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Openness to Experience Rather Than Overexcitabilities: Call It Like It Is


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Openness to experience is a personality factor in the five-factor model of personality, and it is composed of six facets. Facets of openness appear conceptually analogous to overexcitabilities (OEs), which are displays of inner energy guiding individuals toward advanced potential according to the theory of positive disintegration. This study examined the similarity of OEs to corresponding openness to experience facets in a sample of 149 creative adolescents and adults and 312 adults from the general population (total N = 461). Exploratory structural equation modeling tested competing models in which each OE and corresponding openness facet were modeled as separate factors and as joint factors. The separate-factor model had acceptable fit but uninterpretable loadings, while the joint-factor model had acceptable fit and interpretable loadings; thus, openness seems to encompass OEs. Accordingly, the field should align with well-researched psychological theories like the five-factor model of personality and begin to talk about openness rather than OEs.
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Gifted Child Quarterly
2016, Vol. 60(3) 192 –211
© 2016 National Association for
Gifted Children
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DOI: 10.1177/0016986216645407
A controversy exists in gifted education regarding certain
personality traits that appear to be related to giftedness, yet
when describing those traits the majority of the literature
does not use known personality theories. Psychology can
provide an answer to this problem with the five-factor model
of personality (FFM). This is a well-researched and general-
izable personality model that is valid across ages and cul-
tures (McCrae, 2010; McCrae, Terracciano, et al., 2005).
Gifted education would benefit from adopting this interdisci-
plinary stance in scientific studies.
Overexcitabilities (OEs) describe heightened intensity and
sensitivity in five areas, namely imaginational, sensual, emo-
tional, psychomotor, and intellectual, that according to their
original theory, the theory of positive disintegration (TPD;
Dabrowski, 1967), indicate a heightened activity of the ner-
vous system (Mendaglio, 2012; Mendaglio & Tillier, 2006)
and might lead to advanced moral and emotional develop-
ment (Piechowski, 1979, 2006). However, other personality
theories describe similar traits. For example, the most impor-
tant personality theory in psychology is the FFM, a theory
that has strong generalization across cultures and ages
(McCrae, 2010; McCrae, Terracciano, et al., 2005). The FFM
can provide an explanation of behaviors described by OEs in
a more parsimonious theory. In their seminal article, Subotnik,
Olszewski-Kubilius, and Worrell (2011) strongly argued for
the need to incorporate constructs from psychological science
into the gifted education literature. Despite the potential to
inform the field with empirically well-established constructs,
psychological science remains largely underrepresented. This
insufficient representation occurs especially in resources for
parents and educators; for example, the award-winning and
popular book A Parent’s Guide to Gifted Children (Webb,
Gore, Amend, & DeVries, 2007) does not mention well-
researched personality theories such as the FFM, but includes
a section devoted to OEs.
Openness to experience, one of the personality factors in
the FFM, closely relates to and may in fact explain OEs.
According to Costa and McCrae (1992), individuals who are
open to new experiences enjoy both outer and inner worlds,
are curious, and hold novel ideas. They have high aesthetic
sensitivity, intellectual curiosity, vivid imagination, and
evolving value systems. This description appears extraordi-
narily analogous to descriptions of OEs, which describe
active imaginations, enjoyment of sensory pleasures such as
art and beauty, intensity of feelings, love of learning, and a
pull for action (Piechowski, 1979, 2006). The bulk of OE
research appears to be atheoretical, thus misrepresenting
Dabrowski’s original TPD (Mendaglio, 2012) and making it
more plausible to say that the behaviors called OE are in
645407GCQXXX10.1177/0016986216645407Gifted Child QuarterlyVuyk et al.
1The University of Kansas, Lawrence, KS, USA
Corresponding Author:
M. Alexandra Vuyk, Department of Educational Psychology, The
University of Kansas, 1122 West Campus Road, Room 621, Lawrence,
KS 66045, USA.
Openness to Experience Rather Than
Overexcitabilities: Call It Like It Is
M. Alexandra Vuyk1, Thomas S. Krieshok1, and Barbara A. Kerr1
Openness to experience is a personality factor in the five-factor model of personality, and it is composed of six facets. Facets
of openness appear conceptually analogous to overexcitabilities (OEs), which are displays of inner energy guiding individuals
toward advanced potential according to the theory of positive disintegration. This study examined the similarity of OEs to
corresponding openness to experience facets in a sample of 149 creative adolescents and adults and 312 adults from the
general population (total N = 461). Exploratory structural equation modeling tested competing models in which each OE
and corresponding openness facet were modeled as separate factors and as joint factors. The separate-factor model had
acceptable fit but uninterpretable loadings, while the joint-factor model had acceptable fit and interpretable loadings; thus,
openness seems to encompass OEs. Accordingly, the field should align with well-researched psychological theories like the
five-factor model of personality and begin to talk about openness rather than OEs.
openness to experience, overexcitabilities, five-factor model of personality, structural equation modeling, quantitative
methodologies, social and/or emotional development and adjustment
Vuyk et al. 193
reality openness to experience. In this study, we will explore
the potential connection between OEs and facets of openness
to experience, suggesting that they represent similar or
equivalent constructs.
OEs and TPD
The few published empirical studies focus primarily on
OEs without connecting them to Dabrowski’s larger TPD
and the role they play in achieving one’s developmental
potential (Mendaglio, 2012). Despite the popularity of OEs,
empirical evidence supporting their existence is scant, and
patterns of OEs in gifted individuals are inconsistent
(Mendaglio, 2012; Winkler, 2014). Many studies have low
sample sizes (e.g., Gallagher, 1986; Schiever, 1985), and
not all studies are published in peer-reviewed journals (e.g.,
Falk, Yakmaci-Guzel, Chang, Pardo de Santayana Sanz, &
Chavez-Eakle, 2008). Even with these problems, the OE
literature continues to cite them.
Certain proponents of OEs even claim that personality-
based measures, especially ones based on OEs, should be at
the basis of identification for giftedness (Carman, 2011).
This becomes a problematic circular definition of giftedness.
TPD states that the five OEs must be present for a person to
reach their full developmental potential (Mendaglio, 2012),
yet only some studies found that gifted individuals surpassed
the general population on the five OEs (C. M. Ackerman,
1997; Siu, 2010; Tucker & Hafenstein, 1997), and other
studies found differences only in one or two OEs (Wirthwein,
Becker, Loehr, & Rost, 2011; Yakmaci-Guzel & Akarsu,
2006). However, empirical evidence does not support identi-
fication based on personality or OEs (Mendaglio, 2012;
Wirthwein & Rost, 2011), and the usefulness or even exis-
tence of the OE construct is debated (Rost, Wirthwein, &
Steinmayr, 2014).
Openness to Experience and the FFM
Along with intelligence, personality is the construct that
most consistently predicts a wide variety of human behav-
iors, including achievement, job success, well-being, and life
satisfaction (DeYoung, 2011). The FFM is the most widely
accepted personality theory in psychology (McCrae, 2010)
and has support across the lifespan and in various cultures
(McCrae, 2010; McCrae, Terracciano, et al., 2005). This per-
sonality theory encompasses five major factors or domains:
extraversion (E), neuroticism (N), openness to experience or
openness/intellect (O), agreeableness (A), and conscien-
tiousness (C). Each of these domains is divided into six fac-
ets or subscales, with the openness facets reporting the aspect
of life in which a person remains open. The six openness
facets are labeled O1 Fantasy, O2 Aesthetics, O3 Feelings,
O4 Actions, O5 Ideas, and O6 Values, and are backed by
theory (Costa & McCrae, 1992) and empirical studies
(Furnham, Guenole, Levine, & Chamorro-Premuzic, 2013).
We will describe these facets, highlighting the conceptual
similarity found in OEs and supporting the claim that open-
ness can explain behaviors seen in OEs. Given the strong
research support for the FFM and its parsimonious nature,
we propose that the FFM should be favored.
O1 Fantasy describes people with an active and detailed
imagination who believe in the power of fantasy and day-
dreaming and engage vividly in those activities (Costa &
McCrae, 1992), analogous to imaginational OE (Piechowski,
1979, 2006). High O2 Aesthetics indicates an ability to
become absorbed in beauty and arts, with strong enjoyment
of these activities (Costa & McCrae, 1992). Sensual OE,
aesthetics’ corresponding OE, refers to being moved by
sensory experiences and a need for pleasure and beauty
(Piechowski, 1979, 2006). Openness to a full range of feel-
ings, both in variety and in intensity, defines the O3 Feelings
facet (Costa & McCrae, 1992) as well as the emotional OE
(Piechowski, 1979, 2006). O4 Actions describes a love of
novelty and moving out of one’s comfort zone (Costa &
McCrae, 1992), while psychomotor OE refers to high
energy and even restlessness to take action (Piechowski,
1979, 2006). O5 Ideas describes extraordinary curiosity, a
passion for learning, and a need to understand theories and
reasoning (Costa & McCrae, 1992), similar to the intellec-
tual OE (Piechowski, 1979, 2006). People who do not place
importance on authority or tradition score high on O6
Values. They do not support dogmas and can revise rules
whenever needed (Costa & McCrae, 1992). There does not
seem to be a clear overlap of O6 Values and any OE, though
it might relate to emotional OE as Piechowski (2006)
claimed that people with emotional OE have a strong sense
of social justice, but this theoretical correspondence is the
weakest connection in the two sets of constructs.
Studies find a relationship between openness to experi-
ence and intelligence in the general population, mostly with a
small to medium effect size (P. L. Ackerman & Heggestad,
1997; DeYoung, Quilty, Peterson, & Gray, 2014; Gignac,
Stough, & Loukomitis, 2004; Harris, 2004; Moutafi,
Furnham, & Crump, 2006). Studies with gifted samples show
similar results. McCrae et al. (2002) as well as Zeidner and
Shani-Zinovich (2011) found a small to medium effect size
on openness to experience when comparing gifted adoles-
cents with the general population, and Altaras Dimitrijević
(2012) found that a composite factor, mainly constituted of
facets of openness, could discriminate among gifted and non-
gifted samples. Cross, Speirs Neumeister, and Cassady (2007)
and Sak (2004) found their gifted samples had a stronger pref-
erence for intuition over sensory information, a preference
that relates to openness to experience (Costa & McCrae,
1992). Openness to experience is high in creative individuals
regardless of creative domain (Feist, 1998; Gorman & Feist,
2014; Ivcevic & Mayer, 2007; Kerr & McKay, 2013) and can
predict creative performance and participation in creative
activities (Batey, Chamorro-Premuzic, & Furnham, 2010;
Kaufman, 2013). Not surprisingly, the literature shows a
194 Gifted Child Quarterly 60(3)
relationship between openness to experience at the domain
level and OEs (Botella et al., 2015; Limont, Dreszer-
Drogorób, Bedyńska, Śliwińska, & Jastrzębska, 2014; Rost
et al., 2014), yet no studies to date have explored this relation-
ship at the facet level, where we would expect to see the
strongest relationships as each OE appears to correspond to
an openness facet.
The Present Study
Two hypothesized models tested the hypothesis that open-
ness facets and their corresponding OEs represent the same
latent constructs. In the separate-factor model, indicators of
OEs and indicators of openness facets were modeled as two
separate constructs expected to show a very strong correla-
tion. Openness facets and their corresponding OEs are as fol-
lows: O1 Fantasy and imaginational OE, O2 Aesthetics and
sensual OE, O3 Feelings and emotional OE, O4 Actions and
psychomotor OE, O5 Ideas and intellectual OE, and O6
Values on its own. The joint-factor model made this hypoth-
esized relationship more explicit by having all openness and
OE items belonging to each combination load into one single
latent variable. Different personality tests measuring the
exact same constructs have correlations ranging between .70
and .80 (Goldberg, 1999). Therefore, if OEs show similar
relationships with openness facets, or if items of OEs and
openness load onto the same factor, it could be assumed that
they are measuring very similar or equivalent constructs.
Participants and Procedure
For this study, 461 participants from two distinct samples
were recruited. This was to ensure the inclusion of the pop-
ulation of interest, creatively gifted individuals, yet prevent
restriction of range due to their expected high scores on
openness to experience facets and OEs. Therefore, one
sample was composed of persons judged to be creatively
gifted, and the second sample was composed of adults from
the general population. According to FFM theorists, per-
sonality traits are normally distributed in the population
(DeYoung, 2015; McCrae, Terracciano, et al., 2005), yet
OEs are not supposed to be normally distributed (Mendaglio,
2012). Including two samples expected to have a wide
range of scores on openness and OEs would allow testing
for normal distributions.
The decision to select highly creative individuals was
based on the literature reviewed, in which creatively gifted
individuals generally score higher than the general popula-
tion on OEs (Falk et al., 2008; Yakmaci-Guzel & Akarsu,
2006), while intellectually gifted individuals show an incon-
sistent pattern of scores. Both creatively gifted and intellec-
tually gifted are covered under the umbrella of the federal
giftedness definition (Elementary and Secondary Education
Act, 2002) and thus represent the population that is consid-
ered pertinent to proponents of OE.
Sample 1: Creative Adolescents and Adults. Participants in the
first sample were 149 creatively and intellectually gifted
adolescents and adults from the Midwest identified via a
profiling technique developed by Kerr and McKay (2013;
see the appendix). They were recruited via invitations to
high schools (in particular their gifted programs), as well as
creative programs at universities (e.g., arts, creative writing,
graphic, and industrial design). Schools received profiles
that described eminent adults who achieved high creativity
in their domains when they were younger, and school per-
sonnel selected students who fit the profiles. Previous
research indicated the promise of this identification method
as many of these adolescents and adults already had creative
accomplishments, and their personalities resembled those of
creative individuals (Kerr & McKay, 2013). Demographic
information can be found in Table 1.
Data collection for the first sample took place in the con-
text of a larger project approved by the institutional review
board in 2007. Schools received informed consent forms and
distributed them to potential participants. Participants
younger than 18 years signed and turned in their own assent
forms along with consent forms signed by their parents or
legal guardians. Participants aged 18 years or older signed
their own informed consent form before participating in the
study. Recruitment of participants and completion of ques-
tionnaires occurred between February 2014 and May 2015.
Sample 2: Adults From the General Population. The second sam-
ple included 312 adults recruited via Amazon Mechanical
Turk or MTurk, a crowdsourcing platform, by posting a
request for completion of the study via a screener survey with
demographic information and a follow-up survey with the
assessments. MTurk members typically perform tasks such as
completing surveys posted on the platform and receive pay-
ment for completion of those surveys. MTurk only allows
adults to use its services, and no other prerequisite for partici-
pation was requested. Research has shown that results
obtained with MTurk participants are similar to those obtained
in college and community samples, and thus MTurk is gain-
ing acceptance in the behavioral sciences (Shapiro, Chandler,
& Mueller, 2013). Demographic information is in Table 1.
For the second sample, institutional review board approval
was secured, and both questionnaires were set up in Qualtrics.
A Human Intelligence Task was posted on MTurk with a
request for participants, the information statement, and a
screener survey asking for demographic information. First,
472 potential participants completed the screener survey for
which they received a payment of $0.02. After we approved
the screener survey, those 472 potential participants received a
$0.01 bonus payment with an embedded custom link to the
assessments in Qualtrics via a private message. This custom
link was related to that MTurk unique Worker ID, and was a
Vuyk et al. 195
one-time use link. We checked which participants completed
the assessments in Qualtrics using the custom links and paid
those participants an additional bonus of $1.97, for a total pay-
ment for $2.00. In total, 312 participants completed the instru-
ments on Qualtrics. These additional steps were part of the
license agreement for online use of the NEO PI-3, while abid-
ing by terms of service of MTurk. Recruitment of participants
and completion of questionnaires occurred in March 2015.
NEO Personality Inventory-3. The NEO Personality Inven-
tory-3 (NEO PI-3 (McCrae, Costa, & Martin, 2005) is a 240-
item measure based on the FFM. Five domain scales of 48
items per domain, each corresponding to a personality trait,
make up six facet subscales of eight items in each subscale.
The facet scales for openness to experience are openness to
fantasy, aesthetics, feelings, actions, ideas, and values.
Copyright prevents the inclusion here of sample items.
Results are presented as raw scores that can be converted to
T scores to compare results with the suitable norming group.
The normative sample of the NEO PI-3 included adolescents
and improved readability compared with previous iterations
(McCrae, Costa, & Martin, 2005).
Overexcitabilities Questionnaire–Two. The Overexcitabilities
Questionnaire–Two (OEQ-II; Falk, Lind, Miller, Piechowski,
& Silverman, 1999) is at present the only quantitative instru-
ment available to assess OEs, for which reason it was used in
this study. The OEQ-II measures psychomotor, sensual,
imaginational, intellectual, and emotional OEs on a 5-point
Likert-type scale (50 items) for group comparison purposes
only. Copyright prevents the inclusion here of sample items.
Psychometric quality might be a concern with the OEQ-II, as
one published confirmatory factor analysis (CFA) found that
OE models did not fit and did not hold measurement invari-
ance across genders (Warne, 2011), and a later study using
exploratory structural equation modeling within a CFA
framework (ESEM-within-CFA or EWC; Morin, Marsh, &
Nagergast, 2013) found acceptable fit only with model modi-
fications and partial measurement invariance across genders
(Van den Broeck, Hofmans, Cooremans, & Staels, 2013).
Data Analysis
Items in the NEO PI-3 were converted to a 1-to-5 Likert-type
scale as used by the OEQ-II for ease of interpretability. Data
were screened with normality tests. Measurement models
were designed including each openness/OE pair as separate
latent factors or as a single latent factor. Kline (2010) stated
that latent variable modeling studies could be advantageous
to gifted education research to test relationships among
hypothetical constructs such as openness or OEs. Latent
variable models define constructs with multiple indicators
Table 1. Demographic Information.
Sample 1: Creative adolescents and adults (n = 149) Sample 2: Regular adults (n = 312)
Gender, n (%)
Female 83 (55.7) 144 (46.4)
Male 62 (41.6) 166 (50.0)
Other (e.g., nonbinary, transgender) 4 (2.7) 2 (0.6)
Age, M (SD) 17.12 (4.83) 35.92 (10.88)
Highest education level, n (%)
Some high school 134 (89.9) 0 (0)
High school/GED diploma 0 (0) 37 (11.9)
Some college or technical training 14 (9.4) 60 (19.4)
2-year college graduate 0 (0) 25 (8.1)
4-year college graduate 0 (0) 130 (41.9)
Master’s degree 1 (0.7) 54 (17.4)
Doctorate or professional degree 0 (0) 4 (1.3)
Race/ethnicity, n (%)
African American 4 (2.7) 10 (3.2)
Asian American 5 (3.4) 102 (32.7)
Latino/Latina 4 (2.7) 6 (1.9)
Native American 3 (2.0) 3 (1.0)
Other race/ethnicity or multiracial 9 (6.0) 8 (2.6)
Caucasian 123 (82.6) 183 (58.7)
Country of origin, n (%)
United States 149 (100) 217 (70.5)
India 0 (0) 86 (27.9)
Other 0 (0) 4 (1.6)
196 Gifted Child Quarterly 60(3)
correcting for measurement error, and can separate reliable
and unreliable indicators (Little, 2013). Population parame-
ters estimated in latent variable models are unbiased and thus
more exact and generalizable.
Models were tested using CFA in the R package lavaan
(Rosseel, 2012) using the robust maximum likelihood (MLR)
estimator to account for the ordinal nature of data. ESEM
was conducted in MPlus 7.1.3 (Muthén & Muthén, 2013)
with MLR estimation and Geomin rotation, as CFA is not
always suitable for personality tests (Marsh et al., 2010). The
appropriateness of CFA for personality instruments is
debated in the literature, with several FFM researchers sup-
porting the position that CFA is not the optimal choice for
these instruments (Gignac, Bates, & Jang, 2007; Marsh et al.,
2010; McCrae, Zonderman, Costa, Bond, & Paunonen, 1996;
Morin et al., 2013). McCrae et al. (1996) stated that person-
ality instruments have many correlated residuals and cross-
loadings due to the nature of the constructs and the manner in
which instruments are created, and thus would have poorer
fit indices in CFA or would need multiple modifications to
achieve good fit. This need for modifications ultimately
results in data-driven models, which go against the basic
rationale of CFA that relies on theory-driven models (Gignac
et al., 2007). Facing this dilemma, ESEM was introduced as
a theory-driven alternative to CFA to assess structure of per-
sonality instruments (Marsh et al., 2010).
Confirmatory Factor Analysis. Two competing measurement
models indicated the possible relationships among each
openness/OE combination. In the separate-factor model,
each openness facet and each OE were represented as sepa-
rate latent variables, with indicators corresponding to test
items of each openness facet and OE. To be able to observe
correlations among constructs, the fixed-factor method set
the scale. In the joint-factor model, each openness facet/OE
combination represented a single construct, with O6 Values
not matching with any OE. Again, the scale setting method
fixed the factor variance. However, previous studies using
pure CFA models for personality tests and the OEQ-II
resulted in poor fit for the instruments (Gignac et al., 2007;
Van den Broeck et al., 2013; Warne, 2011), and this poor fit
might stem from excessive correlated residuals and cross-
loadings found in personality instruments (Gignac et al.,
2007), thus, we elected to conduct additional ESEM analyses
to address these problems.
Exploratory Structural Equation Modeling. Models of openness
facets and their corresponding OEs as separate or joint fac-
tors were tested using ESEM. ESEM differs from traditional
exploratory factor analysis in that it incorporates advanced
methodological estimation procedures for latent variables
used in SEM and CFA that exploratory factor analysis cannot
estimate (Morin et al., 2013). Unlike CFA, ESEM permits
small cross-loadings for indicators; thus, models using
ESEM (Morin et al., 2013) allowed openness facets and OEs
to be included in one model without compromising model fit
as in CFA (Gignac et al., 2007), as indicators can load on
multiple factors. Researchers are increasingly applying
ESEM when working with personality instruments as the
methodology is more flexible to manage the minor cross-
loadings that are expected in these personality tests (Morin
et al., 2013; Van den Broeck et al., 2013).
Model Fit. Model fit statistics followed Hu and Bentler’s
(1999) and Little’s (2013) suggested definitions of accept-
able fit if comparative fit index (CFI) > .90, root mean square
error of approximation (RMSEA) < .08, and standardized
root mean square residual (SRMR) < .11, or very good fit if
CFI > .95, RMSEA < .05, and SRMR < .06, following com-
binational rules based on SRMR and other fit indices’ rejec-
tion rate of Type I and Type II errors. The combination rules
of RMSEA and SRMR presented by Hu and Bentler indicate
that with a sample size close to 500, the combination of
RMSEA between .05 and .08 and SRMR between .06 and .11
yields an acceptable ratio of Type I and Type II errors and
thus can be used to select useful models. However, these val-
ues were used as guides rather than stringent cutoff values as
advised by Fan and Sivo (2005), particularly because instru-
ments with 5 to 10 factors and 5 to 10 items per factor will
inherently have difficulties achieving restrictive fit conven-
tions (Marsh, Hau, & Wen, 2004).
Initial Analyses
There were no missing data as all items required a response
in the Qualtrics environment as we set it up. All indicators in
the models appeared normally distributed with skewness
<|1.5| and kurtosis <|2|. To calculate descriptive statistics,
item scores of openness facets and OEs on a Likert-type
scale of 1 to 5 were added to create a subscale score.
Reliability was good for all subscales with Cronbach’s alpha
greater than .70. Means, standard deviations, and Cronbach’s
alpha can be found in Table 2.
Correlations of Openness Facets and OEs
Intercorrelations among openness facets and OEs can be
found in Table 3. Below the diagonal are the subscale Pearson
correlations calculated according to the manuals’ instruc-
tions for subscale scores; however, these correlations must
be interpreted carefully as they contain measurement error
from treating latent variables as manifest variables (Little,
2013). Above the diagonal are the interfactor latent correla-
tions from the Model 1 CFA.
Target correlations among openness facets and their cor-
responding OEs were in the expected range, with the excep-
tion of psychomotor OE and O4 Actions. Subscale
correlations include measurement error and were slightly
Vuyk et al. 197
lower than interfactor correlations. O1 Fantasy and imagina-
tional OE had correlations of .76 and .63, O2 Aesthetics and
sensual OE had correlations of .87 and .78, O3 Feelings and
emotional OE had correlations of .84 and .62, and O5 Ideas
and intellectual OE had correlations of .81 and .682. These
correlations suggest that these could be equivalent constructs
from different instruments (Goldberg, 1999). O4 Actions and
psychomotor OE had correlations of .19 and .17. O6 Values
was not expected to correlate to OEs or perhaps to emotional
OE, yet the only significant correlation was a negative cor-
relation of −.307 with psychomotor OE.
Latent Variable Analyses
Confirmatory Factor Analysis. First, CFA models were tested
with the entire sample using MLR estimation. The CFA for
Model 1, where openness facets and their corresponding
OEs were modeled as separate latent constructs, had indices
that varied; χ2(4600, N = 461) = 11971.632; CFI = .688,
Bayesian information criterion (BIC) = 120854.264, SRMR
= .086, RMSEA = .059 (.058-.060). The Model 2 CFA, with
indicators loading on a single latent construct for each open-
ness facet and corresponding OE combination, yielded a
relatively worse fit, χ2(4640, N = 461) = 13400.236; CFI =
.629, BIC = 122037.532, SRMR = .095, RMSEA = .064
Exploratory Structural Equation Modeling. When testing with
ESEM, the first model did not converge, as three variables
were uncorrelated to all other variables in the model. These
variables were from the NEO PI-3; one was a part of O5
Ideas (Q143), and the other two part of O6 Values (Q178
and Q238R). A prerequisite of ESEM is having variables
that correlate with all other variables in the model and thus
Table 2. Descriptive Statistics on Openness Facets and OEs.
Sample 1: Creative adolescents and adults (n = 149) Sample 2: Regular adults (n = 312) Cronbach’s
Openness Domain 180.03 20.01 165.84 20.36 .902
O1: Ideas 30.47 5.31 26.56 5.56 .815
Imaginational OE 31.39 8.11 26.59 8.00 .887
O2: Aesthetics 29.64 6.66 27.58 5.90 .839
Sensual OE 36.28 8.44 34.85 8.21 .905
O3: Feelings 30.32 4.74 28.76 4.79 .742
Emotional OE 35.54 7.30 32.51 6.80 .820
O4: Actions 24.77 4.64 23.49 4.55 .729
Psychomotor OE 30.96 8.25 28.28 8.30 .891
O5: Ideas 32.55 4.82 29.83 5.62 .831
Intellectual OE 38.61 5.85 36.81 7.31 .883
O6: Values 32.28 5.02 29.62 5.89 .831
Table 3. Subscale and Interfactor Correlations Among Openness Facets and OEs (N = 461).
O1 .375*** .328*** .261*** .384*** .354*** .761*** .332*** .229*** −.106* .225***
O2 .330*** .569*** .355*** .477*** .113* .397*** .865*** .526*** .218*** .338***
O3 .290*** .479*** .182** .392*** .194*** .208*** .523*** .826*** .156** .302***
O4 .243*** .351*** .182*** .400*** .378*** .077 .288*** .015 .186*** .214***
O5 .340*** .389*** .317*** .347*** .400*** .200*** .396*** .191*** .179*** .813***
O6 .323*** .120* .201*** .309*** .360*** −.061 .030 –.131* −.370*** .051
MOE .629*** .339*** .169*** .113* .143** −.019 .476*** .461*** .188*** .334***
SOE .287*** .782*** .463*** .314*** .334*** .062 .409*** — .620*** .335*** .434***
EOE .199*** .443*** .623*** .036 .133** −.050 .407*** .524*** .355*** .320***
POE −.086 .188*** .127** .169*** .141** −.307*** .182*** .297*** .291*** .380***
TOE .190*** .294*** .272*** .226*** .682*** .075 .294*** .398*** .252*** .325***
Note. O1 = O1 Fantasy; O2 = O2 Aesthetics; O3 = O3 Feelings; O4 = O4 Actions; O5 = O5 Ideas; O6 = O6 Values; MOE = Imaginational OE; SOE
= Sensual OE; EOE = Emotional OE; POE = Psychomotor OE; TOE = Intellectual OE. Correlations below the diagonal correspond to subscale scores,
calculated according to the NEO PI-3 and OEQ-II scoring manuals. Correlations above the diagonal correspond to interfactor scores, calculated from the
CFA for Model 1 with 11 factors. Target correlations among openness facets and their corresponding OEs are marked in boldface.
*p < .05. **p < .01. ***p < .001.
198 Gifted Child Quarterly 60(3)
those three variables were removed from further ESEM
analyses. Model 1 in ESEM, with openness facets and OEs
as separate latent constructs, fit the data; χ2(3475, N = 439)
= 5944.441; CFI = .875, BIC = 117579.070, SRMR = .028,
RMSEA = .040 (.038-.042). The Model 2 ESEM, with indi-
cators loading on a single latent construct for each openness/
OE combination, yielded worse fit, χ2(3910, N = 439) =
8457.564; CFI = .790, BIC = 117491.513, SRMR = .041,
RMSEA = .051 (.050-.053).
Comparison of CFA Versus ESEM. Indices showed fit that
ranged from very good to acceptable for both CFA and
ESEM, except CFI which fell below the guideline of .90
(Little, 2013) for all models. All fit indices performed better
with ESEM analyses, in accordance with claims of Morin
et al. (2013) regarding personality tests. With the exception
of CFI, other indices were very good in ESEM while barely
reaching acceptable guidelines in traditional CFA; moreover,
CFI seemed consistently worse in the CFA models compared
with ESEM models. For those reasons, ESEM analyses were
selected for interpretation of the results.
Marsh et al. (2004) cautioned against conventional fit
guidelines being too restrictive for models with numerous
factors and numerous indicators. In fact, Kenny and McCoach
(2003) demonstrated empirically that CFI may worsen in
models with more indicators per factor, which adds a caveat
to interpretation. This problem of lower CFI in models with
multiple factors and indicators can be seen in McCrae et al.
(2002) where RMSEA showed excellent fit while CFI
appeared poor. The present study has even more factors and
indicators than McCrae et al. (2002), which warrants caution
in interpreting the overall impact of CFI. High sensitivity to
misspecified factor loadings is another drawback of CFI
(Sun, 2005), and FFM measurement models are particularly
prone to this issue due to the cross-loadings that naturally
exist in FFM instruments (McCrae et al., 1996), which can
explain the significantly lower CFI indices in CFA compared
with ESEM. Additionally, CFI appears to favor models that
are more complex (Sun, 2005), which can explain why in
this study Model 1 had relatively better fit compared with
Model 2 both for CFA and ESEM.
Selection of ESEM Model. Both proposed models fit the data
in an acceptable manner. However, one model could not be
meaningfully interpreted based on theory. Model 1, in which
each openness facet and each OE were presented as separate
constructs, did not follow the expected factor structure (see
Table 4). One factor that was among the first ones extracted
was uninterpretable, as it did not have meaningful item
loadings. Items mostly loaded on their openness facet or
OE, with some expected items not loading on their expected
factor. Items for sensual OE, psychomotor OE, and O6 Val-
ues all loaded on the expected factors based on significance
tests, with sensual OE having two items from O2 Aesthetics
with meaningful loadings (higher than .3 with p less than
.05). O1 Fantasy, imaginational OE, O2 Aesthetics, and O4
Actions had one item each that did not load on the expected
factor based on significance testing. Moreover, O1 Fantasy
had two meaningful loadings from imaginational OE, and
O2 Aesthetics had three meaningful loading items from sen-
sual OE. O3 Feelings and emotional OE items appeared to
load onto a single factor based on significance tests, yet with
two expected items not loading for O3 Feelings and four
expected items for emotional OE. Additionally, emotional
OE loaded onto a separate factor with two expected items
that did not load based on significance, and one meaningful
loading from O3 Feelings. Items for O5 Ideas and intellec-
tual OE loaded onto one single factor based on significance
tests, with one expected item not loading. Thus, Model 1,
despite appropriate fit indices, was not useful in interpreting
the relationship of OEs and openness given the discrepancy
between theory and actual results.
Model 2, in which openness facets and their correspond-
ing OEs were specified as joint factors, fit the data well
with the exception of CFI, and results were interpretable.
Every openness facet except O6 Values loaded onto one
factor in combination with their equivalent OE (see Table
5). O2 Aesthetics and sensual OE, O3 Feelings and emo-
tional OE, and O5 Ideas and intellectual OE loaded onto
the same factor; all expected items loaded based on signifi-
cance tests and most with high loadings. O1 Fantasy and
imaginational OE loaded onto the same factor, with one
expected item not loading based on significance. O4
Actions and psychomotor OE loaded onto the same factor
even though this combination was the most diverse based
on theory, with two expected items not loading based on
significance and O4 items having lower loadings than psy-
chomotor OE items. O6 Values was a single factor with no
OEs loading in conjunction as a block, though with several
items from other openness facets and OEs. Theory sup-
ports the results in this model, and most fit indices are
good. Therefore, this model was selected as the best one
and was used to interpret the results obtained.
Based on the results, openness to experience and OEs seem
to represent largely the same construct. O1 Fantasy and
imaginational OE, O2 Aesthetics and sensual OE, O3
Feelings and emotional OE, O4 Actions and psychomotor
OE, and O5 Ideas and intellectual OE appear to be equivalent
to each other as they loaded onto the same factor. O6 Values
did not load with any OEs per ESEM analyses. Subscale
Pearson correlations among openness facets and OEs, even
though containing measurement error because they do not
treat constructs as latent, as well as interfactor correlations
from Model 1 CFA, show that intercorrelations between each
openness facet and its corresponding OE are high enough
that they can be considered as an equivalent construct mea-
sured by different instruments (Goldberg, 1999).
Table 4. ESEM Loadings for Openness Facets and OEs as Separate Factors (N = 461).
Item O1 MOE Unint SOE O3/EOE O2 POE EOE O5/TOE O4 O6
NEO-O1-1 0.344* −0.063 0.403*** 0.134* 0.058 0.064 0.070 0.020 0.102 0.020 −0.026
NEO-O1-2 0.446*** 0.104 0.090 0.022 0.131 0.068 −0.052 −0.092 −0.094 0.236** 0.143
NEO-O1-3 0.656*** 0.125 0.352 0.025 0.058 0.022 −0.114* 0.039 −0.020 −0.001 0.012
NEO-O1-4 0.358* −0.229* 0.208 −0.003 0.209 0.103 −0.069 −0.027 −0.065 0.093 0.194
NEO-O1-5 0.409 0.127 0.663** −0.013 0.007 0.081 0.024 −0.055 0.024 −0.036 −0.035
NEO-O1-6 0.496*** 0.168 0.071 −0.122 −0.009 0.012 −0.032 −0.068 −0.086 0.076 0.275**
NEO-O1-7 0.455*** −0.175* −0.065 0.020 0.114 0.076 0.052 0.079 −0.131* 0.054 0.251*
NEO-O1-8 0.850*** −0.035 0.089 −0.022 0.010 0.039 −0.133*** 0.018 −0.043 0.082 0.200***
OEQ-MOE-1 1.000*** 0.069 0.043 0.056 0.059 0.041 −0.007 −0.020 0.067 0.136*** 0.000
OEQ-MOE-2 0.275 0.581* 0.516 0.020 −0.030 0.145* −0.038 0.031 0.048 −0.159 −0.140
OEQ-MOE-3 0.956*** 0.196* 0.060 −0.012 0.029 −0.101* −0.009 −0.005 0.044 0.037 0.021
OEQ-MOE-4 0.157* 0.792*** 0.119 0.015 −0.002 −0.013 −0.062 −0.080 0.025 0.100 −0.003
OEQ-MOE-5 0.324* 0.445** 0.335 0.148* 0.011 0.042 0.012 0.137 0.085 −0.081 −0.094
OEQ-MOE-6 0.146 0.734*** 0.422 −0.039 0.055 0.129 0.025 −0.050 −0.025 −0.061 −0.007
OEQ-MOE-7 0.090 0.706*** 0.199 −0.046 0.175 −0.013 0.207** −0.028 −0.018 0.004 0.097
OEQ-MOE-8 0.044 0.558*** 0.220 0.032 0.098 0.047 0.069 0.114 −0.059 −0.085 −0.126
OEQ-MOE-9 0.239** 0.483*** 0.068 0.305*** 0.132 0.046 0.017 0.097 0.084 0.072 0.006
OEQ-MOE-10 0.438 0.330 0.555 0.093 0.026 0.040 −0.016 −0.031 0.118 0.002 −0.100
NEO-O2-1 0.074 −0.094 0.150 0.367*** 0.038 0.405*** −0.071 −0.002 −0.074 0.121 0.120
NEO-O2-2 0.046 0.095 0.052 0.191 0.063 0.218* 0.123 0.125 −0.197** −0.030 0.133
NEO-O2-3 −0.035 −0.002 0.045 0.143 0.045 0.811*** −0.031 0.160 −0.166 0.085 −0.019
NEO-O2-4 −0.045 0.113 0.207* 0.197* 0.067 0.704*** −0.015 0.265** −0.171* 0.083 −0.050
NEO-O2-5 −0.065 −0.065 0.032 0.172* −0.018 0.487*** −0.033 0.322*** −0.102 0.237** −0.104
NEO-O2-6 0.048 0.109 0.064 0.214* 0.130 0.183 0.084 0.065 −0.104 −0.096 0.155
NEO-O2-7 0.118* −0.040 −0.004 0.527*** 0.045 0.273*** −0.041 −0.039 0.064 0.159** 0.039
NEO-O2-8 −0.125 0.063 0.165 0.129 0.005 0.715*** −0.009 0.214* −0.116 0.073 −0.267*
OEQ-SOE-1 0.012 0.013 0.080 0.514*** 0.078 0.117 0.063 0.193** −0.023 0.040 0.108
OEQ-SOE-2 −0.076 0.155** 0.106 0.493*** 0.011 0.525*** −0.018 0.058 0.005 0.188** −0.102
OEQ-SOE-3 −0.075 0.146* 0.158* 0.478*** −0.003 0.429*** −0.013 0.145* 0.036 0.105 0.000
OEQ-SOE-4 0.003 0.301** −0.003 0.327*** 0.219 0.317* 0.174 0.171 −0.098 −0.073 0.006
OEQ-SOE-5 0.006 0.190** 0.127 0.753*** 0.047 0.146 0.110* 0.105 0.061 0.048 0.008
OEQ-SOE-6 0.055 −0.116 0.032 0.543*** 0.077 0.107 0.112 0.195* 0.042 0.068 −0.054
OEQ-SOE-7 −0.044 −0.176* 0.136 0.606*** 0.217* 0.142 −0.110 0.006 −0.046 0.071 0.165
OEQ-SOE-8 −0.010 0.136* 0.071 0.777*** 0.059 0.060 0.020 0.098 0.032 0.009 0.047
OEQ-SOE-9 0.002 0.042 0.062 0.552*** 0.071 −0.005 0.004 0.111 0.091 0.035 −0.024
OEQ-SOE-10 0.047 −0.136 0.023 0.571*** −0.005 0.012 0.089 0.200* 0.103 0.056 −0.034
Item O1 MOE Unint SOE O3/EOE O2 POE EOE O5/TOE O4 O6
NEO-O3-1 −0.034 −0.001 0.085 0.004 0.595*** −0.006 −0.068 0.076 0.031 0.047 −0.007
NEO-O3-2 0.167* −0.132 −0.138 −0.112* 0.664*** 0.041 −0.050 0.089 −0.065 0.032 0.077
NEO-O3-3 0.013 0.130 0.050 0.260*** 0.345*** 0.047 −0.070 0.093 −0.051 0.078 −0.014
NEO-O3-4 0.042 −0.395*** 0.042 −0.025 0.374* 0.229** 0.052 0.066 0.003 −0.073 0.156
NEO-O3-5 −0.057 −0.045 0.190 −0.033 0.471** 0.031 0.173** 0.135 −0.094 0.029 0.023
NEO-O3-6 0.169 −0.258* −0.176 0.060 0.242 0.137 −0.091 0.177 0.088 0.119 0.133
NEO-O3-7 0.076 −0.134** −0.017 −0.088 0.042 0.047 −0.042 0.801** −0.005 0.007 0.105
NEO-O3-8 0.015 −0.139 0.035 0.071 0.291** 0.050 0.039 0.040 0.088 −0.022 0.001
OEQ-EOE-1 0.026 0.016 0.021 −0.016 0.013 0.051 0.030 0.892*** 0.061 −0.106* −0.053
OEQ-EOE-2 0.434* 0.376 −0.431* −0.015 0.350 0.052 −0.089 −0.044 −0.129 −0.238* 0.073
OEQ-EOE-3 0.027 −0.034 −0.133 0.095 0.110 0.001 0.115 0.538*** −0.083 −0.012 0.086
OEQ-EOE-4 −0.013 0.320 −0.107 0.065 0.428** −0.008 0.121 0.279*** 0.001 0.215** −0.144
OEQ-EOE-5 0.031 0.360* 0.023 0.127* 0.589*** 0.034 0.147** 0.076 0.073 −0.067 −0.012
OEQ-EOE-6 −0.062 0.089 −0.079 −0.001 0.073 −0.013 0.079 0.695*** 0.116* −0.001 0.070
OEQ-EOE-7 −0.130 0.391* −0.053 0.073 0.569*** 0.048 −0.175** 0.286*** 0.002 0.001 −0.193*
OEQ-EOE-8 0.028 0.376* −0.145 0.137 0.442** −0.105 0.007 0.134* 0.147* 0.050 0.070
OEQ-EOE-9 0.004 −0.195* 0.164 −0.061 0.518*** −0.002 −0.038 0.433*** −0.110 0.018 −0.036
OEQ-EOE-10 0.045 0.176 0.003 0.035 0.332*** −0.007 0.041 0.288*** 0.053 −0.160* −0.127
NEO-O4-1 0.083 0.001 −0.016 0.046 −0.152 −0.030 −0.123* 0.109 −0.014 0.372*** 0.229*
NEO-O4-2 −0.170 −0.025 0.283 0.176** 0.130 −0.072 0.173** 0.034 −0.075 0.285*** 0.091
NEO-O4-3 0.076 −0.044 −0.066 −0.008 0.055 0.004 0.087 −0.083 0.014 0.638*** 0.009
NEO-O4-4 −0.142 0.113 0.242 0.012 0.119 0.049 0.084 −0.085 0.071 0.430*** 0.047
NEO-O4-5 0.015 0.052 −0.097 −0.085 0.023 0.048 0.058 −0.089* −0.029 0.754*** −0.019
NEO-O4-6 −0.075 −0.107 0.126 0.111 0.022 0.066 0.112* 0.015 0.091 0.061 0.181*
NEO-O4-7 −0.060 0.076 0.078 −0.012 −0.095 0.020 −0.051 0.054 0.065 0.426*** 0.212**
NEO-O4-8 0.063 −0.032 0.006 0.018 −0.050 −0.029 −0.027 0.036 −0.058 0.641*** −0.145*
OEQ-POE-1 −0.067 0.053 0.194 −0.209* 0.192 0.043 0.577*** −0.015 0.128 0.008 −0.164
OEQ-POE-2 −0.005 −0.082 −0.048 −0.088 0.001 0.111 0.765*** 0.019 0.143* 0.070 −0.071
OEQ-POE-3 −0.012 −0.033 0.028 0.094* −0.014 0.016 0.796*** 0.120 0.008 0.124 −0.051
OEQ-POE-4 −0.052 −0.016 −0.112 0.068 0.040 0.013 0.882*** 0.085 0.009 0.010 0.037
OEQ-POE-5 −0.142 0.065 0.223* −0.029 −0.126 −0.048 0.758*** 0.193** 0.031 0.191** −0.093
OEQ-POE-6 0.124 0.227* −0.107 0.099 0.078 −0.122 0.524*** 0.014 −0.103 0.057 −0.036
OEQ-POE-7 −0.085 0.188*** 0.115 0.046 0.070 0.070 0.643*** 0.079 0.052 0.005 −0.187**
OEQ-POE-8 0.173 0.108 −0.297** 0.186* 0.029 −0.017 0.609*** 0.011 0.061 0.003 −0.075
OEQ-POE-9 −0.076 −0.098 0.180 0.132* −0.055 −0.041 0.742*** 0.164* −0.012 −0.017 −0.177*
OEQ-POE-10 −0.095 0.014 0.205 −0.109* 0.029 0.000 0.963*** −0.031 0.034 0.025 −0.054
Table 4. (continued)
Item O1 MOE Unint SOE O3/EOE O2 POE EOE O5/TOE O4 O6
NEO-O5-1 0.033 −0.013 0.181 −0.137* 0.047 0.367*** 0.040 −0.006 0.547*** 0.124* 0.107
NEO-O5-2 0.092 −0.040 −0.225 −0.196** 0.051 0.653*** 0.043 −0.119 0.276 0.172 0.214*
NEO-O5-3 −0.063 −0.131 0.223 −0.014 0.057 −0.020 0.019 0.032 0.375*** 0.028 0.111
NEO-O5-4 0.217* −0.176 −0.200 −0.050 0.047 0.467*** −0.025 0.009 0.301* 0.136 0.219*
NEO-O5-5 0.040 −0.040 −0.017 −0.172** −0.016 0.699*** 0.048 −0.069 0.335* 0.053 0.252**
NEO-O5-6 0.068 −0.059 0.008 0.059 0.099 0.109 0.036 −0.084 0.370*** 0.046 0.159*
NEO-O5-7 0.027 −0.082 0.084 0.063 0.096 0.069 0.093* 0.002 0.434*** 0.039 0.118
OEQ-TOE-1 0.071 −0.057 0.117 0.072 0.020 0.013 0.001 −0.009 0.288*** 0.027 0.035
OEQ-TOE-2 −0.005 0.066 0.054 0.008 −0.088 0.016 0.010 0.148* 0.625*** 0.079 −0.045
OEQ-TOE-3 0.144 0.068 −0.131 0.062 0.088 0.158 0.101 −0.013 0.495*** −0.012 0.006
OEQ-TOE-4 −0.073 0.099 0.182 0.034 −0.089 −0.019 0.050 0.162** 0.656*** 0.121 −0.041
OEQ-TOE-5 0.048 0.029 0.016 0.143** 0.072 0.061 0.107* −0.003 0.537*** −0.102 −0.059
OEQ-TOE-6 0.072 0.207** −0.030 0.049 0.040 0.186 0.087 −0.049 0.636*** 0.009 0.153*
OEQ-TOE-7 −0.128 −0.085 0.257 0.004 0.047 0.027 0.093 0.083 0.746*** 0.041 0.023
OEQ-TOE-8 0.067 0.044 −0.166 0.076 0.142 0.139 −0.019 0.097 0.564*** 0.060 0.080
OEQ-TOE-9 0.001 0.017 0.000 0.028 0.104 0.067 0.081 0.067 0.463*** −0.108 −0.016
OEQ-TOE-10 0.034 −0.049 0.193 0.082 −0.011 0.061 0.096* 0.008 0.672*** 0.031 −0.017
NEO-O6-1 0.145 0.058 −0.144 0.030 −0.011 −0.005 −0.201*** −0.267*** −0.034 0.175* 0.652***
NEO-O6-2 −0.155* 0.066 0.140 0.008 0.030 −0.036 −0.047 0.003 0.011 0.023 0.482***
NEO-O6-3 0.175 −0.082 −0.230* −0.002 −0.035 0.025 −0.052 0.096 −0.127 0.046 0.571***
NEO-O6-4 −0.107* 0.033 0.068 0.085 −0.026 −0.016 0.000 0.152** 0.066 −0.034 0.586***
NEO-O6-5 0.063 0.039 −0.077 −0.039 −0.009 0.013 −0.045 0.058 −0.095 0.162* 0.755***
NEO-O6-6 0.138 −0.057 −0.162 0.033 0.023 0.105 −0.096* −0.138 0.090 0.108 0.518***
Note. ESEM = exploratory structural equation modeling; O1 = O1 Fantasy; O2 = O2 Aesthetics; O3 = O3 Feelings; O4 = O4 Actions; O5 = O5 Ideas; O6 = O6 Values; MOE = Imaginational OE; SOE = Sensual OE; EOE = Emotional
OE; POE = Psychomotor OE; TOE = Intellectual OE; Unint = Uninterpretable Factor. Loadings greater than .4 are noted in boldface. Factors appear in the order in which they were extracted.
*p < .05. **p < .01. ***p < .001.
Table 4. (continued)
202 Gifted Child Quarterly 60(3)
Table 5. ESEM Loadings for Openness Facets and OEs as Joint Factors (N = 461).
NEO-O1-1 0.432*** 0.184*** 0.000 0.100 0.100* 0.189***
NEO-O1-2 0.456*** 0.070 0.015 0.475*** −0.030 −0.059
NEO-O1-3 0.761*** 0.035 0.072 0.233 −0.111** 0.028
NEO-O1-4 0.212* 0.071 0.101 0.453*** −0.090 0.046
NEO-O1-5 0.757*** 0.136* −0.120* −0.015 0.052 0.150**
NEO-O1-6 0.500*** −0.135** −0.035 0.464*** −0.084 −0.056
NEO-O1-7 0.142 −0.020 0.199** 0.564*** −0.012 −0.078
NEO-O1-8 0.623*** −0.092 0.065 0.635*** −0.194*** −0.007
OEQ-MOE-1 0.806*** −0.090 0.093 0.586*** −0.029 0.057
OEQ-MOE-2 0.912*** 0.192*** −0.003 −0.340** −0.007 0.113**
OEQ-MOE-3 0.872*** −0.241*** 0.108* 0.455** −0.046 −0.004
OEQ-MOE-4 0.745*** 0.071 −0.028 −0.126 −0.002 −0.024
OEQ-MOE-5 0.739*** 0.192*** 0.133** −0.179 0.055 0.103*
OEQ-MOE-6 0.862*** 0.154* 0.012 −0.243* 0.062 0.033
OEQ-MOE-7 0.673*** 0.002 0.143 −0.103 0.238*** −0.013
OEQ-MOE-8 0.557*** 0.136* 0.189** −0.329*** 0.122* −0.060
OEQ-MOE-9 0.561*** 0.303*** 0.230*** −0.007 0.063 0.067
OEQ-MOE-10 0.883*** 0.173*** −0.055 −0.078 0.044 0.181***
NEO-O2-1 0.060 0.655*** −0.006 0.245*** −0.070 0.075
NEO-O2-2 0.100* 0.348*** 0.174** 0.110* 0.105 −0.121*
NEO-O2-3 −0.036 0.726*** 0.134* 0.152* −0.023 0.064
NEO-O2-4 0.116* 0.786*** 0.195*** 0.021 0.047 0.044
NEO-O2-5 −0.136* 0.641*** 0.134 0.095 0.077 0.017
NEO-O2-6 0.136** 0.293*** 0.213*** 0.078 0.039 −0.029
NEO-O2-7 0.074 0.619*** 0.020 0.215*** −0.023 0.127**
NEO-O2-8 0.023 0.710*** 0.098 −0.153** 0.076 0.069
OEQ-SOE-1 0.042 0.570*** 0.222*** 0.060 0.090* 0.029
OEQ-SOE-2 0.115** 0.885*** 0.008 −0.020 0.059 0.125**
OEQ-SOE-3 0.117** 0.817*** 0.067 −0.038 0.044 0.156***
OEQ-SOE-4 0.211*** 0.483*** 0.382*** −0.053 0.179** −0.035
OEQ-SOE-5 0.222*** 0.773*** 0.145** −0.081 0.163*** 0.094
OEQ-SOE-6 −0.024 0.544*** 0.214*** 0.035 0.164** 0.067
OEQ-SOE-7 −0.059 0.662*** 0.169** 0.163** −0.096 0.043
OEQ-SOE-8 0.148** 0.711*** 0.176** −0.066 0.057 0.037
OEQ-SOE-9 0.080 0.485*** 0.167** −0.073 0.053 0.081
OEQ-SOE-10 −0.054 0.498*** 0.158* 0.004 0.137* 0.100
NEO-O3-1 0.055 0.032 0.500*** 0.045 −0.011 0.073
NEO-O3-2 −0.022 −0.149** 0.635*** 0.337*** −0.058 −0.029
NEO-O3-3 0.149** 0.303*** 0.361*** 0.017 −0.014 −0.034
NEO-O3-4 −0.228*** 0.064 0.349*** 0.291*** −0.013 0.151**
NEO-O3-5 0.031 0.065 0.425*** 0.040 0.228*** −0.012
NEO-O3-6 −0.180 0.085 0.339*** 0.407*** −0.095* 0.136**
NEO-O3-7 −0.182 0.122 0.557*** 0.090 0.028 0.036
NEO-O3-8 −0.052 0.047 0.272*** 0.061 0.036 0.138*
OEQ-EOE-1 −0.085 0.176** 0.620*** −0.176*** 0.126* 0.074
OEQ-EOE-2 0.389*** −0.258*** 0.500*** 0.217 −0.226** −0.212**
OEQ-EOE-3 −0.166** 0.143* 0.516*** 0.070 0.150** −0.089
OEQ-EOE-4 0.149* 0.127 0.540*** −0.022 0.268*** −0.051
OEQ-EOE-5 0.330*** 0.078 0.593*** −0.069 0.172** 0.084
OEQ-EOE-6 −0.141 0.110 0.553*** −0.064 0.159** 0.110*
OEQ-EOE-7 0.179** 0.166** 0.704*** −0.271*** −0.070 −0.029
OEQ-EOE-8 0.221 0.012 0.519*** 0.017 0.047 0.091
OEQ-EOE-9 −0.073 0.082 0.658*** 0.038 0.055 −0.045
Vuyk et al. 203
OEQ-EOE-10 0.162 −0.008 0.548*** −0.209*** 0.065 0.042
NEO-O4-1 −0.012 0.177** −0.137* 0.369*** −0.047 −0.009
NEO-O4-2 −0.004 0.308*** −0.009 0.065 0.291*** −0.015
NEO-O4-3 −0.024 0.129* −0.165** 0.454*** 0.242*** 0.012
NEO-O4-4 0.100 0.250*** −0.143 0.137* 0.236*** 0.137
NEO-O4-5 −0.026 0.149** −0.219*** 0.439*** 0.239*** −0.029
NEO-O4-6 −0.088* 0.174*** −0.012 0.122** 0.107** 0.177***
NEO-O4-7 −0.008 0.198*** −0.169** 0.282*** 0.053 0.107*
NEO-O4-8 0.007 0.213*** −0.192** 0.283*** 0.181** −0.086
OEQ-POE-1 0.105 −0.177** 0.077 −0.138* 0.635*** 0.183**
OEQ-POE-2 −0.114* −0.108* −0.012 0.082 0.781*** 0.184***
OEQ-POE-3 −0.055 0.070* 0.029 0.072 0.870*** 0.026
OEQ-POE-4 −0.144*** −0.051 0.119** 0.083 0.876*** 0.025
OEQ-POE-5 0.002 0.065 −0.094 −0.106* 0.897*** 0.056
OEQ-POE-6 0.192*** −0.072 0.123 0.056 0.552*** −0.170**
OEQ-POE-7 0.130** 0.071 0.089* −0.207*** 0.721*** 0.069
OEQ-POE-8 0.047 −0.057 0.133* 0.106 0.584*** −0.011
OEQ-POE-9 −0.050 0.095* 0.022 −0.188*** 0.813*** 0.009
OEQ-POE-10 0.035 −0.128** −0.075 −0.060 1.004*** 0.098**
NEO-O5-1 0.085* 0.108* −0.051 0.161*** 0.049 0.707***
NEO-O5-2 −0.111 0.138 −0.041 0.496*** −0.036 0.447***
NEO-O5-3 −0.032 −0.003 −0.006 0.002 0.032 0.454***
NEO-O5-4 −0.111 0.137* 0.060 0.524*** −0.097 0.433***
NEO-O5-5 −0.049 0.220*** −0.069 0.362*** −0.044 0.561***
NEO-O5-6 0.014 0.023 0.024 0.202*** −0.001 0.441***
NEO-O5-7 −0.001 0.032 0.055 0.114*** 0.086** 0.506***
OEQ-TOE-1 0.077* 0.038 −0.015 0.042 0.006 0.327***
OEQ-TOE-2 0.043 −0.011 0.002 −0.102** 0.068 0.631***
OEQ-TOE-3 0.090* −0.026 0.124* 0.085 0.064 0.518***
OEQ-TOE-4 0.080 0.053 −0.037 −0.164*** 0.145*** 0.675***
OEQ-TOE-5 0.085* 0.003 0.106* −0.116** 0.085* 0.556***
OEQ-TOE-6 0.174*** 0.018 0.035 0.089 0.047 0.691***
OEQ-TOE-7 −0.033 0.008 0.007 −0.129*** 0.144*** 0.832***
OEQ-TOE-8 −0.025 0.022 0.232*** 0.131** −0.032 0.590***
OEQ-TOE-9 0.014* −0.059 0.177*** −0.104* 0.054 0.497***
OEQ-TOE-10 0.092 0.038 −0.051 −0.063 0.121** 0.738***
NEO-O6-1 0.042 −0.010 −0.166 0.655*** −0.325*** 0.018
NEO-O6-2 −0.043 0.079 0.003 0.189*** −0.098 0.100*
NEO-O6-3 −0.135 −0.020 0.107 0.597*** −0.171*** −0.083
NEO-O6-4 −0.101* 0.125* 0.100 0.253*** −0.083 0.162***
NEO-O6-5 −0.067 0.044 0.039 0.642*** −0.144* 0.003
NEO-O6-6 −0.063 0.018 −0.033 0.576*** −0.212*** 0.162**
Note. ESEM = exploratory structural equation modeling; O1 = O1 Fantasy; O2 = O2 Aesthetics; O3 = O3 Feelings; O4 = O4 Actions; O5 = O5 Ideas;
O6 = O6 Values; MOE = Imaginational OE; SOE = Sensual OE; EOE = Emotional OE; POE = Psychomotor OE; TOE = Intellectual OE. Loadings greater
than .4 are noted in boldface. Factors appear in the order in which they were extracted.
*p < .05. **p < .01. ***p < .001.
Table 5. (Continued)
These results were obtained with our data set that included
two different samples; one of the samples was composed of cre-
ative individuals, and the other included individuals from the
general population. Creative individuals were expected to score
higher on openness and OEs based on previous research (Batey
et al., 2010; Falk et al., 2008; Feist, 1998; Furnham, Batey,
Booth, Patel, & Lozinskaya, 2011; Furnham, Hughes, &
Marshall, 2013; Gorman & Feist, 2014; Ivcevic & Mayer, 2007;
Kaufman, 2013; Kerr & McKay, 2013; Wolfradt & Pretz, 2001;
Yakmaci-Guzel & Akarsu, 2006) and thus are a helpful criterion
for studies such as this one. Considering these findings, all five
OEs can be entirely represented by a facet of openness.
204 Gifted Child Quarterly 60(3)
Conceptual Similarity
Openness to fantasy and imagination, which is measured in
O1 Fantasy, seems to encompass the construct measured by
imaginational OE as evidenced by their joint factor in ESEM,
correlations, and conceptual descriptions. Individuals open
to fantasy are prone to daydreaming, which likely is of adap-
tive value to them and serves personal goals (McMillan,
Kaufman, & Singer, 2013). Piechowski (2006) agreed that
daydreaming and using imagination in general opens a myr-
iad possibilities. Fantasy, along with aesthetics, feelings, and
actions, is related to creative potential (Nusbaum & Silvia,
2011), creative achievement in the arts (Kaufman, 2013), and
implicit learning (Kaufman et al., 2010).
Openness to sensory pleasures and aesthetic experiences
is measured by O2 Aesthetics and sensual OE. From their
conceptual descriptions to the results of this study from
ESEM and correlations, these two factors appear undifferen-
tiated. One single factor fit the data even though items in the
NEO PI-3 focus more on enjoyment of the arts, while items
on the OEQ-II focus on everyday sensorial experiences.
Aesthetics, just like fantasy, relates to implicit learning
(Kaufman et al., 2010) and creativity (Kaufman, 2013;
Nusbaum & Silvia, 2011). Individuals high in openness to
aesthetic experiences tend to be strongly moved by beauty
found in nature and in arts, and often experience aesthetic
chills in their bodies in response to these stimuli (McCrae,
1997; Silvia & Nusbaum, 2011).
Regarding personal emotional life, O3 Feelings and emo-
tional OE also appeared as a single factor in ESEM and had
high correlations. Both seem to describe the same openness
to a wide variety and depth of feelings that individuals have
related to creative achievement and potential (Kaufman,
2013; Nusbaum & Silvia, 2011), as well as to the experience
of aesthetic chills (McCrae, 1997; Silvia & Nusbaum, 2011).
Individuals who are open to feelings value emotions as an
important part of life and are in tune with their emotional
states; both their positive and negative emotional experi-
ences are more intense than those of others (Costa & McCrae,
1992). Piechowski (2006) also describes extremes from
ecstasy and emotional aliveness to fears and preoccupation
with death. Although one might consider that such a wide
range could render individuals vulnerable to mood disorders,
particularly bipolar types, openness to feelings does not pre-
dict either unipolar or bipolar mood disorders (Quilty,
Pelletier, DeYoung, & Bagby, 2013).
O4 Actions and psychomotor OE loaded onto the same
factor, with the exception of two reverse-scored items refer-
ring to enjoying one’s old ways of doing things. In ESEM
analyses, items of O4 Actions had lower loadings when
compared with psychomotor OE items; correlations were
also in the low range. O4 Actions describe an openness to
change in general, adaptability to novel situations, and
refusal of routines (Costa & McCrae, 1992). These individu-
als continuously revise their actions, trying to find
alternative ways of doing things (Costa & McCrae, 1992).
O4 Actions negatively predicts depression (Quilty et al.,
2013), likely due to the adaptability and willingness to
change until satisfying alternatives emerge, and is less
related to cognitive abilities than the other facets of open-
ness (DeYoung, Peterson, & Higgins, 2005). Psychomotor
OE refers to increased general activity and expression
through motor modes as well as an excess of physical energy
(Piechowski, 2006). People who continually seek novel
alternatives are probably in constant motion, yet these two
can be mutually exclusive for some individuals.
Intellect is one of the most widely studied aspects of
openness to experience, with many theorists calling the
domain Openness/Intellect rather than simply openness
(DeYoung, 2015). The model measuring O5 Ideas and
intellectual OE as a single construct fit the data in ESEM,
and correlations were high. O5 Ideas and intellectual OE
appeared to describe the same construct of intellect, which
has been previously linked with working memory
(DeYoung, Shamosh, Green, Braver, & Gray, 2009), fluid
intelligence (DeYoung et al., 2005; Nusbaum & Silvia,
2011), and crystallized intelligence (DeYoung et al., 2005).
Intellect serves as a predictor of creative achievement in the
sciences (Kaufman, 2013).
Openness to revising one’s values and conceptions of the
world as measured by O6 Values was not a part of OEs, and
in correlations had a moderate negative relationship with
psychomotor OE. Openness to values should theoretically
relate in a positive way to OE descriptions of Piechowski
(2006) about self-examination and moral awareness, which
should be encompassed in the OEQ-II under the emotional
OE subscale. Perhaps said items do not adequately capture
the vastness of Piechowski’s descriptions, or perhaps items
that related to that construct were left out during the devel-
opment of the OEQ-II. Theory cannot feasibly explain the
negative relationship of O6 Values with the energy described
in psychomotor OE. An alternative explanation would
involve the findings of DeYoung et al. (2005) who found
that O6 Values and O5 Ideas more closely related to fluid
intelligence and dorsolateral prefrontal functions than the
other openness facets did, and explored a potential relation-
ship between intellectual curiosity, intelligence, moral rela-
tivism, and rejection of dogmatic beliefs. In this case, O6
Values would be related to intellectual OE, given that O5
Ideas and intellectual OE were practically indistinguishable
in this study. Thus, further research is needed to empirically
elucidate this question.
Problems With OEs and TPD
Research on OEs and TPD has two elemental problems.
According to Dabrowski’s TPD, OEs serve a purpose within
a larger theory and are meaningless on their own (Dabrowski,
1967; Dabrowski, Kawczak, & Piechowski, 1970;
Mendaglio, 2012). TPD and OEs supporters appear to imply
Vuyk et al. 205
that the scarce OEs research validates the existence of OEs
and therefore supports TPD. However, this link is missing in
the literature. First, OEs research is atheoretical and does not
connect OEs to the original theory (Mendaglio, 2012), and
second, TPD presently lacks sufficient empirical support
(Mendaglio, 2012). No studies have yet validated the
assumptions of the overactive nervous systems, the different
brain wirings, and the enhanced experiences attributed to
people presenting with OEs. In fact, neural efficiency theory
and corroborating studies indicate that intelligence is associ-
ated with less brain activity to accomplish tasks (Langer
et al., 2012). The enactment of personality-related behaviors
is also associated with lower brain activity (Knyazev,
Pylkova, Slobodskoj-Plusnin, Bocharov, & Ushakov, 2015).
The only study that used brain imaging for OEs (Kuo
et al., 2012, as cited in Chang & Kuo, 2013) found similar
results as brain imaging studies of openness (Adelstein et al.,
2011; DeYoung, 2010; DeYoung et al., 2010). Mendaglio
(2012) suggested that assuming a normal distribution for
OEs would be incongruent with TPD; however, all OE items
and subscales had a reasonably normal distribution in this
study, which is more consistent with the FFM (Costa &
McCrae, 1992). Thus, at present, OEs merely describe
behaviors and cannot be linked to any biological etiology.
Parsimony is the reason to avoid a complicated theory if a
simple one provides better explanations for the phenomena
studied. The relationships among OEs and openness indicate
that they are the same underlying construct with different
names. As Wirthwein et al. (2011) posited, OEs are possibly
“old wine in new bottles” (p. 150) instead of a distinct and
useful personality construct that can describe characteristics
of gifted and creative individuals. Researchers such as Rost
et al. (2014) and Winkler (2014) concluded that the relation-
ship between giftedness and OEs is unclear and thus the use-
fulness of the construct is limited.
Limitations and Suggestions for Future Studies
Choice of instruments, sample size, and sample selection
can improve in future studies. Quality of instruments has
likely affected results of this study, as the OEQ-II has
shown inadequate fit in the literature (Van den Broeck
et al., 2013; Warne, 2011); however, it is thus far the only
instrument available in English to measure OEs in adoles-
cents and adults. With poor fit for the instrument it would
be difficult to obtain adequate fit for other models that
included this instrument. Inspecting both instruments
showed that the NEO PI-3 had overall longer items than the
OEQ-II. This might be a purely psychometric reason that
could differentiate among openness facets and OEs that
would not relate to the constructs themselves, but would be
an artifact of measurement tools.
Self-report instruments rely on participants for accuracy
of results, which is a major limitation. Studies with observ-
ers’ reports of personality such as the NEO PI-3 Observer
Rating Forms (McCrae, Costa, & Martin, 2005) will add to
these findings. Additionally, if the relationship of openness
to OEs is robust it should hold with different personality
instruments such as the IPIP (Goldberg, 1999). Future stud-
ies could include large-scale samples to confirm these results.
Samples in this study had a disparity in age; means in one
sample did not overlap with the other sample’s range. It was
not possible to find comparable samples of the same age for
this study, though sample comparison was not the principal
aim in this study. Covarying age would be particularly
important in studies with the main focus of comparing sam-
ples. Future studies could include age as a covariate in a
multiple indicators multiple causes or MIMIC model in
structural equation modeling, to prevent spurious effects
due to age differences.
Replications of this same study in other samples will
facilitate further generalization. This study included a cre-
atively gifted sample as a criterion sample, as creative indi-
viduals tend to score highest on openness to experience, and
a sample of adults from the general population. However, OE
research has largely focused on intellectually gifted individ-
uals. Thus, the inclusion of intellectually gifted individuals
as a separate group would be advantageous. If proponents of
OEs continue to believe that OEs and openness to experience
are separate constructs, then it is on them to conduct future
studies to validate the conceptual differences in deeper detail,
as well as empirically support Dabrowski’s TPD.
Openness facets and OEs appear to represent the same con-
struct, and thus the giftedness field would benefit from dis-
cussing the construct as the personality trait of openness to
experience. Subotnik et al. (2011) urged gifted education to
use the vast body of psychological research to inform prac-
tice. In this case, the FFM is the personality model with the
strongest research support and professional acceptance in the
present day (Costa & McCrae, 1992; Goldberg, 1999).
The reason for this change from OEs to openness to expe-
rience goes beyond a mere change in names; the change will
positively affect interpretation of behaviors. Adding the FFM
and openness to experience to the daily vocabulary of gifted
education researchers, teachers, counselors, and parents can
connect these behaviors seen in creatively and intellectually
gifted individuals to the vast literature base on personality. It
will provide a sounder explanation of the behaviors linked to
openness facets. The literature also can predict a develop-
mental trajectory of openness for most individuals (McCrae
et al., 2002). Openness as a personality trait can even affect
career choice as it relates to artistic and investigative voca-
tional interests, and working within realms of one’s voca-
tional interests leads to higher career satisfaction (Larson,
Rottinghaus, & Borgen, 2002).
Another reason to favor openness to experience and the
FFM is the leap one might make based on the explanation
206 Gifted Child Quarterly 60(3)
for the behaviors seen. OEs have a place in a theory, TPD,
which has insufficient empirical support. When reading
about OEs, parents and practitioners can gravitate toward
the theory and make assumptions that go beyond the descrip-
tion of openness- or OE-related behaviors. Such a leap is
dangerous as it might present individuals who are open to
experience as more moral following the original tenets of
TPD (Dabrowski, 1967), an assumption not rooted in sci-
ence. The leap becomes even more dangerous when OE is
presented as an identification tool for giftedness, when stud-
ies have consistently shown that intelligence and openness
have correlations in the .20 to .40 range (P. L. Ackerman &
Heggestad, 1997; Austin, Deary, & Gibson, 1997; Austin
et al., 2002; Moutafi et al., 2006; Moutafi, Furnham, &
Crump, 2003; Zeidner & Shani-Zinovich, 2011).
Gifted education researchers and practitioners would
benefit from the adoption of the FFM of personality as used
by psychologists across the globe. The FFM of personality
is a better option as it will permit meta-analyses and further
generalization of findings. In addition, it will allow practi-
tioners and parents to have a shared vocabulary with other
sciences to describe a personality trait commonly seen in
creatively and intellectually gifted individuals, such as
openness to experience.
Profiles for Selection of Participants (From Kerr &
McKay, 2013)
Core Creativity Characteristics. Creatively gifted students may
be spontaneous, expressive, intuitive, and perceptive, with
evidence of intellectual sophistication and childlike playful-
ness. They are very likely to be curious, open to new experi-
ences, and innovative in many areas of their lives. They may
express originality in thoughts, and are probably unafraid of
what others might think of their ideas. Most likely, these stu-
dents have a wide range of interests and abilities, and may be
comfortable with ambiguity and disorder. Likely to be
unconventional, creatively gifted students are imaginative,
and may challenge the status quo. By late adolescence, truly
creative individuals usually have significant creative accom-
plishments that have earned them recognition by experts in
their domain. Most important, many of these students may
not have qualified for gifted education programs because of
their concentration on their areas of interest rather than being
“well-rounded” students (Amabile, 1983; Csíkszentmihályi,
1996; Goertzel, Goertzel, Goertzel, & Hansen, 2004; Runco,
2004; Simonton, 1999; Torrance, 1984).
Specific Domain Characteristics
Language; verbal/linguistic creativity; potential writers, jour-
nalists, translators, and linguists. The student is likely to be a
precocious and avid reader with an extensive knowledge of
literature; a sophisticated writer; may have advanced ability
to learn other languages. The student should have outstand-
ing verbal accomplishments. He or she may be witty and
expressive. Verbal precocity may get him or her in trou-
ble. The student is likely to have excellent grades in Lan-
guage Arts/English/Foreign language when interested, and
have high scores on verbal achievement tests. May have
mood swings, ranging from expansive, energetic, optimism
when he or she works day and night with intensity on a
project, to periods of self-doubt, low energy, and cynicism
(Andreasen, 1987; Barron, 1969; Jamison, 1989; Kaufman,
2001, 2002; Piirto, 2002; Valdés, 2003; VanTassel-Baska,
Johnson, & Boyce, 1996).
Mathematical and scientific inventiveness. The student may
be a natural mathematician with an ability to perform com-
plex computations in his or her head or who possesses an
advanced understanding of mathematical and scientific con-
cepts. The student loves science, experimentation, and new
technology. In addition, the student enjoys manipulating
materials and information, tinkering, adjusting the designs
of objects, apparel, hardware and software. Intense curiosity
and fascination with enigmas and unsolved problems leads
this student to read widely and in depth. If challenged, the
student has good grades in math, science, and laboratory
classes; if not, the student may expend little effort. Most sci-
entists and inventors had significant accomplishments such
as winning regional or national math and science competi-
tions, or having patentable inventions or designs that are
income producing. These students are usually well adjusted,
but are likely to have just a few like-minded friends (Assou-
line & Lupkowski-Shoplik, 2005; Innamorato, 1998; Park,
Park, & Choe, 2005; Simonton, 1988; Sriraman, 2005; Sub-
otnik, Maurer, & Steiner, 2001).
Interpersonal/emotional creativity. These students are char-
acterized by emotional intelligence, meaning they have the
ability to understand and manage their own emotions and
those of others. The student may be a natural mimic, able
to do impressions, absorb accents, and “get inside another’s
skin.” The student may be the kind of helper that other stu-
dents seek out for help and or a natural leader who is usually
selected by peers to lead in both formal and informal situa-
tions. They are extraverted and people-oriented, able to form
relationships across cultures and age groups; agreeable and
friendly toward all. They thrive on connection, and experi-
ence deep empathy. They may have excellent grades in social
sciences, debate, rhetoric, and leadership courses, as well as
recognition for performance, leadership, or volunteerism
(Bolton & Thompson, 2004; Daloz, Keen, Keen, & Parks,
1996; Hogan, Curphy, & Hogan, 1994; Salovey & Grewal,
2005; Simonton, 2008).
Musical and dance creativity. The student has the ability to
sing or play instruments—usually multiple instruments—or
to dance with technical expertise and imagination. She or he
Vuyk et al. 207
may have an intuitive understanding of music or movement,
and often has perfect pitch, excellent rhythm, and musical
memory. The student can compose or choreograph; his or
her own creations have won the recognition of experts. The
student dances, sings, and performs as often as possible—
but may be defensive, anxious, or perfectionistic, sometimes
leading to denial of coveted roles while in school. These stu-
dents possess excellent musical knowledge in one or more
genres, such as hip hop, jazz, pop, or classical, and may
have sought out rare and little known pieces for inspiration.
Although more introverted than extraverted, the student is
likely to be transformed on stage into an expressive, creative
performer, entering a flow state that conquers shyness or
anxiety (Oreck, Owen, & Baum, 2003; Sloboda, 1988, 2005;
Van Rossum, 2001).
Spatial visual creativity. The student has a powerful ability
to visualize designs, colors, and to manipulate 3D images in
mind and an ability to draw models and designs with techni-
cal skill. The student is imaginative and original in think-
ing, conversation, and attire. He or she creates cartoons,
websites, paintings, graphic art, sculpture, photography,
video, or architecture that has already earned the recogni-
tion of experts. The student may have excellent grades in art,
photography, shop, drawing, or other course emphasizing
spatial/visual ability, but may underperform in other classes.
Like writers, artists are likely to have mood swings, but those
students who lean more toward design and architecture may
be more stable in mood. The student is more introverted than
extroverted, reflective, and easily enters flow states (Barron,
1972; Csíkszentmihályi & Getzels, 1971; Dudek & Hall,
1991; Kay, 2000; MacKinnon, 1961; Pariser & Zimmerman,
2004; Stohs, 1992).
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Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
investigation was supported through awards from the University of
Kansas Office of Graduate Studies Doctoral Student Research
Fund, University of Kansas School of Education Graduate Student
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Author Biographies
M. Alexandra Vuyk, MS, is a doctoral candidate in Counseling
Psychology with a specialty area in Quantitative Psychology at the
University of Kansas, and has an MS in Special Education with a
concentration in Gifted, Talented and Creative from Emporia State
University. She applies her quantitative and statistical expertise to
study the social and emotional development of intellectually and
creatively gifted individuals. She has won several student research
awards from the National Association for Gifted Children Research
and Evaluation Network. She is a native of Paraguay and hopes to
advance gifted education in her home country.
Thomas S. Krieshok, PhD, is a professor in the Department of
Educational Psychology at the University of Kansas, a Fellow of the
American Psychological Association and of the American
Educational Research Association, and a founding member of the
Society for Vocational Psychology. He is a Kemper Teaching Fellow
and a Budig Teaching Professor at the University of Kansas. His
teaching and research interests include decision making, vocational
assessment and career counseling, process and outcome of counsel-
ing interventions, and training issues in professional psychology.
Barbara A. Kerr, PhD, holds an endowed chair as Distinguished
Professor of Counseling Psychology at the University of Kansas
and is a Fellow of the American Psychological Association. Her
research focuses on the development of talent, creativity, and opti-
mal states, while training psychologists and counselors to be talent
scouts who provide positive, strengths-based services. She authored
several books and more than 200 articles, chapters, and articles in
the field of psychology of giftedness and creativity. She currently
directs the Counseling Laboratory for the Exploration of Optimal
States (CLEOS) at the University of Kansas, a research-through-
service program that identifies and guides creative adolescents.
... Questions such as "Why do gifted individuals behave as they do?" and "What motivates them?" have yet to be considered in terms of established personality theory and models [34]. In light of what we know as therapists but also on the basis of relevant personality theory, we therefore set out to better understand the gifted adolescent and, in particular, how their high developmental potential distinguish them from other adolescents. ...
... Fourth, the well-researched and well-established Five-Factor Model of personality (FFM) has been found to be valid across ages and cultures, and it thus provides an excellent starting point for examining the personality characteristics of gifted adolescents [34]. The five identified personality factors are: Openness to Experience (e.g., creativity, imagination, eagerness to learn); Conscientiousness (e.g., goal orientation, orderliness, trustworthiness, ambition, self-discipline); Extraversion (e.g., energy and dominance as opposed to being reserved, withdrawn, and submissive); Agreeableness (e.g., trust, sincerity, compassion as opposed to aggressiveness and egocentrism); and Neuroticism (e.g., emotional stability as opposed to self-doubt, general feelings of anxiety, and depression) (OCEAN). ...
... Literature review showed a relationship, with a small to medium effect size between Openness to Experience and cognitive intelligence in the general population as well as in a gifted population compared to non-gifted population samples [34]. The association of intelligence with Openness to Experience for gifted adolescents is confirmed but also were gifted adolescents found to score lower on Neuroticism than non-gifted adolescents [20,35]. ...
... In another study comparing 374 gifted and 478 typically developing high school students in Israel, gifted students scored significantly higher on openness (Zeidner & Shani-Zinovich, 2011). A growing list of studies replicate this general finding across different cultures and using a variety of Big Five measures and intelligence assessments (Vuyk et al., 2016;Limont et al., 2014;Wirthwein et al., 2019). Using a finer-grained analysis, Altaras-Dimitrijević (2012) applied a discriminate analysis to comparisons of OtE facet scores across three samples of gifted and non-gifted students in high school and college in Serbia. ...
... The most detailed look at the similarities between OE and OtE to date was conducted by Vuyk et al. (2016). They used the 240-item NEO-PI-3 inventory (McCrae, Costa, & Martin, 2005), which allowed for comparisons of individual OEs and the six OtE facets. ...
... The research in OtE provides independent support of many of Dąbrowski's ideas, including that these traits are (1) heritable, (2) associated with neural activity, (3) related to advanced ability, and (4) associated with seeing the world differently in ways that contribute to creativity and some social-emotional challenges. Vuyk et al. (2016) propose that the similarity between OtE and OE makes Dąbrowski "redundant" (p. 68). ...
A sample of 108 highly gifted middle school students participated in a study of the relationships between Big Five factors and overexcitabilities. Students completed the NEO-FFI and Overexcitabilities Questionnaire-II (OEQ-II). A cutoff score applied to the OEQ-II created a threshold for overexcitability, ensuring only extreme responses. Analysis groups were based on the number of OEs students possessed based on the cutoff score. An analysis of variance assessed differences in students’ NEO-FFI scores according to the number of OEs they reported. Students with three or more overexcitabilities had significantly higher scores on NEO-FFI openness to experience than students with fewer overexcitabilities. Gifted females had significantly higher scores on NEO-FFI neuroticism scale than gifted males. The results hold implications for understanding the academic and social-emotional needs of highly gifted students and justify use of the Big Five model and overexcitabilities together to further understand the relationship between intelligence, personality, and giftedness.
... The developmental significance of overexcitability has sometimes been lost in discussions about OE since Piechowski's work first appeared. For instance, two papers by Vuyk et al. (2016aVuyk et al. ( , 2016b attempted to displace the OEs with the claim that they are better understood as openness to experience, and such a view completely ignores the reality of levels of overexcitability and its place within a broader developmental framework. The theory of positive disintegration cannot be replaced by the five factor model of personality because Dąbrowski's theory is an alternative framework to the biomedical model in psychiatry, and his essential thesis that psychoneurosis is not an illness is not even remotely addressed by the research on openness to experience. ...
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The construct of overexcitability originated from the condition known as “nervousness.” Dąbrowski differentiated it into types many years before publishing the first outline of his theory of positive disintegration. In this paper, we establish the origins of psychic overexcitability (OE), tracing its evolution in Dąbrowski’s work prior to developing his theory and later through its placement within the concept of developmental potential. Based on our study of Dąbrowski’s early Polish work, we challenge the belief that overexcitability is often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Piechowski’s elaboration of OE in gifted education is explored, and current misconceptions and misuses of OEs are critiqued. Based on our review, we present possible future applications and elaborations of overexcitability.
... For example, it has been suggested that they are typically overexcited (Dabrowski, 1964) and oversensitive (Mendaglio, 1995) and, therefore, more emotionally involved in their activities than their peers. Reference to the construct of overexcitability has been criticized (Vuyk et al., 2016), but it continues to influence opinions in the field of giftedness, not only of families and practitioners but also of researchers (e.g., Ackerman, 1997;Chang & Kuo, 2019). Gaesser (2018) argued that there are many anxiety-inducing stressors in the everyday experience of gifted individuals (see also Cross & Cross, 2015;C. ...
Whether intellectually gifted children have a greater emotional response when tested is still unclear. This may be due to the marked heterogeneity of this particular population, and the fact that most studies lack the power to reduce the noise associated with this heterogeneity. The present study examined the relationship between performance and emotional response in 468,423 Italian fifth-graders taking a national test on mathematics and language. Analyses were performed using statistical models with polynomial terms. Special attention was paid to estimating the mean emotional response of the children who were gifted (1.5-2.5 standard deviations above the mean) or highly gifted (more than 2.5 standard deviations above the mean). The results showed that, although a lower emotional response correlated with a higher achievement, this relationship is nonlinear, and the estimates for gifted and highly gifted children were virtually the same. Girls showed a greater emotional response than boys on all levels of performance. The theoretical and practical implications of these findings are discussed.
The Marland Report included many correct observations about gifted education. Some findings, for example, were based on Project Talent, a large‐scale population representative longitudinal study of the US high school population. This paper uses the intersection of cognitive aptitudes and gifted education as a framework and synthesizes studies using prospective longitudinal data from numerous sources. Additional retrospective data on US high achievers are reviewed, as are longitudinal findings from other countries. All these sources will be used to reevaluate a selected set of claims made in the Marland Report. Specifically, we explore (a) the definition and understanding of gifted students; (b) the identification of and longitudinal research on gifted students; and (c) we briefly discuss the context of the Marland Report in the wider history of education policy and reform in the US, including how to best support talented students using information from the field of education policy.
Some research has investigated the big five personality dimensions among gifted individuals, but these individual studies have provided inconclusive results. The current meta-analysis examined the nature of the relationship between the big five dimensions and giftedness among individuals. Hedge’s unbiased g was used as the effect size metric, and a 3-level multilevel meta-analytic approach was applied, due to the dependency among the effect sizes obtained from the same study. The analyses used 82 effect sizes, from 13 published studies, and indicated that there was a significant difference between gifted and non-gifted participants in terms of Openness to Experience in favor of gifted individuals (g = .473, p = . 005, 95% CI [.199, .747]). However, there were no significant differences in terms of extraversion, agreeableness, conscientiousness, and neuroticism. The implications and limitations of the findings are discussed.
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Bullying is a common experience of childhood and adolescence that is characterized by repeated actions over time with an intent to harm (Olweus, 1993). Students involved in bullying and victimization are more likely to have academic and social adjustment challenges and are at risk for long-term problems, such as anxiety, depression, and posttraumatic stress (Bosworth et al., 1999; Mynard et al., 2000; Olweus, 1993; Peterson & Ray, 2006a). Security and safety are foundational components of Maslow’s Hierarchy of Needs to enhance quality learning in school and home settings. When students experience anxiety, fear, or negative situations, their openness to learning experiences may be adversely influenced. Quality learning materials and equipment are also important to learning environments. Technologies are ever-changing and advancing to benefit society, and with the use of new tools for communication, how people interact in both kind and unkind ways has also advanced. Today’s technology including social media—such as Snapchat, Instagram, Facebook, Twitter, smartphones, and instant messaging—has connected people in more ways than ever before. As technology has advanced for positive human interaction, so too, has the platform availability for negative interaction. Understanding the possibilities and the risks associated with harnessing educational technology in effective educational ways is an important responsibility of both parents and teachers. For students to optimize their talent development, their learning needs for safety, security, and advanced learning experiences should be tended to and met. This chapter focuses upon what is known about bullying, cyberbullying, and gifted students in the literature
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Today's concept of giftedness has been broadened to include considerably more than academic capability as measured by I.Q. tests, yet, the call for broader conceptualization has essentially resulted in further test orientation. There is a need for a model that would enable one to conceptualize giftedness in terms other than testable skills. This paper presents a psychological model of giftedness that accounts for intellective and non-intellective dimensions, especially those of imagination and feeling. The model rests on the concept of developmental potential taken from a theory of human development. The value of this concept lies in that it gives readily identifiable components: special talents and abilities and five forms of psychic overexcitability: psychomotor, sensual, intellectual, imaginational, and emotional. Specific examples of expressions of overexcitability identified in case data with gifted are given.
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Goodness-of-fit (GOF) indexes provide "rules of thumb"—recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses potential problems underlying the hypothesis-testing rationale of their research, which is more appropriate to testing statistical significance than evaluating GOF. Many of their misspecified models resulted in a fit that should have been deemed acceptable according to even their new, more demanding criteria. Hence, rejection of these acceptable-misspecified models should have constituted a Type 1 error (incorrect rejection of an "acceptable" model), leading to the seemingly paradoxical results whereby the probability of correctly rejecting misspecified models decreased substantially with increasing N. In contrast to the application of cutoff values to evaluate each solution in isolation, all the GOF indexes were more effective at identifying differences in misspecification based on nested models. Whereas Hu and Bentler (1999) offered cautions about the use of GOF indexes, current practice seems to have incorporated their new guidelines without sufficient attention to the limitations noted by Hu and Bentler (1999).
To test hypotheses about the universality of personality traits, college students in 50 cultures identified an adult or college-aged man or woman whom they knew well and rated the 11,985 targets using the 3rd-person version of the Revised NEO Personality Inventory. Factor analyses within cultures showed that the normative American self-report structure was clearly replicated in most cultures and was recognizable in all. Sex differences replicated earlier self-report results, with the most pronounced differences in Western cultures. Cross-sectional age differences for 3 factors followed the pattern identified in self-reports, with moderate rates of change during college age and slower changes after age 40. With a few exceptions, these data support the hypothesis that features of personality traits are common to all human groups.
This new edition completely up-dates the text and takes account of recent work. New material replaces existing information so that individuals such as Michelle Mone (taking on giants) and Ken Morrison, and the stories of Yo Sushi and Lonely Planet are included.The following features are incorporated :Social enterprises (which generate income) are separated from community based ventures which are more grant dependent. The story of Aspire will be introduced and The Storm Model Agency The chapter on the Entrepreneurs of Silicon Valley is to be re-crafted and moved towards the end of the book. It covers both the entrepreneurs and the process and context issues that have helped explain the Silicon Valley phenomenon. The New Internet Entrepreneurs chapter is now to come immediately after Chapter 4 and will be rewritten to include new stories on E-Bay (success) and e-Toys (failure).. There is to be a stronger section on the characteristics of 'The Entrepreneur Enabler' - people who advise and support entrepreneurs . Web support materials and worked examples are to be written for academic adoptions. © 2000, 2004, Bill Bolton and John Thompson. All rights reserved.