This document examines how gender and personality traits relate to divergent thinking and insight problem solving abilities. It proposes that gender and traits may influence the choice of processing mode (System 1 intuitive/associative vs. System 2 logical/analytic) and thereby affect performance on the two creativity measures differently. The study found that Openness correlated with divergent thinking while Emotionality correlated negatively with insight problems. Women performed better at divergent thinking while men performed better at insight problems, and Openness mediated the gender-divergent thinking relationship.

1. The Relations of Gender and Personality Traits on Different
Creativities:
A Dual-Process Theory Account
Wei-Lun Lin
Fo Guang University
Kung-Yu Hsu
National Chung Cheng University
Hsueh-Chih Chen
National Taiwan Normal University
Jenn-Wu Wang
Fo Guang University
In the present study we examine the ways in which gender and
personality traits are related to divergent
thinking and insight problem solving. According to the dual-
process theory account of creativity, we
propose that gender and personality traits might influence the
ease and choice of the processing mode
and, hence, affect 2 creativity measures in different ways. Over
300 participants’ responses on the
Abbreviated Torrance Test for Adults (Chen, 2006), HEXACO
Personality Inventory (Ashton & Lee,
2009; Lee & Ashton, 2004), Raven’s Advanced Progressive
Matrices (Yu, 1993), and performance while
conducting insight-problem tasks, are collected. The results
show that Openness was positively correlated
with divergent thinking performance, whereas Emotionality was
negatively correlated with insight

2. problem-solving performance. Women performed better on
divergent thinking tests, whereas men’s
capabilities were superior on insight problem tasks.
Furthermore, Openness exhibited a mediating effect
on the relationships between gender and divergent thinking. The
relationships among gender, personality,
and creative performance, as well as the implications of these
findings on cultural differences and
real-field creativity, are discussed.
Keywords: HEXACO, personality trait, gender, divergent
thinking, insight problem-solving
Creativity is closely related to human development and achieve-
ment at the individual or societal level. Researchers have
investi-
gated creativity from several aspects (e.g., the 4P model, or the
Person, the Product, the Process, and the Press; Mooney, 1963;
Rhodes, 1961). Various measures are used to differentiate high
from low creative abilities. When considering people’s creative
potentials, divergent thinking is the main focus of the “psycho-
metric approach,” and creative problem solving (e.g., insight
prob-
lem solving) is extensively investigated in the “cognitive ap-
proach” (Sternberg & Lubart, 1999; Sternberg, Lubart,
Kaufman,
& Pretz, 2005). Both are representative indexes that were
widely
applied. Nevertheless, theories and accumulating evidence have
shown that these two measures of creativity might involve two
distinct processes (Perkins, 1998; Sternberg et al., 2005; Wake-
field, 1989). These two processes are correlated with different
cognitive factors (Lin, 2006; Lin, Hsu, Chen, & Chang, 2011;
Lin
& Lien, 2011). Further, individuals’ performances on the two

3. measures are not correlated (Lin, Lien, & Jen, 2005). In the
present
study we investigate how personality traits, as well as gender,
correlate differently with divergent thinking and creative
problem-
solving abilities in accordance with the dual-process theory ac-
count of creativity (Lin & Lien, 2011). In the following para-
graphs, we briefly review the distinction between these two
types
of creativity measures. Our predictions about the ways they
relate
to individuals’ personality traits and gender, based on the dual-
process theory account of creativity, are then proposed.
Distinction Between Divergent Thinking and Creative
Problem Solving
The concept of divergent thinking refers to the ability to gener-
ate diverse and numerous responses to a given question that
increases the likely output of creative ideas (Guilford, 1956). It
serves as the theoretical basis for many creativity tests
(Guilford,
1963; Torrance, 1966; Wallach & Kogan, 1965) in the psycho-
metric approach for creativity (Sternberg & Lubart, 1999; Stern-
berg et al., 2005). For example, the examinee is asked to list as
many as possible interesting and unusual uses for a brick in
accordance with the Unusual Uses Test (Guildford, 1956). Four
main indexes are then applied to assess responses and reflect
divergent thinking ability: (1) The fluency index refers to the
This article was published Online First December 12, 2011.
Wei-Lun Lin and Jenn-Wu Wang, Department of Psychology, Fo
Guang
University, Yilan County, Taiwan; Kung-Yu Hsu, Department
of Psychol-

4. ogy, National Chung Cheng University, Chiayi County, Taiwan;
Hsueh-
Chih Chen, Department of Educational Psychology and
Counseling, Na-
tional Taiwan Normal University, Taipei, Taiwan.
This research was supported partially by a grant to Wei-Lun Lin
from
National Science Council of Taiwan (NSC 97–2410 –H– 431–
014). The
work was also supported by the Ministry of Education, Taiwan,
under the
Aiming for the Top University Plan at National Taiwan Normal
University.
We thank James Kaufman and other anonymous reviewers for
their ex-
tremely helpful comments.
Correspondence concerning this article should be addressed to
Kung-Yu
Hsu, Department of Psychology, National Chung Cheng
University, Tai-
wan, No. 168, University Road, Minhsiung Township, Chiayi
County
62102, Taiwan. E-mail: [email protected]
Psychology of Aesthetics, Creativity, and the Arts © 2011
American Psychological Association
2012, Vol. 6, No. 2, 112–123 1931-3896/11/$12.00 DOI:
10.1037/a0026241
112
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.
ability to generate many responses; (2) the flexibility index
refers
to the ability to switch categories between responses; (3) the
originality index refers to the ability to generate rarely seen re-
sponses according to the norm; and (4) the elaboration index is
specifically used with respect to a figural question that refers to
the
degree of elaboration that is achieved by adding detailed
decora-
tions.
On the other hand, the tradition of investigating creative
problem-solving ability in the cognitive approach traces back to
studies about insight problems by Gestalt psychologists.
Problem
solvers usually encounter obstacles at first, then later invent a
sudden “a-ha!” solution (Dominowski, 1995; Ohlsson, 1984;
Wal-
las, 1926). For example, in a situation in which participants
were
required to attach a candle to a wall by using the only objects
that
were available, they usually encountered obstacles (functional
fixedness of the tack box as a container, not a candlestick) and
could not successfully solve the problem (i.e., the candle
problem;

10. Duncker, 1945). This type of problem is considered a
“productive
problem” and requires more creativity than a “reproductive
prob-
lem” (such as algebraic problems) because it cannot be solved
by
the existing rules. Instead, a reconstruction of the problem
repre-
sentation is required to achieve success (Weisberg, 1995). The
number of correctly solved insight problems is usually used as
an
index of creative ability in this approach.
Wakefield (1989) differentiated problem types along ill- versus
well-defined and open- versus closed-solution dimensions. A di-
vergent thinking task is described as a well-defined, open-
solution
problem, whereas an insight problem is considered as an ill-
defined, closed-solution one. With respect to the essential
proper-
ties of creativity (i.e., novelty and appropriateness; Mayer,
1999),
an open-solution, divergent thinking question better emphasizes
the novelty aspect of creativity (e.g., numerating the uses of a
brick
without considering whether they are practical. Only the number
and unusualness of the responses are scored). In contrast, an
insight or creative problem with a specific solution goal (e.g.,
successfully attaching a candle to a wall) demands ideas that are
novel as well as appropriate, which are consistent with the
problem
constraints (e.g., using only objects available; Lin & Lien,
2011;
Lin et al., 2005).
Empirical evidence indicates that individuals’ performance on

11. the two tasks did not correlate with each other (e.g., Lin et al.,
2005), as well as that various cognitive factors correlated
differ-
ently to these two measures. For example, intelligence is found
to
exhibit moderate positive correlations with creative problem
solv-
ing, but it correlates with divergent thinking to a lesser extent
(Sternberg et al., 2005). Similarly, working memory capacity is
found to be positively correlated with insight problem solving,
but
not with divergent-thinking performance (Lin & Lien, 2011).
Individuals with better divergent thinking performance are
found
to exhibit lower cognitive inhibition ability (as measured by a
retrieval-induced-forgetting paradigm, Anderson, Bjork, &
Bjork,
1994) than controls, whereas individuals with better insight
problem-solving performance do not (Lin, 2006). In addition,
the
aspects of the breadth of attention are found to exhibit different
roles in divergent thinking and insight problem solving (Lin et
al.,
2011).
Lin and Lien (2011) suggested that these results reflected dif-
ferent processes that might involve in divergent thinking and
creative problem solving with respect to the framework of dual-
process theories of cognition (J. St. B. T. Evans, 2003, 2007;
Sloman, 1996; Stanovich & West, 2000). Dual-process theories
have been proposed to account for individuals’ cognitive
process-
ing on wide-ranging cognitive functions, including learning and
memory, reasoning, decision making, and judgment (J. St. B. T.
Evans, 2008). These theories assume that people possess two

12. alternative process systems under one cognitive function.
System
1 (or the heuristic system) processes information in an
associative,
intuitive, and effortless manner without capacity limits. It is
evo-
lutionarily early and comprises prior knowledge and experience.
On the other hand, System 2 (or the analytic system) involves
logical and rule-based processes in which execution relies on
cognitive resources. It is considered evolutionarily recent and
permits abstract thinking. For example, in a syllogism task, a
logically correct validity judgment is proposed to be derived
from
System 2 processing, whereas a judgment influenced by prior
experiences or beliefs without considering its logical status is
derived from System 1 processing (i.e., the belief-bias effect; J.
St.
B. T. Evans, 2003).
Different tasks also might require differential involvement of
both systems (Stanovich, 1999). With respect to these view-
points pertaining to the context of creativity, Lin and Lien
(2011) proposed that idea generation in divergent thinking,
which emphasizes novelty, primarily relies on associative, ef-
fortless System 1 processing. On the other hand, the ability to
generate plausible solutions in creative problem solving reflects
both novelty and appropriateness. It requires System 1 as well
as rule-based, resource-limited System 2 processing.
According to Stanovich (1999), some individual difference vari-
ables could account for the choice of processing mode; for
exam-
ple, cognitive style. Cognitive style1 is a combination of mental
abilities and personality (Guastello, Shissler, Driscoll, & Hyde,
1998). Research also revealed that there were gender
differences in

13. cognitive style (e.g., Sadler-Smith, 2001). Therefore, we
hypoth-
esize that personality traits and gender might also influence the
ease and choice of the processing mode. We explored how per-
sonality traits and gender correlate differently with respect to
the
differential involvement of System 1 and System 2 processing
during the divergent thinking and insight problem-solving mea-
sures.
1 Zhang and Sternberg (2006) proposed a “mode of thinking” in
discus-
sions about the cognitive styles. The three modes of thinking,
which
include analytic, holistic, and integrative, are similar
conceptions to the
dual-process theory (Stanovich & West, 2000). However, the
dual-process
theory (System 1 and System 2) focuses on depicting the
fundamental
information processing modes and is evident in various
cognitive func-
tions. On the other hand, cognitive style represents a higher
level concep-
tion, a combination of mental abilities and personality, or the
ways in
which people prefer to use their abilities (Guastello et al.,
1998). Besides,
there are various concepts or taxonomies of cognitive styles
(Zhang &
Sternberg, 2009). As Stanovich and West (2000) pointed out,
the dual-
process theory depicted computational capacity at the
algorithmic level of
analysis, whereas cognitive or thinking styles indexed
individual differ-

14. ences at the intentional level of analysis. Both contributed
independently to
the variances of individual differences in performance on
various cognitive
tasks (Stanovich & West, 1998). Further, the two conceptions
are different
in malleability that might be affected by long- or short-term
practice
(Baron, 1985).
113GENDER, PERSONALITY TRAITS AND CREATIVENESS
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The Relationships Between Personality Traits and
Creativity
Several personality traits have been identified as being related
to
creativity. At the surface trait level, creative individuals are
found to
be more reserved, dominant, serious, inattentive to rules,
sensitive,
and self-sufficient (Guastello, 2009; Runco, 2007). At the

19. source trait2
level, studies based on the five-factor model (McCrae & Costa,
2008)
consistently find positive relationships between Extraversion,
Open-
ness to Experience, and creativity (Batey, Chamorro-Premuzic,
&
Furnham, 2010; Batey & Furnham, 2006; for the meta-analysis
of the
Big Five source traits with creative behavior, see Guastello,
2009).
Conscientiousness is sometimes found to be negatively related
to
creativity (Batey et al., 2010; Batey & Furnham, 2006).
However,
Feist’s (1998) meta-analysis indicated that although artists were
less
conscientious than nonartists, scientists were more
conscientious than
nonscientists. Researchers also found that creative individuals
possess
some negative traits, such as deviance (Eisenman, 1997) and
psy-
choticism (Eysenck, 1995). Although the results are fruitful,
some
paradoxical personalities existed. As Csikszentmihalyi (1996)
pointed
out in his interviews with highly successful individuals, these
inter-
viewees seemed to be both logical and naı̈ ve, disciplined yet
playful,
introverted and extraverted, realistic but imaginative, objective
but
passionate, and feminine and masculine.

20. The above results were usually derived from studying eminent
people in various domains or assessing normal people’s creative
potentials using certain measurements. As mentioned above, al-
though different personality traits have been linked to scientific
and artistic creativity (other artists’ traits included norm
doubting,
nonconformity, independence, hostility, and lack of warmth,
and
traits of scientists were dominance, arrogance, and high self-
confidence; Feist, 1999), studies that investigated normal
people
mostly utilized divergent thinking tests to differentiate high
from
low creative people. The differences of personality traits
between
people with high and low creative problem-solving potentials
seem
to have been less extensively investigated.
Given that the two measures of divergent thinking and creative
problem solving exhibit different properties and involve
different
processes, their relationships with different personality traits
merit
clarification. For example, Neuroticism or Emotionality, a con-
struct in the five-factor model, is found closely related to
mental
pathology (Matthews, Deary, & Whiteman, 2003). It might
hinder
System 2 processing in which an objective, rational, and rule-
based process is required that underlies creative problem
solving.
However, it might not necessarily hinder associative, intuitive
System 1 processing that divergent thinking mainly requires.
Some
evidence gave support to this speculation. For example,

21. research
has found that Psychoticism (Eysenck, 1995) or mood disorders
(Nowakowska, Strong, Santosa, Wang, & Ketter, 2005) were
correlated with divergent thinking performance, whereas
positive
and stable emotions facilitated insight problem-solving abilities
(G. Kaufmann, 2003). On the other hand, Openness to
Experience
might especially facilitate System 1 processing (and hence
diver-
gent thinking performance) when the richness of ideas, but not
necessarily appropriateness, could effortlessly be associated.
Pre-
vious research did find that Openness to Experience and
divergent
thinking were closely related (Batey & Furnham, 2006; Batey et
al., 2010). It is still questionable whether Openness to
Experience
helps to generate novel and appropriate ideas that could fulfill
the
constraints of solving goals in creative problem solving.
The present study utilizes the HEXACO Personality Inventory
(Ashton & Lee, 2009; Lee & Ashton, 2004), which is considered
more generalizable and inclusive than the five-factor model.
Hon-
esty/Humility, Emotionality, Extraversion, Agreeableness,
Consci-
entiousness, and Openness to Experience are assessed to
determine
the different relationships between personality traits and the
two
creativity measures.
The Relationships Between Gender and Creativity

22. Inconsistent results of gender differences in creativity have
been
obtained in the past (J. C. Kaufman, 2006). Some studies
showed that
no differences existed (e.g., P. C. Cheung, Lau, Chan, & Wu,
2004),
but other studies found that women performed better on the
divergent
thinking tests than men (Dudek, Strobel, & Runco, 1993; Kim &
Michael, 1995; Kuhn & Holling, 2009). Few studies have
investigated
gender differences in creative problem solving. However, some
re-
search has found that women possessed a more intuitive
cognitive
style, whereas men were found to be more sensational (Sadler-
Smith,
2001). Further, women were determined to be more interested in
people, while men were more attracted by objects (Baron-
Cohen,
2003; Connellan, Baron-Cohen, Wheelwright, Batki, &
Ahluwalia,
2000). Some researchers thus proposed that women preferred
holistic,
intuitive thinking and relied more on System 1 processing,
whereas
men were good at analytic thinking and System 2 processing
(Wang,
2011). According to the dual-process theory account of
creativity (Lin
& Lien, 2011), divergent thinking mainly relies on System 1
process-
ing, while creative problem solving also requires System 2
process-
ing. It is hypothesized that women could perform better on

23. divergent
thinking tests, as previously findings have shown, whereas men
could
be more adept at solving creative or insight problems.
In the following experiment, we use a divergent thinking test
(Abbreviated Torrance Test for Adults [ATTA]; Chen, 2006), an
insight problem task (one of the representative creative
problems),
the HEXACO–PI–R (Revised HEXACO Personality Inventory,
Lee & Ashton, 2004), as well as Raven’s Advanced Progressive
Matrices (APM; Yu, 1993; a test for general intelligence3), to
investigate the relationships between personality traits, gender,
and
2 The surface traits refer to personality traits that are the most
specific
and proximally related to external behaviors. The source traits
refer to
personality traits that are the results of factor-analysis on
numerous per-
sonality tests (Guastello, 2009). The present study examined the
relation-
ships between creativity and the source traits (HEXACO)
because the
source traits, which were more basic, were thought to contain
surface-level
traits. We also could provide exploratory data of the Taiwanese
sample and
compare it with past findings on the relationship between the
Big Five and
creativity in different cultures.
3 According to the reviews of Sternberg and colleagues (2005),
the
relationships between intelligence and divergent thinking were

24. more con-
sistent with the threshold theory. It stated that intelligence and
creativity
are correlated up to an IQ of 120, and the relationship dissolves
for higher
IQs. However, the relationships between intelligence and
creative problem
solving are consistently correlated even for higher IQs. Besides,
our
participants were recruited from five universities and were
possibly diverse
in intelligence. Thus, we collected intelligence scores in the
present study
as a controlled variable. We also inspected its different roles on
divergent
thinking and creative problem solving.
114 LIN, HSU, CHEN, AND WANG
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o
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o
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p
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T
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r a
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to
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ad
ly
.
the two creativity measures (i.e., divergent thinking and insight
problem solving). Different relationships between personality
traits and gender with different creativity measures are

29. expected.
In addition, previous studies have inspected the relationships
between personality and creativity, as well as the connection
between gender and creativity (as described in the previous re-
views, these analyses were often with a single creativity
measure,
divergent thinking) and the relationships between personality
and
gender (e.g., Costa, Terracciano, & McCrae, 2001; Schmitt,
Realo,
Voracek, & Allik, 2008). To our knowledge, there are no studies
about how personality and gender interact or mediate each other
to
contribute to creative behavior. The moderation and mediation
effects of personality and gender on divergent thinking and
insight
problem-solving performance also are explored in the present
study.
Method
Participants and General Procedure
A total of 320 undergraduate students from five universities in
Taiwan participated in this study (57.8% women; M age �
19.45
years, SD � 1.89). The collection of different university
students
prevents homogeneity and enables greater variance of our data.
Each participant provided informed consent and participated in
some or all subtests (i.e., insight problem task and HEXACO: n
�
284; ATTA: n � 301; APM: n � 291)4 in consecutive 2-week
anonymous group testing sessions that were 1-hr in duration.
All

30. participants received a gift and were debriefed when they
finished
all of the tasks.
Instruments and Procedures
Creative problem-solving measure—Insight problem task.
Eighteen insight problems (nine verbal and nine figural) were
first
selected from an insight-problems inventory Web site at Indiana
University (http://www.indiana.edu/�bobweb/d4.html). The in-
clusion of these problems fulfilled the criterion of “pure”
insight
problems that necessarily require a reconstructing process, as
suggested by Weisberg (1995). A pretest (n � 52) on these 18
problems was then conducted. According to the results of the
pretest, we chose five verbal problems and five figural problems
with moderate difficulty levels (i.e., ranging from 25% to 70%
for
verbal problems and 21.6% to 62.7% for figural problems). The
10
insight problem task for this study can be found in the
Appendix.
The proceedings of the verbal and figural problems were coun-
terbalanced between participants. Participants were given 20
min
to complete the task. At the end, they were required to report
whether they had previously seen and/or known the answers to
any
of the problems. The performance scores were calculated as the
percentage of unfamiliar problems that were answered correctly
within the verbal, figural, and 10 total insight problems. The
participants’ average accuracy rates on the verbal, figural, and
total
problems were 38% (SD � .29), 43% (SD � .28), and 40% (SD

31. �
.25). The correlation of accuracy rates between verbal and
figural
problems was .55, p � .01. Cronbach’s alpha coefficients were
.54, .51, .68 for the scores on the verbal, figural, and whole
problems, respectively.
Divergent thinking measure—Abbreviated ATTA. The
Chinese version of the ATTA (Chen, 2006) is a translated
version
of ATTA (Goff & Torrance, 2002). It includes three subtests: a
verbal test (question enumeration) and two figural tests (figural
completion). Three minutes is allowed for each subtest. The
test,
which was developed as a large-sample norm in Taiwan for
undergraduate students, established satisfactory reliability and
va-
lidity results (Chen, 2006).
Participants’ responses were scored by two independent raters
for fluency, flexibility, originality, and elaboration, which are
among the most representative indexes for divergent thinking
abilities. The interrater reliability coefficients for these four
kinds
of scores ranged from .83 to .97. The fluency scores were
simply
the total number of responses generated by each participant.
The
flexibility scores represented the number of different categories
of
responses. The originality scores represented the sum of scores
on
each response in comparison to the norm and scored as either 0
or
1. The elaboration scores of the figural subtests were scored as
the

32. number of elaborated decorations in each response.
Participants’ performance on the ATTA was scored as follows:
fluency: M � 12.11, SD � 4.01; flexibility: M � 7.87, SD �
2.69;
originality: M � 3.22, SD � 2.42; elaboration: M � 5.36, SD �
4.31. The scores for these indexes were mostly significantly
cor-
related with those of each of the others (the Pearson’s r ranged
from .31 to .83, p � .01); however, originality and elaboration
exhibited no correlation (r � .07, p � .24).
Personality measure—HEXACO–PI–R. The HEXACO–
PI–R (Ashton & Lee, 2009; Lee & Ashton, 2004) measures six
personality traits: Honesty/Humility, Emotionality,
Extraversion,
Agreeableness, Conscientiousness, and Openness to Experience.
These six personality traits came from the factor analysis results
of
personality lexicons of six different languages. As previously
mentioned, the inventory was considered to be more
generalizable
and inclusive than the five-factor model. There are 16 items for
each personality trait, and a 5-response Likert scale is used
from 1
(strongly disagree) to 5 (strongly agree). This inventory was
translated into the Chinese version by one of the authors. Two
Chinese personality psychologists who were trained in the
United
States and the United Kingdom examined the quality of this
translation. The Chinese version of this inventory then was
trans-
lated into English. The back-translation version of this
inventory
was checked by authors of this inventory and the incorrect
trans-

33. lations were modified. Cronbach’s alpha coefficients, which
ranged from .74 to .82, were obtained for the scores on these six
scales of the Chinese version in this study. The different types
of
measurement equivalence (i.e., configural, metric, and error
vari-
ance) of the HEXACO–PI–R were found in a multiple age group
ranging from early adolescence to adulthood by the use of
confir-
matory factor analysis (Hsu, 2010).
General ability measure—APM. The Chinese version of
APM (Yu, 1993) was administered to assess participants’
general
intelligence. In this test, every test item consists of the
presenta-
tions of several graphs, including an empty one. Participants
have
to find the relationships between them by choosing the most
4 The participants were also assessed with the Adult
Temperament
Questionnaire (D. E. Evans & Rothbart, 2007). The results are
reported in
another paper.
115GENDER, PERSONALITY TRAITS AND CREATIVENESS
T
hi
s
do
cu
m

34. en
t i
s
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al

35. A
ss
oc
ia
tio
n
or
o
ne
o
f i
ts
a
lli
ed
p
ub
lis
he
rs
.
T
hi
s

36. ar
tic
le
is
in
te
nd
ed
s
ol
el
y
fo
r t
he
p
er
so
na
l u
se
o
f t
he

37. in
di
vi
du
al
u
se
r a
nd
is
n
ot
to
b
e
di
ss
em
in
at
ed
b
ro
ad

38. ly
.
reasonable graph to accompany the empty one from among the
eight graphs. The test has established stable reliability and
validity
results: The retest reliability was .91 (Fould, Forbes, & Bevans,
1962), and the correlations between the APM scores and intelli-
gence tests were high (e.g., rs � .4 to .6, Schweizer &
Moosbrug-
ger, 2004).
For a proper time control, we excluded the first 12 items, which
were found to add little to the discriminative power of the test
(Bors & Stokes, 1998), and used Items 13 to 36. The total
admin-
istrating time was 25 min. The performance scores were
calculated
as the total number of items correct. Our participants’ scores
ranged from 0 to 23, with Ms � 12.45, SD � 5.16.
Results
The Relationships Between the Two Measures of
Creativity and General Intelligence
We first examined the relationships between participants’ per-
formances on divergent thinking and insight problem solving.
As
shown in Table 1, although fluency and flexibility scores
exhibited
small positive correlations with verbal, figural, and total insight
problem-solving accuracy (rs � .19, .22, .24; rs � .23, .24, .26,

39. p � .01, respectively), the indexes of originality and elaboration
were not correlated with insight problem-solving performance
(rs � .01 to .11, ns). Because the correlations between the four
indexes of divergent thinking and the two indexes of insight
problem solving were high, we partialed out the effects of other
indexes within one task. The extent of correlations was smaller
between fluency, flexibility, and insight problem-solving
perfor-
mance (rs ranged from .12 to .19, p � .05). These results
indicate
that individuals’ performances on the two creativity measures
were
only slightly related on some of the indexes.
The correlations between the participants’ creative performance
and APM scores, referred to as their general intelligence and
representing their cognitive abilities, were also computed. As
Table 1 shows, participants’ divergent thinking performance—
fluency, flexibility, and elaboration—were positively correlated
with their APM scores (rs � .21, .23, .20, respectively, small
effect
sizes as defined by Cohen, 1988; p � .01). There was no corre-
lation between intelligence scores and the originality index (r �
.01, ns). On the other hand, participants’ APM scores were more
highly correlated to their insight problem-solving performance,
with rs � .39, .43, and .46, p � .01, to verbal, figural, and total
problem-solving accuracy, respectively. All of these
coefficients
achieved a medium level of effect sizes, as defined by Cohen
(1988). These results fit well with previous findings that
creative
problem-solving ability depended more on intelligence than did
the divergent thinking performance (Sternberg et al., 2005).
The Relationships Between Two Creativity Measures

40. and Personality Traits
One of the main purposes of the present study was to inspect
how personality traits correlated differently with divergent
think-
ing and creative problem-solving performance. As Table 2
shows,
Openness to Experience was positively correlated to divergent
thinking indexes (rs � .24, .22, .24, .19, p � .01, for fluency,
flexibility, originality, and elaboration indexes, respectively).
Ex-
traversion was also positively correlated to most of the
divergent
thinking indexes (rs � .14, .13, .16, p � .05, for fluency, flexi-
bility, and elaboration indexes, respectively, but not to the
origi-
nality index, r � .10, ns). Nevertheless, neither Openness to
Experience or Extraversion exhibited correlations with most of
the
participants’ insight problem-solving performance (rs ranged
from
.00 to .09, ns, except the verbal insight problem-solving perfor-
mance exhibited a weak positive correlation with Openness to
Experience, r � .13, p � .05). On the other hand, Emotionality
was found to be negatively correlated to insight problem-
solving
performance (rs � �.16, �.13, �.17, p � .01, for verbal,
figural,
and total insight problem-solving accuracy, respectively), but
not
to divergent thinking performance (rs ranged from .01 to .08,
ns;
even emotionality and elaboration index were positively corre-
lated, r � .12, p � .05). Honesty, Agreeableness, and Conscien-
tiousness were not correlated to either divergent thinking or
insight problem-solving performance. The above results

41. showed a pattern as predicted: Divergent thinking performances
related positively to Openness to Experience and Extraversion,
whereas insight problem-solving performances were hindered
by Emotionality.
Table 1
The Relationships Between Creativity Measures and
Intelligence
Creativity
measures Fluency
Divergent thinking
Elaboration
Insight problem
APMFlexibility Originality Verbal Figural Total
Divergent thinking
Fluency — .83�� .35�� .31�� .19�� .22�� .24�� .21��
Flexibility — .37�� .35�� .23�� .24�� .26�� .23��
Originality — .07 .08 .11 .11 .01
Elaboration — .01 .09 .06 .20��
Insight problem
Verbal — .55�� .88�� .39��
Figural — .88�� .43��
Total — .46��
Note. APM was used to measure participants’ intelligence. APM

42. � Raven’s Advanced Progressive Matrices.
�� p � .01.
116 LIN, HSU, CHEN, AND WANG
T
hi
s
do
cu
m
en
t i
s
co
py
ri
gh
te
d
by
th
e
A
m
er

43. ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
tio
n
or
o
ne
o
f i
ts
a
lli
ed

44. p
ub
lis
he
rs
.
T
hi
s
ar
tic
le
is
in
te
nd
ed
s
ol
el
y
fo
r t
he

45. p
er
so
na
l u
se
o
f t
he
in
di
vi
du
al
u
se
r a
nd
is
n
ot
to
b
e

46. di
ss
em
in
at
ed
b
ro
ad
ly
.
The Relationships Between Two Creativity Measures
and Gender
Another purpose of the present study was to inspect how gender
correlated differently to the two creativity measures. We
separately
analyzed the performance of our female (n � 181 for ATTA and
n � 169 for insight problem task) and male (n � 118 for ATTA
and n � 111 for insight problem task) participants on the ATTA
and insight problem task. The results, which are shown in Table
3,
indicate that our male participants performed better on most of
the
insight problem-solving indexes than their female counterparts:
.48 � .28 versus .40 � .28, t(282) � 2.57, p � .05, d5 � .31,
for
the verbal accuracy; and .45 � .26 versus .37 � .24, t(282) �

47. 2.45,
p � .05, d � .30, for the total accuracy. Except for the figural
accuracy, only marginally significant effect of gender existed,
.41 � .30 vs. .35 � .28, t(282) � 1.7, p � .09. On the contrary,
female participants performed better in most of the divergent
thinking indexes over male participants: 12.48 � 4.0 versus
11.53 � 4.19, t(297) � �1.97, p � .05, d � .23, for fluency;
8.15 � 2.59 versus 7.42 � 2.80, t(297) � �2.29, p � .05, d �
.27,
for flexibility; and 6.04 � 4.43 versus 4.33 � 3.94, t(297) �
�3.41, p � .01, d � .40, for elaboration. The exception was that
female participants did not perform better on the originality
index
than male counterparts: 3.36 � 2.31 vs. 2.98 � 2.58, t(297) �
�1.33, ns. These results indicate the different gender effects on
divergent thinking and insight problem-solving performance and
support our prediction.
The Relationships Between Personality Traits, Gender,
and Two Creativity Measures
To understand how gender and personality traits contribute to
the performance of divergent thinking and insight problem
solving,
several hierarchical multiple-regression analyses were
conducted.
In all of these analyses, the steps of the variables that entered
the
equations were as follows: (1) APM (where intelligence scores
could be controlled), (2) gender, (3) Honesty-Humility,
Extraver-
sion, Emotionality, Agreeableness, Conscientiousness, and
Open-
ness to Experience. All of the personality variables in the final
step
were entered simultaneously.

48. As Table 4 shows, the APM score accounted for 4 to 18% of
variance of two creativity indexes, �F(1, 318) � 11.91 to
67.65,
p � .001, except for the originality index of divergent thinking.
When the gender variable was entered into the equation (Model
2),
the amount of variance explained in both creativity measures
(�R2 � .01 to .04, p � .05) significantly increased from Model
1,
in which only the APM scores was included, �F(1, 317) � 3.91
to
12.51, p � .05. When personality variables were further
included
in Model 3, the amount of variance explained (�R2 � .05, for
all
the above three indexes, p � .01) in divergent thinking perfor-
mance increased further, �F(6, 311) � 3.10 to 3.15, p � .01,
but
not in the insight problem-solving performance.
When intelligence, gender, and personality were included as
predictors (Model 3), APM scores could predict significantly
both
creative performances (with �s � .34 to .41 for insight
problem-
solving, p � .001, and �s � .20 to .21 for divergent thinking, p
�
.001, except originality). The predictive powers were larger for
the
insight problem-solving consideration. Gender significantly pre-
dicted some of the indexes of both creative performances, but in
an
opposite direction. For insight problem solving, �s � �.14 ( p
�
.01) and �.12 ( p � .01) for verbal and total accuracy. On the

49. other
hand, � � .16 ( p � .01) for the elaboration index on divergent
thinking.
As for the predictive powers of personality traits on creativity
measures, Openness to Experience could significantly predict
all
of the four indexes of divergent thinking (with �s � .19, .18,
.20,
.15, p � .05, for fluency, flexibility, originality, and elaboration
indexes, respectively). Nevertheless, it could only predict one
index, verbal accuracy, of insight performance (with � � .13, p
�
.01). In addition, Extraversion could only predict elaboration
scores in divergent thinking (� � .13, p � .05).
The above results indicate that, after controlling the effects of
intelligence on two creativity performances, our male and
female
participants performed better at different creativity tasks (i.e.,
insight
problem solving and divergent thinking), respectively. As for
the roles
5 The effect size, d, is computed according to the formula: d �
t[(1/
n1)�(1/n2)]
.5 (Cortina & Nouri, 2000), where t refers to the statistical
value
of t test, n1 and n2 refer to the numbers of female and male
participants.
This formula is used for an unequal cell condition.
Table 2
The Relationships Between Creativity Measures and Personality

50. Traits
Creativity
measures H E X A C O
Divergent thinking
Fluency �.02 .01 .14� .05 .06 .24��
Flexibility .03 .08 .13� �.03 .03 .22��
Originality .04 .01 .10 .05 .02 .24��
Elaboration �.02 .12� .16� �.03 �.02 .19��
Insight problem
Verbal �.03 �.16�� .00 .07 .01 .13�
Figural �.07 �.13� .05 .09 .05 .03
Total �.05 �.17�� .03 .10 .03 .09
Note. H � Honesty-Humility; E � Emotionality; X �
Extraversion; A �
Agreeableness; C � Conscientiousness; O � Openness to
Experience.
� p � .05. �� p � .01.
Table 3
Means and Standard Deviations of Two Creativity Measures
Between Male and Female Participants
Creativity
measures
Participants
Male Female

51. Divergent thinking
Fluency 11.53 (4.2) 12.48� (4.0)
Flexibility 7.42 (2.8) 8.15� (2.6)
Originality 2.98 (2.6) 3.36 (2.3)
Elaboration 4.33 (3.9) 6.04�� (4.4)
Insight problem
Verbal 0.48 (0.28) 0.40� (0.28)
Figural 0.41 (0.30) 0.35 (0.28)
Total 0.45 (0.26) 0.37� (0.24)
Note. Standard deviations are in parentheses.
� p � .05. �� p � .01.
117GENDER, PERSONALITY TRAITS AND CREATIVENESS
T
hi
s
do
cu
m
en
t i
s
co
py
ri
gh

52. te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
tio
n
or

53. o
ne
o
f i
ts
a
lli
ed
p
ub
lis
he
rs
.
T
hi
s
ar
tic
le
is
in
te
nd

54. ed
s
ol
el
y
fo
r t
he
p
er
so
na
l u
se
o
f t
he
in
di
vi
du
al
u
se

55. r a
nd
is
n
ot
to
b
e
di
ss
em
in
at
ed
b
ro
ad
ly
.
of personality traits, the regression results were more complex,
al-
though most of the patterns were preserved as in the correlation
analysis (see Table 2). Personality also seemed to exhibit a
lesser

56. effect on insight problem solving than divergent thinking.
Inspecting Table 4, it was found that, compared to Model 2, the
predictive powers of gender were diminished when the
personality
variables were entered into the regression analysis in Model 3
for
the indexes of fluency and flexibility. These results revealed
that
the mediation effects of personality traits (especially Openness
to
Experience) could be found. The mediation analyses were
further
conducted to investigate whether different gender predicted
differ-
ent creative performances through personality traits. The results
revealed (see Figure 1A) that the initial associations between
gender and fluency scores were eliminated by the inclusion of
Openness to Experience, indicating a full mediation effect
(Sobel
z value � 2.05, p � .05). The associations between gender and
flexibility scores were also attenuated by the mediation of
Open-
ness (see Figure 1B), to a marginally significant degree (Sobel z
value � 1.74, p � .08). Other analyses did not show any signif-
icant mediation effects of personality on the relationships
between
gender and creative performances.
In addition, we examined whether gender and personality
variables
interacted on the two creative performances. Therefore, Model 4
was
constructed and Gender Personality variables were entered as
predictors. The results showed no significant moderation

57. effects.
Discussion
The present study specifies and examines how personality traits
and gender are related to different creativity measures:
divergent
thinking and insight problem solving. These two creativity mea-
sures are investigated and applied extensively but independently
by the psychometric and cognitive approach in creativity
research
literature. Our results mainly showed that different personality
traits in the HEXACO–PI–R were related to different creativity
measures. Our male and female participants performed
differently
in the two creativity measures. Interesting findings were also
obtained when considering the relationships between gender,
per-
sonality traits, and creativity. The more detailed discussions of
these findings are stated as follows.
As our data showed (see Table 2), Openness to Experience and
Extraversion correlated significantly to most of the participants’
divergent thinking performances. These results replicated the
pre-
vious findings in which these two personality traits were
identified
as being important in creativity, when only the divergent
thinking
sort of measurement was used (Batey & Furnham, 2006; Batey
et
al., 2010). However, Openness to Experience and Extraversion
were not so related to close-ended, insight problem solving
when
we took this measurement into consideration. Instead,
individuals

58. who performed better on insight problem solving possessed less
Emotionality, as our data showed a negative correlation between
Emotionality and insight problem-solving performance.
Table 4
Intelligence, Gender, and Personality Traits as Predictors of the
Two Creativity Measures
Predictors in model
Insight problem Divergent thinking
Verbal Picture Total Fluency Flexibility Originality Elaboration
Model 1
APM .350��� .389��� .419��� .195��� .205��� .013
.190���
�R2 .123��� .151��� .175��� .038��� .042��� .000
.036���
Model 2
APM .352��� .390��� .420��� .193��� .204��� .013
.188���
Gender �.150�� �.102� �.144�� .111� .129�� .077
.191���
�R2 .023�� .010� .021�� .012� .017�� .006 .037���
Model 3
APM .342��� .389��� .413��� .197��� .213��� .009
.197���
Gender �.141�� �.069 �.121�� .096† .092 .043 .157��

59. Honesty �.028 �.062 �.051 �.026 .039 .036 �.004
Emotionality �.091 �.081 �.096† .011 .065 .024 .094
Extraversion �.019 .044 .014 .083 .108† .053 .128�
Agreeableness .003 .037 .028 �.009 �.073 .003 �.049
Conscientiousness .029 .072 .051 .050 .025 �.013 �.027
Openness to Experience .127� �.006 .069 .187��� .175���
.204��� .149��
�R2 .028 .019 .023 .054�� .053�� .048�� .053��
Note. APM was used to measure participants’ intelligence;
Gender: Male 0, Female 1.
† p � .10. � p � .05. �� p � .01. ���p � .001.
A
B
Gender
Openness to Experience
Fluency
β = .09 (.11*)
β = .12 * β = .21 *** (.24***)
Gender
Openness to Experience
Flexibility

60. β = .11 (.12*)
β = .12 * β = .18 *** (.22**)
Figure 1. The mediation effects of personality trait (Openness to
Expe-
rience) on the relationships between gender and the fluency (A),
flexibility
(B) index of divergent thinking. � p � .05. �� p � .01. ��� p
� .001.
118 LIN, HSU, CHEN, AND WANG
T
hi
s
do
cu
m
en
t i
s
co
py
ri
gh
te
d

61. by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
tio
n
or
o
ne

62. o
f i
ts
a
lli
ed
p
ub
lis
he
rs
.
T
hi
s
ar
tic
le
is
in
te
nd
ed
s

63. ol
el
y
fo
r t
he
p
er
so
na
l u
se
o
f t
he
in
di
vi
du
al
u
se
r a
nd

64. is
n
ot
to
b
e
di
ss
em
in
at
ed
b
ro
ad
ly
.
An interesting finding in the regression analysis (see Table 4)
revealed that Openness to Experience also significantly
predicted
verbal insight problem-solving performance even after
intelligence
and gender were controlled. Given that Openness to Experience
is
assumed to be related to the richness of ideas an individual

65. holds
and processes (Batey et al., 2010), one possible explanation is
that
some less dominant concepts (but that are keys to the correct
answers) that lie in the descriptions of our particular verbal
insight
problems were equally and properly processed as the dominant
concepts by individuals with a high level of Openness to
Experi-
ence; hence, the likelihood of correct solutions increased. The
role
of Openness to Experience on insight problem solving still
needs
further investigation with different problems as measures.
Turning to the role of gender, our data showed a pattern of
disso-
ciation between gender and different creativity measures (see
Table
3). Although women performed better on most of the indexes of
divergent thinking test, which was also demonstrated by
previous
studies (Dudek et al., 1993; Kim & Michael, 1995; Kuhn &
Holling,
2009), our data first showed that men were better at an insight
problem-solving task. When inspecting the relationships
between
gender, personality traits, and creativity, our results showed
that the
predictive powers of gender generally decreased when
personality
variables were included in the regression analyses, especially
for
divergent thinking indexes (see Table 4). Although the
moderation
effects of gender and personality were not significant, the

66. mediation
analyses showed some interesting findings. Women performed
better
on divergent thinking tasks and are more open to experience
than men
(additional analysis that we performed showed a significant
correla-
tion between gender and Openness to Experience, r � .12, p �
.05).
The significant mediation effects of Openness to Experience on
the
relationships between gender and divergent thinking indexes
(fluency,
in particular) indicated that women possessed a higher level of
Open-
ness to Experience, which further enhanced their fluency in
divergent
thinking.
There was no significant mediation effect of personality on the
relationships between gender and insight problem-solving
perfor-
mance, although analysis showed that gender and Emotionality
were significantly correlated (r � .21, p � .01). In addition,
Table
4 also showed that the amount of variance explained in insight
problem-solving performance did not increase when personality
variables were further included in the regression analysis. These
results indicate that gender did not predict insight problem-
solving
performance through Emotionality and personality traits played
smaller roles on insight problem solving. Instead, insight
problem-
solving performance was mainly predicted by intelligence
(APM)
and gender differences. Although the regression weights that

67. were
obtained, as shown in Table 4, were rather low, the present
study
may provide an exploratory direction. The ways in which gender
and personality interact to influence different creativity
measures
remain an interesting issue that merits further exploration.
As previously mentioned, Lin and Lien (2011) proposed a dual-
process account of creativity to explore the differential
involvement of
Systems 1 and 2 processing in divergent thinking and creative
prob-
lem solving. In respect to this theory, the present study
hypothesizes
that different gender and personality traits might influence the
ease
and choice of the processing mode and, hence, affect divergent
thinking or creative problem solving differently on either side
(as we
used insight problems as one kind of creative problems). Our
results
support this theory. First, the participants’ performances on the
two
measures were only slightly correlated, indicating different
processes
that the two measures might possibly involve. Second, although
women have been suggested to be prone to holistic, intuitive,
and
System 1 thinking, and men have been suggested to be
dominated by
analytic and System 2 thinking (Wang, 2011), our results
showed that
female and male participants performed better at divergent
thinking

68. and insight problem solving, respectively. Third, Emotionality,
which
was hypothesized to hinder analytical, System 2 processing with
its
high level, was found to be negatively related to insight
problem-
solving performance. Openness to Experience, which might
espe-
cially facilitate associative, System 1 processing, was found to
be
positively related to divergent thinking performance. These
results
support the notions of dual-process account of creativity (Lin &
Lien,
2011) in that the idea generation in divergent thinking mainly
relies on
associative, intuitive, and effortless System 1 processing.
However,
generating plausible solutions in creative problem solving
additionally
requires rule-based, analytical, and resource-limited System 2
pro-
cessing.
Another interesting issue that requires further discussion is how
culture may have played a role in our study on the relationships
between gender, personality, and creativities. Our data was
collected
in Taiwan, but some researchers pointed out that gender
differences in
creativity (divergent thinking performance, in particular) could
be best
expected in Western cultures (e.g., Kuhn & Holling, 2009). As
men-
tioned, P. C. Cheung and colleagues (2004) found no gender
differ-

69. ences in the fluency index in a Hong Kong sample. Our results
showed that Taiwanese women performed better in divergent
thinking
than men, consistent with the findings in Western culture
(Dudek et
al., 1993; Kuhn & Holling, 2009) as well as Kim and Michaels’
(2005) finding on a significant gender difference in creativity in
a
Korean sample. In addition, although creativity could be
hindered by
the social structure of the collectivism culture (Runco, 2007)
and
Openness to Experience could be not encouraged in Chinese
society
(McCrae, Yik, Trapnell, Bond, & Paulhus, 1998), we found that
Openness to Experience and Extraversion were positively
related to
divergent thinking. As F. M. Cheung and colleagues (2008)
argued,
the construct of openness in Chinese culture should incorporate
the
idea- or interest-related cognitive styles in Western culture with
in-
terpersonal potency and Extraversion in the collective culture.
Our
measures of Openness to Experience and Extraversion could
capture
the part of the Chinese notion of Openness to Experience. Our
results
also imply that, even in a detrimental context (e.g., collectivism
culture) for creativity, the personal dispositions could enhance
one’s
creativity.
Although many factors may contribute to the real-life
achievement

70. of creativity, creativity potentials or abilities (such as divergent
think-
ing and creative problem solving) were considered the essential
com-
ponents of this achievement (Eysenck, 1995; Sternberg et al.,
2005).
The present study that focuses on individuals’ creative
potentials
might provide some implications for real-world creativity. A
scientist
discovers the principles of the world according to phenomena
and
evidence, whereas an artist creates works of art without
constraining
the work to concrete explanations of objectives (Simonton,
2008;
Stent, 2001). Creative problem solving, with its closed-ended
prop-
erty, could be closer to the scientific discovery processes,
whereas
divergent thinking tasks might characterize more artistic
processes by
their open-ended nature (Lin & Lien, 2011). Previous work has
identified some different personality traits that belong to artists
and
scientists (Feist, 1999). Our study suggests that Emotionality
and
119GENDER, PERSONALITY TRAITS AND CREATIVENESS
T
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71. cu
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72. ic
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oc
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or
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a
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rs
.
T

73. hi
s
ar
tic
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is
in
te
nd
ed
s
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fo
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he
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74. f t
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75. ro
ad
ly
.
Openness to Experience might be other traits that could
differentiate
them with a possible theoretical base (i.e., the dual-process
account of
creativity, Lin & Lien, 2011). Our study also gives support to
and a
possible explanation concerning the observations that women
are
more interested in social and artistic events, whereas men are
more
interested in scientific activities (Betz & Fitzgerald, 1987).
Further,
men are better than women at scientific studies (Ceci, Williams,
&
Barnett, 2009; Martin, Mullis, Gonzalez, & Chrostowski, 2004).
If factors are found to be correlated differently with divergent
thinking and creative problem solving, they might be used to
differentiate individuals with distinct potentials and design for
various training programs that are applied to improve different
creative achievements.
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(Appendix follows)

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94. P
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Appendix A
Ten Insight Problems and
Solution
s Used
Problems