the factor structures of ability and trait emotional
TRANSCRIPT
UNLV Theses, Dissertations, Professional Papers, and Capstones
8-1-2020
The Factor Structures of Ability and Trait Emotional Intelligences The Factor Structures of Ability and Trait Emotional Intelligences
Relative to General Intelligence and Personality Relative to General Intelligence and Personality
Nicholas Carfagno
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THE FACTOR STRUCTURES OF ABILITY AND TRAIT EMOTIONAL INTELLIGENCES
RELATIVE TO GENERAL INTELLIGENCE AND PERSONALITY
By
Nicholas Carfagno
Bachelor of Arts – Psychology University of California, Santa Cruz
2013
Master of Arts - Psychology University of Nevada, Las Vegas
2017
A dissertation submitted in partial fulfillment of the requirements for the
Doctor of Philosophy - Psychology
Psychology Department College of Liberal Arts The Graduate College
University of Nevada, Las Vegas August 2020
ii
Dissertation Approval
The Graduate College The University of Nevada, Las Vegas
July 17, 2020
This dissertation prepared by
Nicholas Carfagno
entitled
The Factor Structures of Ability and Trait Emotional Intelligences Relative to General Intelligence and Personality is approved in partial fulfillment of the requirements for the degree of
Doctor of Philosophy - Psychology Psychology Department
Stephen Benning, Ph.D. Kathryn Hausbeck Korgan, Ph.D. Examination Committee Chair Graduate College Dean Michelle Paul, Ph.D. Examination Committee Member Kimberly Barchard, Ph.D. Examination Committee Member Harsha Perera, Ph.D. Graduate College Faculty Representative
TABLE OF CONTENTS
Abstract iii
List of Figures iv
Chapter 1: Literature Review 1
Chapter 2: Current Studies 13
Chapter 3: Method 14
Chapter 4: Data Analysis 22
Chapter 5: Discussion 34
Appendix A: R Studio Syntax 40
Appendix B: GSCI Assessment 44
Appendix C: Additional Fit Statistics 56
References 57
Curriculum Vitae 73
ABSTRACT
Emotional Intelligence is a popular term used to describe one’s experience with their own
emotions and awareness of others’. Although emotional intelligence is thought to be a helpful
predictor of various variables such as work performance and wellbeing, there is disagreement
within the field of the precise definition and method of measurement. The current study
examines two of the most popular models of emotional intelligence, ability and trait models, to
determine if they are the same construct or two different constructs that share a moniker.
Additionally, both ability and trait emotional intelligences’ factor structures are examined
through hierarchical and bifactor models to determine the best fitting model for each. Finally,
ability emotional intelligence is explored relative to how it fits with current understandings of
cognitive ability and trait emotional intelligence is explored relative to how it fits with current
understandings of normal-range personality.
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LIST OF FIGURES
Figure 1. Hierarchical and bifactor models of the MSCEIT 25
Figure 2. Hierarchical and bifactor models of the TEIQue 26
Figure 3. Hierarchical and bifactor models of general intelligence 27
Figure 4. Hierarchical and bifactor models of general personality 28
Figure 5. Hierarchical models of ability EI and general intelligence 30
Figure 6. Scatterplot of the correlation between ability EI and trait EI 32
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CHAPTER 1: LITERATURE REVIEW
Emotional intelligence (EI) represents an individual’s experience with their own
emotions and awareness of others’ (Fernandez et al., 2012); it is a predictor of life satisfaction
(Urquijo et al., 2016), academic performance (Fernandez et al., 2012), and career adaptability
(Coetzee & Harry, 2014), among other variables. Despite EI’s apparent utility, dominant
researchers of the field disagree in regards to both its definition and method of measurement.
Until a relative consensus is formed, it is difficult to determine a standardized utility for
measuring EI. This dissertation will address the relationship between two of the most researched
forms of EI: ability EI and trait EI. Ability EI is defined as a mental ability that must be tested,
and trait emotional intelligence is defined as a facet of personality that can be assessed through
self-report (Cherniss, 2010).
Previous research suggests that ability and trait models of EI vary from being orthogonal
constructs to displaying a weak relationship with each other (r = .18; Brannick et al., 2009; Di
Fabio & Saklofske, 2014; Petrides, 2011; van der Linden et al., 2017). Any correlation between
the two models is likely due to the relationship between mental abilities (such as crystallized and
fluid intelligences) and a general factor of personality (van der Linden et al., 2017). Moreover,
substantial disagreements about the structure of emotional intelligence within each model have
plagued the field (MacCann et al., 2014; Van der Linden et al., 2012; van der Linden et al.,
2017). I aim to bridge the divide within the EI literature by examining both ability EI and trait EI
with their commonly associated constructs of general intelligence and normal-range personality
within the same study. Each construct’s factor structure will be examined to determine which
model has the best fit. The sample used in this study will consist of a greater range of intellectual
functioning than samples typically used when examining ability EI; as a result, my findings will
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be more generalizable relative to many previous studies. In summary, this dissertation will
address the following questions: What is the factor structure of ability and trait emotional
intelligences? Is ability EI a stratum II broad ability in the hierarchical model of general
intelligence and is trait EI truly synonymous with a general factor of personality? Lastly, will
ability EI and trait EI be correlated after controlling for general intelligence and personality?
Ability Model Emotional Intelligence
The ability model of EI was originally outlined by Mayer, DiPaolo, and Salovey (1990)
and was more concretely defined by Mayer and Salovey (1997, pp. 3–34). Ability EI is defined
as the ability to recognize emotions, to effectively use emotions, perceive the meanings
associated with particular emotions, and to effectively regulate emotions (Mayer & Salovey,
1997). More recently, ability EI has been further described as an intelligence that is associated
with efficient problem solving and quality of social relationships (Mayer et al., 2008). Ability EI
was created from a theoretical standpoint of a four-branch model consisting of the above
definition, with each branch contributing to an overall global factor of EI (Mayer & Salovey,
1997; Salovey & Grewal, 2005). The branches have been designated as a) managing emotions so
as to attain specific goals, b) understanding emotions, emotional language, and the signals
conveyed by emotions, c) using emotions to facilitate thinking, and d) perceiving emotions
accurately in oneself and others (Mayer et al., 2008). Ability EI is typically measured by the
Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Brannick, Wahi, & Goldin, 2011;
Maul, 2012). Not only was the MSCEIT created by the researchers responsible for one of the
modern definitions of ability EI, it also represents one of the most examined assessments for any
form of EI (Kong, 2014).
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However, the MSCEIT’s factor structure has been the subject of many psychometric
critiques. Gardner and Qualter (2011) observed very similar scores and high overlapping
variance between emotion facilitation and perceiving emotions via confirmatory factor analysis
(CFA). As a result, they suggest that these two factors of ability EI be collapsed into one factor,
yielding a three-factor model consisting of emotion perception, emotion understanding, and
emotion management (Rossen et al., 2008). Fan et al. (2010) also concluded that a three-factor
model better fits the MSCEIT after conducting a review of CFAs of 18 different studies using the
measure; they also found emotion facilitation and perceiving emotions to be highly correlated (r
= .90). The authors of the MSCEIT have acknowledged these findings and attribute them to a
potential issue with the test design or unintended participant response styles. Regardless, they
have chosen to maintain a four-factor model based on theory and emotion facilitation’s
association with the global EI score (Mayer et al., 2016).
Though most of the literature surrounding the factor structure has modeled the MSCEIT’s
structure at the subtest level, Maul (2012a) demonstrated that a finer-grained consideration of the
MSCEIT’s structure at the item-level and recommended either a unidimensional or two-factor
structure of the instrument. Maul also points out that the MSCEIT is especially difficult to model
from a sub test level due to its different item types, which causes some of the variance observed
to result from the different formats of different items. Additionally, not all items are independent
of each other. Several groups of items are dependent on a test-taker’s interpretation of a single
vignette or image. Although an item-level analysis would help rectify some of these difficulties
in measurement, these analyses require large sample sizes (N = 758 in that study) and item-level
data that the test publisher does not provide. This renders verification of Maul’s results
challenging in the scope of this dissertation.
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The MSCEIT has been found to be associated with both increased adaptive and decreased
maladaptive emotional functioning but only reduced maladaptive social functioning. First, the
MSCEIT is associated with wellbeing. Sanchez-Alvarez, Extremera, and Fernández-Berrocal
(2016) conducted a meta-analysis of three studies and found a correlation of .22, while Brackett
and Mayer (2003), which was not included in the meta-analysis for unknown reasons, reported a
correlation of .28. The MSCEIT is negatively correlated with assessments that measure the
presence of overall depressive and anxious symptomatology (r = -.27; Cejudo, 2016; r = -.31;
Brackett & Salovey, 2006). Additionally, the MSCEIT is associated with less aggressive
behaviors, r = -.14 (Megías et al., 2018), and less interpersonal conflict, r = -.45, but not more
positive interpersonal experiences (Lopes et al., 2003). Thus, high scores on the MSCEIT may be
associated with better emotional health, but they are not as strongly associated with positive
social relationships as the test’s authors suggest they should be.
Although the authors of the MSCEIT suggest that the MSCEIT measures one’s ability to
use feelings to enhance thought (Mayer et al., 2002), the MSCEIT is in fact more strongly related
to one’s overall intelligence rather than its application measured through real world achievement.
Kong (2014) conducted a meta-analysis of 46 publications, consisting of 53 study samples,
examining the relation of the MSCEIT (and its predecessor, the MEIS) with cognitive ability.
Kong reported a correlation of the MSCEIT/MEIS of .30 with general intelligence. Correlations
of .26 (Kong, 2014) and .23 (Pardeller et al., 2017) were found for the MSCEIT and verbal
intelligence measures. Finally, a correlation of .23 was found with nonverbal intelligence
measures (Kong, 2014). In regards to the application of intelligence, Brackett and Mayer (2003)
found that the MSCEIT correlated with high school GPA .21, college GPA .16, and Verbal SAT
scores .32. If the MSCEIT did truly measure one’s ability to enhance thought through the use of
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emotion, there should be a greater correlation between the MSCEIT and GPA rather than the
MSCEIT and general intelligence. The only instance in which the authors’ suggestion appears to
be true is in regards to verbal tasks. The small to moderate correlations suggest that ability EI is a
separate, but related construct to g.
Ability Emotional Intelligence and General Intelligence
In current theories, ability EI exists as a stratum II factor, while general intelligence (g) is
a stratum III; rather, ability EI is a factor that loads onto one one’s overall g (MacCann et al.,
2014; Mayer et al., 2016). General human intelligence is commonly viewed within a hierarchical
or bifactor model (Barbey, 2018; Beaujean, 2015; McGrew, 2009). Studies examining
intelligence measures often implement both models without finding significant differences
between the two (Beaujean et al., 2014; Canivez et al., 2017; Reynolds & Keith, 2017). Because
ability EI and intelligence is typically understood through a hierarchical model, I will begin with
this model to discuss both constructs.
The hierarchical model of intelligence, also referred to as the Cattell–Horn–Carroll
(CHC) model, combines the tenets of the Cattell and Horn's Gf–Gc model and the Carroll Three-
Stratum model of intelligence (McGrew, 2009). The CHC model breaks intelligence down into
three strata, the highest stratum (stratum III) refers to general intelligence. Below general
intelligence lies stratum II, which refers to broad abilities. These broad abilities include fluid
reasoning (Gf), comprehension-knowledge (Gc), short-term memory (Gsm), visual processing
(Gv), auditory processing (Ga), long-term storage and retrieval (Glr), cognitive processing speed
(Gs), decision and reaction speed (Gt), reading and writing (Grw), and quantitative knowledge
(Gq). Although currently still under investigation, the following broad abilities have been
tentatively included as part of the CHC model: general (domain specific) knowledge (Gkn),
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tactile abilities (Gh), kinesthetic abilities (Gk), olfactory abilities (Go), psychomotor abilities
(Gp), and psychomotor speed (Gps). The lowest stratum (stratum I) refers to narrow abilities that
make up different broad abilities. For example, Gs consists of perceptual speed, rate-of-test-
taking, number facility, speed of reasoning, reading speed, and writing speed.
MacCann et al. (2014) used structural equation modeling to demonstrate that ability EI
meets the correlational criteria of a stratum II broad ability. “Correlational criteria” refers to
three standards that must be met: 1) the mental abilities that make up a broad ability must form a
coherent construct, 2) a broad ability must demonstrate a positive correlation with other
previously established tests of intelligence, and 3) a broad ability must demonstrate some unique
variance relative to other broad abilities. In regards to the first criterion, the three primary mental
abilities (emotion perception, emotion understanding, and emotion management) of ability EI
form a single construct, which is represented by a global score. The second criterion has also
been met; the subtests of ability EI have an average correlation of .30 (ranging from .05 to .63)
with 15 different subtests of cognitive ability (which were primarily taken from the Educational
Testing Service Kit of Factor Referenced Cognitive Tests; MacCann et al., 2014). The third
criterion also appears to be successfully met due to the three branches of ability EI demonstrating
unique variance relative to other existing stratum II broad abilities; emotion perception, emotion
understanding, and emotion management correlate .62, .84, and .53 with Gc, while Gf-Gc
correlate .87 and Gf-Gv correlate .88 (MacCann et al., 2014). While ability EI does meet the
criteria of a CHC model, trait EI does not meet the aforementioned criterion and is better
measured through self-report.
Assuming ability EI is a stratum II ability, as suggested by MacCann et al. (2014), the
three MSCEIT branches of Emotion Perception, Emotion Understanding, and Emotion
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Management (Fan et al., 2010; Roberts et al., 2001; Rossen et al., 2008) would be considered
Stratum I abilities. A strength of viewing the branches this way is that the MSCEIT provides
formal assessments of these EI-related stratum I abilities, in contrast to the more purely
theoretical nature of most stratum I constructs. However, it is difficult to say with confidence
that the branches represent stratum I abilities unique to the MSCEIT, because these branches
appear to contain other commonly described stratum II skills within their subtests. For example,
the perceiving emotions branch has items that require test-takers to read a story and to identify
emotional content (Mayer et al., 2002). Individuals who demonstrate strengths in the stratum II
abilities Grw and Gv will read and comprehend the stories better than those who do not, which
would suggest that the Stratum II abilities of Grw and Gv would also influence an individual’s
global EI score. Even if these MSCEIT branches represent unique stratum I abilities, Maul
(2012a)’s suggested factor structure raises the question whether two or three narrow abilities
would be appropriate. Indeed, that analysis suggests even finer grained abilities below stratum I
might be necessary to understand influences on MSCEIT scores. For these reasons, I confined
my factor-level analyses to global MSCEIT scores at the stratum II level of the CHC model of
intelligence.
Trait Model Emotional Intelligence and Personality
The trait model of EI (which is alternatively labeled “emotional self-efficacy”) was
initially defined as a construct that examined behavioral dispositions and self-perceived abilities
via self-report consisting of empathy, assertiveness, self-reported social intelligence, and self-
reported ability EI (Petrides, Pita, et al., 2007; Petrides & Furnham, 2001). The Trait Emotional
Intelligence Questionnaire (TEIQue) is an assessment frequently used to measure the above-
described model of trait EI (Petrides, 2009; van der Linden et al., 2017). Trait EI has been
8
associated with several affective variables, many of which are also correlated with personality.
TEIQue global scores correlated -.50 with mental health symptoms in a 10-study meta-analysis
(Martins et al., 2010). This moderate correlation was further supported by Cejudo (2016), who
reported a correlation of .53 between the TEIQue and the absence of psychological distress. The
absence of psychopathology symptoms is possibly explained by successful coping; the TEIQue
is moderately correlated with adaptive psychological coping, r = .44 (Mikolajczak et al., 2008).
Unlike the MSCEIT, which only consistently correlated with the absence of distressing affect,
the TEIQue is also correlated with reported feelings of happiness, (r = .70; Furnham & Petrides,
2003; r = .60; Furnham & Christoforou, 2007).
Though ability EI is thought to fall within the realm of general intelligence, trait EI is
believed to be associated with a general factor of personality as part of a hierarchical model (van
der Linden et al., 2017). The general factor of personality refers to the single highest-order factor
derived from measures of normal-range personality (Musek, 2007). The general factor of
personality is positively correlated with extraversion, agreeableness, conscientiousness, and
openness; it is negatively correlated with neuroticism (Dunkel & van der Linden, 2017).
Through the use of confirmatory factor analyses, Van Der Linden, Te Nijenhuis, and
Bakker (2010) suggest that personality exists in a hierarchical model consisting of three different
levels. This finding has also been supported through meta-analyses of the big five model of
normal-range personality (Davies et al., 2015). At the bottom level of latent variables, there are
the big five (extraversion, agreeableness, conscientiousness, openness, and neuroticism). The
level above that contains “the big two:” alpha and beta. Alpha, also referred to as stability,
consists of conscientiousness, agreeableness, and neuroticism. Alpha is associated with
consistent moods, goals/motivation, and interpersonal styles. Beta, also referred to as plasticity,
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consists of extraversion and openness. Beta is associated with willingness to search for and
develop new goals and experiences (DeYoung et al., 2002; Liu & Campbell, 2017; van der
Linden et al., 2010). The highest level of the hierarchical model contains the aforementioned
general factor of personality that encompasses all of the lower-level factors (Rushton et al., 2009;
van der Linden et al., 2010). The interpretation of the general factor of personality when modeled
by the big five (Costa & McCrae, 1992) is still contested. While initially it was thought to reflect
social desirability (Arias et al., 2018; Davies et al., 2015), some current research suggests that it
better explains social effectiveness (Dunkel et al., 2016; van der Linden et al., 2016) and that
instances of wanting to appear socially desirable only affects the factor structure rather than the
validity of the general factor itself (Schermer & Holden, 2019).
Recently, trait EI has been examined within the hierarchical model of personality (Van
der Linden et al., 2012; van der Linden et al., 2017). Trait EI is the highest loading factor when
examined with big five dimensions and highly correlates with the general factor of personality (r
= .86). Due to the large amount of shared variance between trait EI and the general factor of
personality, it may be argued that the two constructs are fairly redundant (Van der Linden et al.,
2012; van der Linden et al., 2017).
Thus far, comparisons between the general factor of personality and trait EI have been
conducted with general factors produced from the big five model. Although they are all referred
to as the “general factor of personality,” the variance explained by the general factor depends on
the method of measure (Hopwood et al., 2011; Loehlin, 2012). Another popular model of
personality is the Giant Three, which is measured by the Multidimensional Personality
Questionnaire (MPQ; Tellegen & Waller, 2008). While some studies suggest that a meaningful
general factor of the MPQ exists (Rushton et al., 2009), others have had difficulty replicating the
10
factor loadings and deny a robust general factor (Donnellan et al., 2012). Indeed, the correlations
between the NEO-PI-R and MPQ general factors are extremely variable, between .12 (Hopwood
et al., 2011) and -.59 (Loehlin, 2012). Loehlin (2012) suggests that this is likely a result in
different statistical methods and states that he obtained the general factor of personality directly
as the first principal factor from the intercorrelations of the scales of the inventories. In contrast,
Hopwood et al. (2011) obtained the general factors through a series of exploratory hierarchical
factor analyses starting with the lowest-order set of scales, which likely funneled out substantial
variance that was retained in Loehlin (2012). Because the relationship between the MPQ’s
general factor of personality and trait EI has not yet been explored, it is unclear whether it will be
as strong as that involving the Big Five’s general factor.
Points of Contention between Models of Emotional Intelligence
As the field continues to polarize about whether ability or trait EI represents the most
informative form of EI, researchers are becoming more willing to acknowledge that these are
likely different constructs sharing a similar title (Cherniss, 2010; Petrides, Pita, et al., 2007).
Those who favor ability EI argue that other forms of EI are not appropriate because they do not
objectively measure problem-solving skills, reasoning, or behavioral outcomes, whereas
measures of ability EI do (Pisner et al., 2017). Ability EI is also favored by neuroscientists
interested in EI due to its clearer neural correlates (Killgore & Yurgelun-Todd, 2007). Ability
EI’s apparent applicability to neuroscience results from its similarities with traditional
assessments of cognitive abilities and aforementioned more objective nature (Pisner et al., 2017).
Those who support trait EI argue that it is more important to have a measure with greater
incremental validity and ease of administration (Andrei et al., 2016). The TEIQue demonstrates
incremental predictive validity over general personality in regards to facial recognition,
11
loneliness, eating disorders, well-being, emotion regulation, stress response, coping strategies,
depressive symptoms, anxiety symptoms, and somatic complaints (Andrei et al., 2016; Jolić-
Marjanović & Altaras-Dimitrijević, 2014; Petrides, Pérez-González, et al., 2007). The MSCEIT
demonstrates incremental validity over general personality and general intelligence when
predicting academic achievement but not negative affect, positive affect, life-satisfaction, and
psychological distress (Karim & Weisz, 2010; Lanciano & Curci, 2014).
Bifactor and Hierarchical Models
The bifactor model represents an alternative factor structure to the hierarchical model.
The hierarchical model assumes that each subtest of an assessment loads onto a specific factor
and that each specific factor loads onto a higher-order general factor. Therefore, variance within
a subtest is never directly explained via the general factor. The bifactor model assumes that each
subtest loads onto both a general factor and specific factor and that both general and specific
factors exist on the same stratum (Kranzler et al., 2015; MacCann et al., 2014; Murray &
Johnson, 2013). Therefore general factors within a bifactor model are directly measured through
subtests and are not mediated by specific factors. Additionally, the general factor is partialled out
of the lower-order factors, which results in the factors being independent of each other (Arias et
al., 2018; Gignac, 2008).
Bifactor models have a number of interpretive advantages over hierarchical models,
though they may be less able to detect poorly fitting models because they fit large numbers of
parameters. Group factor scores from bifactor models are readily interpretable due to the group
factors being independent of the general factor, which allows for parsing out independent
estimates of test variability due to general and group factors. Separating general and group
factors also reduces multicollinearity among factors in multivariate analyses (Kranzler et al.,
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2015). Without having a factor structure forced into a hierarchy, bifactor models typically
provides a better fitting model. This is because forcing a factor structure requires proportionality
constraints such that the ratio of general and group factor variance is the same for each indicator
within the group factor;the greater the violation of these constraints, the worse fit statistics the
model will have (Gignac, 2016). Even with a likely better fit, this does not imply that the bifactor
model is always advantageous. When using the bifactor model, more parameters must be
estimated, which reduces the overall precision of estimations made. While hierarchical models
are more sensitive to misspecifications and are less likely to accept incorrect models compared to
bifactor models (Murray & Johnson, 2013; Preacher et al., 2013). Fit statistics for cognitive
ability measures, which includes the MSCEIT, computed as part of a CFA tend to favor bifactor
models even when the construct being examined is truly a higher order model in simulation
studies (Morgan et al., 2015). Thus, researchers should not base their models solely on fit
statistics but instead also be cognizant of the theory that the cognitive assessment is grounded in.
Though it must be cautioned that given the complex nature of psychological assessment, it is
unreasonable to assume that the proportionality constraints placed by researchers accurately
reflect the true factor structure of an assessment (Gignac, 2016).
Alternative Models and Other Measurements of Emotional Intelligence
Other models of emotional intelligence combine both self-report and ability, but tend to
use either the term trait model or the term ability model. For example, some examine ability,
personality, motivation, and empathy in a self-report format (van Zyl & de Bruin, 2012;
Whitman et al., 2008). I view the constructs of ability EI and trait EI as two distinct entities that
have differing methods of measurement, and this dissertation aims to examine both as such.
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CHAPTER 2: CURRENT STUDY
The present study seeks to compare the different theories and methods of measures of EI
in an undergraduate sample. Specifically I will contrast the ability model of EI, measured by the
Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), and the trait model of EI,
measured by the Trait Emotional Intelligence Questionnaire (TEIQue). Three specific questions
will be addressed. First, does a hierarchical or bifactor model best represent ability EI, trait EI,
general intelligence, and general personality? Second, in reference to the first question, what is
the factor structure of each construct? Third, are the global scores of ability EI and trait EI
correlated with each other and with general factors of intelligence and personality?
Hypotheses
I predict that although ability EI and trait EI share the moniker of emotional intelligence,
that they in fact represent different constructs that are products of their tools of measurement. I
also predict that ability EI, trait EI, general intelligence, and general personality will best fit a
hierarchical model. Due to ability EI’s similarity in measurement to traditional intelligences, I
believe that it will load as a stratum II broad ability within the CHC model of general
intelligence. Due to trait EI’s similarity in measurement to personality, and its previous
association with the general factor of personality, I believe that trait EI will represent an
analogous construct to the general factor of personality regardless of the factor structure used
(van der Linden et al., 2017).
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CHAPTER 3: METHOD
Participants
Participants consisted of 207 (68% female) undergraduate students recruited from
University of Nevada, Las Vegas via the Sona system. This desired sample size was determined
by using a normal bivariate model in G*Power 3.1.9.2 (Faul et al., 2007). It has 80% power to
detect two-tailed correlations of .25 at an α level of .005, which provides a higher evidentiary
value than a typical α level of .05 (Benjamin et al., 2018). A predicted correlation of .25 was
used because it has been identified to be the average correlation among variables in personality
research (Gignac & Szodorai, 2016). The mean participant age was 19.3 years (SD = 3.14 years).
In total, 41.6% of participants were White, 14.0% were Black, 25.8% were Asian, 2.5% were
Native American or Native Alaskan, and 14.5% identified as some other racial group; 19.8% of
participants identified as Hispanic.
Measures
Demographics
The demographic questionnaire was a self-report measure consisting of 61 items. This
measure inquired about the participant’s age, gender, race/ethnicity, marital status, family
history, educational history, medical/psychological history, substance use, and criminal activity.
Emotional Intelligence
Two measures of emotional intelligence were used in this dissertation. The MSCEIT
(Mayer, Salovey, Caruso, & Sitarenios, 2003) was used to measure ability emotional
intelligence, and the TEIQue (Petrides, 2009) was used to measure trait emotional intelligence.
Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). The MSCEIT is a
141 item, electronically administered assessment of ability EI. It contains four branches that are
15
believed to be components of EI: perceiving emotions, using emotions to facilitate thought,
understanding emotions, and managing emotions. Items on the MSCEIT involve multiple choice
and rating scale responses to stimuli, such as anecdotes and the facial expressions.
Each item is scored with reference to the consensus of either experts in emotion research
or lay community members without this expertise. Because measures of traditional intelligence
are scored in a manner closer to the expert consensus method and the authors recommend it due
better inter-rater agreement (Mayer et al., 2003), I will use expert consensus scores from the
MSCEIT. Expert consensus scoring more closely aligns with traditional intelligence measures
because items are created with the intent that some answers are more correct than others, which
are determined by an individual who specializes in the associated field. Nevertheless, across all
five item responses to the 141 items, expert and lay consensus scores had an agreement rate of r
= .91. Furthermore, expert and lay consensus branch, area, and total scores correlate between .96
and .98. The MSCEIT contains a full test split-half reliability of r = .91 for expert consensus
scores (Mayer et al., 2003).
Trait Emotional Intelligence Questionnaire (TEIQue). The TEIQue is 153-item self-
report measure of trait EI. It consists of the following four factors and 15 facets: 1) Well-being
examines positive affect and self-esteem [Trait optimism, Trait happiness, Self-esteem], 2)
Sociability [Emotion management of others, Assertiveness, Social awareness] examines one’s
effectiveness in affecting others’ emotions, assertiveness, and social skills, 3) Emotionality [Trait
empathy, Emotion perception of self and others, Emotion expression, Relationships] examines
perspective taking, awareness of own and others’ emotions, communicating emotions, and
presence of fulfilling relationships, 4) Self-control [Emotion regulation, Impulsiveness, Stress
management] examines control over one’s own emotions, resistance of urges, and stress
16
regulation. The last two facets do not load onto any factors of trait EI, but they contribute to
one’s overall trait EI: Self-motivation, which examines one’s unwillingness to give up, and
Adaptability, flexibility in new situations (Andrei et al., 2016). Facets typically range in internal
consistency (as calculated by Cronbach alpha) from .68 to .87, with an Cronbach alpha’s of .89
for total EI scores (Petrides, 2009).
Multidimensional Personality Questionnaire - Brief Form (MPQ-BF). The MPQ-BF
is a 155-item self-report measure of normal-range personality. It consists of three higher order
factors and 11 primary trait scales. The inclusion of primary trait scales is an advantage over the
big five models of personality, in which the lowest level factors are facets. The inclusion of
primary trait scales will allow for interpretations with greater specificity (Miller et al., 2011).
The first higher order factor, Positive Emotional Temperament (PEM), refers to one’s
disposition towards the experience of positive emotions. PEM consists of several subscales:
Well-Being (feelings of happiness and optimism), Social Potency (preferring attention and
socially dominant roles), Achievement (hardworking and ambitious attitude), and Social
Closeness (feeling close and affectionate towards many friends). The second high order factor,
NEM, refers to one’s disposition towards the experience of negative emotions. Negative
Emotional Temperament (NEM) consists of the following sub-scales: Stress Reaction (proneness
to anxiety, stress, and becoming easily upset), Alienation (feelings of suspicion and being
betrayed by others), and Aggression (experiencing enjoyment when observing violence and
willingness to mistreat others for personal gain. The final higher-order factor, Constraint (CON),
refers to one’s ability to inhibit behavior and plan before acting. CON consists of the following
subscales: Harm Avoidance (preference for safe, non-thrilling activities), Control (preference for
caution, control, and planning), and Traditionalism (preference for cultural and religious norms).
17
The MPQ-BF also examines the primary trait of absorption, which assesses heightened
responsiveness to sensory experiences and vividness of imaginings. Absorption cross loads
approximately equally onto PEM and NEM (Benning & Freeman, 2017; Eigenhuis et al., 2017;
C. J. Patrick et al., 2002)
The primary trait scales of the MPQ-BF maintain good internal consistency with
Cronbach’s α between .74 and .84 (C. J. Patrick et al., 2002). The MPQ also includes three
validity scales: Variable Response Inconsistency (VRIN), True Response Inconsistency (TRIN),
and Unlikely Virtues. While the first two validity scales act to identify inconsistent responding,
Unlikely Virtues aims to detect impression management and those who are attempting to appear
socially desirable (Benning & Freeman, 2017; C. J. Patrick et al., 2002). This is particularly
advantageous considering Schermer and Holdern (2019)’s warning that trying to appear socially
desirable affects the factor structure of models that include a general factor of personality. Most
other normal-range personality measures do not include a social desirability scale; those that do
contain a social desirability scale, such as the Eysenck Personality Questionnaire, are older
measures and are limited to fewer primary trait scales (Sato, 2005). As a result, the MPQ-BF
presents itself as the most appropriate normal-range personality measure for this dissertation.
Fluid intelligence. Fluid intelligence refers to one’s ability to learn and manipulate new
information; processes involved include problem solving and reasoning (Hülür et al., 2018;
Takaiwa et al., 2018). To measure fluid intelligence, I used select subtests of the International
Cognitive Ability Resource (ICAR). The ICAR is an open-access assessment of general
intelligence that was collaboratively created by several researchers. Three subtests of the ICAR
were used to measure fluid intelligence. Three-dimensional Rotation consists of 24 items that
asks participants which of the six answer choices is a possible rotation of the target stimuli cube;
18
all cubes contain figures on the three visible sides. Participants could also choose “None of the
cubes could be a rotation” and “I do not know the solution.” Letter and Number Series consists
of nine items that provide five numbers or letters and asks participants which letter or number
comes next in the series; six number or letter answer choices are provided along with “None of
these” and “I don’t know.” Matrix Reasoning consists of 11 items, each of which provide a 3 x 3
grid consisting of eight figures with a missing figure in the bottom-right corner. Participants
were asked to choose one of six answer choices that provide the figure that would complete the
pattern established in the grid (Condon & Revelle, 2014).
Crystallized intelligence. Crystallized intelligence refers to one’s ability to recall
information such as facts and vocabulary (Hülür et al., 2018; Takaiwa et al., 2018). To measure
crystallized intelligence, I used three assessments. First, the Shipley Institute of Living Scale:
Vocabulary, which is a 40-item, multiple-choice assessment of one’s spelling ability. Individuals
are given 10 minutes to complete the assessment. It is used to assess crystallized intelligence in
adults (Shipley, 1940).
Secondly, I used certain items of the Verbal Reasoning subtest of the ICAR. This subtest
contains questions regarding factual information with the options of eight responses. Some of
these items ask the test-taker to solve mathematical word problems; these items were excluded.
This left seven factual information questions for participants to answer.
The final measure of crystallized intelligence is the Grammatical Sentences of
Crystalized Intelligence (GSCI). The GSCI is a 30-item measure that asks individuals to identify
a possible grammatical error within a short passage. Each passage contains four underlined
portions. Individuals must identify which of the four underlined portions contains an error. If
none of the underlined portions contain an error, they must specify so. This assessment was
19
created specifically for this dissertation due to the ICAR’s plethora of fluid intelligence
subscales, but minimal crystalized intelligence measures.
Because not all 30 items were likely to be acceptable measures of this construct, I
determined prior to the study that I would prune the final set of items to analyze. I used a parallel
component analysis of the tetrachoric correlation matrix with 1000 replications using the
fa.parallel function in the psych R package to determine whether multiple factors have
eigenvalues greater than the 95th percentile of resampled data. The parallel analysis indicated that
three factors should be extracted, so I subjected these data to a Schmid-Leiman exploratory
bifactor analysis involving promax rotation using the mirt function in the mirt R package. After
inspecting the rotated factor loadings, I concluded that there was only one interpretable factor.
The first factor comprised items indexing a variety of grammatical issues. The second factor
comprised items that were nearly always incorrectly answered in our sample, marking it as a
difficulty factor. The third factor comprised all the items with “No error” as the correct response.
Because only one factor emerged, I retained all items with a loading of >|.45| on the first factor
and <|.25| on the other two factors, which retained nine items to use in my subsequent analyses.
Procedures
Participants read and signed a consent form before being allowed to participate in the
study. Participants were seated in a room containing five desktop computers, three on one side of
the room and two on the other, with computer towers between each participant to ensure privacy
among adjacent participants. Over the course of approximately two hours, participants completed
the MSCEIT, TEIQue, MPQ-BF, and ICAR. Demographic information was collected along with
two other assessments that will not be included in this dissertation. All assessments were
administered electronically, and a research assistant was present in the testing room to answer
20
any potential questions regarding the experiment. Participants received course credit for
engaging in the study after being debriefed about its purpose.
Data Analysis
A stepwise data analysis plan was used in which a series of models were compared. The
first research question addressed whether a hierarchical or bifactor model best represents the
factor structures described above of ability EI, trait EI, general intelligence, and normative
personality. Each structure was modeled using confirmatory analysis via the sem command in the
lavaan package in R.
For each model, I reported χ2, root mean square error of approximation (RMSEA), and
standardized root mean square residual (SRMR) values as measures of absolute fit; Tucker-
Lewis Index (TLI) values as a measure of relative fit; and Akaike and Bayesian Information
Criteria (AIC and BIC) as measures of the relative likelihoods of each model. Following Hu and
Bentler (1999), I aimed to consider well-fitting models with RMSEA values close to or below
.06, SRMR values close to or below .08, and TLI values close to or exceeding .95. I modified ill-
fitting models only with paths that were both theoretically sensible and had a basis in prior
studies. In each pair of models, the model with the lower value of AIC and BIC was selected to
answer subsequent research questions. One univariate outlier that was greater than three standard
deviations from the mean were excluded from the MSCEIT’s Managing branch. I used a
Mahalanobis distance test to identify multivariate outliers. Although the MSCEIT contained
three, the TEIQue contained four, and the general intelligence measures contained three, the
exclusion of these outliers did not significantly change any of the fit statistics or allow models
that did not initially converge to converge. As a result, I did not remove the multivariate outliers
from analyses.
21
The second research question aimed to determine whether ability EI is a second-order
factor of general intelligence or a separate construct, as well as determining whether trait EI is a
second-order factor of the general factor of personality or a separate construct. The best-fitting
models from the first research question was used in these analyses. Structural equation models
were used to model each EI as either a second-order factor beneath the general factor and as a
separate construct to determine the best fit.
The third study question aimed to answer whether ability and trait EI are correlated. I
accomplished this by using the factor scores extracted from the best fitting ability and trait EI
models determined by the second research question.
22
CHAPTER 4: RESULTS
Aim 1
All model fit and comparison statistics for Aim 1 are summarized in Table 1. In general,
the MSCEIT and intelligence measures had superior fits to the TEIQue and MPQ-BF.
Table 1
Fit Statistics for Hierarchical and Bifactor Models of Each Measure
Model RMSEA 90% RMSEA CI SRMR TLI AIC BIC MSCEIT
Hierarchical .045 .000-.081 .051 .935 -3663 -3580 Bifactor N/A N/A N/A N/A N/A N/A
TEIQue Hierarchical .121 .108-.135 .090 .820 7359 7472 Bifactor .112 .098-.126 .074 .828 7305 7448
Intelligence Hierarchical .036 .000-.116 .039 .978 -694 -658 Bifactor .081 .000-.221 .020 .884 -691 -644
MPQ-BF Hierarchical .092 .072-.113 .088 .745 -414 -327 Bifactor .081 .058-.105 .081 .802 -432 -322
MSCEIT
When examining the MSCEIT, I encountered errors regarding inability to invert the
matrix for both the hierarchical and bifactor models; this was most likely due to having latent
variables with only two indicators. As a result, I constrained the Faces and Pictures subtests to
load equally on Perceiving Emotions and the Changes and Blends subtests to load equally onto
Understanding Emotions. These constraints allowed the hierarchical model to converge
23
successfully, but the bifactor model was still unable to invert its matrix. To attempt to create a
bifactor model that converged, I specified starting values for each factor. Unfortunately, even
with the specifications the model could not converge and fit statistics were unable to be
calculated (see Figure 2).
TEIQue
The hierarchical factor model of the TEIQue produced no errors when fitting the model,
whereas the bifactor model had negative variance in the Emotion Regulation facet (see Figure 2).
Although that variance had a small and insignificant z score (z = -0.125, p = .901), it had a very
large standard error of 50.9; the next largest standard error in the model was more than two
orders of magnitude lower at 0.097, even though all indicators were scored on the same scale. As
a result, I did not interpret this negative variance as being indistinguishable from zero due to the
uncertainty caused by the enormous standard error. This led me to conclude that even though the
bifactor model had lower AIC and BIC values, the hierarchical model was the best model when
considering the instability of the bifactor model’s parameters and was used in subsequent
analyses.
General Intelligence
Initially, errors inverting the matrix were encountered when modeling general
intelligence with hierarchical and bifactor structures. This was likely due to the elimination of
the verbal reasoning subtest (which had a Cronbach’s α of .29, even after screening for outliers),
leaving Gc with only two observed variables (Little et al., 1999). To remedy this problem, I
constrained the Shipley and the GSCI to have equal loadings on Gc. This modification allowed
both models to converge successfully without any errors (see Figure 3). Not only did the
hierarchical model produce a RMSEA and a TLI closer to what I had previously indicated would
24
be a “good model,” it also produced a lower BIC than the bifactor model. Therefore, the
hierarchical factor structure of general intelligence was the better fitting model and was used in
subsequent analyses. Crystallized and fluid intelligences had essentially equal and strong
loadings on the general intelligence factor.
MPQ-BF
The hierarchical model of the MPQ-BF produced variances for PEM, NEM, and CON
greater than 1. This prevented the standardized parameters of the GFP model from being
calculated. The bifactor model of the MPQ-BF initially produced a negative residual variance for
the Traditionalism facet. Due to this residual’s low and insignificant z score (z = -0.122, p =
.903), as well as its interpretable standard error of 0.515 (which was within two orders of
magnitude of the next largest standard error at 0.016), I determined that this variance was
indistinguishable from zero. As a result, I constrained the residual variance of Traditionalism to
zero. After this constraint, the bifactor model ran without any errors (see Figure 4). The bifactor
model was the best model of the MPQ-BF based on BIC and having no inadmissible parameter
estimates, as was the case in the hierarchical model. In this model, Control (-), Harm Avoidance
(-), and Aggression were the strongest (albeit moderate) markers of the general factor of
personality.
25
Figure 1. Hierarchical (top panel) and bifactor (bottom panel) models of the MSCEIT. Prc = Perceiving emotions, Und = Understanding emotions, Mng = Managing emotions, Fcs = Faces, Pct = Pictures, Chn = Changes, Bln = Blends, Fcl = Facilitation, Sns = Sensations, E_M = Emotional management, S_M = Social management, AEI = Ability emotional intelligence
26
Figure 2. Hierarchical (top panel) and bifactor (bottom panel) models of the TEIQue. WlB = Well-being, Scb = Sociability, Emt = Emotionality, SlC = Self-Control, Opt = Trait Optimism, Hpp = Trait happiness, SlE = Self-esteem, EmM = Emotion management, Ass = Assertiveness, ScA = Social awareness, Emp = Trait empathy, Emp = Emotion perception, ExE = Emotion expression, Rlt = Relationships, EmR = Emotion regulation, ImC = Impulse control, StM = Stress management, Mtv = Motivation, Adp = Adaptability, TEI = Trait Emotional Intelligence
27
Figure 3. Hierarchical (top panel) and bifactor (bottom panel) models of general intelligence. Gf = Fluid intelligence, Gc = Crystallized intelligence, TD_ = Three-dimensional rotation, LNS = Letter and number series, M_R = Matrix reasoning, Shp = Shipley Institute of Living Scale: Vocabulary, GSC = Grammatical Sentences of Crystalized Intelligence, IQ = General Intelligence.
28
Figure 4. Hierarchical (top panel) and bifactor (bottom panel) models of general personality PEM = Positive emotionality, NEM = Negative emotionality, CON = Constraint, W_B = Wellbeing, S_P = Social potency, Ach = Achievement, S_C = Social closeness, Abs = Absorption, S_R = Stress reaction, Ain = Alienation, Agg = Aggression, Cnt = Control, H_A = Harm avoidance, Trd = Traditionalism.
29
Aim 2
All model fit and comparison statistics for Aim 2 are summarized in Table 2. None of
these models had good absolute levels of fit, which likely reflects a number of unmodeled
potential cross loadings among indicators across factors.
Table 2
Fit Statistics for Models Assessing Relationships among Conceptually Similar General Factors
Model RMSEA 90% RMSEA CI SRMR TLI AIC BIC INTELLIGENCE AND MSCEIT
Separate Factors .160 .134-.187 .184 .710 494 548 Second-Order Factor .132 .106-.160 .133 .803 461 514
MPQ AND TEIQUE Separate Factors N/A N/A N/A N/A N/A N/A Second-Order Factor N/A N/A N/A N/A N/A N/A
General Intelligence and the MSCEIT
Continuing to use the constraints reported for the hierarchical models of general
intelligence and the MSCEIT allowed both the general intelligence and ability EI as separate
general factors model and the second-order factor model successfully converge without errors.
The factor scores of perceiving emotions, understanding emotions, and managing emotions from
their individual models were saved and used in the current models to decrease the number of
parameters that were required to be estimated (see Figure 5). Compared to the separate general
factors model, the ability EI as a second-order factor model had a higher TLI value and lower
SRMR, AIC, and BIC values. In this model, ability EI had a moderate loading on the general
intelligence factor and was substantially smaller than those for crystallized and fluid intelligence.
30
31
Figure 5. Hierarchical models of ability EI and general intelligence. Prc = Perceiving emotions, Und = Understanding emotions, Mng = Managing emotions, Fcs = Faces, Pct = Pictures, Chn = Changes, Bln = Blends, Fcl = Facilitation, Sns = Sensations, E_M = Emotional management, S_M = Social management, AEI = Ability emotional intelligence, Gf = Fluid intelligence, Gc = Crystallized intelligence, TD_ = Three-dimensional rotation, LNS = Letter and number series, M_R = Matrix reasoning, Shp = Shipley Institute of Living Scale: Vocabulary, GSC = Grammatical Sentences of Crystalized Intelligence, IQ = General Intelligence.
32
MPQ-BF and TEIQue
Despite continuing to constrain the traditionalism facet to zero, neither model could be
identified. The GFP and trait EI as separate general factors model produced an error that its
matrix could not be inverted, while the second-order factor model indicated that no solution
could be found. Neither error messages nor models indicated reasonable modifications that could
be used to create a viable factor structure; therefore, the relationship between the GFP and trait
EI in Aim 3 was examined via correlating the general factors from their separate models.
Aim 3
To examine the relationship between ability EI and trait EI, I correlated both general
factors from their best fitting models in Aim 1. The two demonstrated a very small and
insignificant correlation, r(205) = .0006, p = .994 (see Figure 6). For the sake of thoroughness, I
examined the correlations of the ability general factors with one another and the trait general
factors. General intelligence correlated moderately with MSCEIT ability EI, r(205) = .553, p <
.001, but the MPQ-BF’s general factor did not correlate with TEIQue trait EI, r(205) = -.0025, p
= .971.
33
Figure 6. Scatterplot of the correlation between ability EI and trait EI. AEI = ability EI, TEI = trait EI.
−2 −1 0 1
−2
−1
01
Only_EI_Fscores$AEI
Only_EI_Fscores$TEI
34
CHAPTER 5: DISCUSSION
This study examined the factor structures of the MSCEIT, general intelligence as
measured through a number of assessments, the TEIQue, and the MPQ-BF. In accordance to my
first hypothesis, Ability EI, general intelligence, and trait EI all were best described by
hierarchical structures. Contrary to this hypothesis, the bifactor model fit best when examining
personality. The first half of my second hypothesis, which stated ability EI is a second-order
factor of general intelligence, was found to be correct. The second half that stated trait EI and the
GFP, as measured by the MPQ-BF, are analogous constructs was incorrect; in fact, they appear
to be separate and unrelated constructs. Finally, ability EI and trait EI were not significantly
related, which confirmed my final hypothesis that ability EI and trait EI are completely separate
constructs that share only a moniker.
Models of Abilities and Traits
Abilities
Many contemporary models of general intelligence approach the construct via a bifactor
model because of the tendency to yield a better statistical fit (Schult & Sparfeldt, 2016). Despite
a usually better fit, several authors caution against assuming general intelligence is best
represented by a bifactor because of the overall statistical bias towards bifactor model fit indices
(Morgan et al., 2015; Murray & Johnson, 2013). Instead, it is important to consider both
statistical fit and construct conceptualization when choosing a preferred model (Morgan et al.,
2015). Not only does the hierarchical model of general intelligence appeal more to a layperson’s
intuition, it was also a better statistical fit when examining my data. A possible explanation for
the better fit is the limited number of indicator variables used in the study, but in combination
with construct conceptualization, it is the best observed model for this data.
35
Similarly, the hierarchical model of the MSCEIT yielded a relatively good fit, whereas I
was unable to find a solution that allowed the bifactor model to converge. Such difficulties with
the bifactor model of the MSCEIT were also observed by Evans et al. (2020), who were also
unable to find a solution. MacCann et al. (2014) was able to find a bifactor MSCEIT solution,
but only after constraining indicator variables to have equal loadings, therefore making the
bifactor model indistinguishable from the hierarchical model.
Both Evans et al. (2020) and MacCann et al. (2014) suggest ability EI is a second-order
factor of general intelligence. I wanted to test these findings due to neither study examining
whether ability EI would be a better statistical fit as a separate but related construct. Though
neither the second-order factor model nor separate factors model had a good overall fit, the
nested model had a better fit than the separate model based on both absolute and relative fit
statistics. Within the second-order model, ability EI had a moderate loading onto general
intelligence. This finding does not suggest that ability EI should be included in test batteries
assessing general intelligence. Ability EI should instead be conceptualized similarly to other
second-order factors that are not typically included on cognitive intelligence batteries, such as
Ga, Gt, Glr, Gs, Grw, and Gq. Just like ability EI, these factors tend to have weaker loadings on
general intelligence relative to Gc and Gf (MacCann et al., 2014).
Traits
The relationship between the GFP and trait EI is somewhat unclear. The GFP, as modeled
by the MPQ-BF, appears to be unrelated to trait EI. The GFP in this study must be interpreted
with caution. While I had initially chosen the MPQ-BF as a normal-range personality measure
due to it containing an unlikely virtues scale, which would have been used if I was unable to
extract a general factor, I had not considered that its factor scores of PEM, NEM, and CON were
36
created to be independent of each other. Unlike Donnellan et al. (2012), I was able to extract a
general factor from the MPQ-BF. However, this general factor resembles trait externalizing due
to the high loadings of aggression, control, and harm avoidance (Blonigen et al., 2016; Hopwood
& Grilo, 2010) rather than traditional GFPs that are associated with social effectiveness and
social desirability (Arias et al., 2018; Davies et al., 2015; van der Linden et al., 2016). My
findings support the conclusions made by Hopwood et al. (2011) that the GFP is dependent on
which personality inventory is used to obtain it, and that general factors between personality
assessments typically have a weak to non-existent correlation. The TEIQue is typically examined
through a hierarchical model (Petrides, 2009), and my results further support this choice. While
the hierarchical model did not indicate a “good fit” as specified in my methods section, it
successfully converged without any psychometric concerns, whereas the bifactor model
contained unresolvable Heywood cases that rendered it uninterpretable.
Contrary to my hypothesis, trait EI and the GFP did not represent analogous constructs;
instead, they were completely uncorrelated. This is in stark contrast to Van Der Linden et al.'s
(2017) meta-analysis, which reported a correlation of .86 between trait EI and the GFP. A major
difference between the current study and the meta-analysis is that the meta-analysis calculated
the GFP via the big two: alpha and beta. Within this study, the observed GFP created by the
MPQ-BF resembled trait externalizing (Hopwood & Grilo, 2010). Externalizing behaviors are
negatively correlated with alpha and positively correlated with beta (DeYoung et al., 2008);
given the very high correlation between trait EI and the GFP calculated from a big two model, it
is understandable that trait EI would be uncorrelated with a GFP that resembles trait
externalizing. As a result, my findings regarding trait EI and personality may actually offer
37
discriminant validity; thus, only the GFP as calculated by the big five and big two is similar to
trait EI.
The Generality of Emotional Intelligence
In regards to my final hypothesis, my findings support that ability EI and trait EI are
completely separate constructs. In fact, they are a prime example of the jingle fallacy, leading
individuals to assume that they measure the same construct because they both share the title of
emotional intelligence (Gonzalez et al., 2020). Simply placing the “ability” or “trait” descriptor
before the term “EI” is likely to continue the conceptual confusion that has roiled these
literatures. Therefore, I propose that ability EI continue to use the title EI due to it being
measured similarly to traditional intelligences, while trait EI as measured by the TEIQue solely
use its alternative name of “emotional self-efficacy” (Petrides, Pita, et al., 2007).
Limitations and Future Directions
While it would have been ideal to examine the plethora of different EI assessments and
models beyond an undergraduate sample, the scope of this dissertation did not allow for it due to
funding and time constraints. The Situational Test of Emotion Management (STEM) and
Situational Test of Emotional Understanding (STEU) are also measures of ability EI. The STEM
assesses one’s ability to manage emotions, whereas the STEU measures one’s ability to
understand emotions (MacCann et al., 2011). These measures were created due to concerns of
the factor structure of the MSCEIT and the desire to increase the availability of ability EI
measures (Ferguson & Austin, 2011; Libbrecht & Lievens, 2012). These measures are somewhat
commonly used and have been utilized by some medical schools as part of their selection process
(De Leng et al., 2017). The STEM and the STEU were not used in this dissertation because
according to the ability EI model from which these assessments were created, there are four
38
branches, which have been commonly collapsed into three through psychometric rigor in recent
research (MacCann et al., 2014). The STEM and the STEU only contain two of the branches,
managing emotions and understanding emotions. I believed that only two branches would not
thoroughly address the research questions posed in this study.
The Emotional Quotient Inventory 2.0 (EQ-I 2.0) is a self-report measure, but the items
attempt to examine features that are more closely aligned with the ability model (Van Zyl, 2016;
Petrides, 2009, pp. 85–101). The Schutte Self Report Emotional Intelligence Test (SSEIT) is
another popular self-report measure that uses the same model of EI as the MSCEIT (Schutte et
al., 1998). Although the measure was designed based on the model of EI used to develop the
MSCEIT, the two measures only correlate .14 (Schutte et al., 1998).
This study omitted the competency model of EI, which accounts for both EI and social
intelligence (Kunnanatt, 2008). As defined by this model, EI is associated with self-awareness of
one’s internal states and preferences as well as the self-management of internal states and
impulses (Kunnanatt, 2008). The competency model is measured by the Emotional and Social
Competence Inventory (ESCI; Kunnanatt, 2008). It aims to measure EI via a “behavioral
approach” and makes use of several raters (i.e. supervisors, peers, etc.), including the individual
being assessed. The ESCI is associated with performance in certain environments, such as at
one’s occupation, and aids in identifying strengths and weaknesses (Boyatzis, 2009; Kunnanatt,
2008).
Lastly, obtaining a GFP with a personality measure other than the MPQ-BF, which was
originally designed to have three orthogonal factors (C. Patrick & Kramer, 2017), would likely
allow for a more cohesive and meaningful general factor that could be examined relative to trait
EI.
39
Conclusions
In summary, ability EI should be considered a second-order factor of general intelligence.
Therefore, some of the variance observed in ability EI is explained by general intelligence. The
GFP, as measured by the MPQ-BF, and trait EI are best described as unrelated constructs that do
not share or explain variance within one another. However, the radically different natures of
GFPs obtained different personality measures call into question whether it is even meaningful to
posit relationships between the GFP and trait EI measures like the TEIQue. Indeed, the term GFP
shares similar issues with the term EI; it is a moniker shared by many different constructs and is
dependent on the tool of measurement.
40
APPENDIX A: R SYNTAX
############ # Figure 1 # ############ # Hierarchical MSCEIT model <- ' # latent variable definitions #Perceiving =~ Faces + Pictures #Understanding =~ Changes + Blends Perceiving =~ Faces + equal("Perceiving=~Faces")*Pictures Understanding =~ Changes + equal("Understanding=~Changes")*Blends Managing =~ Emotion_Management + Social_Management + Facilitation + Sensations AEI =~ Managing + Perceiving + Understanding ' library(semPlot) fit <- sem(model, data=st025_MSCEITQs, std.lv=TRUE) semPaths(fit, whatLabels = "std", layout = 'tree3', edge.label.cex=1, title = FALSE, curvePivot = TRUE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) # Bifactor MSCEIT model <- ' # latent variable definitions Perceiving =~ Faces + equal("Perceiving=~Faces")*Pictures Understanding =~ Changes + equal("Understanding=~Changes")*Blends Managing =~ Emotion_Management + Social_Management + Facilitation + Sensations AEI =~ Faces + Pictures + Changes + Blends + Emotion_Management + Social_Management + Facilitation + Sensations ' fit <- sem(model, data=st025_MSCEITQs, orthogonal=TRUE, std.lv=TRUE) semPaths(fit, layout = 'tree3', edge.label.cex=1, bifactor = 'AEI', title = FALSE, curvePivot = TRUE, exoCov = FALSE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) corr.test(subset(st025_MSCEITQs, select= Faces:Social_Management)) describe(subset(st025_MSCEITQs, select=c(Emotion_Management,Social_Management,Facilitation,Sensations))) subset(st025_MSCEITQs, FirstName == 1183, select=c(Emotion_Management,Social_Management,Facilitation,Sensations)) ############ # Figure 2 # ############
41
# Hierarchical TEIQue model <- ' # latent variable definitions WellBeing =~ Optimism + Happiness + SelfEsteem Sociability =~ EmotionManagement + Assertiveness + SocialAwareness Emotionality =~ Empathy + EmotionPerceptionIndex + EmotionExpression + Relationships SelfControl =~ EmotionRegulation + ImpulseControl + StressManagement EI =~ WellBeing + Sociability + Emotionality + SelfControl + Motivation + Adaptability ' library(semPlot) fit <- sem(model, data=TEIQue, std.lv=TRUE) semPaths(fit, whatLabels = "std", layout = 'tree3', edge.label.cex=1, title = FALSE, curvePivot = TRUE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) # Bifcator TEIQue model <- ' # latent variable definitions WellBeing =~ Optimism + Happiness + SelfEsteem Sociability =~ EmotionManagement + Assertiveness + SocialAwareness Emotionality =~ Empathy + EmotionPerceptionIndex + EmotionExpression + Relationships SelfControl =~ EmotionRegulation + ImpulseControl + StressManagement EI =~ Optimism + Happiness + SelfEsteem + EmotionManagement + Assertiveness + SocialAwareness + Empathy + EmotionPerceptionIndex + EmotionExpression + Relationships + EmotionRegulation + ImpulseControl + StressManagement + Motivation + Adaptability #Fix negative variance issues test #EmotionRegulation ~~ 0*EmotionRegulation #Happiness ~~ 0*Happiness ' library(semPlot) fit <- sem(model, data=TEIQue, orthogonal=TRUE, std.lv=TRUE) semPaths(fit, whatLabels = "std", edge.label.cex=1, layout = 'tree2', bifactor = 'EI', title = FALSE, curvePivot = TRUE, exoCov = FALSE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) ############ # Figure 3 # ############ # Hierarchical IQ model <- ' # latent variable definitions Gf =~ ThreeD_Rotation + Matrix_Reasoning + LNS
42
Gc =~ GSCI + equal("Gc=~GSCI")*Shipley IQ =~ Gf + Gc ' fit <- sem(model, data=Total_IQ, std.lv=TRUE) semPaths(fit, whatLabels = "std", layout = 'tree3', edge.label.cex=1, title = FALSE, curvePivot = TRUE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) # Bifactor IQ model <- ' # latent variable definitions Gf =~ ThreeD_Rotation + Matrix_Reasoning + LNS Gc =~ GSCI + equal("Gc=~GSCI")*Shipley IQ =~ ThreeD_Rotation + Matrix_Reasoning + LNS + GSCI + Shipley ' fit <- sem(model, data=Total_IQ, orthogonal=TRUE, std.lv=TRUE) semPaths(fit, layout = 'tree3', edge.label.cex=1, bifactor = 'IQ', whatLabels = "std", title = FALSE, curvePivot = TRUE, exoCov = FALSE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) ############ # Figure 4 # ############ # Hierarchical MPQ model <- ' # latent variable definitions PEM =~ Well_Being + Social_Potency + Achievement + Social_Closeness + Absorption NEM =~ Stress_Reaction + Alienation + Aggression + Absorption CON =~ Control + Harm_Avoidance + Traditionalism GFP =~ PEM + NEM + CON ' fit <- sem(model, data=MPQfullscales) semPaths(fit, whatLabels = "std", edge.label.cex=1, layout = 'tree3', title = FALSE, curvePivot = TRUE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) # Bifactor MPQ model <- ' # latent variable definitions GFP =~ Well_Being + Social_Potency + Achievement + Social_Closeness + Absorption + Stress_Reaction + Alienation + Aggression + Control + Harm_Avoidance + Traditionalism PEM =~ Well_Being + Social_Potency + Achievement + Social_Closeness + Absorption NEM =~ Stress_Reaction + Alienation + Aggression + Absorption CON =~ Control + Harm_Avoidance + Traditionalism
43
#Fix traditionalism to 0 due to non-significant negative variance Traditionalism ~~ 0*Traditionalism ' fit <- sem(model, data=MPQfullscales, orthogonal=TRUE) semPaths(fit, whatLabels = "std", layout = 'tree3', edge.label.cex=1, bifactor = 'GFP', title = FALSE, curvePivot = TRUE, exoCov = FALSE) #Model Summary Statistics summary(fit, standardized=TRUE, modindices = TRUE) fitmeasures(fit) ############ # Figure 5 # ############ #Ability EI as a Second-Order Factor model <- ' # latent variable definitions Gf =~ ThreeD_Rotation + Matrix_Reasoning + LNS Gc =~ GSCI + equal("Gc=~GSCI")*Shipley AEI =~ Perceiving + Understanding + Managing IQ =~ Gf + equal("IQ=~Gf")*Gc + AEI ' fit <- sem(model, data=GMSCEIT, std.lv=TRUE) semPaths(fit, whatLabels = "std", layout = 'tree3', edge.label.cex=1, title = FALSE, curvePivot = TRUE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit) # Ability EI as a Separate Factor model <- ' # latent variable definitions Gf =~ ThreeD_Rotation + Matrix_Reasoning + LNS Gc =~ GSCI + equal("Gc=~GSCI")*Shipley IQ =~ Gf + equal("IQ=~Gf")*Gc AEI =~ Perceiving + Understanding + Managing IQ ~~ AEI ' fit <- sem(model, data=GMSCEIT, std.lv=TRUE) semPaths(fit, whatLabels = "std", layout = 'tree', edge.label.cex=1, title = FALSE, curvePivot = TRUE) #Model Summary Statistics summary(fit, standardized=TRUE) fitmeasures(fit)
44
APPENDIX B: GSCI ASSESMENT
Instructions Each passage has four underlined portions. These underlined portions may or may not contain an error (there is only one possible error per question). If you believe the first underlined portion has an error, then answer "1st blank". If the second underlined portion contains an error, then answer "2nd blank", etc. If you do not believe there are any errors in a passage, then answer "No error".
Q1 The otter, blue jay, and weasel were walking among the river when they saw the kitten sitting underneath the tree.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q2 The kitten stood up and became excited when his friends came towards him, and he thought that he could finally catch the dastardly beetle.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
45
Q3 The kitten and his friends strategized a way that they could trap the beetle using sticks and tree leafs. To their displeasure, they were not successful.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q4 The woman, dressed in all black clothing, was mourning the loss of her husband on a cold and rainy friday night.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q5 An extraordinary discovery was made by accident when the chemist made a mistake and dropped a glass flask coated in plastic cellulose nitrate.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
46
Q6 Nina Simone was not only a well known artist but an activist for civil rights during the 1950s and 1960s.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q7 In 2018 the sleek modern hotel tower replaced the old and dusty brown casino that overlooked the Las Vegas Strip.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
47
Q8 Neither the hockey announcer or the referee could be heard over the angry shouting of the team’s fans in the arena.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q9 The brand new parking garage was convenient but over 300 residents were forced to move when they demolished the old apartments.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q10 The poet admired the woman from afar and was inspired to write when they felt the pain of unrequited love.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
48
Q11 Just between you and I, there is something wrong with our boss. She looks tired and yells a lot now.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q12 I felt like I needed a change. I wanted to shake it up, to get a thrill which only comes from something different.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q13 If only he were able to feel love, compassion, or remorse. Any of these emotions would make him seem human.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
49
Q14 If I had a dollar for every silly thing I heard or read, I would be rich like you, too.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q15 This town has such a low crime rate that, for all intense and purposes, one could bike to a convenience store in the middle of the night and not worry.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
50
Q16 The three best things about going to Costa Rica was the hikes, the hidden hot springs, and the food (of course).
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q17 No matter how many points your team is currently losing by, you must give your best effort because you never know how it will effect the game.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q18 The student ran home to show his parents his test score. Everyone in his family was proud of how good he had done on the exam.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
51
Q19 The coach guaranteed without a doubt that his team would eradicate the competition. He believed that they were in a league of their own.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q20 There within the large house, their hearts beat furiously. Although afraid, they’re the ones who had the best chance of completing the job.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q21 The director stated that the reason the series is so successful have been the people who work hard on set everyday.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
52
Q22 While mopping the floor after a fast-paced basketball game, the janitor found a smelly player’s sock.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q23 When the little girl brought home her straight “A” report card to her mother, she was so proud.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q24 Leading voices of the robotics division are focusing on a positive rebranding of artificial intelligence in response to public concern of the possibility of a robot uprising.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
53
Q25 What we consider “good, better, or best” is relative to the available options. Sometimes even the best option is unfavorable.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q26 The store had everything; books, shoes, candles, toys, etc. We all loved to wait outside before the store opened to get the best merchandise.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
54
Q27 The continuous thunder shook the entire neighborhood; my friends and I wondered if the dogs would ever stop barking.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q28 The weight of a thousand concerns stooped his shoulders. He could not bare to think about how he would have to address them.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
Q29 Although a lot of people are terrified of anacondas for their menacingly large bodies, these reptile are actually known to be one of the least dangerous snakes.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error
55
Q30 After having to tie my shoe, I noticed that it was quickly becoming dark. The only ones who hadn’t made it back to the base were Miranda and me.
o 1st blank
o 2nd blank
o 3rd blank
o 4th blank
o No error End of Block: Default Question Block
Questions retained and used in study: 1,4,6,7,8,11,16,18,28
0 5 10 15 20 25 30
01
23
Parallel Analysis Scree Plots
Factor/Component Number
eige
nval
ues
of p
rinci
pal c
ompo
nent
s an
d fa
ctor
ana
lysi
s
PC Actual Data
PC Simulated Data
PC Resampled Data
FA Actual Data
FA Simulated Data
FA Resampled Data
56
APPENDIX C: ADDITIONAL FIT STATISTICS
χ2 and CFI of Each Model
Model χ2 p-value CFI MSCEIT
Hierarchical 28.323 .077 .948
Bifactor N/A N/A N/A TEIQue Hierarchical 348.060 <.001 .834
Bifactor 276.274 .<.001 .874 Intelligence
Hierarchical 5.044 .283 .991
Bifactor 2.355 .125 .988 MPQ-BF
Hierarchical 109.896 <.001 .815
Bifactor 77.888 <.001 .881 Intelligence and MSCEIT Separate Factors 67.981 <.001 .904 Second Order-Factor 67.981 <.001 .904 MPQ-BF and TEIQue Separate Factors N/A N/A N/A Second-Order Factor N/A N/A N/A
57
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Doctoral Intern August 2019 – August 2020
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Pre-Doctoral Practicum Assessment and Therapy Trainee August 2018 – May 2019
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Pre-Doctoral Practicum Therapy and Assessment Trainee August 2017 – August 2018
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Clinical Doctoral Student Graduate Counselor August 2016 – May 2017 Counseling and Psychological Services at the University of Nevada, Las Vegas Therapy and Intake Supervisors: James Jobe, Ph.D.; Alexandria Moorer, M.A. • 9-month practicum at a university counseling center. A weekly caseload of approximately six
therapy clients and one intake client was maintained. Weekly individual supervision, group supervision, and multidisciplinary case rounds were conducted
• Focused on exploring and implementing cultural development models, developing an acceptance and commitment therapy orientation, treatment in a brief-intervention model, and working in an integrative healthcare institution
• Worked with culturally and economically diverse undergraduate and graduate students who presented with depressive symptoms, anxious symptoms, suicidality, relationship concerns, adjustment disorders, and academic concerns
Clinical Doctoral Student Graduate Clinician August 2015 – August 2016; The PRACTICE Clinic University of Nevada, Las Vegas August 2017 – May 2018
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Therapy Supervisors: Kristin Culbert, Ph.D.; Katherine Isaza, Psy.D. Assessment Supervisors: Stephen Benning, Ph.D.; Michelle Paul, Ph.D. • 12-month practicum placement that provides low cost mental health therapy and assessment
services to the university population and community at large. A weekly caseload of approximately five therapy clients and one assessment client is maintained, with weekly individual and group supervision. Intakes were conducted on a biweekly basis
• Explored different theoretical orientations, advocated for underserved and underrepresented populations, researched considerations when working with clients of minority status, and facilitated group therapy
• Served a wide variety of clients as a result of sliding-scale fees; clients consisted of adults who were diverse in socioeconomic status, education, and ethnicity
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Interprofessional Education Day March 2014, March 2015, March 2016 University of Nevada, Las Vegas • Engaged in a professional training aimed to improve client experience by increasing the
cohesiveness and collaboration between healthcare professionals within an integrated healthcare model
• Co-created and presented a lecture regarding the role of clinical psychology within an integrated healthcare model
• Collaborated with seven healthcare graduate degree programs from the University of Nevada, Las Vegas (UNLV) participated in a series of group learning projects, simulations, and mock treatment planning
• Enrolled in a semester-long course (Integrated Behavioral Health Care) to develop and reinforce skills critical to the collaboration and consultation of healthcare professionals
The Counseling and Testing Center, University of Idaho August 2019 – December 2019
• Supervised a graduate student in their fifth year of practicum in the clinical psychology Ph.D. program at Washington State University
• Facilitated the skill development of supervisee professional identity, navigation of ethical concerns, risk management, documentation, and preparation for internship.
• Received group supervision of supervision with the training director of the Counseling and Testing Center and fellow doctoral interns.
The PRACTICE Clinic University of Nevada, Las Vegas • Supervised a graduate student who just completed their first year of practicum in the clinical
psychology Ph.D. program at UNLV • Guided supervisee in regards to growth edges, intervention planning, assisting in case
conceptualizations, exploring multicultural implications, managing client risk, discussing ethical concerns, and defining supervisee supervision goals
• Received supervision of supervision with the training director of the Practice and was concurrently enrolled in the course “Introduction to Supervision.”
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The PRACTICE Clinic University of Nevada, Las Vegas
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Instructor August 2016 – May 2017 University of Nevada, Las Vegas August 2018 – Present Course: General Psychology Created an introductory course for undergraduate students. Solely responsible for course content including lecture material, in-class activities, assignments, and exams. Classes operate through open discussions based on lecture material, different values and beliefs held by students are encouraged to be shared.
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