The Power of Personality The Comparative Validity of Personality Traits, Socioeconomic Status, and Cognitive Ability for Predicting Important Life Outcomes Brent W. Roberts, 1 Nathan R. Kuncel, 2 Rebecca Shiner, 3 Avshalom Caspi, 4,5 and Lewis R. Goldberg 6 1 University of Illinois, 2 University of Minnesota, 3 Colgate University, 4 Institute of Psychiatry at Kings College, London, United Kingdom, 5 Duke University, and 6 Oregon Research Institute ABSTRACT—The ability of personality traits to predict im- portant life outcomes has traditionally been questioned because of the putative small effects of personality. In this article, we compare the predictive validity of personality traits with that of socioeconomic status (SES) and cogni- tive ability to test the relative contribution of personality traits to predictions of three critical outcomes: mortality, divorce, and occupational attainment. Only evidence from prospective longitudinal studies was considered. In addi- tion, an attempt was made to limit the review to studies that controlled for important background factors. Results showed that the magnitude of the effects of personality traits on mortality, divorce, and occupational attainment was indistinguishable from the effects of SES and cognitive ability on these outcomes. These results demonstrate the influence of personality traits on important life outcomes, highlight the need to more routinely incorporate measures of personality into quality of life surveys, and encourage further research about the developmental origins of per- sonality traits and the processes by which these traits in- fluence diverse life outcomes. Starting in the 1980s, personality psychology began a profound renaissance and has now become an extraordinarily diverse and intellectually stimulating field (Pervin & John, 1999). However, just because a field of inquiry is vibrant does not mean it is practical or useful—one would need to show that personality traits predict important life outcomes, such as health and lon- gevity, marital success, and educational and occupational at- tainment. In fact, two recent reviews have shown that different personality traits are associated with outcomes in each of these domains (Caspi, Roberts, & Shiner, 2005; Ozer & Benet- Martinez, 2006). But simply showing that personality traits are related to health, love, and attainment is not a stringent test of the utility of personality traits. These associations could be the result of ‘‘third’’ variables, such as socioeconomic status (SES), that account for the patterns but have not been controlled for in the studies reviewed. In addition, many of the studies reviewed were cross-sectional and therefore lacked the meth- odological rigor to show the predictive validity of personality traits. A more stringent test of the importance of personality traits can be found in prospective longitudinal studies that show the incremental validity of personality traits over and above other factors. The analyses reported in this article test whether personality traits are important, practical predictors of significant life outcomes. We focus on three domains: longevity/mortality, divorce, and occupational attainment in work. Within each domain, we evaluate empirical evidence using the gold standard of prospective longitudinal studies—that is, those studies that can provide data about whether personality traits predict life outcomes above and beyond well-known factors such as SES and cognitive abilities. To guide the interpretation drawn from the results of these prospective longitudinal studies, we provide benchmark relations of SES and cognitive ability with outcomes from these three domains. The review proceeds in three sections. First, we address some misperceptions about personality traits that are, in part, responsible for the idea that personality does not predict important life outcomes. Second, we present a review of the evidence for the predictive validity of personality traits. Third, we conclude with a discussion of the implications of our findings and recommendations for future work in this area. Address correspondence to Brent W. Roberts, Department of Psy- chology, University of Illinois at Urbana-Champaign, 603 East Daniel Street, Champaign, IL 61820; e-mail: broberts@cyrus. psych.uiuc.edu. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE Volume 2—Number 4 313 Copyright r 2007 Association for Psychological Science
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The Power of PersonalityThe Comparative Validity of Personality Traits,Socioeconomic Status, and Cognitive Ability for PredictingImportant Life OutcomesBrent W. Roberts,1 Nathan R. Kuncel,2 Rebecca Shiner,3 Avshalom Caspi,4,5 and
Lewis R. Goldberg6
1University of Illinois, 2University of Minnesota, 3Colgate University, 4Institute of Psychiatry at Kings College, London,
United Kingdom, 5Duke University, and 6Oregon Research Institute
ABSTRACT—The ability of personality traits to predict im-
portant life outcomes has traditionally been questioned
because of the putative small effects of personality. In this
article, we compare the predictive validity of personality
traits with that of socioeconomic status (SES) and cogni-
tive ability to test the relative contribution of personality
traits to predictions of three critical outcomes: mortality,
divorce, and occupational attainment. Only evidence from
prospective longitudinal studies was considered. In addi-
tion, an attempt was made to limit the review to studies
that controlled for important background factors. Results
showed that the magnitude of the effects of personality
traits on mortality, divorce, and occupational attainment
was indistinguishable from the effects of SES and cognitive
ability on these outcomes. These results demonstrate the
influence of personality traits on important life outcomes,
highlight the need to more routinely incorporate measures
of personality into quality of life surveys, and encourage
further research about the developmental origins of per-
sonality traits and the processes by which these traits in-
fluence diverse life outcomes.
Starting in the 1980s, personality psychology began a profound
renaissance and has now become an extraordinarily diverse and
intellectually stimulating field (Pervin & John, 1999). However,
just because a field of inquiry is vibrant does not mean it is
practical or useful—one would need to show that personality
traits predict important life outcomes, such as health and lon-
gevity, marital success, and educational and occupational at-
tainment. In fact, two recent reviews have shown that different
personality traits are associated with outcomes in each of
these domains (Caspi, Roberts, & Shiner, 2005; Ozer & Benet-
Martinez, 2006). But simply showing that personality traits
are related to health, love, and attainment is not a stringent test
of the utility of personality traits. These associations could be
the result of ‘‘third’’ variables, such as socioeconomic status
(SES), that account for the patterns but have not been controlled
for in the studies reviewed. In addition, many of the studies
reviewed were cross-sectional and therefore lacked the meth-
odological rigor to show the predictive validity of personality
traits. A more stringent test of the importance of personality
traits can be found in prospective longitudinal studies that show
the incremental validity of personality traits over and above
other factors.
The analyses reported in this article test whether personality
traits are important, practical predictors of significant life
outcomes. We focus on three domains: longevity/mortality,
divorce, and occupational attainment in work. Within each
domain, we evaluate empirical evidence using the gold standard
of prospective longitudinal studies—that is, those studies that
can provide data about whether personality traits predict
life outcomes above and beyond well-known factors such as SES
and cognitive abilities. To guide the interpretation drawn
from the results of these prospective longitudinal studies, we
provide benchmark relations of SES and cognitive ability with
outcomes from these three domains. The review proceeds in
three sections. First, we address some misperceptions about
personality traits that are, in part, responsible for the idea that
personality does not predict important life outcomes. Second,
we present a review of the evidence for the predictive validity of
personality traits. Third, we conclude with a discussion of the
implications of our findings and recommendations for future
work in this area.
Address correspondence to Brent W. Roberts, Department of Psy-chology, University of Illinois at Urbana-Champaign, 603 EastDaniel Street, Champaign, IL 61820; e-mail: [email protected].
PERSPECTIVES ON PSYCHOLOGICAL SCIENCE
Volume 2—Number 4 313Copyright r 2007 Association for Psychological Science
THE ‘‘PERSONALITY COEFFICIENT’’: ANUNFORTUNATE LEGACY OF THE PERSON–SITUATION
DEBATE
Before we embark on our review, it is necessary to lay to rest a
myth perpetrated by the 1960s manifestation of the person–
situation debate; this myth is often at the root of the perspective
that personality traits do not predict outcomes well, if at all.
Specifically, in his highly influential book, Walter Mischel
(1968) argued that personality traits had limited utility in pre-
dicting behavior because their correlational upper limit ap-
peared to be about .30. Subsequently, this .30 value became
derided as the ‘‘personality coefficient.’’ Two conclusions were
inferred from this argument. First, personality traits have little
predictive validity. Second, if personality traits do not predict
much, then other factors, such as the situation, must be re-
sponsible for the vast amounts of variance that are left unac-
counted for. The idea that personality traits are the validity
weaklings of the predictive panoply has been reiterated in un-
mitigated form to this day (e.g., Bandura, 1999; Lewis, 2001;
Paul, 2004; Ross & Nisbett, 1991). In fact, this position is so
widely accepted that personality psychologists often apologize
for correlations in the range of .20 to .30 (e.g., Bornstein, 1999).
Should personality psychologists be apologetic for their
modest validity coefficients? Apparently not, according to
Meyer and his colleagues (Meyer et al., 2001), who did psy-
chological science a service by tabling the effect sizes for a wide
variety of psychological investigations and placing them side-
by-side with comparable effect sizes from medicine and every-
day life. These investigators made several important points.
First, the modal effect size on a correlational scale for psy-
chology as a whole is between .10 and .40, including that seen in
experimental investigations (see also Hemphill, 2003). It
appears that the .30 barrier applies to most phenomena in
psychology and not just to those in the realm of personality
psychology. Second, the very largest effects for any variables in
psychology are in the .50 to .60 range, and these are quite rare
(e.g., the effect of increasing age on declining speed of infor-
mation processing in adults). Third, effect sizes for assessment
measures and therapeutic interventions in psychology are sim-
ilar to those found in medicine. It is sobering to see that the
effect sizes for many medical interventions—like consuming
aspirin to treat heart disease or using chemotherapy to treat
breast cancer—translate into correlations of .02 or .03. Taken
together, the data presented by Meyer and colleagues make clear
that our standards for effect sizes need to be established in light
of what is typical for psychology and for other fields concerned
with human functioning.
In the decades since Mischel’s (1968) critique, researchers
have also directly addressed the claim that situations have a
stronger influence on behavior than they do on personality traits.
Social psychological research on the effects of situations typi-
cally involves experimental manipulation of the situation, and
the results are analyzed to establish whether the situational
manipulation has yielded a statistically significant difference in
the outcome. When the effects of situations are converted into
the same metric as that used in personality research (typically
the correlation coefficient, which conveys both the direction and
the size of an effect), the effects of personality traits are generally
as strong as the effects of situations (Funder & Ozer, 1983;
Sarason, Smith, & Diener, 1975). Overall, it is the moderate
position that is correct: Both the person and the situation are
necessary for explaining human behavior, given that both have
comparable relations with important outcomes.
As research on the relative magnitude of effects has docu-
mented, personality psychologists should not apologize for
correlations between .10 and .30, given that the effect sizes
found in personality psychology are no different than those
found in other fields of inquiry. In addition, the importance of a
predictor lies not only in the magnitude of its association with
the outcome, but also in the nature of the outcome being pre-
dicted. A large association between two self-report measures of
extraversion and positive affect may be theoretically interesting
but may not offer much solace to the researcher searching for
proof that extraversion is an important predictor for outcomes
that society values. In contrast, a modest correlation between a
personality trait and mortality or some other medical outcome,
such as Alzheimer’s disease, would be quite important. More-
over, when attempting to predict these critical life outcomes,
even relatively small effects can be important because of their
pragmatic effects and because of their cumulative effects across
a person’s life (Abelson, 1985; Funder, 2004; Rosenthal, 1990).
In terms of practicality, the �.03 association between taking
aspirin and reducing heart attacks provides an excellent ex-
ample. In one study, this surprisingly small association resulted
in 85 fewer heart attacks among the patients of 10,845 physi-
cians (Rosenthal, 2000). Because of its practical significance,
this type of association should not be ignored because of the
small effect size. In terms of cumulative effects, a seemingly
small effect that moves a person away from pursuing his or her
education early in life can have monumental consequences for
that person’s health and well-being later in life (Hardarson et al.,
2001). In other words, psychological processes with a statisti-
cally small or moderate effect can have important effects on
individuals’ lives depending on the outcomes with which they
are associated and depending on whether those effects get cu-
mulated across a person’s life.
PERSONALITY EFFECTS ON MORTALITY, DIVORCE,AND OCCUPATIONAL ATTAINMENT
Selection of Predictors, Outcomes, and Studies
for This Review
To provide the most stringent test of the predictive validity of
personality traits, we chose to focus on three objective outcomes:
mortality, divorce, and occupational attainment. Although we
314 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
could have chosen many different outcomes to examine, we
selected these three because they are socially valued; they are
measured in similar ways across studies; and they have been
assessed as outcomes in studies of SES, cognitive ability, and
personality traits. Mortality needs little justification as an out-
come, as most individuals value a long life. Divorce and marital
stability are important outcomes for several reasons. Divorce is
a significant source of depression and distress for many indi-
viduals and can have negative consequences for children,
whereas a happy marriage is one of the most important predic-
tors of life satisfaction (Myers, 2000). Divorce is also linked to
disproportionate drops in economic status, especially for women
(Kuh & Maclean, 1990), and it can undermine men’s health (e.g.,
Lund, Holstein, & Osler, 2004). An intact marriage can also
preserve cognitive function into old age for both men and
women, particularly for those married to a high-ability spouse
(Schaie, 1994).
Educational and occupational attainment are also highly
(odds or probabilities) for the experimental (or numerator)
group, with the reverse being true for values below 1.0 (down to a
lower limit of zero). Because of this asymmetry, the log of these
statistics is often taken.
The primary advantage of ratio statistics in general, and the
risk ratio in particular, is their ease of interpretation in applied
settings. It is easier to understand that death is three times as
likely to occur for one group than for another than it is to make
sense out of a point-biserial correlation. However, there are also
some disadvantages that should be understood. First, ratio sta-
tistics can make effects that are actually very small in absolute
magnitude appear to be large when in fact they are very rare
events. For example, although it is technically correct that one is
three times as likely (risk ratio 5 3.0) to win the lottery when
buying three tickets instead of one ticket, the improved chances
of winning are trivial in an absolute sense.
Second, there is no accepted practice for how to divide con-
tinuous predictor variables when computing odds, risk, and
hazard ratios. Some predictors are naturally dichotomous (e.g.,
gender), but many are continuous (e.g., cognitive ability, SES).
Volume 2—Number 4 315
Brent W. Roberts et al.
Researchers often divide continuous variables into some arbi-
trary set of categories in order to use the odds, rate, or hazard
metrics. For example, instead of reporting an association be-
tween SES and mortality using a point-biserial correlation, a
researcher may use proportional hazards models using some
arbitrary categorization of SES, such as quartile estimates (e.g.,
lowest versus highest quartiles). This permits the researcher to
draw conclusions such as ‘‘individuals from the highest category
of SES are four times as likely to live longer than are groups
lowest in SES.’’ Although more intuitively appealing, the odds
statements derived from categorizing continuous variables
makes it difficult to deduce the true effect size of a relation,
especially across studies. Researchers with very large samples
may have the luxury of carving a continuous variable into very
fine-grained categories (e.g., 10 categories of SES), which may
lead to seemingly huge hazard ratios. In contrast, researchers
with smaller samples may only dichotomize or trichotomize the
same variables, thus resulting in smaller hazard ratios and what
appear to be smaller effects for identical predictors. Finally,
many researchers may not categorize their continuous variables
at all, which can result in hazard ratios very close to 1.0 that are
nonetheless still statistically significant. These procedures for
analyzing odds, rate, and hazard ratios produce a haphazard
array of results from which it is almost impossible to discern a
meaningful average effect size.1
One of the primary tasks of this review is to transform the
results from different studies into a common metric so that a fair
comparison could be made across the predictors and outcomes.
For this purpose, we chose the Pearson product-moment cor-
relation coefficient. We used a variety of techniques to arrive at
an accurate estimate of the effect size from each study. When
transforming relative risk ratios into the correlation metric, we
used several methods to arrive at the most appropriate estimate
of the effect size. For example, the correlation coefficient can be
estimated from reported significance levels (p values) and from
test statistics such as the t test or chi-square, as well as from
other effect size indicators such as d scores (Rosenthal, 1991).
Also, the correlation coefficient can be estimated directly from
relative risk ratios and hazard ratios using the generic inverse
variance approach (The Cochrane Collaboration, 2005). In this
procedure, the relative risk ratio and confidence intervals (CIs)
are first transformed into z scores, and the z scores are then
transformed into the correlation metric.
For most studies, the effect size correlation was estimated
from information on relative risk ratios and p values. For the
latter, we used the requivalent effect size indicator (Rosenthal &
Rubin, 2003), which is computed from the sample size and
p value associated with specific effects. All of these techniques
transform the effect size information to a common correlational
metric, making the results of the studies comparable across
different analytical methods. After compiling effect sizes, meta-
analytic techniques were used to estimate population effect
sizes in both the risk ratio and correlation metric (Hedges &
Olkin, 1985). Specifically, a random-effects model with no
moderators was used to estimate population effect sizes for both
the rate ratio and correlation metrics.2 When appropriate, we
first averaged multiple nonindependent effects from studies that
reported more than one relevant effect size.
The Predictive Validity of Personality Traits for Mortality
Before considering the role of personality traits in health and
longevity, we reviewed a selection of studies linking SES and
cognitive ability to these same outcomes. This information
provides a point of reference to understand the relative contri-
bution of personality. Table 1 presents the findings from 33
studies examining the prospective relations of low SES and low
cognitive ability with mortality.3 SES was measured using
measures or composites of typical SES variables including in-
come, education, and occupational status. Total IQ scores were
commonly used in analyses of cognitive ability. Most studies
demonstrated that being born into a low-SES household or
achieving low SES in adulthood resulted in a higher risk of
mortality (e.g., Deary & Der, 2005; Hart et al., 2003; Osler et al.,
2002; Steenland, Henley, & Thun, 2002). The relative risk ratios
and hazard ratios ranged from a low of 0.57 to a high of 1.30 and
averaged 1.24 (CIs 5 1.19 and 1.29). When translated into the
correlation metric, the effect sizes for low SES ranged from�.02
to .08 and averaged .02 (CIs 5 .017 and .026).
Through the use of the relative risk metric, we determined that
the effect of low IQ on mortality was similar to that of SES,
ranging from a modest 0.74 to 2.42 and averaging 1.19 (CIs 5
1.10 and 1.30). When translated into the correlation metric,
however, the effect of low IQ on mortality was equivalent to a
correlation of .06 (CIs 5 .03 and .09), which was three times
larger than the effect of SES on mortality. The discrepancy
between the relative risk and correlation metrics most likely
resulted because some studies reported the relative risks in
terms of continuous measures of IQ, which resulted in smaller
1This situation is in no way particular to epidemiological or medical studiesusing odds, rate, and hazard ratios as outcomes. The field of psychology reportsresults in a Babylonian array of test statistics and effect sizes also.
2The population effects for the rate ratio and correlation metric were notbased on identical data because in some cases the authors did not report rateratio information or did not report enough information to compute a rate ratioand a CI.
3Most of the studies of SES and mortality were compiled from an exhaustivereview of the literature on the effect of childhood SES and mortality (Galobardeset al., 2004). We added several of the largest studies examining the effect ofadult SES on mortality (e.g., Steenland et al., 2002), and to these we added theresults from the studies on cognitive ability and personality that reported SESeffects. We also did standard electronic literature searches using the termssocioeconomic status, cognitive ability, and all-cause mortality. We also exam-ined the reference sections from the list of studies and searched for papers thatcited these studies. Experts in the field of epidemiology were also contacted andasked to identify missing studies. The resulting SES data base is representativeof the field, and as the effects are based on over 3 million data points, the effectsizes and CIs are very stable. The studies of cognitive ability and mortalityrepresent all of the studies found that reported usable data.
316 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
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.02
Low
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ltin
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eH
R5
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.23
)
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.00
5
r hr5
.03
r e5
.03
Low
adu
ltoc
cup
atio
nal
pre
stig
e
HR
51
.20
(0.9
4,
1.5
3)
r hr5
.01
Volume 2—Number 4 317
Brent W. Roberts et al.
Table
1.
(Cont’
d.)
Stu
dy
NO
utc
ome
Yea
rsC
ontr
ols
Pre
dic
tors
Ou
tcom
eE
st.
r
7,3
31
wom
enfr
om
Con
nec
ticu
t
All
-cau
se
mor
tali
ty
9ye
ars
Age
,ra
ce,
smok
ing,
BM
I,al
cohol
con
sum
pti
on,ac
tivi
tyle
vel,
soci
al
ties
,h
avin
ga
regu
lar
hea
lth
care
pro
vid
er,
nu
mb
erof
chro
nic
con
dit
ion
s,d
epre
ssiv
esy
mp
tom
s,
cogn
itiv
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nct
ion
,p
hys
ical
fun
ctio
n,
hea
lth
stat
us
Low
adu
lted
uca
tion
HR
50
.96
(0.6
4,
1.4
4)
r hr5
.00
Low
adu
ltin
com
eH
R5
1.9
0(1
.09
,3
.32
)
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.05
r hr5
.03
r e5
.02
Low
adu
ltoc
cup
atio
nal
pre
stig
e
HR
51
.15
(0.8
3,
1.5
9)
r hr5
.01
11
,97
7m
enfr
om
Nor
thC
arol
ina
All
-cau
se
mor
tali
ty
9ye
ars
age,
race
,sm
okin
g,d
egre
eof
urb
aniz
atio
n,
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coh
ol
con
sum
pti
on,
soci
alti
es,
hav
ing
a
regu
lar
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lth
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pro
vid
er,
nu
mb
erof
chro
nic
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ion
s,
dep
ress
ive
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pto
ms,
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itiv
e
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ctio
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ical
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ctio
n,h
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h
stat
us
Low
adu
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tion
HR
51
.18
(0.8
4,
1.6
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Low
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atio
nal
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stig
e
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51
.01
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8,
1.3
2)
r hr5
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36
wom
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om
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thC
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se
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ty
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ars
Age
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ce,
smok
ing,
BM
I,al
cohol
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sum
pti
on,
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es,
hav
ing
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vid
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nu
mb
erof
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ress
ive
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pto
ms,
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itiv
e
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ctio
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ical
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ctio
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h
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us
Low
adu
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tion
HR
51
.04
(0.8
4,
1.3
0)
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Low
adu
ltin
com
eH
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.11
,2
.08
)
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.01
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Low
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atio
nal
pre
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e
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51
.21
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7,
1.5
1)
p<
.10
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.02
r e5
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be-
Dim
mer
etal
.,2
00
4
3,0
87
wom
enfr
om
the
Ala
med
aC
oun
ty
Stu
dy
All
-cau
se
mor
tali
ty
30
year
sA
ge,
inco
me,
edu
cati
on,
occu
pat
ion
,sm
okin
g,B
MI,
ph
ysic
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ty
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chil
dh
ood
SE
SH
R5
1.1
2(0
.99
,1
.27
)r h
r5
.03
Low
adu
lted
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tion
HR
51
.17
(0.9
9,
1.3
9)
r hr5
.03
Man
ual
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pat
ion
HR
51
.06
(0.8
7,
1.3
0)
r hr5
.01
Low
adu
ltin
com
eH
R5
1.3
5(1
.14
,1
.60
)r h
r5
.06
Bos
wor
th&
Sch
aie,
19
99
1,2
18
mem
ber
sof
the
Sea
ttle
Lon
gitu
din
alS
tud
y
All
-cau
se
mor
tali
ty
7ye
ars
Sex
,ag
e,ed
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tion
Low
verb
alIQ
F(1
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4)
51
7.5
8,
p<
.00
1
r F5
.12
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.10
Low
mat
hIQ
F(1
,1
,19
8)
53
.75
,
p<
.05
r F5
.06
r e5
.06
Low
spat
ial
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(1,
1,1
19
)5
3.7
2,
p<
.05
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.06
r e5
.06
Bu
cher
&R
agla
nd
,
19
95
3,1
54
mid
dle
-age
d
men
from
the
Wes
tern
Col
lab
orat
ive
Gro
up
Stu
dy
All
-cau
se
mor
tali
ty
22
year
sS
ysto
lic
blo
odp
ress
ure
,
chol
este
rol,
smok
ing,
hei
ght
Low
adu
ltS
ES
RR
51
.45
(1.1
7,
1.8
1)
r rr5
.06
Cla
use
n,
Dav
ey-
Sm
ith
,&
Th
elle
,
20
03
12
8,7
23
Osl
o
nat
ives
All
-cau
se
mor
tali
ty
30
year
sA
ge,
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ltin
com
eL
owin
dex
ofin
equ
alit
yR
Rm
en5
2.4
8
(1.9
4,
3.1
6)
r rr5
.03
RR
wom
en5
1.4
7
(1.0
6,
2.0
4)
r rr5
.01
318 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
Cu
rtis
,S
outh
all,
Con
gdon
,&
Dod
geon
,2
00
4
23
,31
1m
enan
d
35
,29
5w
omen
ofth
e
Nat
ion
alS
tati
stic
s
Lon
gitu
din
alS
tud
y
All
-cau
se
mor
tali
ty
10
year
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ge,
sex,
mar
ital
stat
us,
emp
loym
ent
stat
us
Low
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cial
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en5
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6)
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Dav
eyS
mit
h,
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t,
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ne,
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ole,
19
98
5,7
66
men
aged
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–
64
in1
97
0
All
-cau
se
mor
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ty
25
year
sA
ge,
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ltS
ES
,d
epri
vati
on,
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risk
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ors
Low
fath
er’s
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alcl
ass
HR
51
.19
(1.0
4,
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7)
p5
.04
2
r hr5
.03
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.03
Dea
ry&
Der
,2
00
58
98
mem
ber
sof
the
Twen
ty-0
7S
tud
y
All
-cau
se
mor
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ty
24
year
sSex
,sm
okin
g,so
cial
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s,ye
ars
of
edu
cati
on
Low
IQH
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1.3
8(1
.15
,1
.67
)
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.00
06
r hr5
.15
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.11
Sex
,sm
okin
g,ye
ars
ofed
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tion
,
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Low
soci
alcl
ass
HR
51
.13
(1.0
1,
1.2
6)
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.02
7
r hr5
.07
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.07
Sex
,sm
okin
g,so
cial
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s,IQ
Low
edu
cati
onH
R5
1.0
6(0
.97
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)
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.20
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Doo
rnb
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mh
out,
19
90
78
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5D
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h
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ion
als
All
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se
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igh
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50
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01
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a
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cell
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nk
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20
00
13
,33
2N
atio
nal
Hea
lth
and
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trit
ion
Exa
min
atio
nS
urv
ey
par
tici
pan
ts
All
-cau
se
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ty
12
year
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ge,
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ity,
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ineq
ual
ity,
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lf-r
ated
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7,
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a
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00
21
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4m
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ers
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onga
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Ind
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t
Eld
ers
Su
rvey
All
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se
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ty
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ge,
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on,
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nal
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ity,
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ed
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ion
Low
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onR
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2
Low
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itiv
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51
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2
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.,
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01
9,7
73
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d
9,1
39
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from
the
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dy
All
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se
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ty
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30
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s
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ght,
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hed
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tion
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8)
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hed
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5)
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tet
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92
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ers
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e
Mid
span
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dy
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o
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enta
l
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rvey
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93
2
All
-cau
se
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ty
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tion
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1.2
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)
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8
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onL
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cial
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lop
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mit
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95
8W
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tern
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tlan
d
All
-cau
se
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ty
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year
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ge,
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ure
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7
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03
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om
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h
All
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se
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ty
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6
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ang
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im,
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05
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th
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ean
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ed3
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year
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der
All
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se
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ty
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ars
Age
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atio
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um
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cal
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fact
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hou
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me
RR
52
.24
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0,
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0)
r rr5
.05
Kor
ten
etal
.,1
99
98
97
sub
ject
sag
ed
70
year
san
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der
All
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se
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ty
3.5
year
sA
ge,
sex,
gen
eral
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lth
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s,
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ess,
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ol–
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ter
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alit
ies
Tes
t
Low
IQH
R5
2.4
2
(1.2
7,
4.6
2)
r hr5
.09
Volume 2—Number 4 319
Brent W. Roberts et al.
Table
1.
(Cont’
d.)
Stu
dy
NO
utc
ome
Yea
rsC
ontr
ols
Pre
dic
tors
Ou
tcom
eE
st.
r
Ku
h,
Har
dy,
Lan
gen
ber
g,
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har
ds,
&
Wad
swor
th,
20
02
2,5
47
wom
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d
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men
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h
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nci
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ey
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se
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ty
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year
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ex,
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fath
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alcl
ass
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0,
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0)
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01
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.06
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.05
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h,
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00
4
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47
wom
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d
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12
men
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ical
Res
.
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nci
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surv
ey
All
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se
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ty
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year
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tion
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en5
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0(1
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,
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0)
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3
r hr5
.05
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.05
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sub
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der
All
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se
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ty
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year
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ence
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r5
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me
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6)
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enA
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chil
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ood
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9(1
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ith
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99
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ng
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dy
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year
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d
old
er
All
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se
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ty
4.5
year
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ge,
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51
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9,
1.6
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r rr5
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kn
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.33
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5,
1.5
4)
r rr5
.17
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flu
ency
RR
51
.50
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7,
1.7
8)
r rr5
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Mar
tin
&
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nsk
y,2
00
5
65
9gi
fted
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dre
n
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Ter
man
Lif
e
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leS
tud
y
All
-cau
se
mor
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ty
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year
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ath
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pat
ion
,poo
rh
ealt
hin
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dh
ood
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ex
Les
sh
igh
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HR
50
.73
(0.5
9,
0.9
0)
r hr5
.11
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her
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nH
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0.9
9(0
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03
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08
mem
ber
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Pro
ject
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ropol
itin
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enhag
en
All
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se
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ty
49
year
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irth
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ght
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kin
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ass
stat
us
HR
51
.30
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8,
1.5
7)
r hr5
.03
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bir
thw
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owH
arn
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ist
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02
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tize
ns
of
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en
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en&
13
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)
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se
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ty
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–3
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year
s
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okin
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tyle
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tion
,
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on,
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stru
ctu
re,
Per
cen
tof
hou
seh
old
sw
ith
chil
dre
n
Hig
hh
ouse
hol
din
com
eM
en’s
HR
50
.64
a(0
.57
,
0.7
3)
p<
.01
r hr5�
.06
a
r e5�
.02
a
Wom
en’s
HR
50
.68
a
(0.6
5,
0.8
9)
p<
.01
r hr5�
.04
a
r e5�
.02
a
Pu
dar
ic,
Su
nd
qu
ist,
&Jo
han
sson
,2
00
3
8,9
59
mem
ber
sof
the
Sw
edis
hS
urv
ey
ofL
ivin
gC
ond
itio
ns
All
-cau
se
mor
tali
ty
7–
12
year
s
Age
,h
ealt
hst
atu
sL
owed
uca
tion
RR
51
.22
(1.0
7,
1.3
8)
r hr5
.03
Sh
iple
y,D
er,
Tay
lor,
&D
eary
,2
00
6
6,4
24
mem
ber
sof
the
UK
Hea
lth
and
Lif
esty
leS
urv
ey
All
-cau
se
mor
tali
ty
19
year
sA
ge,
sex,
soci
alcl
ass,
edu
cati
on,
hea
lth
beh
avio
rs,
FE
V,
blo
od
pre
ssu
re,
BM
I
Hig
hve
rbal
mem
ory
HR
50
.95
(0.9
2,
0.9
9)
p<
.00
52
r hr5�
.03
r e5�
.03
Hig
hvi
sual
spat
ial
abil
ity
HR
50
.99
(0.9
6,
1.0
3)
p5
.66
r hr5�
.01
r e5
.00
320 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
Ste
enla
nd
etal
.,
20
02
55
0,8
88
men
from
the
CP
S-I
coh
ort
All
-cau
se
mor
tali
ty
26
year
sA
ge,
smok
ing,
BM
I,d
iet,
alco
hol
,
hyp
erte
nsi
on,
men
opau
sal
stat
us
(wom
en)
Low
edu
cati
onle
vel
Men
’sR
R5
1.1
4(1
.12
,
1.1
6)
r rr5
.02
55
3,9
59
wom
en
from
the
CP
S-I
coh
ort
Wom
en’s
RR
51
.24
(1.2
1,
1.2
8)
r rr5
.02
62
5,6
63
men
from
the
CP
S-I
Ico
hor
t
All
-cau
se
mor
tali
ty
16
year
sA
ge,
smok
ing,
BM
I,d
iet,
alco
hol
,
hyp
erte
nsi
on,
men
opau
sal
stat
us
(wom
en)
Low
edu
cati
onle
vel
Men
’s
RR
51
.28
(1.2
5,
1.3
1)
r rr5
.03
76
7,4
72
wom
en
from
the
CP
S-I
I
coh
ort
Wom
en’s
RR
51
.18
(1.1
5,
1.2
2)
r rr5
.01
St.
Joh
net
al.,
20
02
8,0
99
Sen
iors
from
the
Can
adia
nS
tud
y
ofH
ealt
han
dA
gin
g
Mor
tali
ty5
year
sA
ge,
sex,
edu
cati
on,
mar
ital
stat
us,
fun
ctio
nal
stat
us,
self
-rat
ed
hea
lth
Hig
hM
MS
Esc
ores
OR
50
.95
(0.9
3,
0.9
7)
r or5�
.05
a
Ten
con
i,D
evot
i,
Com
elli
,&
RIF
LE
Res
earc
hG
rou
p,
20
00
12
,36
1It
alia
nm
en
from
the
RIF
LE
poo
lin
gp
roje
ct
All
-cau
se
mor
tali
ty
7ye
ars
Age
,sy
stol
icb
lood
pre
ssu
re,
chol
este
rol,
smok
ing
Low
adu
lted
uca
tion
leve
l
RR
50
.76
(0.5
6,
1.0
1)
p5
.12
2
r rr5�
.02
r e5�
.01
Low
adu
ltoc
cup
atio
nal
leve
l
RR
51
.30
(1.0
4,
1.6
3)
p5
.02
2
r rr5
.02
r e5
.02
Vag
ero
&L
eon
,
19
94
40
4,4
50
Sw
edis
h
men
bor
nin
19
46
–
19
55
Mor
tali
ty3
6ye
ars
Ad
ult
hoo
dso
cial
clas
sL
owch
ild
hoo
dso
cial
clas
s
OR
51
.52
(1.3
2,
1.7
6)
r or5
.01
Wh
alle
y&
Dea
ry,
20
01
72
2M
emb
ers
ofth
e
Sco
ttis
hm
enta
l
surv
eyof
19
32
Lif
e
exp
ecta
ncy
76
year
sF
ath
er’s
SE
S,
over
crow
din
gH
igh
Mor
ayH
ouse
test
scor
es(I
Q)
Par
tial
r5
.19
r5
.19
Note
.C
on
fid
ence
inte
rvals
are
given
inp
are
nth
eses
.S
ES
5so
cioec
on
om
icst
atu
s;H
R5
haza
rdra
tio;
RR
5re
lati
ve
risk
rati
o;
OR
5od
ds
rati
o;
r rr
5C
orr
elati
on
esti
mate
dfr
om
the
rate
rati
o;
r hr
5co
rrel
ati
on
esti
mate
dfr
om
the
haza
rdra
tio;
r or
5co
rrel
ati
on
esti
mate
dfr
om
the
od
ds
rati
o;
r F5
corr
elati
on
esti
mate
dfr
om
Fte
st;
r e5
r eq
uiv
ale
nt—
corr
elati
on
esti
mate
dfr
om
the
rep
ort
edp
valu
ean
dsa
mp
lesi
ze;
BM
I5
bod
ym
ass
ind
ex;
FE
V5
forc
edex
pir
ato
ryvolu
me;
AD
Ls
5act
ivit
ies
of
dail
yli
vin
g;M
MS
E5
Min
iM
enta
lS
tate
Exam
inati
on
;C
PS
5C
an
cer
Pre
ven
tion
Stu
dy;
RIF
LE
5ri
skfa
ctors
an
dli
feex
pec
tan
cy.
aT
he
sign
of
the
rati
os
an
dco
rrel
ati
on
sb
ase
don
hig
hS
ES
an
dh
igh
IQw
ere
rever
sed
bef
ore
thes
eef
fect
size
sw
ere
agg
rega
ted
wit
hre
main
ing
effe
ctsi
zes.
bIQ
score
sare
refe
rred
toas
‘‘le
ssh
igh
’’b
ecau
seth
elo
wes
tIQ
score
inth
esa
mp
lew
as
135.
Volume 2—Number 4 321
Brent W. Roberts et al.
relative risk ratios (e.g., St. John, Montgomery, Kristjansson, &
McDowell, 2002). Merging relative risk ratios from these studies
with those that carve the continuous variables into subgroups
appears to underestimate the effect of IQ on mortality, at least in
terms of the relative risk metric. The most telling comparison of
IQ and SES comes from the five studies that include both vari-
ables in the prediction of mortality. Consistent with the aggre-
gate results, IQ was a stronger predictor of mortality in each case
cluded twelve studies that examined the effect of neuroticism,
pessimism, mental instability, and sense of coherence. The av-
erage relative risk ratio for the Negative Emotionality domain
was 1.15 (CIs 5 1.04 and 1.26), and the corresponding corre-
lation effect size was .05 (CIs 5 .02 and .08). Thus, Neuroticism
was associated with a diminished life span. Nineteen studies
reported relations between Hostility/Disagreeableness and all-
cause mortality, with notable heterogeneity in the effects across
studies. The risk ratio population estimate showed an effect
equivalent to, if not larger than, the remaining personality do-
mains (risk ratio 5 1.14; CIs 5 1.06 and 1.23). With the cor-
relation metric, this effect translated into a small but statistically
significant effect of .04 (CIs 5 .02 and .06), indicating that
hostility was positively associated with mortality. Thus, the
specific personality traits of Conscientiousness, Positive Emo-
tionality/Extraversion, Neuroticism, and Hostility/Disagree-
ableness were stronger predictors of mortality than was SES
when effects were translated into a correlation metric. The effect
4We identified studies through electronic searches that included the termspersonality traits, extraversion, agreeableness, hostility, conscientiousness, emo-tional stability, neuroticism, openness to experience, and all-cause mortality. Wealso identified studies through reference sections of the list of studies andthrough studies that cited each study. A number of studies were not included inthis review because we focused on studies that were prospective and controlledfor background factors.
5We did not examine the domain of Openness to Experience because therewere only two studies that tested the association with mortality.
322 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
TA
BL
E2
Per
son
ali
tyT
rait
sa
nd
Mo
rta
lity
Stu
dy
NO
utc
ome
Len
gth
ofst
udy
Con
trol
sP
redic
tors
Outc
ome
Est
.ra
All
ison
etal
.,2
00
31
01
surv
ivor
sof
hea
dan
dn
eck
can
cer
Mor
tali
ty1
year
Age
,d
isea
sest
age,
coh
abit
atio
nst
atu
sH
igh
Op
tim
ism
OR
51
.12
(1.0
1,
1.2
4)
r or5�
.22
Alm
ada
etal
.,1
99
11
,87
1m
emb
ers
ofth
eW
este
rnE
lect
ric
Stu
dy
All
-cau
sem
orta
lity
25
year
sA
ge,
blo
odp
ress
ure
,sm
okin
g,ch
oles
tero
l,al
cohol
consu
mpti
on
Hig
hN
euro
tici
smR
R5
1.2
0(1
.00
,1
.40
)r r
r5
.05
Hig
hC
ynic
ism
RR
51
.4(1
.2,
1.7
)r r
r5
.09
Bar
efoo
t,D
ahls
trom
,&
Wil
liam
s,1
98
32
55
med
ical
stu
den
tsA
ll-c
ause
mor
tali
ty2
5ye
ars
Hig
hH
osti
lity
p5
.00
5r e
5.1
8
Bar
efoo
t,D
odge
,P
eter
son
,D
ahls
trom
,&
Wil
liam
s,1
98
9
12
8la
wS
tud
ents
29
year
sA
geH
igh
Hos
tili
typ
5.0
12
r e5
.22
Bar
efoo
t,L
arse
n,
von
der
Lie
th,
&S
chro
ll,
19
95
73
0re
sid
ents
ofG
lost
rup
bor
nin
19
14
All
-cau
sem
orta
lity
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year
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ge,
sex,
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odp
ress
ure
,sm
okin
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igly
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d,
FE
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igh
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tili
tyR
R5
1.3
6(1
.06
,1
.75
)r r
r5
.09
Bar
efoo
tet
al.,
19
98
10
0O
lder
men
and
wom
enA
ll-c
ause
mor
tali
ty1
4ye
ars
Sex
,ag
eH
igh
Tru
stR
R5
0.4
6(0
.24
,0
.91
)p<
.03
r rr5�
.23
r e5�
.22
Bar
efoo
tet
al.,
19
87
50
0m
emb
ers
ofth
ese
con
dD
uk
elo
ngi
tud
inal
stu
dy
All
-cau
sem
orta
lity
15
year
sA
ge,s
ex,c
hol
este
roll
evel
s,sm
okin
g,p
hys
icia
nra
tin
gsof
hea
lth
Su
spic
iou
snes
sp
5.0
2r e
5.1
0
Boy
leet
al.,
20
05
1,3
28
Du
ke
Un
iver
sity
Med
ical
Cen
ter
pat
ien
tsA
ll-c
ause
mor
tali
ty1
5ye
ars
Sex
,ag
e,to
bac
coco
nsu
mpti
on,
hyp
erte
nsi
on,
hyp
erli
pid
emia
,n
um
ber
ofco
ron
ary
arte
ries
nar
row
ed,
left
ven
tric
ula
rej
ecti
onfr
acti
on,
arte
ryb
ypas
ssu
rger
y
Hig
hH
osti
lity
HR
51
.25
(1.0
6,
1.4
7)
p<
.00
7r h
r5
.07
r e5
.07
Boy
leet
al.,
20
04
93
6D
uk
eU
niv
ersi
tyM
edic
alC
ente
rp
atie
nts
All
-cau
sem
orta
lity
15
year
sS
ex,
age,
tob
acco
consu
mpti
on,
hyp
erte
nsi
on,
hyp
erli
pid
emia
,n
um
ber
ofco
ron
ary
arte
ries
nar
row
ed,
left
ven
tric
ula
rej
ecti
onfr
acti
on,
arte
ryb
ypas
ssu
rger
y
Hig
hH
osti
lity
HR
51
.28
(1.0
6,
1.5
5)
p<
.02
r hr5
.08
r e5
.08
Ch
rist
ense
net
al.,
20
02
17
4ch
ron
icre
nal
insu
ffici
ency
pat
ien
tsM
orta
lity
4ye
ars
Age
,d
iab
etic
stat
us,
hem
oglo
bin
Hig
hC
onsc
ien
tiou
snes
sH
R5
0.9
4,B
5�
.06
6(.
03
)p<
.05
r B5�
.17
r e5�
.15
Hig
hN
euro
tici
smH
R5
1.0
5,
B5
.04
7(.
02
3)
p<
.05
r hr5
.15
r e5
.15
Dan
ner
etal
.,2
00
11
80
nu
ns
Lon
gevi
ty6
3ye
ars
Age
,ed
uca
tion
,li
ngu
isti
cab
ilit
yH
igh
Pos
itiv
eE
mot
ion
(sen
ten
ces)
HR
52
.50
(1.2
0,
5.3
0)
p<
.01
r hr5
.18
r e5
.19
Hig
hP
osit
ive
Em
otio
n(w
ord
s)H
R5
3.2
0(1
.50
,6
.80
)p<
.01
r hr5
.22
r e5
.19
Volume 2—Number 4 323
Brent W. Roberts et al.
Table
2.
(Cont’
d.)
Stu
dy
NO
utc
ome
Len
gth
ofst
udy
Con
trol
sP
redic
tors
Outc
ome
Est
.ra
Dif
fere
ntP
osit
ive
Em
otio
ns
HR
54
.30
(1.7
0,
10
.40
)p<
.01
r hr5
.24
r e5
.19
Den
olle
tet
al.,
19
96
30
3C
HD
pat
ien
tsM
orta
lity
8ye
ars
CH
D,
age,
soci
alal
ien
atio
n,
dep
ress
ion
,u
seof
ben
zod
iaze
pin
es
Typ
eD
per
son
alit
ybH
R5
4.1
0(1
.90
,8
.80
)p
5.0
00
4r h
r5
.21
r e5
.20
Eve
rson
etal
.,1
99
72
,12
5m
enfr
omth
eK
uop
ioE
sch
emic
Hea
rtD
isea
seR
isk
Fac
tor
Stu
dy
All
-cau
sem
orta
lity
9ye
ars
Age
,S
ES
Cyn
ical
dis
tru
stH
R5
1.9
7(1
.26
,3
.09
)r h
r5
.06
Fri
edm
anet
al.,
19
93
1,1
78
mem
ber
sof
the
Ter
man
Lif
ecyc
leS
tud
yL
onge
vity
71
year
sS
ex,
IQH
igh
Con
scie
nti
ousn
ess
HR
5.3
3,
B5�
1.1
1(0
.37
)p<
.01
r hr5
.09
r e5
.08
Hig
hC
hee
rfu
lnes
scH
R5
1.2
1,
B5
.19
(.0
7)
p<
.05
r hr5�
.08
r e5�
.06
Gil
tay,
Gel
eijn
se,
Zit
man
,H
oek
stra
,&
Sch
oute
n,
20
04
39
7m
enan
d4
18
wom
enof
the
Arn
hem
Eld
erly
Stu
dy
All
-cau
sem
orta
lity
9ye
ars
Age
,sm
okin
g,al
coh
ol,
edu
cati
on,
acti
vity
leve
l,S
ES
,an
dm
arit
alst
atu
s
Dis
pos
itio
nal
opti
mis
mM
en’s
HR
50
.58
(0.3
7,
0.9
1)
p5
.01
r hr5�
.12
r e5�
.13
Wom
en’s
HR
50
.80
(0.5
1–
1.2
5)
p5
.39
r hr5�
.05
r e5�
.04
Gro
ssar
th-M
atic
ek,
Bas
tian
ns,
&K
anaz
ir,1
98
51
,33
5in
hab
itan
tsof
Crv
enk
a,Y
ugo
slav
iaM
orta
lity
10
year
sA
geH
igh
Rat
ion
alit
ydp<
.00
1r e
5.0
9
Hea
rn,M
urr
ay,&
Lu
epk
er,
19
89
1,3
13
Un
iver
sity
ofM
inn
esot
ast
ud
ents
All
-cau
sem
orta
lity
33
year
sA
geH
igh
Hos
tili
typ
5.7
2r e
5.0
1
Hir
okaw
a,N
agat
a,Tak
atsu
ka,
&S
him
izu
,2
00
4
12
,41
7m
ales
and
14
,13
3fe
mal
esof
the
Tak
ayam
aS
tud
y
7ye
ars
Age
,sm
okin
g,m
arit
alst
atu
s,B
MI,
exer
cise
,al
coh
ol,
edu
cati
on,
and
nu
mb
erof
chil
dre
n
Hig
hR
atio
nal
ityd
Men
’sH
R5
0.9
6(0
.83
,1
.09
)W
omen
’sH
R5
0.8
2,
(0.7
0,
0.9
6)
p<
.05
r hr5�
.01
r hr5�
.02
r e5�
.02
Hol
lis,
Con
net
t,S
teve
ns,
&G
reen
lick
,1
99
01
2,8
66
men
from
the
Mu
ltip
leR
isk
Fac
tor
Inte
rven
tion
Tri
al
All
-cau
sem
orta
lity
6ye
ars
Stu
dy
grou
pas
sign
men
t,ag
e,ci
gare
ttes
,b
lood
pre
ssu
re,
chol
este
rol
Hig
hTy
pe
Ap
erso
nal
ity
RR
50
.94
(0.8
9,
0.9
9)
p<
.01
r hr5�
.02
r e5�
.02
Irib
arre
net
al.,
20
05
5,1
15
mem
ber
sof
the
CA
RD
IAst
ud
yN
on-A
IDS
,n
on-
hom
icid
e-re
late
dm
orta
lity
16
year
sA
ge,
sex,
race
Hig
hH
osti
lity
RR
52
.02
(1.0
7,
3.8
1)
r rr5
.03
Kap
lan
etal
.,1
99
42
,46
4m
enfr
omth
eK
uop
ioE
sch
emic
Hea
rtD
isea
seR
isk
Fac
tor
Stu
dy
All
-cau
sem
orta
lity
6ye
ars
Age
,in
com
eS
hyn
ess
HR
51
.01
(0.6
3,
1.6
2)
r hr5
.00
Kor
ten
etal
.,1
99
98
97
sub
ject
sag
ed7
0ye
ars
and
old
erM
orta
lity
4ye
ars
Age
,se
x,ge
ner
alh
ealt
h,
AD
Ls,
illn
ess,
blo
odp
ress
ure
,S
ymb
ol–
Let
ter
Mod
alit
ies
Tes
t,M
MS
E
Hig
hN
euro
tici
smH
R5
0.5
3(0
.31
,0
.90
)r h
r5�
.08
324 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
Ku
sken
vuo
etal
.,1
98
83
,75
0F
inn
ish
mal
etw
ins
All
-cau
sem
orta
lity
3ye
ars
Age
Hig
hH
osti
lity
RR
52
.98
(1.3
1,
6.7
7)
r rr5
.04
Mar
uta
,C
olli
gan
,M
alin
choc
,&
Off
ard
,2
00
08
39
pat
ien
tsfr
omth
eM
ayo
Cli
nic
All
-cau
sem
orta
lity
29
year
sS
ex,
age,
exp
ecte
dsu
rviv
alP
essi
mis
mH
R5
1.2
0(1
.04
,1
.38
)p
5.0
1r h
r5
.09
r e5
.09
Mar
uta
etal
.,1
99
36
20
from
the
May
oC
lin
icA
ll-c
ause
mor
tali
ty2
0ye
ars
Age
,se
x,h
yper
ten
sion
,w
eigh
tH
igh
Hos
tili
typ
5.0
69
r e5
.07
McC
arro
n,
Gu
nn
ell,
Har
riso
n,
Ok
ash
a,&
Dav
ey-S
mit
h,
20
03
8,3
85
form
erm
ale
stu
den
tsA
ll-c
ause
mor
tali
ty4
1ye
ars
Sm
okin
g,fa
ther
’sS
ES
,B
MI,
mat
ern
alan
dp
ater
nal
vita
lst
atu
s
Men
tal
inst
abil
ity
RR
52
.05
(1.3
6–
3.0
9)
p<
.01
r rr5
.04
r e5
.03
McC
ran
ie,
Wat
kin
s,B
ran
dsm
a,&
Sis
son
,1
98
64
78
ph
ysic
ian
sA
ll-c
ause
mor
tali
ty2
5ye
ars
Hig
hH
osti
lity
p5
.78
9r e
5�
.01
Mu
rber
g,B
ru,
&A
arsl
and
,2
00
11
19
hea
rtfa
ilu
rep
atie
nts
Mor
tali
ty2
year
sA
ge,
sex,
dis
ease
seve
rity
Neu
roti
cism
HR
51
.14
0(1
.02
7,
1.2
65
)p
5.0
1
r hr5
.23
r e5
.24
Osl
eret
al.,
20
03
7,3
08
mem
ber
sof
Pro
ject
Met
rop
olit
inC
open
hag
en,
Den
mar
k
All
-cau
sem
orta
lity
49
year
sIQ
,b
irth
wei
ght,
SE
SC
reat
ivit
yH
R5
1.1
7(0
.89
,1
.54
)r h
r5
.01
C.
Pet
erso
n,
Sel
igm
an,
Yu
rko,
Mar
tin
,&
Fri
edm
an,
19
98
1,1
79
mem
ber
sof
the
Ter
man
Lif
ecyc
leS
tud
yM
orta
lity
51
Yea
rsG
lob
alp
essi
mis
mO
R5
1.2
6,
p<
.01
r e5
.08
Sch
ulz
etal
.,1
99
62
38
can
cer
pat
ien
tsC
ance
rm
orta
lity
8m
onth
sS
ite
ofca
nce
r,p
hys
ical
sym
pto
ms,
age
Pes
sim
ism
OR
51
.07
,B
5.0
7(.
05
)r B
5.0
8
Pes
sim
ism�
Age
inte
ract
ion
OR
50
.88
,B
5�
.12
(.0
6),
p<
.05
r B5
.11
r e5
.13
Su
rtee
s,W
ain
wri
ght,
Lu
ben
,D
ay,
&K
haw
,2
00
52
0,5
50
mem
ber
sof
the
EP
IC-N
orfo
lkst
ud
y(8
,95
0m
enan
d1
1,6
00
wom
en)
Mor
tali
ty6
year
sA
ge,
dis
ease
,ci
gare
tte
smok
ing
his
tory
Hos
tili
tyM
en’s
RR
51
.06
(0.9
9,
1.1
4)
r rr5
.02
Wom
en’s
RR
51
.00
(.9
1,
1.0
9)
r rr5
.00
Su
rtee
s,W
ain
wri
ght,
Lu
ben
,K
haw
,&
Day
,2
00
31
8,2
48
mem
ber
sof
the
EP
IC-N
orfo
lkst
ud
yM
orta
lity
6ye
ars
Age
,d
isea
se,
soci
alcl
ass,
ciga
rett
esm
okin
gh
isto
ryS
tron
gse
nse
ofco
her
ence
RR
50
.76
(0.6
5,
0.8
7)
p<
.00
01
(tak
enfr
omab
stra
ct)
r hr5�
.03
r e5�
.03
Wei
ss&
Cos
ta,
20
05
e1
,07
6m
emb
ers
ofth
eM
edic
are
Pri
mar
yan
dC
onsu
mer
-Dir
ecte
dC
are
Dem
onst
rati
on
All
-cau
sem
orta
lity
5ye
ars
Gen
der
,ag
e,ed
uca
tion
,d
iab
etic
stat
us,
card
iova
scu
lar
dis
ease
,fu
nct
ion
alli
mit
atio
ns,
self
-ra
ted
hea
lth
,ci
gare
tte
smok
ing,
dep
ress
ion
,N
euro
tici
sm,
Agr
eeab
len
ess
Con
scie
nti
ousn
ess
HR
50
.51
(0.3
1,
0.8
5)
p<
.05
r hr5�
.08
r e5�
.06
Gen
der
,ag
e,ed
uca
tion
dia
bet
icst
atu
s,ca
rdio
vasc
ula
rd
isea
se,
fun
ctio
nal
lim
itat
ion
s,se
lf-
rate
dh
ealt
h,
ciga
rett
e
Neu
roti
cism
HR
50
.99
(0.9
7,
1.0
0)
p<
.05
r hr5�
.04
r e5�
.06
Volume 2—Number 4 325
Brent W. Roberts et al.
Table
2.
(Cont’
d.)
Stu
dy
NO
utc
ome
Len
gth
ofst
ud
yC
ontr
ols
Pre
dic
tors
Ou
tcom
eE
st.
ra
smok
ing,
dep
ress
ion
,C
onsc
ien
tiou
snes
s,A
gree
able
nes
sG
end
er,
age,
edu
cati
on,
dia
bet
icst
atu
s,ca
rdio
vasc
ula
rd
isea
se,
fun
ctio
nal
lim
itat
ion
s,se
lf-
rate
dh
ealt
h,
ciga
rett
esm
okin
g,d
epre
ssio
n,
Neu
roti
cism
,C
onsc
ien
tiou
snes
s
Agr
eeab
len
ess
HR
50
.99
(0.9
8,
1.0
0)
r hr5�
.06
Wil
son
etal
.,2
00
38
51
mem
ber
sof
the
Rel
igio
us
Ord
ers
Stu
dy
All
-cau
sem
orta
lity
5ye
ars
Age
,se
x,ed
uca
tion
,h
ealt
hTra
itan
xiet
yR
R5
1.0
4(0
.99
,1
.09
)p
5.0
1(u
nad
just
ed)
r rr5
.05
r e5
.09
Tra
itan
ger
RR
51
.03
(0.9
5,
1.1
2)
p5
.64
(un
adju
sted
)r r
r5
.02
r e5
.02
Wil
son
etal
.,2
00
56
,15
8m
emb
ers
(age
d6
5ye
ars
and
old
er)
ofth
eC
hic
ago
Hea
lth
and
Agi
ng
Pro
ject
All
-cau
sem
orta
lity
6ye
ars
Age
,se
x,ra
ce,
edu
cati
onN
euro
tici
smR
R5
1.0
16
(1.0
10
,1
.02
0)
r rr5
.07
Ext
rave
rsio
nR
R5
0.9
84
(0.9
78
,0
.99
1)
r rr5�
.05
Wil
son
etal
.,2
00
48
83
mem
ber
sof
the
Rel
igio
us
Ord
ers
Stu
dy
All
-cau
sem
orta
lity
5ye
ars
Age
,ge
nd
er,
edu
cati
on,
rem
ain
ing
per
son
alit
ytr
aits
Neu
roti
cism
RR
51
.04
(1.0
2,
1.0
8)
p<
.02
(un
adju
sted
)r r
r5
.12
r e5
.09
Ext
rave
rsio
nR
R5
0.9
6(0
.94
,0
.99
)p<
.00
1(u
nad
just
ed)
r rr5�
.08
r e5�
.11
Op
enn
ess
RR
51
.00
5(0
.97
0,
1.0
40
)p
5.0
14
r rr5
.01
r e5
.08
Agr
eeab
len
ess
RR
50
.96
4(0
.93
0,
1.0
00
)p
5.0
11
r rr5�
.06
r e5�
.09
Con
scie
nti
ousn
ess
RR
50
.96
8(0
.94
,0
.99
)p<
.00
1
r rr5�
.07
r e5�
.11
Note
.C
on
fid
ence
inte
rvals
are
given
inp
are
nth
eses
.H
R5
haza
rdra
tio;
RR
5re
lati
ve
risk
rati
o;
OR
5od
ds
rati
o;
r rr
5co
rrel
ati
on
esti
mate
dfr
om
the
rate
rati
o;
r hr
5co
rrel
ati
on
esti
mate
dfr
om
the
haza
rdra
tio;
r or
5co
rrel
ati
on
esti
mate
dfr
om
the
od
ds
rati
o;
r B5
corr
elati
on
esti
mate
dfr
om
ab
eta
wei
ght
an
dst
an
dard
erro
r;r e
5r e
qu
ivale
nt(c
orr
elati
on
esti
mate
dfr
om
the
rep
ort
edp
valu
ean
dsa
mp
lesi
ze);
FE
V5
forc
edex
pir
ato
ryvolu
me;
CH
D5
coro
nary
hea
rtd
isea
se;
SE
S5
soci
oec
on
om
icst
atu
s;B
MI
5b
od
y–m
ass
ind
ex;
AD
Ls
5act
ivit
ies
of
dail
yli
vin
g;M
MS
E5
Min
iM
enta
lS
tate
Exam
inati
on
.aT
he
dir
ecti
on
of
the
corr
elati
on
was
der
ived
by
choosi
ng
ap
osi
tive
pole
for
each
dim
ensi
on
(hig
hE
xtr
aver
sion
,A
gree
ab
len
ess,
Con
scie
nti
ou
snes
s,N
euro
tici
sm,
an
dO
pen
nes
s)an
dass
um
ing
that
each
dim
ensi
on
,w
ith
the
exce
pti
on
of
Neu
roti
cism
,w
ou
ldb
en
egati
vel
yre
late
dto
mort
ali
tyin
its
posi
tive
man
ifes
tati
on
.bT
yp
eD
per
son
ali
tyw
as
cate
gori
zed
as
aN
euro
tici
smm
easu
reas
itco
rrel
ate
sm
ore
con
sist
entl
yw
ith
hig
hN
euro
tici
sm(D
eF
ruyt
&D
enoll
et,
2002),
tho
ugh
itsh
ou
ldb
en
ote
dth
at
ith
as
stro
ng
corr
elati
on
sw
ith
low
Extr
aver
sion
,lo
wA
gree
ab
len
ess,
an
dlo
wC
on
scie
nti
ou
snes
s.c O
nth
eb
asi
sof
the
corr
elati
on
sp
rese
nte
din
Mart
inan
dF
ried
man
(2000),
chee
rfu
lnes
sw
as
cate
gori
zed
as
am
easu
reof
Agr
eeab
len
ess.
dR
ati
on
ali
tyw
as
not
cate
gori
zed
into
the
Big
Fiv
eb
ecau
seit
mea
sure
ssu
pp
ress
ion
of
agg
ress
ion
,w
hic
hd
oes
not
easi
lyfa
llin
toon
eof
the
five
bro
ad
do
main
s.e T
he
dis
crep
an
cyin
the
Haza
rdra
tios
resu
lts
from
the
fact
that
the
Neu
roti
cism
score
sw
ere
con
tin
uou
san
dth
eC
on
scie
nti
ou
snes
ssc
ore
sw
ere
tric
hoto
miz
ed.
326 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
of personality traits on mortality appears to be equivalent to IQ,
although the additive effect of multiple trait domains on mor-
tality may well exceed that of IQ.
Why would personality traits predict mortality? Personality
traits may affect health and ultimately longevity through at least
three distinct processes (Contrada, Cather, & O’Leary, 1999;
cio, 1998). In contrast, those individuals who were more con-
scientious and agreeable tended to remain longer in their
marriages and avoided divorce (Kelly & Conley, 1987; Kin-
nunen & Pulkkenin, 2003; Roberts & Bogg, 2004). Although
these studies did not control for as many factors as the health
studies, the time spans over which the studies were carried out
were impressive (e.g., 45 years). We aggregated effects across
these studies for the trait domains of Neuroticism, Agreeable-
ness, and Conscientiousness with the correlation metric, as too
few studies reported relative risk outcomes to warrant aggre-
gating. When so aggregated, the effect of Neuroticism on divorce
was .17 (CIs 5 .12 and .22), the effect of Agreeableness was
�.18 (CIs 5 �.27 and �.09), and the effect of Conscientious-
ness on divorce was �.13 (CIs 5 �.17 and �.09). Thus, the
predictive effects of these three personality traits on divorce
were greater than those found for SES.
Why would personality traits lead to divorce or conversely
marital stability? The most likely reason is because personality
traits help shape the quality of long-term relationships. For
example, Neuroticism is one of the strongest and most consistent
personality predictors of relationship dissatisfaction, conflict,
abuse, and ultimately dissolution (Karney & Bradbury, 1995).
Sophisticated studies that include dyads (not just individuals)
and multiple methods (not just self reports) increasingly
6We identified studies using electronic searches including the terms divorce,socioeconomic status, and cognitive ability. We also identified studies throughexamining the reference sections of the studies and through studies that citedeach study.
Volume 2—Number 4 327
Brent W. Roberts et al.
TA
BL
E3
SE
Sa
nd
IQE
ffec
tso
nD
ivo
rce
Stu
dy
NO
utc
ome
Len
gth
ofst
ud
yC
ontr
olva
riab
les
Pre
dic
tor
Res
ult
sE
st.
r
Am
ato
&R
oger
s,1
99
71
,74
2co
up
les
from
the
Pan
el
Stu
dy
ofM
arit
alIn
stab
ilit
y
over
the
Lif
eC
ours
e
Div
orce
12
year
sA
geat
mar
riag
e,p
rior
coh
abit
atio
n,
eth
nic
ity,
year
sm
arri
ed,
chu
rch
atte
nd
ance
,ed
uca
tion
,
emp
loym
ent,
hu
sban
d’s
inco
me,
rem
arri
age,
par
ents
div
orce
d
Wif
e’s
inco
me
p5
.01
r e5
.06
Ben
tler
&N
ewco
mb
,1
97
87
7co
up
les
(53
mal
es,
24
fem
ales
)
Div
orce
4ye
ars
Wom
en’s
edu
cati
on
occu
pat
ion
p5
.05
p5
.05
r e5�
.22
r e5�
.22
Fer
guss
on,
Hor
woo
d,
&S
han
non
,
19
84
1,0
02
fam
ilie
sfr
omth
e
Ch
rist
chu
rch
Ch
ild
Dev
elop
men
tS
tud
y
Fam
ily
bre
akd
own
5ye
ars
Mat
ern
alag
e,fa
mil
ysi
ze,
chu
rch
atte
nd
ance
,
mar
riag
ety
pe,
len
gth
of
mar
riag
e,p
lan
nin
gof
pre
gnan
cy
SE
ST
52
.86
r t5�
.09
Hel
son
,2
00
69
8w
omen
Div
orce
31
year
sS
AT
Ver
bal
SA
TM
ath
r5�
.06
r5
.08
Hol
ley,
Yab
iku
,&
Ben
in,
20
06
67
0m
oth
ers
from
the
Inte
rgen
erat
ion
alS
tud
yof
Par
ents
and
Ch
ild
ren
Div
orce
13
year
sA
geat
mar
riag
e,re
ligi
on,
chu
rch
atte
nd
ance
,
pre
viou
sco
hab
itat
ion
,
nu
mb
erof
chil
dre
n
Sim
ilar
itie
ssu
bte
st
from
WA
IS
t5�
3.0
2r t
5�
.12
Jalo
vaar
a,2
00
17
66
,63
7fi
rst
mar
riag
esfr
om
Fin
lan
d
Div
orce
2ye
ars
Du
rati
onof
mar
riag
e,w
ife’
s
age
atm
arri
age,
fam
ily
com
pos
itio
n,
deg
ree
of
urb
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328 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
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dy
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en.
Volume 2—Number 4 329
Brent W. Roberts et al.
TA
BL
E4
Per
son
ali
tyT
rait
sa
nd
Ma
rita
lO
utc
om
es
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dy
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ome
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eC
ontr
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dic
tors
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r
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tler
&N
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330 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
Kin
nu
nen
&P
ulk
kin
en,
20
03
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omen
and
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gitu
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al
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die
s
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Volume 2—Number 4 331
Brent W. Roberts et al.
Table
4.
(Cont’
d.)
Stu
dy
NO
utc
ome
Tim
eC
ontr
ols
Pre
dic
tors
Res
ult
sE
st.
r
Tu
cker
etal
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99
87
73
from
the
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mat
ive
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ng
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dy
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orce
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year
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geat
mar
riag
e,
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cati
on
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acy
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5)
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xiet
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R5
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1
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siti
vity
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.80
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5.2
5)
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.12
r e5
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ger
OR
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.70
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1)
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.00
1
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sion
OR
51
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1,
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1)
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96
8m
emb
ers
ofth
e
Ter
man
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eC
ycle
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dy
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53
to7
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ars
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,ed
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tion
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eat
mar
riag
e
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scie
nti
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ess
OR
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ent
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ng
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(0.8
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1)
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teac
her
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ng
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1)
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ng
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ng
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ent
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ent
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teac
her
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on
fid
ence
inte
rvals
are
given
inp
are
nth
eses
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R5
haza
rdra
tio;
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lati
ve
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o;
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ds
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o;
r d5
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elati
on
esti
mate
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om
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ore
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r5
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mate
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om
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od
ds
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r F5
corr
elati
on
esti
mate
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om
Fte
st;
r e5
r eq
uiv
ale
nt
(corr
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on
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mate
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om
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rep
ort
edp
valu
ean
dsa
mp
lesi
ze);
MM
PI
5M
inn
esota
Mu
ltip
hasi
cP
erso
nali
tyIn
ven
tory
;IH
S5
Inst
itu
teof
Hu
man
Dev
elop
men
t.
332 Volume 2—Number 4
The Comparative Predictive Validity of Personality Traits
demonstrate that the links between personality traits and rela-
tionship processes are more than simply an artifact of shared
method variance in the assessment of these two domains (Don-
traits shape people’s reactions to the behavior of their partners.
For example, disagreeable individuals may escalate negative
affect during conflict (e.g., Gottman, Coan, Carrere, & Swanson,
1998). Similarly, agreeable people may be better able to regulate
emotions during interpersonal conflicts (Jensen-Campbell &
Graziano, 2001). Cognitive processes also factor in creating
trait-correlated experiences (Snyder & Stukas, 1999). For ex-
ample, highly neurotic individuals may overreact to minor
criticism from their partner, believe they are no longer loved
when their partner does not call, or assume infidelity on the basis
of mere flirtation. Third, personality traits evoke behaviors from
partners that contribute to relationship quality. For example,
people high in Neuroticism and low in Agreeableness may be
more likely to express behaviors identified as detrimental to
relationships such as criticism, contempt, defensiveness, and
stonewalling (Gottman, 1994).
The Predictive Validity of Personality Traits for
Educational and Occupational Attainment
The role of personality traits in occupational attainment has
been studied sporadically in longitudinal studies over the last
few decades. In contrast, the roles of SES and IQ have been
studied exhaustively by sociologists in their programmatic re-
search on the antecedents to status attainment. In their seminal
work, Blau and Duncan (1967) conceptualized a model of status
attainment as a function of the SES of an individual’s father.
Researchers at the University of Wisconsin added what they
considered social-psychological factors (Sewell, Haller, &
Portes, 1969). In this Wisconsin model, attainment is a function
of parental SES, cognitive abilities, academic performance,
occupational and educational aspirations, and the role of sig-
nificant others (Haller & Portes, 1973). Each factor in the model
has been found to be positively related to occupational attain-
ment (Hauser, Tsai, & Sewell, 1983). The key question here is to
what extent SES and IQ predict educational and occupational
attainment holding constant the remaining factors.
A great deal of research has validated the structure and
content of the Wisconsin model (Sewell & Hauser, 1980; Sewell
& Hauser, 1992), and rather than compiling these studies, which
are highly similar in structure and findings, we provide repre-
sentative findings from a study that includes three replications of
the model (Jencks, Crouse, & Mueser, 1983). As can be seen in
Table 5, childhood socioeconomic indicators, such as father’s
occupational status and mother’s education, are related to out-
comes, such as grades, educational attainment, and eventual
occupational attainment, even after controlling for the remain-
ing variables in the Wisconsin model. The average beta weight of
SES and education was .09.7 Parental income had a stronger
effect, with an average beta weight of .14 across these three
studies. Cognitive abilities were even more powerful predictors
of occupational attainment, with an average beta weight of .27.
Do personality traits contribute to the prediction of occupa-
tional attainment even when intelligence and socioeconomic
background are taken into account? As there are far fewer
studies linking personality traits directly to indices of occupa-
tional attainment, such as prestige and income, we also included
prospective studies examining the impact of personality traits on
related outcomes such as long-term unemployment and occu-
pational stability. The studies listed in Table 6 attest to the fact
that personality traits predict all of these work-related outcomes.
For example, adolescent ratings of Neuroticism, Extraversion,
Agreeableness, and Conscientiousness predicted occupational
status 46 years later, even after controlling for childhood IQ
(Judge, Higgins, Thoresen, & Barrick, 1999). The weighted-
average beta weight across the studies in Table 6 was .23 (CIs 5
.14 and .32), indicating that the modal effect size of personality
traits was comparable with the effect of childhood SES and IQ
on similar outcomes.8
Why are personality traits related to achievement in educa-
tional and occupational domains? The personality processes
involved may vary across different stages of development, and at
least five candidate processes deserve research scrutiny (Rob-
erts, 2006). First, the personality-to-achievement associations
may reflect ‘‘attraction’’ effects or ‘‘active niche-picking,’’
whereby people choose educational and work experiences
whose qualities are concordant with their own personalities. For
7We did not transform the standardized beta weights into the correlationmetric because almost all authors failed to provide the necessary information forthe transformation (CIs or standard errors). Therefore, we averaged the resultsin the beta weight metric instead. As the sampling distribution of beta weights isunknown, we used the formula for the standard error of the partial correlation(p
N�k�2) to estimate CIs.8In making comparisons between correlations and regression weights, it
should be kept in mind that although the two are identical for orthogonalpredictors, most regression weights tend to be smaller than the correspondingzero-order validity correlations because of predictor redundancy (R.A. Peterson& Brown, 2005).
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example, people who are more conscientious may prefer con-
ventional jobs, such as accounting and farming (Gottfredson,
Jones, & Holland, 1993). People who are more extraverted may
prefer jobs that are described as social or enterprising, such as
teaching or business management (Ackerman & Heggestad,
1997). Moreover, extraverted individuals are more likely to as-
sume leadership roles in multiple settings (Judge, Bono, Ilies, &
Gerhardt, 2002). In fact, all of the Big Five personality traits
have substantial relations with better performance when the
personality predictor is appropriately aligned with work criteria
(Hogan & Holland, 2003). This indicates that if people find jobs
that fit with their dispositions they will experience greater levels
of job performance, which should lead to greater success, ten-
ure, and satisfaction across the life course (Judge et al., 1999).
Second, personality-to-achievement associations may reflect
‘‘recruitment effects,’’ whereby people are selected into
achievement situations and are given preferential treatment on
the basis of their personality characteristics. These recruitment
effects begin to appear early in development. For example,
children’s personality traits begin to influence their emerging
relationships with teachers at a young age (Birch & Ladd, 1998).
In adulthood, job applicants who are more extraverted, consci-
entious, and less neurotic are liked better by interviewers and
are more often recommended for the job (Cook, Vance, &
Spector, 2000).
Third, personality traits may affect work outcomes because
people take an active role in shaping their work environment
(Roberts, 2006). For example, leaders have tremendous power to
shape the nature of the organization by hiring, firing, and pro-
moting individuals. Cross-sectional studies of groups have
shown that leaders’ conscientiousness and cognitive ability af-
fect decision making and treatment of subordinates (LePine,
Hollenbeck, Ilgen, & Hedlund, 1997). Individuals who are not
leaders or supervisors may shape their work to better fit them-
selves through job crafting (Wrzesniewski & Dutton, 2001) or
job sculpting (Bell & Staw, 1989). They can change their day-
to-day work environments through changing the tasks they do,
organizing their work differently, or changing the nature of the
relationships they maintain with others (Wrzesniewski & Dut-
ton, 2001). Presumably these changes in their work environ-
ments lead to an increase in the fit between personality and
work. In turn, increased fit with one’s environment is associated
with elevated performance (Harms, Roberts, & Winter, 2006).
Fourth, some personality-to-achievement associations emerge
as consequences of ‘‘attrition’’ or ‘‘deselection pressures,’’
whereby people leave achievement settings (e.g., schools or
jobs) that do not fit with their personality or are released from
these settings because of their trait-correlated behaviors (Cairns
& Cairns, 1994). For example, longitudinal evidence from
different countries shows that children who exhibit a combina-
tion of poor self-control and high irritability or antagonism are at
heightened risk of unemployment (Caspi, Wright, Moffitt, &
in turn, educational attainment is the best predictor of occu-
pational attainment. This observation about cumulative
indirect effects applies equally well to SES and personality
traits.
Furthermore, the effect sizes associated with SES, cognitive
abilities, and personality traits were all uniformly small-to-
medium in size. This finding is entirely consistent with those
from other reviews showing that most psychological constructs
have effect sizes in the range between .10 and .40 on a corre-
lational scale (Meyer et al., 2001). Our hope is that reviews like
this one can help adjust the norms researchers hold for what the
modal effect size is in psychology and related fields. Studies are
often disparaged for having small effects as if it is not the norm.
Moreover, small effect sizes are often criticized without any
understanding of their practical significance. Practical signifi-
cance can only be determined if we ground our research by both
predicting consequential outcomes, such as mortality, and by
translating the results into a metric that is clearly understand-
able, such as years lost or number of deaths. Correlations and
ratio statistics do not provide this type of information. On the
other hand, some researchers have translated their results into
metrics that most individuals can grasp. As we noted in the
introduction, Rosenthal (1990) showed that taking aspirin pre-
vented approximately 85 heart attacks in the patients of 10,845
physicians despite the meager �.03 correlation between this
practice and the outcome of having a heart attack. Several other
studies in our review provided similar benchmarks. Hardarson
et al., (2001) showed that 148 fewer people died in their high
education group (out of 869) than in their low education group,
despite the effect size being equal to a correlation of �.05.
Danner et al. (2001) showed that the association between pos-
itive emotion and longevity was associated with a gain of almost
7 years of additional life, despite having an average effect size of
around .20. Of course, our ability to draw these types of con-
clusions necessitates grounding our research in more practical
outcomes and their respective metrics.
There is one salient difference between many of the studies of
SES and cognitive abilities and the studies focusing on per-
sonality traits. The typical sample in studies of the long-term
effect of personality traits was a sample of convenience or was
distinctly unrepresentative. In contrast, many of the studies of
SES and cognitive ability included nationally representative
and/or remarkably large samples (e.g., 500,000 participants).
Therefore, the results for SES and cognitive abilities are gen-
eralizable, whereas it is more difficult to generalize findings from
personality research. Perhaps the situation will improve if future
demographers include personality measures in large surveys of
the general population.
Recommendations
One of the challenges of incorporating personality measures in
large studies is the cost–benefit trade off involved with including
a thorough assessment of personality traits in a reasonably short
period of time. Because most personality inventories include
many items, researchers may be pressed either to eliminate them
from their studies or to use highly abbreviated measures of
personality traits. The latter practice has become even more
common now that most personality researchers have concluded
that personality traits can be represented within five to seven
broad domains (Goldberg, 1993b; Saucier, 2003). The tempta-
tion is to include a brief five-factor instrument under the as-
sumption that this will provide good coverage of the entire range
0
0.05
0.10
0.15
0.20
0.25
0.30
SES
Co
rrel
atio
n
C N A
Fig. 2. Average effects (in the correlation metric) of low socioeconomicstatus (SES), low Conscientiousness (C), Neuroticism (N), and lowAgreeableness (A) on divorce. Error bars represent standard error.
0
0.05
0.10
0.15
0.20
0.25
0.30
SES
Sta
nd
ard
ized
Bet
a W
eig
ht
Parental income IQ Personality Traits
Fig. 3. Average effects (in the standardized beta weight metric) of highsocioeconomic status (SES), high parental income, high IQ, and highpersonality trait scores on occupational outcomes.
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