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Factor analyses of 75 facet scales from 2 major Big Five inventories, in the Eugene-Springfield community sample (N=481), produced a 2-factor solution for the 15 facets in each domain. These findings indicate the existence of 2 distinct (but correlated) aspects within each of the Big Five, representing an intermediate level of personality structure between facets and domains. The authors characterized these factors in detail at the item level by correlating factor scores with the International Personality Item Pool (L. R. Goldberg, 1999). These correlations allowed the construction of a 100-item measure of the 10 factors (the Big Five Aspect Scales [BFAS]), which was validated in a 2nd sample (N=480). Finally, the authors examined the correlations of the 10 factors with scores derived from 10 genetic factors that a previous study identified underlying the shared variance among the Revised NEO Personality Inventory facets (K. L. Jang et al., 2002). The correspondence was strong enough to suggest that the 10 aspects of the Big Five may have distinct biological substrates.
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Between Facets and Domains: 10 Aspects of the Big Five
Colin G. DeYoung
Yale University
Lena C. Quilty
Centre for Addiction and Mental Health
Jordan B. Peterson
University of Toronto
Factor analyses of 75 facet scales from 2 major Big Five inventories, in the Eugene-Springfield
community sample (N 481), produced a 2-factor solution for the 15 facets in each domain. These
findings indicate the existence of 2 distinct (but correlated) aspects within each of the Big Five,
representing an intermediate level of personality structure between facets and domains. The authors
characterized these factors in detail at the item level by correlating factor scores with the International
Personality Item Pool (L. R. Goldberg, 1999). These correlations allowed the construction of a 100-item
measure of the 10 factors (the Big Five Aspect Scales [BFAS]), which was validated in a 2nd sample
(N 480). Finally, the authors examined the correlations of the 10 factors with scores derived from 10
genetic factors that a previous study identified underlying the shared variance among the Revised NEO
Personality Inventory facets (K. L. Jang et al., 2002). The correspondence was strong enough to suggest
that the 10 aspects of the Big Five may have distinct biological substrates.
Keywords: personality, Big Five, five factor model, aspects, facets
Personality trait dimensions can be categorized by arranging
them into hierarchies, based on their intercorrelations. Broad do-
mains (e.g., Extraversion), each encompassing many related traits,
are located near the top of the hierarchy, and very specific patterns
of behavior and experience (e.g., talking a lot) are located near the
bottom. The arrangement of these hierarchies has been a central
preoccupation of personality psychologists for the better part of a
century. Considerable progress has been made, leading to a rea-
sonable degree of consensus regarding the makeup of an adequate
categorization scheme. The five-factor model, or Big Five, which
originated from studies of trait-descriptive adjectives drawn from
the lexicon, is the most widely used classification system for
personality traits, identifying five broad domains of personality:
Extraversion, Agreeableness, Conscientiousness, Neuroticism, and
Openness/Intellect (Costa & McCrae, 1992a; Digman, 1990; Gold-
berg, 1993; John & Srivastava, 1999). Like any dominant para-
digm, the Big Five model has drawn its fair share of criticisms and
proposals for alternatives (e.g., Ashton et al., 2004; Saucier, 2003;
Waller, 1999; Zuckerman, Kuhlman, Joireman, Teta, & Kraft,
1993). Nonetheless, the Big Five has proved extremely useful in
providing a common language for researchers and organizing
personality research.
Much research on the Big Five has focused on a two-level
hierarchy, with the five domains at the top subsuming narrower
traits called “facets” at a second level. This approach is exempli-
fied by the widely used Revised NEO Personality Inventory
(NEO-PI-R; Costa & McCrae, 1992b), which breaks each of the
five domains down into six facets.
1
More than two levels can be
identified, however. Since the discovery by Digman (1997) that
the regular pattern of correlations among the Big Five has a higher
order factor solution, there has been increasing discussion of levels
of the hierarchy above the Big Five domains (DeYoung, 2006;
DeYoung, Peterson, & Higgins, 2002; Jang et al., 2006; Markon,
Krueger, & Watson, 2005; Saucier, 2003). Two constructs, labeled
Alpha and Beta (Digman, 1997), or Stability and Plasticity
(DeYoung, 2006; DeYoung et al., 2002), appear to constitute the
highest level of personality organization in the hierarchy built
around the Big Five and have been described as “metatraits.” Less
attention has been paid to a level of trait organization located
between facets and domains. Reasons exist, however, to suspect
that this level might be both interesting and important.
A behavior genetic study in large Canadian and German sam-
ples found that two genetic factors are responsible for the shared
variance of the six facet scales that make up each of the Big Five
in the NEO-PI-R (Jang, Livesley, Angleitner, Riemann, & Vernon,
1
One might well argue that this approach includes three levels, as the
items that make up each facet scale typically describe multiple distinguish-
able patterns of behavior and experience (Digman, 1990). Most research
linking personality ratings to other phenomena does not investigate indi-
vidual items, however, for psychometric reasons.
Colin G. DeYoung, Department of Psychology, Yale University; Lena
C. Quilty, Clinical Research Department, Centre for Addiction and Mental
Health, Toronto, Ontario, Canada; Jordan B. Peterson, Department of
Psychology, University of Toronto, Toronto, Ontario, Canada.
This study was supported in part by a grant from the Social Sciences and
Humanities Research Council of Canada awarded to Jordan B. Peterson.
We thank Weronika Sroczynski for her help in running this study, Lewis
R. Goldberg for his generosity in making data available from the Eugene-
Springfield community sample, Brian P. O’Connor for advice on factor
analysis, and Kerry L. Jang for advice on calculating genetic factor scores.
Correspondence concerning this article should be addressed to Colin G.
DeYoung, Department of Psychology, Yale University, Box 208205, New
Haven, CT 06520. E-mail: cdeyoung@post.harvard.edu
Journal of Personality and Social Psychology Copyright 2007 by the American Psychological Association
2007, Vol. 93, No. 5, 880 896 0022-3514/07/$12.00 DOI: 10.1037/0022-3514.93.5.880
880
2002). Each of the Big Five domains, therefore, appears potentially
divisible into two subdomains with distinct biological sources.
This finding would, by itself, be sufficient to motivate investiga-
tion into an intermediate level of personality structure. Additional
sources of motivation can be found in the personality literature,
where the possibility that one or more of the Big Five might
subsume two separable subdomains has been raised in a variety of
contexts.
Depue and Collins (1999) reviewed the literature on Extraver-
sion, for example, and noted a primary division within the domain,
between agency (“social dominance and the enjoyment of leader-
ship roles, assertiveness, exhibitionism, and a subjective sense of
potency in accomplishing goals,” p. 492) and sociability. (They
note a third traditional conception of Extraversion as impulsivity
but argue that impulsivity is in fact a compound trait combining
Extraversion with low Conscientiousness or Constraint.) Some
empirical support for such a division can be found in factor
analyses of the NEO Personality Inventory (NEO-PI; McCrae &
Costa, 1985, which predated the NEO-PI-R and did not include
facet scales for Agreeableness and Conscientiousness). These anal-
yses demonstrated that the Assertiveness and Activity facets of
Extraversion split off in a separate factor from the other four
Extraversion facets (Church, 1994; Church & Burke, 1994). At
least one widely used instrument loosely based on the Big Five, the
Hogan Personality Inventory, reflects this division, dividing the
assessment of Extraversion between “Ambition” and “Sociability”
scales (Hogan & Hogan, 1992).
Costa, McCrae, and Dye (1991) described Conscientiousness
“as having both proactive and inhibitive aspects” (p. 887), the
proactive aspect including such traits as “need for achievement and
commitment to work,” and the inhibitive aspect including such
traits as “moral scrupulousness and cautiousness.” Empirical sup-
port for a similar division is offered by a study that performed
factor analysis of scales from seven major personality inventories,
including only scales identified by their authors as conceptually
related to Conscientiousness (Roberts, Chernyshenko, Stark, &
Goldberg, 2005). Two of these instruments, the NEO-PI-R and the
Abridged Big Five Circumplex scales from the International Per-
sonality Item Pool (AB5C-IPIP; Goldberg, 1999) were specifically
designed to assess facets of the Big Five. Although Roberts et al.
found six factors in total, all but two of the NEO and AB5C facets
were subsumed within two factors, labeled Industriousness and
Order, suggesting that, at least as defined in Big Five space,
Conscientiousness has two primary subdomains. This finding is
similar to that of Jackson, Paunonen, Fraboni, and Goffin (1996),
who found that a factor solution splitting Conscientiousness into
Achievement and Methodicalness was better than the standard Big
Five solution in their instrument, the Personality Research Form.
In relation to Agreeableness, Ashton and Lee (2005) have re-
cently noted that two facets of Agreeableness in the NEO-PI-R,
Straightforwardness and Modesty, have relatively weak loadings
on Agreeableness. They demonstrated that these two facets were
good markers of a factor labeled Honesty-Humility, in their six-
factor model presented as an alternative to the Big Five. This
finding suggests the possibility that, within the Big Five, Agree-
ableness might be separable into two subdomains. Perhaps, rather
than adding a sixth domain, as Ashton and Lee (2005; Ashton et
al., 2004) suggest, one could instead discriminate between two
aspects of Agreeableness at a level of personality organization
between facets and domains.
Some of the most intense debate on the Big Five has centered on
how best to characterize the fifth factor, commonly labeled either
Openness to Experience or Intellect. The compound label Open-
ness/Intellect has become increasingly popular precisely because
both labels apparently identify distinct but equally important as-
pects of the domain (DeYoung, Peterson, & Higgins, 2005; John-
son, 1994; Saucier, 1992). Johnson (1994) noted that two of the
purest representations of the Openness/Intellect domain, from a
factoring standpoint, are the Ideas and Aesthetics facets of the
NEO-PI-R. These were characterized elegantly by Johnson as
representing interests in truth and beauty, respectively, which may
begin to capture the conceptual distinction between Intellect and
Openness.
Less attention has been paid to the presence of different subdo-
mains within Neuroticism. In reviewing lexical studies of person-
ality structure, however, Saucier and Goldberg (2001) identified
anxiety/fearfulness and irritability as distinct trait clusters and
indicated that irritability does not always fall unambiguously
within the Neuroticism factor, though it is included within Neu-
roticism in the NEO-PI-R’s Angry-Hostility facet.
Jang et al.’s (2002) finding that two genetic factors underlie the
shared variance of the facets in each of the Big Five suggests that
the trend toward identifying exactly two subfactors within each of
the Big Five may represent more than mere coincidence or desire
for parsimony. The purpose of the present study was to extend the
investigation of this level of organization within the Big Five by
addressing some of the limitations of Jang et al.’s study. Most
important is the necessity of analyzing a reasonably comprehen-
sive selection of facets within each of the Big Five domains. Jang
et al. examined the covariance of the six facets within each domain
of the NEO-PI-R, but the facet structure of the NEO-PI-R was
derived theoretically, based on a review of the literature (Costa &
McCrae, 1992b), and nothing guarantees that its facets sample the
space within each domain thoroughly. In addition to the NEO-
PI-R, therefore, we used another instrument in the present study,
the AB5C-IPIP (Goldberg, 1999), whose facet level structure was
devised by an algorithm that provided more thorough coverage of
the universe of personality descriptors.
The AB5C-IPIP facets were derived from the AB5C lexical
model developed by Hofstee, de Raad, and Goldberg (1992). The
AB5C model takes advantage of the fact that almost all trait-
descriptive adjectives can be represented as a blend of two Big
Five dimensions. Each of the 10 possible pairs of Big Five dimen-
sions can therefore be used to define a circumplex, or circular
arrangement of traits, with Big Five axes at and 90°. Facets
were defined by dividing each of these 10 circumplexes with six
axes, located at 15°, 45°, 75°, etc., thus defining 12 sections of 30°
each. Adjectives falling within each section or its polar opposite
represent a facet. There are two “factor-pure” facets in each
circumplex, spanning the x- and y-axes, plus four facets that
represent a positive primary loading on one of the Big Five and a
positive or negative secondary loading on the other. Across all 10
circumplexes, 9 facets are thus defined for each of the Big Five
domains—1 factor-pure and 8 with secondary loadings. Each of
the AB5C-IPIP facets targeted the content of the adjectives in one
of the AB5C lexical facets, using short descriptive phrases, which
are more consistently interpreted than single adjectives (Goldberg,
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TEN ASPECTS OF THE BIG FIVE
1999). The AB5C-IPIP provides the most thorough facet-level
coverage of the Big Five of any instrument presently available.
Study 1 reports the factor analysis of facets within each Big Five
domain. Study 2 uses the IPIP to characterize the resulting factors
at the item level and to provide an instrument for assessing them.
Study 3 examines how similar these phenotypic factors are to the
genetic factors reported by Jang et al. (2002).
Study 1
We investigated the number of factors present within the facets
of two major Big Five personality questionnaires, which provided
a total of 15 facets for each domain. The NEO-PI-R was used
because it is the most widely used measure of the Big Five and it
facilitated comparisons with Jang et al.’s (2002) genetic findings.
The AB5C-IPIP was used to achieve more thorough coverage of
facet-level traits than would be provided by the NEO-PI-R alone.
Our hypothesis was that the most likely result for each domain was
a two-factor solution.
Method
Participants. Participants were 481 members of the Eugene-
Springfield community sample (ESCS; 200 men and 281 women),
ranging in age from 20 to 85 years (M 52.51, SD 12.63), who
completed both the NEO-PI-R and AB5C-IPIP. They were re-
cruited by mail from lists of homeowners and agreed to complete
questionnaires, delivered by mail, for pay, over a period of many
years, beginning in 1994. The sample spanned all levels of edu-
cational attainment, with an average of 2 years of postsecondary
schooling. Most participants identified as White (97%), and 1% or
less (for each category) identified as Hispanic, Asian American,
Native American, or did not report their ethnicity.
Measures. The NEO-PI-R (Costa & McCrae, 1992b) contains
240 5-point Likert scale items and breaks each of the Big Five
down into six facets, each assessed by eight items. Costa and
McCrae (1992b) list internal reliabilities for the facet scales rang-
ing from .62 to .82. Similar reliabilities were obtained in the
present sample. The NEO-PI-R was administered to the ESCS in
the summer of 1994.
The AB5C-IPIP (Goldberg, 1999) contains 485 5-point Likert
scale items and breaks each of the Big Five down into nine facets,
each assessed by 9 –13 items. The 45 AB5C-IPIP facet scales were
created on the basis of the content of the lexical AB5C facets,
using the IPIP, which was administered to the ESCS between 1994
and 1996. Internal reliabilities range from .67 to .90.
2
Analysis. Factor analyses were performed using principal-axis
factoring (also known as common factor analysis), with direct
oblimin rotation (⌬⫽0) to allow correlated factors. For the factor
analyses within each domain, the number of factors to extract was
determined using Velicer’s minimum average partial (MAP) test
(O’Connor, 2000). In the MAP test, a complete principal compo-
nents analysis is performed, after which the first principal compo-
nent is partialed out of the correlations among the variables, and
the average squared partial correlation is noted. This procedure is
repeated using the first two principal components, then the first
three, and so on. The number of factors to extract is the number of
components that resulted in the minimum average squared partial
correlation. This is the number of factors that are related to
systematic variance in the original correlation matrix.
The MAP test’s ability to identify only those factors that are
related to systematic variance in the matrix is particularly useful in
the present context because of the likelihood of redundancy among
facets across the two inventories. Two facet scales measuring the
same construct and thus having very similar content might be
correlated strongly enough to split off and form their own factor.
Such a factor would simply reiterate the existence of that specific
facet and would be uninformative for the purpose of investigating
a level of organization between facets and domains. The MAP test
would be unlikely to identify such a small factor.
Results
Before factoring the 15 facets within each domain separately,
we examined the factor structure of all 75 facets together to make
sure they conformed to the Big Five structure, as expected.
3
The
first 10 eigenvalues were 15.19, 10.47, 8.57, 6.23, 5.02, 1.74, 1.54,
1.42, 1.34, 1.24. After extracting and rotating five factors, all
facets had their highest loading on the expected factor, except for
Trust and Assertiveness from the NEO-PI-R and Reflection from
the AB5C-IPIP, and these three had strong secondary loadings on
the expected factor. (Trust loaded at .52 on Neuroticism and at
.43 on Agreeableness; Assertiveness loaded at .56 on Conscien-
tiousness and at .50 on Extraversion; Reflection loaded at .51 on
Agreeableness and at .50 on Openness/Intellect.) Thus, there ap-
pears to be no reason to exclude any facets from the analysis of
individual Big Five domains.
For the 15 facets within each Big Five domain, mere examina-
tion of the eigenvalues (see Table 1) might suggest only one large
factor. Nonetheless, the MAP test indicated two factors in each
domain (see Table 2), with one exception, Extraversion, for which
three factors were indicated. However, when Excitement Seeking
was excluded from the MAP test for Extraversion, only two factors
were indicated (see Table 2). A factor created by the presence of
a single-facet scale seems unlikely to be sufficiently broad to
represent a meaningful factor at the level between facets and
domains. Furthermore, Excitement Seeking is the best marker of
impulsivity within Extraversion (Whiteside & Lynam, 2001), and
impulsivity is likely to be relatively peripheral to Extraversion
(Depue & Collins, 1999). We therefore extracted two factors from
each of the Big Five domains. We retained Excitement Seeking in
the analysis of Extraversion facets in order to examine its loadings
in the two-factor solution. (Excluding it did not noticeably change
the solution or scores for this factor, which were correlated at .999,
with the factor scores from the analysis reported here.)
Table 3 shows the factor loadings and correlations within each
domain and provides labels that attempt to capture the essence of
each factor. An additional column at the left in Table 3 contains
codes for secondary loadings on the basis of the AB5C lexical
model (Goldberg, 1999; Hofstee et al., 1992; Johnson, 1994). Note
that these secondary loadings were not derived from the present
factor analyses, but from calculations of the AB5C model in other
samples. These codes are discussed below.
2
The AB5C-IPIP is publicly available at http://ipip.ori.org/
3
Descriptive statistics, the correlation matrix for all 75 facets, and the
factor loadings for the five-factor solution are available from Colin G.
DeYoung upon request.
882
DEYOUNG, QUILTY, AND PETERSON
Discussion
Each of the Big Five was found to contain two distinct, though
correlated, factors underlying the variance shared among 15 facet
scales. Before attempting to interpret the content of these factors,
we asked ourselves whether the presence of exactly two factors in
all five domains might simply be an artifact stemming from the
manner in which the facets of the AB5C-IPIP were constructed.
Remember that 40 of the 45 AB5C facets are defined by a positive
loading on their primary domain and either a positive or negative
secondary loading on one other domain (the other five facets are
defined by descriptors loading exclusively on their primary do-
main and are thus factor-pure). All of the positive poles of the Big
Five are socially desirable, whereas all of the negative poles are
socially undesirable (Neuroticism is reversed in the AB5C and
labeled Emotional Stability), which might lead to two-factor solu-
tions in which traits with desirable and undesirable secondary
loadings clustered separately.
In other words, our findings could be nothing but a social
desirability artifact. In order to evaluate this possibility, we exam-
ined the division of positive and negative secondary loadings
(noted in Table 3) among the two factors for each domain. Johnson
(1994) calculated the AB5C primary and secondary loadings for
the NEO-PI-R facets, so we were able to assign all 75 facets’
secondary loadings on the basis of the AB5C model. (Note that the
codes for NEO Neuroticism facets are reversed in sign in order to
maintain the association between positive secondary loadings and
social desirability across all scales.)
What is immediately clear is that the facets do not consistently
split according to the social desirability of their secondary load-
ings. All but 2 of the 10 factors are marked by facets with both
positive and negative secondary loadings. Of interest as well is
that, within Agreeableness, Neuroticism, and Openness/Intellect,
factor-pure facets serve as markers of both factors. These findings
bolster our supposition that factors within the facets of each Big
Five domain are likely to represent substantive and meaningful
distinctions in content rather than mere artifacts. (Of course, Jang
et al.’s, 2002, finding of two genetic factors within each of the Big
Five offers additional support for this position, as genes cannot be
affected by social desirability.)
Each of the Big Five can thus be said to have two aspects,
representing related but separable trait dimensions. How should
these dimensions be interpreted and labeled? The task is most
straightforward for Openness/Intellect. The long-running debate
over the interpretation of this domain has left us with obvious
choices to represent factors marked by facets like Quickness,
Ingenuity, and Ideas, on the one hand, and Aesthetics, Imagination,
and Fantasy on the other: Intellect and Openness. As other re-
searchers have noted, it appears that the two sides of this debate
were simply focusing on different aspects of the larger domain
(DeYoung et al., 2005; Johnson, 1994; Saucier, 1992). The factors
that emerged here do not merely reflect the agendas of the authors
of our two instruments, who happen to fall on opposite sides of the
Openness/Intellect debate, because two AB5C-IPIP facets are
good markers of Openness and one NEO-PI-R facet is a good
marker of Intellect.
The two aspects of Extraversion are consistent with distinctions
drawn in the literature between agency or dominance and socia-
bility. We suggest Assertiveness and Enthusiasm as labels for these
two aspects of Extraversion. While Assertiveness should be rela-
tively uncontroversial as a compromise between the more general
and abstract idea of agency and the more socially specific idea of
dominance, Enthusiasm probably needs more thorough justifica-
tion. Sociability is problematic as a descriptor of this aspect of
Extraversion because it focuses exclusively on the manner in
which this trait is manifested socially, ignoring the crucial affec-
tive component. Along with Gregariousness and Friendliness, the
Positive Emotions facet is a strong marker of this factor, and
conceptions of Extraversion often focus on the tendency to expe-
rience positive emotions associated with anticipation or enjoyment
of reward (Depue & Collins, 1999; Lucas, Diener, Grob, Suh, &
Table 1
Eigenvalues for Factor Analysis of 15 Facets in Each Big Five
Domain
Factor N A C E O
1 7.70 6.65 7.57 6.59 6.57
2 1.44 1.81 1.27 1.84 1.97
3 1.10 1.20 1.20 1.44 1.15
4 0.87 1.01 0.83 1.09 0.96
5 0.80 0.61 0.71 0.91 0.68
6 0.55 0.57 0.65 0.64 0.62
7 0.44 0.54 0.53 0.42 0.59
8 0.39 0.46 0.48 0.39 0.51
9 0.35 0.43 0.38 0.35 0.40
10 0.31 0.43 0.33 0.28 0.36
11 0.28 0.35 0.26 0.27 0.32
12 0.25 0.30 0.24 0.23 0.25
13 0.20 0.24 0.20 0.20 0.23
14 0.18 0.20 0.19 0.17 0.22
15 0.17 0.20 0.15 0.16 0.18
Note. N 481. Principal-axis factoring. N Neuroticism; A Agree-
ableness; C Conscientiousness; E Extraversion; O Openness/
Intellect.
Table 2
MAP Test for Facets in Each Big Five Domain
Component N A C E O
0 .238 .175 .229 .177 (.196) .173
1 .041 .044 .035 .056 (.062) .051
2 .031 .028 .034 .048 (.049) .027
3 .040 .031 .039 .0448 (.052) .032
4 .048 .035 .043 .0451 (.051) .039
5 .052 .046 .049 .050 (.060) .050
6 .064 .060 .058 .065 (.074) .059
7 .081 .082 .081 .086 (.090) .091
8 .100 .108 .113 .100 (.119) .116
9 .135 .148 .134 .128 (.158) .137
10 .188 .179 .171 .168 (.210) .181
11 .262 .226 .226 .218 (.291) .217
12 .393 .318 .305 .294 (.466) .316
13 .574 .502 .526 .465 (1.000) .488
14 1.000 1.000 1.000 1.000 1.000
Note. Numbers in parentheses are based on calculations excluding NEO
Excitement Seeking. The lowest average square partial correlation for each
domain is in bold. N Neuroticism; A Agreeableness; C Consci-
entiousness; E Extraversion; O Openness/Intellect.
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TEN ASPECTS OF THE BIG FIVE
Table 3
Two-Factor Solutions for Each Big Five Domain
Secondary
loading
code Facet and instrument
Neuroticism Secondary
loading
code Facet and instrument
Extraversion
Volatility Withdrawal Enthusiasm Assertiveness
P Stability (AB5C) .86 .69
II Calmness (AB5C) .81 .53
II Angry hostility (NEO) .76 .54
V Tranquility (AB5C) .75 .52
I Impulse control (AB5C) .70 .32
III Moderation (AB5C) .70 .63
I Impulsiveness (NEO) .59 .43
II Imperturbability (AB5C) .54 .45
III Cool-headedness (AB5C) .30 .27
I Happiness (AB5C) .64 .88
III Depression (NEO) .57 .85
III Vulnerability (NEO) .59 .78
P Anxiety (NEO) .57 .78
V Toughness (AB5C) .66 .77
P Self-consciousness (NEO) .41 .76
Factor correlation .64
Agreeableness
Compassion Politeness
I Warmth (AB5C) .87 .45
III Sympathy (AB5C) .86 .46
P Understanding (AB5C) .83 .52
V Empathy (AB5C) .69 .36
I Altruism (NEO) .65 .64
IV Tenderness (AB5C) .65 .28
I Tender-mindedness (NEO) .50 .42
IV Trust (NEO) .42 .42
V Nurturance (AB5C) .63 .80
I Cooperation (AB5C) .37 .74
IV Pleasantness (AB5C) .60 .72
P Compliance (NEO) .35 .71
III Morality (AB5C) .40 .67
III Straightforwardness (NEO) .30 .67
IV Modesty (NEO) .22 .44
Factor correlation .54
Conscientiousness
Industriousness Orderliness
IV Purposefulness (AB5C) .86 .57
I Efficiency (AB5C) .84 .61
IV Self-discipline (NEO) .83 .55
IV Competence (NEO) .75 .34
V Organization (AB5C) .74 .54
I Achievement striving
(NEO)
.65 .42
II Dutifulness (NEO) .63 .50
I Deliberation (NEO) .55 .43
II Dutifulness (AB5C) .52 .49
V Orderliness (AB5C) .54 .87
P Conscientiousness (AB5C) .78 .79
V Order (NEO) .61 .79
IV Perfectionism (AB5C) .42 .67
II Rationality (AB5C) .60 .60
I Cautiousness (AB5C) .44 .46
Factor correlation .64
Note. N 481. Principal-axis factoring with direct oblimin rotation. AB5C Abridged Big Five Circumplex Scales from the International Personality
Item Pool; I Extraversion; II Agreeableness; III Conscientiousness; IV Emotional Stability; V Openness/Intellect; P factor-pure; see text
for discussion of these codes.
II Friendliness (AB5C) .88 .48
II Warmth (NEO) .79 .35
P Gregariousness (AB5C) .77 .71
IV Poise (AB5C) .71 .57
IV Gregariousness (NEO) .71 .32
P Positive emotions (NEO) .67 .43
III Self-disclosure (AB5C) .60 .48
V Sociability (AB5C) .48 .21
V Leadership (AB5C) .56 .85
III Assertiveness (AB5C) .42 .83
III Assertiveness (NEO) .40 .72
II Provocativeness (AB5C) .25 .71
III Activity (NEO) .38 .59
IV Talkativeness (AB5C) .39 .58
II Excitement seeking (NEO) .27 .27
Factor correlation .53
Openness/Intellect
Intellect Openness
IV Quickness (AB5C) .86 .38
II Creativity (AB5C) .85 .48
P Intellect (AB5C) .81 .57
P Ideas (NEO) .76 .57
I Ingenuity (AB5C) .73 .45
III Competence (AB5C) .71 .20
IV Depth (AB5C) .55 .52
I Introspection (AB5C) .41 .31
P Aesthetics (NEO) .33 .87
III Imagination (AB5C) .50 .85
II Reflection (AB5C) .27 .73
III Fantasy (NEO) .45 .64
I Feelings (NEO) .34 .58
I Actions (NEO) .36 .54
III Values (NEO) .32 .44
Factor correlation .51
884
DEYOUNG, QUILTY, AND PETERSON
Shao, 2000; Watson & Clark, 1997). Social interaction is often
rewarding, which appears to provide the motivation for the socia-
bility associated with Extraversion (Lucas & Diener, 2001). En-
thusiasm is a good label for this factor because it is broad enough
to describe both positive emotion and outgoing friendliness or
sociability. John (1990) demonstrated that enthusiastic is an ex-
cellent descriptor of prototypical Extraversion.
Our two Conscientiousness factors are nearly identical to factors
found in the same sample by Roberts et al. (2005), in their analysis
of scales conceptually related to Conscientiousness from seven
different instruments.
4
We have therefore elected to use labels very
similar to theirs, Industriousness and Orderliness. Orderliness
seems preferable to their term “Order” because the former de-
scribes a tendency of the individual, whereas the latter describes an
outcome of behavior or some other ordering process.
The two aspects of Agreeableness appear to distinguish between
compassionate emotional affiliation with others (e.g., Warmth, Sym-
pathy, Tenderness) and a more reasoned (or at least cognitively
influenced) consideration of and respect for others’ needs and desires
(e.g., Cooperation, Compliance, Straightforwardness). We therefore
suggest Compassion and Politeness as labels for these factors. Polite-
ness appears similar to Ashton and Lee’s (2005; Ashton et al., 2004)
Honesty-Humility factor, as both are marked by the NEO-PI-R facets
Straightforwardness and Modesty. Given that AB5C-IPIP facets like
Morality and Compliance also mark this factor, Ashton and Lee’s
(2005) assertion that the NEO-PI-R is unlike other Big Five measures,
in containing content that could be included in their Honesty-Humility
factor, may be unfounded.
The two factors within Neuroticism, which we labeled Volatility
and Withdrawal, are consistent not only with the lexical division
noted by Saucier and Goldberg (2001) between irritability and anxi-
ety/fearfulness but also with a tradition that distinguishes between
externalizing and internalizing problems (Achenbach & Edelbrock,
1978, 1984; Krueger, 1999). Facets like Stability (reversed), Angry
Hostility, and Impulsiveness imply problems of disinhibition, leading
to the outward expression of negative affect, whereas facets like
Depression, Vulnerability, and Anxiety imply problems of inhibition,
negative affect directed inward. We chose the label Volatility because
it seems broad enough to encompass emotional lability, irritability or
anger, and difficulty controlling emotional impulses. The second
factor appears to reflect susceptibility to a class of negative affect that
has commonly been described as withdrawal (Davidson, 2001). The
label Happiness, for the facet of the AB5C-IPIP that (reversed in sign)
is the strongest marker of the Withdrawal factor, is potentially mis-
leading because its items emphasize negative affect (“Seldom feel
blue,” “Feel threatened easily”) rather than positive affect.
Choosing suitable labels for each factor obviously depends
heavily on interpretation of the factors’ content, which can be
difficult when based merely on facet labels. Furthermore, inter-
preting factors that are fairly strongly correlated poses an addi-
tional challenge, as many facets load strongly on both factors. We
therefore defer further justification of our interpretations until
Study 2, in which we examine individual items that best mark each
of the 10 aspect factors.
Study 2
The IPIP contains over 2,000 public domain items that have
been administered to the ESCS, on which we performed our
analysis in Study 1. It is thus uniquely well suited to the empirical
characterization of factor content at the item level. We examined
correlations between scores for the 10 aspect factors presented in
Table 3 and every IPIP item.
In addition to allowing more precise characterization of the
aspect factors, this undertaking had the advantage of allowing the
creation of an instrument to measure the 10 aspects of the Big Five.
Such an instrument would allow the aspects to be assessed in other
samples without having to administer two very long questionnaires
and perform multiple factor analyses. Given that the NEO-PI-R is
widely used, another strategy, especially for existing data, would
be to use the factor loadings presented in Table 3 to identify NEO
facets or combinations of facets that are good markers for each
aspect. One limitation of this strategy, however, is that no good
markers for Compassion appear in the NEO-PI-R. Two of the
NEO-PI-R Agreeableness facets (Altruism and Tender-
Mindedness) load strongly on Compassion, but they load almost
equally on Politeness. They are good markers, therefore, of Agree-
ableness as a whole, but they cannot discriminate Compassion
from Politeness. Additionally, administration of the NEO-PI-R is
costly and time-consuming, and a shorter instrument designed
specifically to assess the 10 aspects of the Big Five might be
preferable in many situations. We therefore took advantage of the
IPIP to develop such an instrument, the Big Five Aspect Scales
(BFAS).
4
A question raised by differences between Roberts et al.’s (2005) results
and ours is why they found six factors, whereas we found only two. The
statistical answer is that we used only two of the seven instruments that
they used, and, even in their study, all but two of the scales from these two
instruments fell within two factors. Of course, the real question is whether
the NEO-PI-R and AB5C-IPIP neglect some facets of Conscientiousness.
We suspect not. Rather, it appears that Roberts et al.’s (2005) additional
factors are best viewed as compound traits, stemming from the conjunction
of Conscientiousness with other traits, rather than as aspects or facets of
Conscientiousness itself. Roberts et al.’s Self-Control factor is marked by
two scales from the Hogan Personality Inventory (HPI; Hogan & Hogan,
1992), Impulse Control and Not Spontaneous, that have their primary
loading on Extraversion rather than on Conscientiousness in the AB5C
model (Johnson, 1994). Similarly, their Virtue factor is marked by two HPI
scales, Moralistic and Virtuous, that do not have their primary or secondary
AB5C loadings on Conscientiousness (Johnson, 1994). (This situation
highlights one pitfall of personality research: The fact that a scale has been
conceptually located in one of the Big Five domains may not be the best
guide to determine whether it is statistically located in that domain.)
Traditionalism and Responsibility also seem likely to be compound traits
(though AB5C codes have not been calculated for all of the scales that
mark them). Traditionalism appears to indicate conformity with moral
norms, which we (DeYoung et al., 2002) have demonstrated can best be
located within the Big Five hierarchy at the metatrait level, as a compound
trait resulting from the combination of high Stability (the shared variance
of Emotional Stability, Conscientiousness, and Agreeableness) and low
Plasticity (the shared variance of Extraversion and Openness/Intellect).
Roberts et al. described Responsibility as reflecting enjoyment of cooper-
ation and being of service to others, which suggests Agreeableness as much
if not more than Conscientiousness. We conclude that additional
Conscientiousness-related factors beyond Industriousness and Orderliness
do not appear best described as lower order traits within the domain of
Conscientiousness, though they are interesting constructs in their own right
and may be useful in the prediction of behavior.
885
TEN ASPECTS OF THE BIG FIVE
Following selection of items that were good markers of each
aspect in the ESCS, these items were administered to a large
university sample. Once the final items were selected on the basis
of their psychometric properties in the university sample, we were
able to examine the reliability and validity of the instrument in
both samples.
Method
Initial item selection. Factor scores for each of the 10 factors
presented in Table 3 were calculated using the regression method.
These scores were then correlated with all of the IPIP items. As an
initial item pool, we chose 15 items showing the highest correla-
tions with each factor, excluding those that seemed overly redun-
dant and making sure to include roughly equal numbers of posi-
tively and negatively keyed items. In order to provide adequate
discrimination between the two aspects in each domain, and to
prevent excessive cross-loadings on other domains, we excluded
items that showed a correlation with another factor within .10 of
the primary correlation. For example, if the strongest correlation
for a particular item was .58 with Compassion, then we would
exclude it if its correlation with Politeness or any of the other eight
aspect factors was .48 or greater.
5
Having selected 150 IPIP items to mark the 10 aspects, we
administered them to a large undergraduate sample, intending to
choose 10 items to measure each aspect, based on their psycho-
metric properties in the new sample, for a total of 100 items. Prior
to administration, we changed the wording for three of the nega-
tively keyed items selected for Politeness, in order to reverse their
keying direction, because only two positively keyed items in the
IPIP met our selection criteria for this aspect (see Table 4).
Additionally, we added a new item, “Am not a very enthusiastic
person,” to test our hypothesis that Enthusiasm is a good label for
this aspect of Extraversion.
Participants and measures. Participants were 480 undergradu-
ates in southern Ontario (299 women and 180 men; 1 with no gender
reported), enrolled at the University of Toronto, Toronto, Ontario,
Canada, or the University of Waterloo, Waterloo, Ontario, Canada.
They ranged in age from 17 to 61 years (M 19.32, SD 3.33) and
came from diverse ethnic backgrounds (45% White; 34% East Asian;
9% South Asian; 3% Black; 3% Middle Eastern; 1% Hispanic; 5%
unknown). All participants received course credit for completing the
study. The potential BFAS items and the Big Five Inventory (BFI;
John & Srivastava, 1999), were completed via the Web, using Likert
scales ranging from 1 to 5. The BFI, which was completed by 472
participants, is an excellent short measure of the Big Five and thus
makes a good benchmark against which to validate new Big Five
scales. (Additionally, 423 of our ESCS participants also completed
the BFI, allowing comparison across samples.)
Approximately 1 month following their completion of the study,
participants were contacted by e-mail and asked to complete the
BFAS items again via the Web in order to obtain an index of
test–retest reliability. Ninety participants completed the retest, and
the average number of days between first and second completion
of the BFAS was 38.44 (SD 10.71).
Results
Final item selection. Principal-axis factoring with direct ob-
limin rotation (⌬⫽0) was used to extract two factors from the
items in each of the Big Five domains. In order to reduce col-
linearity in the final scales, items were included only if their
loading on the intended aspect factor was at least .10 greater than
on the other aspect factor. This criterion was used to exclude 20
items, but it was relaxed for 5 other items in order to maintain
balanced keying. No scale was allowed a ratio of positively to
negatively keyed items (or vice versa) greater than 6/4. Addition-
ally, a five-factor solution was extracted from all items across all
five domains, and items were excluded if they did not have their
highest loading on the intended Big Five domain; 14 items were
excluded by this criterion.
6
Table 4 shows the 10 final items for each of the 10 scales. Right
columns in Table 4 show the correlation of each item with the
relevant factor score from the ESCS in Study 1 and the factor
loading of each item on the relevant aspect factor in the university
sample from Study 2. Items were averaged (with appropriate
reversals) to create scale scores for each aspect, and these scores
were averaged across the two aspects in each domain to create Big
Five domain scores. Thus, in addition to 10-item scales for the 10
aspects, the BFAS includes 20-item scales for the Big Five.
Reliability and validity of the BFAS. Table 5 provides descrip-
tive statistics for the BFAS, including Cronbach’s alpha for the
ESCS (M 0.83, SD 0.03), the initial university sample (M
0.81, SD 0.05), and the retest university sample (M 0.83,
SD 0.05). (There were no significant differences in BFI or
BFAS scores between those who completed the retest and those
who did not, nor did scores change significantly from test to
retest.) Correlations between scale scores and factor scores from
Study 1 are given for the ESCS (M 0.89, SD 0.02), and
test–retest correlations are given for the university sample (M
0.81, SD 0.04). Table 6 contains correlations between all BFI
and BFAS scales. Correlations between the same Big Five do-
mains across scales (in bold italics) were high; when corrected for
attenuation, based on reliability, they ranged from .85 to .96 (M
0.90, SD 0.05) for the university sample and from .72 to .91
(M 0.84, SD 0.07) for the ESCS. Table 6 also reveals that
patterns of correlation among the Big Five within each instrument
5
One effect of this selection procedure was to exclude items that appear
most central to each of the Big Five domains because they are related
strongly but almost equally to both aspects. These items are potentially
informative conceptually. For example, the item “Have a vivid imagina-
tion” was associated almost equally with Intellect and Openness, support-
ing Saucier’s (1992) suggestion of Imagination as an alternative label for
the Openness/Intellect domain. The argument that unconventionality is
also important to this domain (de Raad, Perugini, Hrebickova, & Szarota,
1998) finds some support in the excluded item “Like to be viewed as
proper and conventional.” Other insights from these excluded items include
the fact that the talkativeness associated with Extraversion is characteristic
of both Enthusiasm and Assertiveness (“Usually like to talk a lot”; “Have
little to say”) and that susceptibility to stress and negative emotions appears
common to both Volatility and Withdrawal (“Get stressed out easily”; “Am
often in a bad mood”).
6
For example, the item “Tend to vote for liberal political candidates”
was a clear marker of Openness in the ESCS but had its strongest load-
ing—negatively— on Conscientiousness in the university sample. This
finding is not particularly surprising, as Goldberg and Rosolack (1994)
found that conservatives were low in Openness/Intellect but high in Con-
scientiousness, but it does suggest that this item is not a good specific
marker of Openness.
886
DEYOUNG, QUILTY, AND PETERSON
Table 4
The Big Five Aspect Scales
Scale
r with factor
score (ESCS)
Factor loading
(University)
Neuroticism
Volatility
Get angry easily. .67 .75
Rarely get irritated. (R) .64 .64
Get upset easily. .68 .75
Keep my emotions under control. (R) .55 .51
Change my mood a lot. .59 .63
Rarely lose my composure. (R) .54 .39
Am a person whose moods go up and down
easily.
.56 .71
Am not easily annoyed. (R) .54 .57
Get easily agitated. .56 .75
Can be stirred up easily. .56 .70
Withdrawal
Seldom feel blue. (R) .65 .41
Am filled with doubts about things. .64 .65
Feel comfortable with myself. (R) .63 .47
Feel threatened easily. .62 .62
Rarely feel depressed. (R) .56 .51
Worry about things. .60 .58
Am easily discouraged. .58 .65
Am not embarrassed easily. (R) .44 .42
Become overwhelmed by events. .57 .57
Am afraid of many things. .54 .63
Agreeableness
Compassion
Am not interested in other people’s problems. (R) .62 .50
Feel others’ emotions. .66 .60
Inquire about others’ well-being. .62 .64
Can’t be bothered with other’s needs. (R) .58 .65
Sympathize with others’ feelings. .62 .72
Am indifferent to the feelings of others. (R) .57 .51
Take no time for others. (R) .48 .59
Take an interest in other people’s lives. .61 .70
Don’t have a soft side. (R) .42 .47
Like to do things for others. .57 .60
Politeness
Respect authority. .43 .33
Insult people. (R) .55 .58
Hate to seem pushy. .42 .30
Believe that I am better than others. (R) .49 .51
Avoid imposing my will on others.
a
.49 .42
Rarely put people under pressure.
a
.48 .48
Take advantage of others. (R) .48 .69
Seek conflict. (R) .48 .52
Love a good fight. (R) .48 .54
Am out for my own personal gain. (R) .46 .50
Conscientiousness
Industriousness
Carry out my plans. .59 .54
Waste my time. (R) .60 .62
Find it difficult to get down to work. (R) .56 .64
Mess things up. (R) .55 .54
Finish what I start. .52 .54
Don’t put my mind on the task at hand. (R) .54 .45
Get things done quickly. .49 .46
Always know what I am doing. .49 .49
Postpone decisions. (R) .53 .51
Am easily distracted. (R) .52 .53
(table continues)
887
TEN ASPECTS OF THE BIG FIVE
Table 4 (continued)
Scale
r with factor
score (ESCS)
Factor loading
(University)
Orderliness
Leave my belongings around. (R) .63 .47
Like order. .63 .56
Keep things tidy. .61 .60
Follow a schedule. .54 .54
Am not bothered by messy people. (R) .51 .26
Want everything to be “just right.” .53 .56
Am not bothered by disorder. (R) .48 .31
Dislike routine. (R) .48 .41
See that rules are observed. .47 .45
Want every detail taken care of. .47 .52
Extraversion
Enthusiasm
Make friends easily. .70 .60
Am hard to get to know. (R) .68 .61
Keep others at a distance. (R) .63 .61
Reveal little about myself. (R) .56 .46
Warm up quickly to others. .65 .66
Rarely get caught up in the excitement. (R) .45 .44
Am not a very enthusiastic person.
b
(R)
.56
Show my feelings when I’m happy. .54 .46
Have a lot of fun. .48 .63
Laugh a lot. .43 .62
Assertiveness
Take charge. .67 .71
Have a strong personality. .65 .69
Lack the talent for influencing people. (R) .57 .57
Know how to captivate people. .58 .53
Wait for others to lead the way. (R) .56 .62
See myself as a good leader. .57 .69
Can talk others into doing things. .56 .47
Hold back my opinions. (R) .48 .52
Am the first to act. .53 .63
Do not have an assertive personality.
a
(R)
.69 .61
Openness/Intellect
Intellect
Am quick to understand things. .65 .65
Have difficulty understanding abstract ideas. (R) .68 .55
Can handle a lot of information. .64 .65
Like to solve complex problems. .61 .51
Avoid philosophical discussions. (R) .61 .45
Avoid difficult reading material. (R) .58 .39
Have a rich vocabulary. .61 .48
Think quickly. .57 .65
Learn things slowly. (R) .48 .55
Formulate ideas clearly. .56 .60
Openness
Enjoy the beauty of nature. .43 .47
Believe in the importance of art. .66 .64
Love to reflect on things. .42 .48
Get deeply immersed in music. .60 .44
Do not like poetry. (R) .60 .51
See beauty in things that others might not notice. .52 .47
Need a creative outlet. .48 .40
Seldom get lost in thought. (R) .40 .40
Seldom daydream. (R) .38 .35
Seldom notice the emotional aspects of paintings and pictures. (R) .60 .47
Note. Items from all 10 scales should be interspersed for administration, and 5-point Likert scales should be
used for responses. (R) indicates items to be reverse scored; ESCS Eugene-Springfield community sample.
a
These items were keyed in the opposite direction for the ESCS.
b
This item is new; it was not included in the International Personality Item Pool or administered to the ESCS.
888
DEYOUNG, QUILTY, AND PETERSON
(in bold) are similar, offering further support for similarity of
measurement across instruments.
In the ESCS, we were additionally able to validate the BFAS
against NEO-PI-R domain scores and Saucier’s (1994) Mini-
Markers, a well-validated adjective marker set for the lexical Big
Five, which participants completed at the same time as the BFI
(see Table 7). High correlations between the same Big Five do-
mains across scales (in bold) provide an additional demonstration
that the BFAS is measuring the standard Big Five. When corrected
for attenuation, these correlations ranged from .80 to .92 (M
0.88, SD 0.05) for the NEO-PI-R and from .80 to .85 (M 0.82,
SD 0.02) for the Mini-Markers.
Discriminant validity and an example of suppression. Given
the fairly strong correlations between the two aspect factors in
each domain, one important question is: To what degree do the two
aspects of each domain possess discriminant validity? If the two
aspects within each Big Five domain are indeed distinct traits, then
they should not show overly similar patterns of correlation with
other variables. Table 6 confirms that they do not, for all five
aspect pairs. The differential associations of the aspect pairs of
Extraversion and Agreeableness provide one clear example:
Whereas Assertiveness is negatively correlated with Politeness,
Enthusiasm is positively correlated with Politeness.
Because each pair of aspects is positively correlated, assessing
discriminant validity can be more complicated than simply looking
for divergent patterns of zero-order correlations. Being positively
correlated and presumably sharing some of the same sources, the
two aspects in each domain should predict many variables simi-
larly. Furthermore, whenever they do not predict some variable
similarly, they may act as suppressors on each other. When two
positively correlated variables are related to a third variable in
opposite directions, one or both of their associations with the third
variable may be suppressed (Paulhus, Robins, Trzesniewski, &
Tracy, 2004). Multiple regression or partial correlation may then
be necessary to control for the positive association between the
first two variables in order to examine the unique associations of
their nonshared variance with the third variable. (Although the
correlations between aspects are fairly strong, none of them reach
the threshold [r .9] at which multicollinearity typically becomes
a problem for such analyses; Tabachnick & Fidell, 2001).
As one example of suppression, consider the associations of the
aspects of Conscientiousness with BFI Neuroticism (see Table 6).
In previous research, the negative correlation between Conscien-
tiousness and Neuroticism has proved to be one of the most robust
cross-domain correlations among the Big Five (Mount, Barrick,
Scullen, & Rounds, 2005). Using the BFAS, however, one can see
that this correlation holds only for Industriousness. Orderliness is
almost uncorrelated with Neuroticism. Not only that, but when one
controls for Industriousness, Orderliness is significantly positively
correlated with Neuroticism, in both the university sample and the
ESCS (University: partial r .24, p .01; ESCS: partial r .20,
p .01). Thus, the negative association between Industriousness
and Neuroticism was suppressing a positive association between
Orderliness and Neuroticism.
Correlations among the aspects. Patterns of correlation
among the aspect-level traits (bottom right corner of Table 6) are
more varied than correlations among domains, and stronger cross-
domain correlations appear at the aspect level than at the Big Five
level. In several cases, correlations between two aspects across two
domains are at least as strong as correlations between the two
aspects within each of those two domains. This is true of the
correlations between Intellect and Industriousness and between
Intellect and Assertiveness. (In fact, Intellect, Industriousness, and
Assertiveness form a cluster of related scales from three different
domains.) Could this finding be a product of our final item selec-
tion procedure, which intentionally reduced correlations between
aspects within the same domain, by choosing items that discrim-
inated well between the two aspects? This explanation seems
Table 5
Descriptive Statistics for the BFAS in Two Samples
Factor
ESCS University
MSD r
a
MSD
1
2
r
b
Neuroticism 2.46 0.63 .89 2.82 0.70 .89 .89 .85
Volatility 2.48 0.70 .85 .90 2.72 0.82 .87 .89 .85
Withdrawal 2.45 0.71 .84 .91 2.92 0.75 .81 .80 .81
Agreeableness 4.11 0.45 .84 3.70 0.56 .85 .89 .79
Compassion 4.11 0.54 .84 .90 3.87 0.65 .84 .91 .79
Politeness 4.10 0.53 .75 .85 3.52 0.67 .76 .76 .74
Conscientiousness 3.76 0.51 .84 3.06 0.56 .81 .82 .86
Industriousness 3.80 0.61 .81 .87 2.84 0.70 .79 .82 .82
Orderliness 3.73 0.62 .80 .89 3.28 0.64 .72 .74 .79
Extraversion 3.48 0.60 .85 3.37 0.63 .88 .86 .83
Enthusiasm 3.59 0.72 .81 .88 3.52 0.73 .81 .80 .73
Assertiveness 3.36 0.70 .85 .88 3.21 0.71 .84 .88 .86
Openness/Intellect 3.72 0.53 .85 3.47 0.52 .80 .82 .82
Intellect 3.70 0.68 .84 .93 3.39 0.67 .79 .81 .86
Openness 3.74 0.61 .78 .88 3.52 0.64 .72 .77 .79
Note. BFAS Big Five Aspect Scales; ESCS Eugene-Springfield Community Sample;
1
internal reliability in original sample (N 480);
2
internal reliability in retest sample (N 90).
a
Correlation with factor scores from Study 1, Table 3.
b
Test–retest correlation.
889
TEN ASPECTS OF THE BIG FIVE
Table 6
Correlations Between the BFI and the BFAS in Two Samples
Domain
BFI BFAS 10 aspects
NACEONACEON
V
N
W
A
C
A
P
C
I
C
O
E
E
E
A
O
I
O
O
N (BFI) .38 .27 .18 .11 .75 .13 .14 .26 .04 .67 .67 .06 .17 .30 .06 .27 .16 .15 .10
A (BFI) .24 .25 .10 .03 .38 .59 .13 .21 .00 .44 .24 .45 .55 .17 .06 .38 .04 .02 .02
C (BFI) .24 .38 .25 .15 .29 .18 .71 .33 .13 .19 .32 .15 .16 .65 .54 .20 .34 .27 .07
E (BFI) .33 .15 .18 .29 .14 .07 .21 .76 .17 .05 .30 .22 .11 .24 .12 .60 .67 .22 .06
O (BFI) .13 .11 .11 .26 .14 .10 .01 .35 .77 .06 .20 .25 .09 .10 .11 .16 .42 .64 .64
N (BFAS) .80 .34 .33 .26 .15 .20 .22 .32 .12 .89 .89 .09 .25 .41 .04 .28 .27 .26 .07
A (BFAS) .01 .68 .36 .06 .09 .14 .18 .13 .12 .25 .10 .85 .84 .18 .12 .33 .11 .01 .23
C (BFAS) .15 .24 .77 .08 .04 .25 .22 .25 .01 .13 .26 .11 .20 .83 .84 .14 .29 .12 .14
E (BFAS) .36 .31 .33 .78 .34 .33 .23 .24 .34 .10 .47 .32 .10 .35 .07 .85 .84 .40 .15
O (BFAS) .21 .17 .31 .22 .67 .20 .28 .19 .37 .04 .18 .27 .07 .14 .15 .19 .38 .85 .81
Volatility (N
V
)
.67 .40 .25 .10 .08 .90 .24 .17 .16 .15 .59 .09 .34 .28 .06 .12 .06 .14 .07
Withdrawal (N
W
)
.76 .20 .34 .38 .19 .88 .00 .29 .44 .21 .59 .08 .10 .46 .01 .38 .43 .32 .05
Compassion (A
C
)
.02 .54 .32 .22 .19 .03 .84 .18 .40 .40 .07 .02 .43 .13 .05 .44 .10 .11 .35
Politeness (A
P
)
.04 .62 .28 .12 .04 .20 .86 .20 .00 .08 .32 .02 .45 .18 .17 .12 .29 .14 .03
Industriousness (C
I
)
.32 .25 .72 .17 .04 .42 .17 .84 .31 .23 .28 .49 .12 .16 .39 .21 .39 .31 .09
Orderliness (C
O
)
.09 .14 .55 .05 .11 .02 .20 .81 .07 .07 .01 .03 .17 .17 .38 .03 .09 .10 .15
Enthusiasm (E
E
)
.27 .42 .24 .69 .20 .25 .36 .16 .88 .22 .15 .31 .46 .15 .20 .06 .43 .18 .13
Assertiveness (E
A
)
.36 .11 .34 .68 .39 .33 .04 .25 .87 .44 .13 .46 .22 .15 .34 .06 .52 .49 .13
Intellect (O
I
)
.37 .10 .39 .25 .46 .37 .15 .31 .42 .82 .25 .41 .24 .01 .40 .10 .21 .52 .37
Openness (O
O
)
.03 .17 .09 .09 .62 .06 .33 .02 .18 .80 .02 .08 .42 .14 .04 .01 .14 .17 .33
Note. The university sample is below the diagonal; the Eugene-Springfield community sample is above. Validity coefficients across instruments are in bold italics. Correlations among the Big Five
within instrument are in bold; BFI Big Five Inventory; BFAS Big Five Aspect Scales; N Neuroticism; A Agreeableness; C Conscientiousness; E Extraversion; O Openness/Intellect;
subscript letters represent the first letter of the aspect.
890
DEYOUNG, QUILTY, AND PETERSON
unlikely because correlations among the factor scores for the
aspects from Study 1 (see Table 8) demonstrate that this pattern is
not merely an artifact of our scale construction technique. Even in
the factor scores, Intellect is correlated almost equally with Open-
ness and Assertiveness, and Industriousness shows sizable corre-
lations with both Intellect and Assertiveness. Compassion and
Enthusiasm are another cross-domain pair that show strong corre-
lations in Tables 6 and 8.
Despite these patterns of cross-domain correlation, the Big Five
are readily recoverable from the aspects by factor analysis. Tables
9 and 10 show eigenvalues and five-factor solutions for factor
scores and scale scores in the ESCS and for scale scores in the
university sample.
Discussion
The items selected for the BFAS (see Table 4), which were
among the best markers for each of the 10 factors, offer additional
support for the interpretations of these factors that we offered in
Study 1. Compassion, for example, is clearly marked by the
tendency to affiliate with others emotionally and to take interest in
others’ emotions, whereas Politeness contrasts the tendency to
respect others with the tendency to pursue one’s own desires at the
expense of others, even to the point of belligerence. Consistent
with the apparent similarity between Politeness and Ashton and
Lee’s (2005; Ashton et al., 2004) Honesty-Humility factor, hints of
narcissism and Machiavellianism can be detected in Politeness
items like “Believe that I am better than others” and “Take advan-
tage of others” (Lee & Ashton, 2005). As hypothesized, the new
item, “Am not a very enthusiastic person,” was a good marker of
the factor we labeled Enthusiasm.
The BFAS appears to provide excellent representations of the
two factors underlying the shared variance of the facets in each
domain. Additionally, averaging the two aspects in each domain
provides good representations of the Big Five. The strong demo-
graphic differences between the two samples used to construct the
BFAS suggest that this instrument is likely to be valid in a wide
variety of English-speaking populations. (One sample was a
largely middle-aged, American, community sample, almost en-
tirely White; the other was an ethnically diverse sample of young
adults enrolled in two Canadian universities.) Although the BFAS
might be improved upon psychometrically by using item response
theory or by developing additional new items specifically targeting
Table 7
Correlation of the BFAS With NEO-PI-R and Mini-Markers Big Five Domain Scores
Domain
NEO-PI-R Mini-Markers
NACEONACEO
N (BFAS) .84 .30 .36 .25 .07 .69 .22 .20 .17 .09
A (BFAS) .13 .69 .13 .12 .17 .32 .66 .14 .04 .00
C (BFAS) .25 .11 .77 .21 .16 .09 .08 .72 .18 .02
E (BFAS) .36 .03 .23 .78 .33 .17 .27 .25 .69 .28
O (BFAS) .11 .04 .06 .28 .78 .07 .17 .12 .15 .71
Volatility .68 .37 .27 .07 .01 .68 .26 .12 .03 .03
Withdrawal .81 .16 .37 .38 .12 .56 .14 .24 .33 .13
Compassion .04 .50 .06 .31 .34 .19 .63 .11 .19 .17
Politeness .18 .67 .15 .11 .05 .35 .48 .13 .13 .17
Industriousness .45 .11 .72 .29 .01 .24 .11 .59 .22 .13
Orderliness .03 .07 .56 .06 .25 .09 .02 .61 .07 .08
Enthusiasm .29 .25 .10 .68 .24 .24 .43 .15 .56 .08
Assertiveness .32 .21 .30 .64 .32 .05 .02 .28 .59 .39
Intellect .25 .14 .22 .30 .56 .15 .07 .23 .20 .64
Openness .09 .08 .15 .15 .73 .05 .22 .05 .03 .52
Note. Validity coefficients are in bold. BFAS Big Five Aspect Scales; NEO-PI-R Revised NEO Personality Inventory; N Neuroticism; A
Agreeableness; C Conscientiousness; E Extraversion; O Openness/Intellect.
Table 8
Correlations Among the Factor Scores for the 10 Aspects From Study 1
Factor 1 2 3 4 5 6 7 8 9 10
1. Volatility
2. Withdrawal .71
3. Compassion .13 .13
4. Politeness .48 .17 .61
5. Industriousness .43 .50 .15 .25
6. Orderliness .10 .06 .03 .19 .71
7. Enthusiasm .12 .40 .59 .17 .18 .00
8. Assertiveness .03 .46 .13 .37 .34 .05 .59
9. Intellect .11 .36 .12 .20 .32 .03 .19 .56
10. Openness .11 .05 .41 .01 .08 .25 .29 .28 .55
891
TEN ASPECTS OF THE BIG FIVE
the 10 aspect factors, use of the IPIP allowed us to create a public
domain instrument with excellent psychometric properties.
The BFAS should be useful for exploring the discriminant
validity of the different aspects within each domain, especially in
cases of suppression, the phenomenon in which a positive associ-
ation between two variables may obscure the association of one or
both with a third variable. Our results highlighted one example of
suppression: Orderliness showed almost no zero-order correlation
with Neuroticism, but it was significantly positively correlated
with Neuroticism when controlling for Industriousness. It is im-
portant to understand what such a result means in commonsense
terms: Given two people (or groups) with equal levels of Indus-
triousness, the one higher in Orderliness is likely to show higher
levels of Neuroticism. This finding may be substantively impor-
tant. One of the facet scales that marked the Orderliness factor was
Perfectionism (see Table 3), and two Orderliness items (“Want
everything to be ‘just right’” and “Want every detail taken care
of”) appear conceptually related to perfectionism. Perfectionism
has been described as a “pervasive neurotic style” (Hewitt & Flett,
1991, p. 456) and is associated with anxiety, depression, and other
psychopathologies (Dunkley, Sanislow, Grilo, & McGlashan,
2006; Sherry, Hewitt, Flett, Lee-Baggely, & Hall, 2007). Despite
the fact that Conscientiousness is typically negatively associated
with Neuroticism, the BFAS reveals that its Orderliness aspect
may be positively associated with Neuroticism. This finding might
lead to an advance in researchers’ understanding of how some
forms of Conscientiousness can be maladaptive.
In addition to demonstrating the reliability and validity of the
BFAS, Study 2 demonstrated that the aspect-level traits show more
striking patterns of cross-domain correlations than do the Big Five.
We see this as a potential advantage rather than a disadvantage, as
correlations among the aspects may reveal meaningful cross-
domain connections that are given short shrift in much of the
literature on the Big Five. For example, Enthusiasm and Compas-
sion are strongly correlated, perhaps because both encompass the
tendency toward social affiliation. However, item content suggests
that Enthusiasm is linked to the rewarding nature of social affili-
ation, whereas Compassion appears to reflect affiliation driven by
concern or empathy. It is of interest that their complementary
aspects, Assertiveness and Politeness, are negatively correlated.
Separating the aspects of Extraversion and Agreeableness, there-
fore, may further researchers’ understanding of how these two
domains are both similar and different in their description of
interpersonal behavior.
As another example, Assertiveness, Intellect, and Industrious-
ness were strongly intercorrelated. All three of these traits seem
likely to be related to industrial performance, which might make
them particularly useful in research on leadership or personnel
selection. Although the 10 aspects displayed the standard Big Five
factor structure, if the correlations among Assertiveness, Intellect,
and Industriousness were a bit stronger, then one could imagine
them forming a factor of their own. Such a factor, labeled Prowess/
Heroism, appears to have emerged in a Greek lexical study (Sauc-
ier, Georgiades, Tsaousis, & Goldberg, 2005). This Greek factor
was most similar to Extraversion in the standard Big Five, but it
excluded descriptors related to “sociability” and included descrip-
tors related to “giftedness/brilliance” and “competence,” which, in
the Big Five, would fall within Openness/Intellect and Conscien-
tiousness, respectively (Saucier et al., 2005), and which corre-
spond to Intellect and Industriousness, at the aspect level. This
finding suggests, as Saucier et al. (2005) noted, that different
languages may yield different factor structures, not because the
underlying structure of personality in different cultures is very
different, but because different languages emphasize different con-
Table 9
Eigenvalues for Factor Analyses of the 10 Aspects
Factor
ESCS
scale score
ESCS
factor score
University
scale score
1 2.79 3.15 3.03
2 1.71 2.17 1.70
3 1.57 1.72 1.39
4 1.26 1.19 1.16
5 0.90 0.92 0.93
6 0.48 0.28 0.49
7 0.37 0.19 0.41
8 0.36 0.14 0.34
9 0.30 0.13 0.30
10 0.27 0.12 0.25
Note. ESCS Eugene-Springfield community sample.
Table 10
Five-Factor Solutions for the 10 Aspects
Aspect
ESCS scale score ESCS factor score University scale score
NACEONACEONACEO
Volatility .78 .22 .01 .02 .00 .89 .20 .12 .08 .03 .75 .28 .01 .01 .01
Withdrawal .79 .08 .13 .34 .10 .83 .00 .13 .38 .14 .84 .14 .13 .27 .02
Compassion .06 .63 .03 .44 .26 .02 .87 .02 .31 .21 .11 .56 .12 .42 .42
Politeness .22 .78 .15 .04 .10 .31 .81 .15 .20 .16 .14 .77 .14 .01 .04
Industriousness .37 .02 .73 .14 .17 .37 .07 .89 .15 .15 .40 .01 .75 .14 .07
Orderliness .10 .10 .61 .03 .15 .02 .06 .83 .01 .14 .08 .14 .54 .03 .01
Enthusiasm .14 .14 .05 .75 .09 .12 .39 .01 .77 .10 .13 .17 .05 .82 .08
Assertiveness .18 .39 .28 .55 .38 .09 .24 .16 .82 .38 .27 .29 .18 .61 .34
Intellect .24 .18 .10 .13 .75 .18 .13 .12 .18 .87 .37 .14 .25 .16 .65
Openness .15 .21 .19 .11 .60 .10 .25 .21 .17 .69 .11 .19 .07 .09 .62
Note. Principal-axis factoring with varimax rotation. ESCS Eugene-Springfield Community Sample; N Neuroticism; A Agreeableness; C
Conscientiousness; E Extraversion; O Openness/Intellect.
892
DEYOUNG, QUILTY, AND PETERSON
nections in a web of relations among lower level traits that exist in
all cultures. Whether the 10 aspects of the Big Five might consti-
tute such lower level traits is a question of interest for future
research.
Study 3
Having found two factors within the facets of each of the Big
Five and characterized them through empirical scale construction,
we turn to the question of how well these phenotypic factors
correspond to the genetic factors identified by Jang et al. (2002).
Before examining this question empirically, one must consider the
strengths and limitations of Jang et al.’s behavior genetic findings.
In two large samples, they tested models with one, two, or three
genetic factors in each domain, and, in every case, models with
two factors fit best. This provides strong evidence that a single
factor cannot completely explain the pattern of genetic covariance
among the facets in each domain.
However, Jang and colleagues (2002) did not allow the two
factors within each domain to correlate, which might have pre-
vented their estimated factor loadings from providing the best
representation of the true factors (though it does not invalidate
their finding that more than one factor is necessary). Another
behavior genetic study of the same samples (Yamagata et al.,
2006), which factor-analyzed all 30 facets of the NEO-PI-R to-
gether, found a five-factor genetic solution that was highly con-
gruent across cultures and in which all of the facets in each domain
loaded strongly on a single factor. This study did not factor the
facets within each domain separately, nor did it compare factor
solutions with different numbers of factors within each domain, so
it is not incompatible with Jang et al.’s (2002) findings, but it does
suggest that the two genetic factors within each domain should be
correlated. That correlation would indicate the existence of genes
that influence both factors, whereas the two factors themselves
indicate genes that specifically influence one subfactor of the
domain but not the other.
Another limitation of Jang et al.’s (2002) study was that it used
only the NEO-PI-R. To the degree that the NEO-PI-R underrep-
resents any content at the facet level, their factors may be distorted.
Despite these limitations, Jang et al.’s work provides the best
estimates presently available of two genetic factors underlying
each of the Big Five domains. We therefore used their estimated
factor loadings and the NEO-PI-R to calculate genetic factor
scores in the ESCS, allowing us to quantify the similarities be-
tween the phenotypic factors found by us and the genetic factors
found by Jang et al.
Our general hypothesis was that, within each domain, each
phenotypic factor would be most strongly correlated with a differ-
ent genetic factor. These correlations may not be extremely strong
because phenotypic factors include environmental as well as ge-
netic influences and because Jang et al.’s (2002) results may be
less accurate than they would have been had they allowed corre-
lated factors or used a wider range of facets in each domain.
Nonetheless, the correlations should show a pattern of correspon-
dence between phenotypic and genetic factors, moderated by three
additional hypotheses: The results of Study 1 suggested the hy-
pothesis that the phenotypic factors of Compassion and Intellect
would correspond less closely to Jang et al.’s genetic factors
because the NEO-PI-R does not include any facets that specifically
marked the Compassion factor without also marking the Politeness
factor and includes only one facet (Ideas) that strongly marked the
Intellect factor. A second moderating hypothesis was developed on
the basis of the fact that Jang et al. did not rotate their factors. In
an unrotated factor solution, many variables tend to load on a large
first factor, and additional factors model relations that were not
captured in that first factor. Thus, in the present case, the first
genetic factor is more likely than the second to be correlated with
both phenotypic factors. Finally, because Jang et al.’s factor load-
ings showed some divergence between Canadian and German
samples for Conscientiousness and Extraversion, we expected that
there might be less agreement across the two sets of estimates in
those two domains.
Method
Participants were the 481 ESCS participants from Study 1.
Genetic factor scores were calculated using the NEO-PI-R and the
method of Thomson (1951). Let X be a matrix of standardized
facet scores, with number of rows equal to sample size and six
columns representing the facets in one domain. The genetic factor
scores Y are then the expected values of the two factors in that
domain conditional on X, which are given by:
Y X
1
,
where is the correlation matrix of the six facets, and is a
matrix of the estimated genetic factor loadings for those facets
reported by Jang et al. (2002).
7
These genetic factor scores were
computed twice for each Big Five domain, using values of
derived for Jang et al.’s Canadian and German samples separately.
These two estimates of the genetic factor scores were then
correlated with the aspect factor scores from Study 1 (very similar
correlations were found if scores from the BFAS were used instead
of factor scores). Partial correlations were used to control for the
shared variance of the two phenotypic factors in each domain,
allowing us to investigate only the unique association between
each aspect and the estimated genetic factor scores. We report the
zero-order correlations as well, but these are less interpretable
because of the lack of specificity introduced by the nonrotation of
the genetic factors in conjunction with the fairly strong correla-
tions between the phenotypic factors.
Results and Discussion
Table 11 presents the associations between genetic and pheno-
typic factor scores. As predicted, when considering the partial
correlations, in most cases, each genetic factor was more strongly
associated with one phenotypic factor than the other. Of the 10
phenotypic factors, 8 were correlated at r .5 with one, and only
one, of the two genetic factors. The only two phenotypic factors
that were not correlated at r .5 with one of the genetic factors
were Compassion and Intellect, and this was predicted because of
the underrepresentation of Compassion- and Intellect-related fac-
ets in the NEO-PI-R. Some of the aspects, like Openness and
7
NEO-PI-R facet correlation matrices from the ESCS were used for
these calculations. Results were very similar using the normative matrices
from the NEO manual (Costa & McCrae, 1992b).
893
TEN ASPECTS OF THE BIG FIVE
Withdrawal, showed correlations as high as .90 with one genetic
factor, suggesting high degrees of similarity.
Also as predicted, the first genetic factor was positively corre-
lated with both phenotypic factors in most domains. This lack of
complete specificity is likely to be due to the fact that Jang et al.’s
(2002) factors were unrotated, rendering the first factor less spe-
cific than it could have been had their factors been rotated toward
simple structure. Finally, for Extraversion and Conscientiousness,
the two domains in which Jang et al.’s (2002) Canadian and
German samples showed the most divergence, the phenotypic
factors more closely resembled the German than the Canadian
genetic factors.
Comparison of the genetic factors reported by Jang et al. (2002)
and the phenotypic factors found in Study 1 thus suggests a
reasonably high degree of correspondence between the two sets of
factors, given the limitations discussed. The correspondence is
strong enough to suggest that the aspect-level phenotypic factors
may have separable genetic substrates similar to those associated
with Jang et al.’s genetic factors. Future research could provide a
stronger test of this hypothesis by including the AB5C-IPIP with
the NEO-PI-R in a behavior genetic study.
General Discussion
The studies reported here demonstrate a level of organization in
personality between the narrow facets and the broad domains.
Factor analysis indicated that each Big Five domain is divisible
into two correlated aspects, subsuming multiple facets. A process
of empirical scale construction using the IPIP allowed us to pro-
vide a detailed characterization of the aspect factors at the item
level, while simultaneously creating the BFAS, a reliable and valid
public domain instrument to assess the 10 aspects of the Big Five.
The larger significance of the existence of the aspects, like the
two genetic factors found within each Big Five domain by Jang et
al. (2002), is that they reveal a novel level of personality structure
that demands further investigation. The degree of correspondence
between our phenotypic factors and Jang et al.’s genetic factors
suggests the possibility that both sets of findings may be tapping
the same underlying structure and that this structure may be partly
genetically based. The optimal facet-level structure of personality
in the five-factor model is still unknown, but at this intermediate
level of the hierarchy between facets and domains, each of the Big
Five appears divisible into two aspects, each subsuming many
facets. By analyzing a broader range of facets than just those of the
NEO-PI-R, the present studies may have provided a more accurate
characterization of the two aspects of each domain.
Like the Big Five themselves, the aspects are phenotypic factors
that presumably have both genetic and environmental causes. The
sources of the aspects may be easier to identify than the specific
genetic and environmental causes of individual facets. The aspect-
level traits are broader, more parsimonious, and less arbitrary than
the facets. They allow differentiation of two trait dimensions
within each of the Big Five that are likely to have partially distinct
biological substrates, environmental influences, and effects.
Consider Openness and Intellect, for example. The distinction
between these two aspects of Openness/Intellect may explain why
the NEO-PI-R facets of Fantasy, Aesthetics, and Feelings (markers
of Openness) sometimes show different patterns of association
from Ideas (a marker of Intellect). Fantasy, Aesthetics, and Feel-
ings are all correlated with crystallized intelligence but not fluid
intelligence or working memory, whereas Ideas is correlated with
fluid intelligence and working memory (DeYoung et al., 2005).
Given what is known about the biological substrates of fluid
intelligence and working memory (DeYoung et al., 2005; J. R.
Gray & Thompson, 2004), this finding suggests that those sub-
strates might be among the sources of Intellect but not Openness.
As another example, we hypothesize that the two aspects of
Neuroticism, Volatility and Withdrawal, may represent the pri-
mary manifestations in personality of two brain systems governing
sensitivity to threat and punishment, which J. A. Gray and Mc-
Naughton (2000) have called the fight-flight-freeze system (FFFS)
and the behavioral inhibition system (BIS). J. A. Gray and Mc-
Naughton (2000) described Neuroticism as a predisposition for
general sensitivity to threat and punishment, involving both the
FFFS and the BIS. Whereas Neuroticism as a whole may reflect
the joint sensitivity of these systems, we suggest that the two
aspects of Neuroticism may reflect the sensitivities of the two
systems individually. This hypothesis is plausible because Gray
and McNaughton describe the BIS as responsible for anxiety and
also relate it to depression, whereas they describe the FFFS as
responsible for panic and anger. Withdrawal clearly encompasses
traits related to anxiety and depression, whereas Volatility clearly
encompasses traits related to anger, as well as items like “Get upset
easily” and “Rarely lose my composure,” which may be related to
panic. Various psychologists have proposed the relation of Neu-
roticism to the BIS (e.g., Elliot & Thrash, 2002), but our findings
may indicate what aspect of Neuroticism is associated with the BIS
and what is associated with the FFFS.
Table 11
Partial Correlations Between Genetic Factor Estimates (From
Jang et al., 2002) and Aspect Factor Scores, Controlling for
Complementary Aspects
Aspect
Canadian estimate German estimate
F1 F2 F1 F2
Neuroticism
Volatility .68 (.85) .68 (.18) .59 (.80) .76 (.19)
Withdrawal .44 (.77) .85 (.72) .38 (.73) .90 (.75)
Agreeableness
Compassion .27 (.49) .19 (.30) .40 (.65) .08 (.29)
Politeness .30 (.50) .65 (.68) .48 (.68) .52 (.57)
Conscientiousness
Industriousness .42 (.66) .58 (.70) .38 (.72) .56 (.61)
Orderliness .24 (.60) .04 (.48) .52 (.76) .18 (.33)
Extraversion
Enthusiasm .71 (.79) .34 (.56) .77 (.83) .01 (.45)
Assertiveness .03 (.48) .37 (.58) .05 (.46) .69 (.76)
Openness/Intellect
Intellect .24 (.59) .29 (.31) .13 (.35) .31 (.46)
Openness .93 (.95) .05 (.13) .73 (.76) .20 (.40)
Note. N 481. Predicted strongest partial correlations are in bold.
Zero-order correlations are in parentheses. Correlations .10 are signifi-
cant at p .05. F1 Genetic Factor 1; F2 Genetic Factor 2.
894
DEYOUNG, QUILTY, AND PETERSON
Untangling the sources of the two aspects of each Big Five
domain is one major challenge posed by our findings. We hope
that the BFAS may prove useful in research aimed at this goal. At
the very least, the BFAS provides a demonstration of the unique
possibilities for research and empirical scale construction provided
by the IPIP (Goldberg, 1999), allowing characterization of factors
with much more confidence than would have been possible on the
basis of variable labels and factor loadings. (It would be interesting
to test how similar the BFAS is to the Big Five Questionnaire
[Caprara, Barbaranelli, Borgogni, & Perugini, 1993], an instru-
ment derived theoretically rather than empirically, which divides
each Big Five domain into two subscales.) It is hoped that our
factor analyses will be replicated in other samples, despite the
difficulty of administering both the NEO-PI-R and the AB5C-IPIP.
Nonetheless, the size of the ESCS and the use of scale scores rather
than item scores as variables in our factor analyses in Study 1
provide some confidence that the factors assessed by the BFAS are
likely to be replicable.
The aspect-level factors are lower order factors specific to the
Big Five. In other personality models, other traits of similar
magnitude might appear. However, the Big Five remains a prom-
ising and widely used model. New knowledge about its substruc-
ture could have important ramifications for personality psychology
in its efforts to identify the sources of personality and to under-
stand associations between the Big Five and a wide variety of other
phenomena.
References
Achenbach, T. M., & Edelbrock, C. S. (1978). The classification of child
psychopathology: A review and analysis of empirical efforts. Psycho-
logical Bulletin, 85, 1275–1301.
Achenbach, T. M., & Edelbrock, C. S. (1984). Psychopathology of child-
hood. Annual Review of Psychology, 35, 227–256.
Ashton, M. C., & Lee, K. (2005). Honesty-Humility, the Big Five, and the
five-factor model. Journal of Personality, 73, 1322–1354.
Ashton, M. C., Lee, K., Perugini, M., Szarota, P., de Vries, R. E., Blas,
L. D., et al. (2004). A six-factor structure of personality descriptive
adjectives: Solutions from psycholexical studies in seven languages.
Journal of Personality and Social Psychology, 86, 356 –366.
Caprara, G. V., Barbaranelli, C., Borgogni, L., & Perugini, M. (1993). The
Big Five questionnaire: A new questionnaire to assess the five factor
model. Personality and Individual Differences, 15, 281–288.
Church, A. T. (1994). Relating the Tellegen and five-factor models of
personality structure. Journal of Personality and Social Psychology, 67,
898 –909.
Church, A. T., & Burke, P. J. (1994). Exploratory and confirmatory tests of
the Big Five and Tellegen’s three- and four-dimensional models. Journal
of Personality and Social Psychology, 66, 93–114.
Costa, P. T., Jr., & McCrae, R. R. (1992a). Four ways five factors are basic.
Personality and Individual Differences, 13, 653– 665.
Costa, P. T., Jr., & McCrae, R. R. (1992b). NEO PI-R Professional
Manual. Odessa, FL: Psychological Assessment Resources.
Costa, P. T., Jr., McCrae, R. R., & Dye, D. A. (1991). Facet scales for
Agreeableness and Conscientiousness: A revision of the NEO Person-
ality Inventory. Personality and Individual Differences, 12, 887– 898.
Davidson, R. J. (2001). Toward a biology of personality and emotion.
Annals of the New York Academy of Sciences, 935, 191–207.
Depue, R. A., & Collins, P. F. (1999). Neurobiology of the structure of
personality: Dopamine, facilitation of incentive motivation, and extra-
version. Behavioral and Brain Sciences, 22, 491–569.
de Raad, B., Perugini, M., Hrebickova, M., & Szarota, P. (1998). Lingua
franca of personality: Taxonomies and structures based on the psy-
cholexical approach. Journal of Cross-Cultural Psychology, 29, 212–
232.
DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-
informant sample. Journal of Personality and Social Psychology, 91,
1138 –1151.
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2002). Higher-order
factors of the Big Five predict conformity: Are there neuroses of health?
Personality and Individual Differences, 33, 533–552.
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2005). Sources of
Openness/Intellect: Cognitive and neuropsychological correlates of the
fifth factor of personality. Journal of Personality, 73, 825– 858.
Digman, J. M. (1990). Personality structure: Emergence of the five-factor
model. Annual Review of Psychology, 41, 417– 440.
Digman, J. M. (1997). Higher-order factors of the Big Five. Journal of
Personality and Social Psychology, 73, 1246 –1256.
Dunkley, D. M., Sanislow, C. A., Grilo, C. M., & McGlashan, T. H.
(2006). Perfectionism and depressive symptoms 3 years later: Negative
social interactions, avoidant coping, and perceived social support as
mediators. Comprehensive Psychiatry, 46, 106 –115.
Elliot, A. J., & Thrash, T. M. (2002). Approach-avoidance motivation in
personality: Approach and avoidance temperaments and goals. Journal
of Personality and Social Psychology, 82, 804 818.
Goldberg, L. R. (1993). The structure of phenotypic personality traits.
American Psychologist, 48, 26 –34.
Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality
inventory measuring the lower-level facets of several five-factor models.
In I. Mervielde, I. Deary, F. De Fruyt, & F. Ostendorf (Eds.), Personality
psychology in Europe (Vol. 7, pp. 7–28). Tilburg, the Netherlands:
Tilburg University Press.
Goldberg, L. R., & Rosolack, T. K. (1994). The Big Five factor structure
as an integrative framework: An empirical comparison with Eysenck’s
PEN model. In C. F. Halverson, Jr., G. A. Kohnstamm, & R. P. Martin
(Eds.), The developing structure of temperament and personality from
infancy to adulthood (pp. 7–35). New York: Erlbaum.
Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety:
An enquiry into the functions of the septo-hippocampal system (2nd ed.).
New York: Oxford University Press.
Gray, J. R., & Thompson, P. M. (2004). Neurobiology of intelligence:
Science and ethics. Nature Reviews Neuroscience, 5, 471– 482.
Hewitt, P. L., & Flett, G. L. (1991). Perfectionism in the self and social
contexts: Conceptualization, assessment, and association with psycho-
pathology. Journal of Personality and Social Psychology, 60, 456 470.
Hofstee, W. K., de Raad, B., & Goldberg, L. R. (1992). Integration of the
Big Five and circumplex approaches to trait structure. Journal of Per-
sonality and Social Psychology, 63, 146 –163.
Hogan, R., & Hogan, J. (1992). Hogan Personality Inventory manual.
Tulsa, OK: Hogan Assessment Systems.
Jackson, D. N., Paunonen, S. V., Fraboni, M., & Goffin, R. G. (1996). A
five-factor versus a six-factor model of personality structure. Personality
and Individual Differences, 20, 33– 45.
Jang, K. L., Livesley, W. J., Angleitner, A., Reimann, R., & Vernon, P. A.
(2002). Genetic and environmental influences on the covariance of
facets defining the domains of the five-factor model of personality.
Personality and Individual Differences, 33, 83–101.
Jang, K. L., Livesley, W. J., Ando, J., Yamagata, S., Suzuki, A., Angleit-
ner, A., et al. (2006). Behavioral genetics of the higher-order factors of
the Big Five. Personality and Individual Differences, 41, 261–272.
John, O. P. (1990). The “Big Five” factor taxonomy: Dimensions of
personality in the natural language and in questionnaires. In L. A. Pervin
(Ed.), Handbook of personality: Theory and research (pp. 66 –100).
New York: Guilford Press.
John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History,
measurement, and theoretical perspectives. In L. A. Pervin & O. P. John
895
TEN ASPECTS OF THE BIG FIVE
(Eds.), Handbook of personality: Theory and research (2nd ed., pp.
102–138). New York: Guilford Press.
Johnson, J. A. (1994). Clarification of factor five with the help of the
AB5C model. European Journal of Personality, 8, 311–334.
Krueger, R. F. (1999). The structure of common mental disorders. Archives
of General Psychiatry, 56, 921–926.
Lee, K., & Ashton, M. C. (2005). Psychopathy, Machiavellianism, and
Narcissism in the Five-Factor Model and the HEXACO model of per-
sonality structure. Personality and Individual Differences, 38, 1571–
1582.
Lucas, R. E., & Diener, E. (2001). Understanding extraverts’ enjoyment of
social situations: The importance of pleasantness. Journal of Personality
and Social Psychology, 81, 343–356.
Lucas, R. E., Diener, E., Grob, A., Suh, E. M., & Shao, L. (2000).
Cross-cultural evidence for the fundamental features of extraversion.
Journal of Personality and Social Psychology, 79, 452– 468.
Markon, K. E., Krueger, R. F., & Watson, D. (2005). Delineating the
structure of normal and abnormal personality: An integrative hierarchi-
cal approach. Journal of Personality and Social Psychology, 88, 139
157.
McCrae, R. R., & Costa, P. T., Jr. (1985). Updating Norman’s “adequate
taxonomy”: Intelligence and personality dimensions in natural language
and in questionnaires. Journal of Personality and Social Psychology, 49,
110 –121.
Mount, M. K., Barrick, M. R., Scullen, S. M., & Rounds, J. (2005).
Higher-order dimensions of the big five personality traits and the big six
vocational interest types. Personnel Psychology, 58, 447– 478.
O’Connor, B. P. (2000). SPSS and SAS programs for determining the
number of components using parallel analysis and Velicer’s MAP test.
Behavior Research Methods, Instruments, & Computers, 32, 396 402.
Paulhus, D. L., Robins, R. W., Trzesniewski, K. H., & Tracy, J. L. (2004).
Two replicable suppressor situations in personality research. Multivari-
ate Behavioral Research, 39, 303–328.
Roberts, B. W., Chernyshenko, O. S., Stark, S., & Goldberg, L. R. (2005).
The structure of Conscientiousness: An empirical investigation based on
seven major personality questionnaires. Personnel Psychology, 58, 103–
139.
Saucier, G. (1992). Openness versus intellect: Much ado about nothing?
European Journal of Personality, 6, 381–386.
Saucier, G. (1994). Mini-markers: A brief version of Goldberg’s unipolar
Big-Five markers. Journal of Personality Assessment, 63, 506 –516.
Saucier, G. (2003). An alternative multi-language structure for personality
attributes. European Journal of Personality, 17, 179 –205.
Saucier, G., Georgiades, S., Tsaousis, I., & Goldberg, L. R. (2005). The
factor structure of Greek personality adjectives. Journal of Personality
and Social Psychology, 88, 856 875.
Saucier, G., & Goldberg, L. R. (2001). Lexical studies of indigenous
personality factors: Premises, products, and prospects. Journal of Per-
sonality, 69, 847– 879.
Sherry, S. B., Hewitt, P. L., Flett, G. L., Lee-Baggely, D. L., & Hall, P. A.
(2007). Trait perfectionism and perfectionistic self-presentation in per-
sonality pathology. Personality and Individual Differences, 42, 477–
490.
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th
ed.). Boston: Allyn & Bacon.
Thomson, G. H. (1951). The factorial analysis of human ability. London:
University of London Press.
Waller, N. G. (1999). Evaluating the structure of personality. In C. R.
Cloninger (Ed.), Personality and psychopathology (pp. 155–200). Wash-
ington, DC: American Psychiatric Association.
Watson, D., & Clark, L. A. (1997). Extraversion and its positive emotional
core. In R. Hogan, J. Johnson, & S. Briggs (Eds.), Handbook of per-
sonality psychology (pp. 767–793). San Diego, CA: Academic Press.
Whiteside, S. P., & Lynam, R. W. (2001). The five factor model and
impulsivity: Using a structural model of personality to understand im-
pulsivity. Personality and Individual Differences, 30, 669 689.
Yamagata, S., Suzuki, A., Ando, J., Ono, Y., Kijima, N., Yoshimura, K., et
al. (2006). Is the genetic structure of human personality universal? A
cross-cultural twin study from North America, Europe, and Asia. Jour-
nal of Personality and Social Psychology, 90, 987–998.
Zuckerman, M., Kuhlman, D. M., Joireman, J., Teta, P., & Kraft, M.
(1993). A comparison of three structural models of personality: The Big
Three, the Big Five, and the Alternative Five. Journal of Personality and
Social Psychology, 65, 757–768.
Received December 11, 2006
Revision received June 1, 2007
Accepted June 5, 2007
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... Importantly, these five domains represent just one level of a personality trait hierarchy, where the broad domains subsume much narrower facets, and are in turn nested within very broad meta-traits (Costa & McCrae, 1995;DeYoung, 2006;Digman, 1997). The most recently proposed level of this hierarchy consists of 10 aspects lying between domains and facets, such that each domain (e.g., extraversion) can be divided into two aspects (e.g., assertiveness and enthusiasm) (DeYoung et al., 2007). The ten aspects (Table 1) are thought to provide insight into the distinct processes that underlie each of the personality domains (e.g., assertiveness and enthusiasm have been suggested to reflect dopamine and opioid function, respectively; (Allen & DeYoung, 2016). ...
... The Big Five Aspects Scale (BFAS) was used to measure the Big Five personality trait domains and their aspects (DeYoung et al., 2007). Higher scores indicate that the participant is higher on that domain/aspect. ...
... Higher scores indicate that the participant is higher on that domain/aspect. The BFAS has demonstrated good reliability, with Cronbach's alpha for all scales exceeding 0.70 (DeYoung et al., 2007). Excellent validity is demonstrated by strong convergent correlations with other related personality measures, such as the NEO-PI-R (r ¼ 0.90) across a number of samples (DeYoung et al., 2007Ludeke et al., 2019;Quilty et al., 2013). ...
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... Informants were asked to rate targets on 24 items mod-The Convergence of Self and Informant Reports in a Large Online Sample Collabra: Psychology eled after the 12 aspects of feedback provided on the SAPA-Project during the data collection period. This feedback was based on the 10 Big Five Aspect Scales (DeYoung et al., 2007) plus Honesty and Humility. Response choices for these informant report items ranged from 1 = "Very inaccurate" to 6 = "Very accurate". ...
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... Comparisons with several other self-report frameworks are possible using the publicly available data at https://dataverse.harvard.edu/dataverse/SAPA-Project. To contextualize the validity of the SPI-5 relative to other frameworks, we report the self-informant associations for three of the more widely-used self-report frameworks: the BFAS (DeYoung et al., 2007), the IPIP correlates of the Big Five Factor Markers (IPIPB5, Goldberg et al., 2006), and the IPIP correlates of the NEO-PI-R (IPIPNEO, Goldberg et al., 2006). The number of pairwise administrations between all of the self-report items in these frameworks (minimum = 1,988; Mdn = 2,770; m = 3,128.1) ...
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... The openness to experience factor was assessed via the Big Five Aspect scale(20 items;DeYoung et al., 2007). The choice of a different scale than Study 1 was motivated by a desire to conceptually replicate the findings with openness to experience. ...
... Participants answered on a 5-points scale ranging from 1 (very inaccurate) to 5 (very accurate). The factor structure of the scale, involving two facets, has been supported in the study ofDeYoung et al. (2007). They further demonstrated that social desirability did not account for the scores on the scale. ...
... We computed a composite variable by averaging all the items (α = .78; which was similar to the findings ofDeYoung et al. (2007) who reported an average α = .82 across their samples).Associative Abilities. ...
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The standards that a person pursue in life can be set in a rigid or flexible way. The recent literature has emphasized a distinction between high and realistic standards of excellence, from high and unrealistic standards of perfection. In two studies, we investigated the role of striving towards excellence (i.e., excellencism) and striving towards perfection (i.e., perfectionism) in relation to divergent thinking, associative thinking, and openness to experience, general self‐efficacy, and creative self‐beliefs. In Study 1, 279 university students completed three divergent thinking items, which called for creative uses of two common objects and to name original things which make noise. A measure of openness to experience was included. Results from multiple regression indicated that participants pursuing excellence tended to generate more answers and more original ones compared with those pursuing perfection. Openness to experience was positively associated to excellencism and negatively associated to perfectionism. In Study 2 (n = 401 university students), we replicated these findings and extended them to associative tasks requiring participants to generate chains of unrelated words. Additional individual differences measures included general self‐efficacy, creative self‐efficacy, and creative personal identity. The results suggested that excellencism was associated with better performance on divergent thinking and associative tasks, compared with perfectionism. Excellencism was positively associated with all four personality variables, whereas perfectionism was significantly and negatively associated with openness to experience only. Implications for the distinction between perfectionism and excellencism with respect to creative indicators are discussed. In addition, the paradoxical finding that perfection strivers had high creative self‐efficacy and creative personal identity but lower openness to experience and poorer performance on objective indicators of creative abilities is discussed.
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... There is also evidence that personality traits of the healthcare provider, specifically when low in neuroticism, might also significantly reduce the severity of complaints associated with chronic diseases such as arthritis (12) . This is consistent with the results of the BELL trial, in which the instructor's personality (low in neuroticism and volatility (13) ) was reported by the participants to have been a major factor in their engagement (5) . There is evidently more to the effective management of osteoarthritic knee pain than changing kinetics. ...
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... The Big Five Aspects Scale (BFAS) is a 10 facet self-report personality measure based on the big five personality traits of Extraversion, Agreeableness, Openness, Neuroticism, and Conscientiousness (DeYoung, Quilty, & Peterson, 2007;Sun, Kaufman, Smillie, 2018). Each of the big five personality traits is composed of two facets, with Extraversion containing Enthusiasm and Assertiveness, Agreeableness having Compassion and Politeness, Openness with Openness and Intellect, Neuroticism composed by Withdrawal and Volatility, and Conscientiousness containing Industriousness and Orderliness. ...
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