency

Internal consistency 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 (a) the interrelatedness of the items that comprise a scale and (b) the number of items that comprise a scale (John & Benet-Martinez, 2000). Higher values reflect higher internal consistency.

Cronbach’s alpha values for the EQ-i 2.0 scales in the normative sample are presented in Table B.7. Given that Cronbach’s alpha is influenced by the number of items on a scale (with more items generally leading to higher alphas), the number of items per scale is also displayed in this table. The majority of the values found in Table B.7 demonstrate excellent reliability for the EQ-i 2.0. In the Overall column, the alpha value for the Total EI scale was .97, values for the composite scales ranged from .87 to .92, and values were .75 or higher for every subscale. These values were similar within the various age and gender normative groups, including a Total EI alpha of at least .95 in each norm group. The high level of internal consistency found in the EQ-i 2.0 Total EI score supports the idea that the EQ-i 2.0 items are measuring a single cohesive construct—namely, emotional intelligence.

Comparing the General Population and Professional Norms

Because the individuals in the Professional Norm sample have professional careers and are more educated as a group compared to those in the General Population Norm sample, it was hypothesized that the professionals should score higher than the general population. In order to test this hypothesis, EQ-i 2.0 scale scores for these two groups were compared to each other. As expected, the Professional Norm sample scored 4.5 to 9.7 standard score points higher (M difference = 6.4 standard score points) than the General Population Norm sample (see Table B.8).

Large Scale Analysis of Professional Norms

In order to determine what the distribution of scores from EQ-i 2.0 customers would look like when scored with the Professional Norms, data from a sample of 4,000 EQ-i 2.0 customers (collected from July, 2011 to December, 2011) were scored using the Professional Norms. Individuals in this sample came from both the United States (76.5%) and Canada (23.5%); 53.6% of the sample were male, and ranged in age from 18 to 80 years (M age = 40.8 years; SD = 12.4 years). Results from this sample (see Table B.9) revealed that when using the Professional Norms, the average scores were very close to 100 (M = 100.2 to 103.5), and the standard deviations were very close to 15 (SD = 13.7 to 16.3). Furthermore, skewness and kurtosis values for this sample were small (skewness values ranged from -0.95 to -0.34; kurtosis values ranged from -0.15 to 1.11) indicating that the distribution of scores approximates a bell-shaped (Gaussian) curve. These values, combined with an examination of the scale histograms, indicate a very slight negative skew, with no significant departures from a bell-shaped (Gaussian) curve (see Figure B.1).

Factorial Validity

EXPLORATORY FACTOR ANALYSIS

Exploratory factor analysis (EFA) was used to determine whether the subscales established with the North American EQ-i 2.0 General Population normative data empirically emerge from the Professional normative dataset. Five EFAs were conducted, where the items within each composite scale were analyzed separately. In each EFA, a three-factor solution was forced to examine whether the items that corresponded to each subscale within the composite also loaded together in the Professional 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 on the three subscales (Self-Regard, Self-Actualization, and Emotional Self-Awareness) loaded significantly onto their respective factors as expected by the established factor structure, with no cross-loading items.

For the Self-Expression Composite EFA, all items on the Emotional Expression, Assertiveness, and Independence subscales loaded onto their respective factors, with one Independence item that cross-loaded with Assertiveness.

For the Interpersonal Composite EFA, items on the Interpersonal Relationships, Empathy, and Social Responsibility subscales loaded significantly onto their respective factors, with no cross-loading items.

For the Decision Making Composite EFA, all Problem Solving, Reality Testing, and Impulse Control items loaded onto their respective factors, with the exception of one Reality Testing item that loaded onto Problem Solving.

For the Stress Management Composite EFA, all items on the three subscales (Flexibility, Stress Tolerance, and Optimism) loaded significantly onto their respective factors, with no cross-loading items.

To summarize, the EFAs generated solutions that strongly correspond to the established EQ-i 2.0 factor structure, with 117 out of 118 items empirically grouping together onto the expected factors.

CORRELATIONS AMONG EQ-i 2.0 COMPOSITE SCALES AND SUBSCALES

Correlations among the EQ-i 2.0 composite scales and subscales were examined. These correlations were expected to be generally high, given that they all measure the same underlying construct of emotional intelligence; however, they should not be so high as to indicate redundancy between the scales. Correlations observed in the Professional normative sample are presented in Tables B.10 (composite scales) and B.11 (subscales).

The composite scale correlations ranged from r = .51 (Decision Making/Interpersonal) to r = .78 (Stress Management/Self-Perception), with an average correlation of r = .67. For the subscales, correlations ranged from r = .13 (Independence/Empathy) to r = .79 (Self-Regard/Happiness), with an average correlation of r = .46. As highlighted in Table B.11, subscale correlations within composite scales ranged from r = .28 (Emotional Expression/Independence) to r = .63 (Self-Regard/Self-Actualization). 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 of the multidimensional nature of the assessment, and support the existence of composite scales and subscales.