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Personality and Intelligence: Examining the Associations of Investment-Related Personality Traits With General and Specific Intelligence

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In this study, we examine the associations of the scales of the California Psychological Inventory (CPI; a measure of personality traits) with intelligence measured by four cognitive ability tests, completed by a sample of 4876 working adults. We framed our analyses of the correlations around the investment perspective on the personality-intelligence relationship that proposes traits are associated with investment in intellectual activity, which develops cognitive abilities over time. In particular, we report associations between investment-related scales (Intellectual Efficiency, Flexibility, Achievement via Independence, Psychological-mindedness, and Tolerance) and a higher-order personality factor (Originality) of the CPI with intelligence measured at broad and narrow levels of abstraction. We found positive associations between investment-related scales, and Originality with observed ability test scores and factor g extracted from test scores. We found positive associations of traits with unique variance in verbal ability measures, but negative with measures of quantitative and visuo-spatial abilities. Our study extends the literature on investment theories of intelligence-personality relations, is the first study to examine the associations of multiple scales of the CPI with intelligence measures, and adds much needed data to the literature from a working adult sample.
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PERSONALITY AND INTELLIGENCE 1
Running head: PERSONALITY AND INTELLIGENCE
Personality and Intelligence: Examining the Associations of Investment-related Personality
Traits with General and Specific Intelligence
Stephen A. Woods PhD
Surrey Business School
University of Surrey
Daniel P. Hinton PhD
1
Aston Business School
Aston University
Sophie von Stumm PhD
Department of Psychology
Goldsmiths University of London
&
James Bellman-Jeffries MSc
Aston Business School
Aston University
Corresponding Author:
Prof. Stephen A. Woods PhD CPsychol
Surrey Business School
University of Surrey
Guildford
Surrey GU2 7XH, UK
s.a.woods@surrey.ac.uk
1
Present address: Institute of Psychology, University of Wolverhampton
PERSONALITY AND INTELLIGENCE 2
In this study, we examine the associations of the scales of the California
Psychological Inventory (CPI; a measure of personality traits) with intelligence measured by
four cognitive ability tests, completed by a sample of 4876 working adults. We framed our
analyses of the correlations around the investment perspective on the personality-intelligence
relationship that proposes traits are associated with investment in intellectual activity, which
develops cognitive abilities over time. In particular, we report associations between
investment-related scales (Intellectual Efficiency, Flexibility, Achievement via Independence,
Psychological-mindedness, and Tolerance) and a higher-order personality factor (Originality)
of the CPI with intelligence measured at broad and narrow levels of abstraction. We found
positive associations between investment-related scales, and Originality with observed ability
test scores and factor g extracted from test scores. We found positive associations of traits
with unique variance in verbal ability measures, but negative with measures of quantitative
and visuo-spatial abilities. Our study extends the literature on investment theories of
intelligence-personality relations, is the first study to examine the associations of multiple
scales of the CPI with intelligence measures, and adds much needed data to the literature
from a working adult sample.
Keywords: Personality Traits, Intelligence, Intellectual Investment, California
Psychological Inventory
PERSONALITY AND INTELLIGENCE 3
Introduction
Traditionally, intelligence and personality have been conceptualised and treated as
separate entities (Eysenck, 1994; Zeidner & Matthews, 2000). However, contemporary theory
on intelligence-personality relations proposes mechanisms by which intelligence and
personality are linked. Arguably the most influential of these is investment theory (e.g. von
Stumm & Ackerman, 2013), which proposes that intelligence and personality are associated
at the conceptual level, whereby personality traits determine where, when and how people
apply and invest their abilities and thus, their development of intelligence across the lifespan.
In this study, our main contribution is to extend the literature on investment perspectives on
intelligence-personality relations by examining associations of investment-related personality
traits at the facet and higher-order level, with intelligence also modelled at broad and narrow
levels. To do this, we report associations of scales of the California Psychological Inventory
with four tests of cognitive ability. Our study further contributes data from a non-student
(working adult) sample.
The investment perspective on personality and cognitive ability is based on the
observation that personality traits that refer to the tendency to seek out, engage in and pursue
learning opportunities, such as Openness to Experience from the Five Factor Model (Costa &
McCrae, 1992) or Need for Cognition (Cacioppo & Petty, 1982), have been shown to be
associated with markers of adult intelligence (von Stumm & Ackerman, 2013; Ziegler et al.,
2012). Elevated investment traits may influence intelligence by promoting greater
engagement with a wider range of activities and stimuli, which could enhance intellectual
abilities (Arteche, Chamorro-Premuzic, Ackerman, & Furnham, 2009). For example,
Openness from the Big Five model is associated with artistic and intellectual job preferences
(Ackerman & Heggestad, 1997), and may influence motivation to attempt tasks in those
domains of activity and work (e.g. Woods & Hampson, 2010), with higher ability
PERSONALITY AND INTELLIGENCE 4
determining likely success in those tasks. Success could consequently influence motivation to
attempt further tasks, increasing the variety and complexity of experience, reciprocally
deepening corresponding investment traits, reinforcing interests, and developing intellectual
capabilities. This reasoning is in line with recent theory in the personality and development
literature (e.g. the Dynamic Developmental Model of Woods, Lievens, De Fruyt & Wille,
2013), and longitudinal research on the interplay of investment-related traits and intelligence
(Ziegler, Danay, Heene, Asendorpf & Bühner, 2012).
Most previous research on intelligence-personality associations has focused on
studying personality traits either from the Five Factor Model (e.g. Austi, Deary, & Gibson,
1997; Wolf & Ackerman, 2005; Zeidner & Matthews, 2000; Wainwright et al., 2008) or
narrowly defined investment-related personality traits (DeYoung et al., 2005; von Stumm &
Ackerman, 2013). Example investment traits that have been found to correlate positively with
intelligence include judging and perceiving from the Myers-Briggs Type Indicator, and the
facet Openness to Ideas from the NEO PIR (Furnham et al., 2007), the Tough-mindedness
(comprising Conceptual, Intuitive, and Radical) scales of the Fifteen Factor Questionnaire
(Moutafi, Furnham & Paltiel, 2005) and Culture (see studies of Reeve, Meyer & Bonaccio,
2006, and Major, Johnson & Deary, 2014), and Openness from the Big Five (von Stumm &
Ackermann, 2013).
Examination of the patterns of reported correlations between investment traits and
intelligence at different levels of abstraction provides further insight into the nature of the
relations of these individual differences. At a general level (i.e. with factor g) investment
traits demonstrate a rather consistent positive association in the studies highlighted
previously. However, there is variation across facets of intelligence, with studies reporting
higher correlations with crystallized ability (e.g. Reeve et al., 2006), and in particular verbal
reasoning (Furnham et al., 2007; Moutafi et al., 2005). This pattern of associations supports
PERSONALITY AND INTELLIGENCE 5
the developmental explanation of the relations of investment traits and intelligence. Given
that crystallized intelligence represents acquired and learned abilities (e.g. Woods & West;
2014; Carroll, 1993), greater investment in intellectual pursuits and verbal education,
logically would lead to greater developed crystallized ability. Further examination of
correlations in these studies also shows some differences in the relations for men and women
(e.g. Reeve et al., 2006 reported stronger correlations for women compared to men).
With respect to fluid ability, the picture is less clear. Some studies report correlations
of investment traits with fluid and visuo-spatial abilities as zero (e.g. von Stumm et al., 2009)
or even negative (Reeve et al., 2006). Yet in other studies, longitudinal data suggest that
Openness to Experience does predict development of fluid ability in children (e.g. Asendorpf
& Van Aken, 2003), and in later life stages (e.g. Soubelet & Salthouse, 2011; Zimprich,
Allemand, & Dellenbach, 2009; Ziegler, Cengia, Mussell, & Gerstof, 2015). Ziegler and
colleagues (Ziegler, Danay, Heene, Asendorpf, & Buhner, 2012; Ziegler et al., 2015) have
proposed a developmental theoretical model to explain the possible pathways (the Openness-
Fluid-Crystallized-Intellignece; OFCI model). In this model, Openness and fluid ability are
proposed to interact when people have opportunity to experiment and act openly and freey.
Openness promotes environmental experimentation, with fluid ability promoting success in
problem solving and task activity, in turn prompting greater curiosity. The reciprocal
interplay is similar to developmental mechanisms proposed for personality development
generally (e.g. Woods et al., 2013), and fosters development of crystallized ability. Ziegler
and colleagues have proposed that the mechanism may apply at certain critical time periods
when people experience lower environment constraints (e.g. in childhood and adolescence).
Without some contradiction among data in the literature, and based on the emergent nascent
nature of these theoretical explanations, the continuing need for new data is underlined.
PERSONALITY AND INTELLIGENCE 6
To further add to the literature on intelligence-personality associations, we report here
associations between scales of the California Psychological Inventory (CPI; Gough, 1987;
Gough & Bradley, 1996) and a battery of intelligence tests completed by a sample of working
adults. The CPI is a widely used personality inventory that offers a compelling prospect for
examining a broader range of investment-related personality traits than in previous studies.
For example, the inventory has distinct features as compared to the Big Five (McCrae, Costa
& Piedmont, 1993; Soto & John, 2009). Von Stumm and Ackerman (2013) included the CPI
scale Intellectual Efficiency as a marker of intellectual investment in their meta-analysis
reporting an association with g of .31 (fixed effect model; .42 random effects model) across
11 studies. However, here we propose that the CPI contains a wider range of investment-
related traits. Rushton and Irwing (2009) presented an analysis of the higher-order factors of
the CPI, with one extracted component (labelled Originality) being particularly potentially
relevant to examining personality-intelligence relations from an investment perspective.
The CPI scales loading most strongly on the Originality Factor were Intellectual
Efficiency (comfort with conceptual or intellectual thinking), Flexibility (adaptability and
openness to change), Tolerance (open-mindedness and openness of attitudes), Achievement
via Independence (preference and motivation for unstructured, independent settings), and
Psychological-mindedness (intuition and insight into the thoughts of others). The scales
capture various aspects of intellectual investment and cultural openness in a broad way. For
example in Woods and Anderson’s (2016) Periodic Table of Personality, Psychological-
mindedness, Intellectual Efficiency and Achievement via Independence were found to cluster
together based on their pattern of Big Five loadings under a facet labelled Efficiency of
Thought/Inquisitiveness. Flexibility was located (and negatively correlated) with other
personality scales related to lack of spontaneity or inflexibility, and Tolerance was clustered
with scales relating to Calmness, but with a small loading on Openness/Intellect, reflecting
PERSONALITY AND INTELLIGENCE 7
the theme of the scale of open-mindedness, and psychological adjustment (i.e. emotional
stability) with respect to acceptance of alternative attitudes and values. Interpreting the scales,
people higher on Originality and its associated scales are therefore more likely to invest time
in inquisitive, flexible, critical and more complex thinking about concepts generally, their
work and social environment. We propose that these tendencies ultimately represent greater
investment in intellectual activity and conceptual thinking, consistent with the investment
perspective of personality-intelligence relations.
Following the investment perspective, we reason that these personality traits are
related to intellectual development, manifesting in observed positive associations with
intelligence, leading us to hypothesize:
Hypothesis 1: Investment related traits are positively associated with intelligence.
We moreover expect that investment-related traits will be more strongly associated
with intelligence than non-investment traits. Although we set no formal hypothesis, the
pattern of correlations among traits and intelligence will be examined.
As previously discussed, in studies of personality and intelligence, it has been
informative to examine associations at various levels specificity of personality traits and
ability facets (e.g. Djapo et al., 2011). Such examination is informative from both conceptual
and empirical points of view. From a conceptual perspective, facet-level associations clarify
more specific relations of narrow personality and ability constructs (De Young, 2012). On the
other hand, from an empirical perspective, partialing g from observed facet-level associations
helps to clarify more precisely the magnitude and nature of personality-intelligence relations
(Reeve, Meyer & Bonaccio, 2006; von Stumm, Chamorro-Premuzic, Quiroga, & Colom,
2009).
Following this line, in our study, we examined associations of facet-level and higher-
order personality traits, with specific facets of ability, and higher-order g. Following Reeve et
PERSONALITY AND INTELLIGENCE 8
al. (2006) and von Stumm et al. (2009), we approach these analyses based on data from
observed test scores, but also by separating unique variance in cognitive ability test scores
from general variance represented in factor g. We present these analyses in order to explore
personality-intelligence relations in more depth. We expect the general trend of positive
associations between investment-related traits and intelligence to be maintained across levels
of construct specificity, but that following past findings (e.g. Reeve et al., 2006) the
associations will be strongest for crystallized ability:
Hypothesis 2: Investment related personality traits measured in the CPI are related to
intelligence at general (higher-order) and specific (i.e. scale) levels of specificity.
We tested our hypotheses in a sample of working adults, who completed the CPI and
a battery of four cognitive ability tests that measured verbal, visuo-spatial and quantitative
abilities.
Method
Participants and Procedure
Participants for this study were 4876 adults included in a data archive obtained from
test publishing consultancy Personnel Development International (PDI). The anonymized
archive contained demographic data of each participant along with each participant’s raw test
score on each measure (the 23 CPI scales, two of the Employee Aptitude Survey tests, the
Watson-Glaser Critical Thinking Appraisal Form A and the Wesman Personnel Classification
Test). Of these participants, 30.4% were female and 69.0% were male, with 0.6% of
participants not reporting their gender. The mean age of participants was 39.6 years (SD =
8.0 years, Range = 18 62 years). 73.8% of participants listed their ethnic background as
White/Caucasian, 3.3% as Black/African, 1.7% as Hispanic, 1.1% as Oriental/Asian, 0.4% as
‘other’, 0.2% as Amerind/Aleutian and 19.6% did not report their ethnic background. Of the
total participants, 54.9% held managerial level posts, 23.5% professional/technical posts,
PERSONALITY AND INTELLIGENCE 9
1.5% clerical/administrative posts and 18.5% did not report their occupation background. The
remaining 1.7% was made up by participants who held posts identified as skilled trade,
unskilled trade, home maker or ‘other’.
Not all participants completed all measures so the number of participants in each of
the analyses varies slightly. However, each of the measures was completed by a minimum of
4750 or more participants. Incomplete data rows (i.e. cases with one or more missing values
for the 20 CPI folk scales and the 4 measures of cognitive ability) were deleted listwise from
the dataset and excluded from analyses. After this screening, data for 4705 participants
remained in the dataset. Note that because only scale-level data were available for analysis
(not uncommon in data of proprietary assessment instruments; e.g. Woods & Hardy, 2012),
we consulted alpha reliability data in the technical manuals of all measures in the study
(which we report in our descriptions of measures below).
Measuring Personality Traits
California Psychological Inventory (CPI-462; Gough, 1987; Gough & Bradley,
1996): The CPI is a personality inventory that measures a candidate’s personality on 20
“folk” scales and 3 “vector” scales. The version of the CPI used for this study is made up of
462 true/false items. The folk scales all measure aspects of personality easily understood by
laypeople across different cultures. The vector scales attempt to classify people’s personality
as being in one sector of a 3-dimensional grid. Evidence of the structural properties and
validity of the instrument is reported by Rushton and Irwing (2009) and Soto and John
(2009). Although not included in our paper, exploratory factor analyses of our data indicated
a conceptually sensible five-factor structure underlying the 20 folk scales (available on
request from the second author). The reliabilities of the CPI scales from a standardization
sample of the instrument, are shown in Table 2 in the diagonal of the correlation matrix. We
noted that two of the scales measuring investment-related traits had reliabilities below 0.70
PERSONALITY AND INTELLIGENCE 10
(Flexibility and Psychological-mindedness). To check that these scales did not confound our
higher-order findings, we ran analyses removing each and both of these scales, finding no
substantive difference in the correlations of the Originality factor with the cognitive ability
tests or g. We therefore leave them in our analyses for completeness and to enable integration
of our findings against other studies that include the CPI.
Measuring Intelligence
General intelligence (g) was measured based on a combination of different ability
tests, as described below.
Wesman Personnel Classification Test (PCT; Wesman, 1965; α = .78-89; Pearson
Talent Lens, 2007): The PCT is a test designed to measure a candidate’s verbal reasoning
skills. It is administered online in supervised conditions and takes 20 minutes to complete.
The test is made up of 45 items. Candidates are required to be fluent in English to be able to
complete the test. Research has shown the test to display acceptable levels of reliability and
validity for a variety of occupational group samples (Pearson Talent Lens, 2007).
Watson-Glaser Critical Thinking Appraisal Form A (W-GCTA; Watson & Glaser,
2008; split-half r = .80-85): The W-GCTA is a test designed to assess critical thinking skills
and high-level reasoning in graduates and managers. Candidates are presented with
problems, statements, interpretations and arguments and are required for each one to assess
the logical validity of its propositions. The test is made up of 80 questions and is
administered in supervised conditions in no more than 50 minutes.
Employee Aptitude Survey (EAS; Ruch et al., 1994): The EAS is a battery of tests
measuring specific aspects of cognitive ability. Participants in this study completed two tests:
EAS 5 Space Visualisation (α = .89; Ruch et al., 1994), which measures the ability to
visualise forms and objects in space, and to mentally rotate and manipulate them, and EAS 6
PERSONALITY AND INTELLIGENCE 11
Numerical Reasoning (α = .81; Ruch et al., 1994), which measures the ability to perform
basic mathematical operations quickly and accurately.
Analyses
We analysed our data following a systematic approach to examine patterns of
associations between investment-related traits and intelligence at different levels of
specificity. Firstly, we computed a correlation matrix of all individual CPI scales and the
ability tests to provide context to our results. Next, we used Principal Axis Factoring to
extract general factors from the four ability tests (i.e. to extract g) and the five investment-
related personality scales (i.e. to extract the Originality factor). In the case of extracting g,
standardized residuals of the individual test scores regressed onto factor g were saved as
variables to represent the unique variance in each ability test (following Reeve et al., 2006,
and von Stumm et al., 2009). We examined the associations of g with the facet-level
investment-related personality traits in two ways; first by correlating the personality scales
with extracted factor g, second by following von Stumm et al. (2009) and averaging the
correlations of each personality scale across the four ability tests. To examine the associations
of unique variance in each test with investment-related personality traits, we correlated the
standardized residuals for each with personality scale scores and the extracted Originality
factor. Finally, to examine the higher-order association of Originality and g, we used a
confirmatory factor analysis to test the correlation of the latent constructs.
Results
Descriptive Statistics and Scale-level Correlations
Means, standard deviations, skewness, kurtosis, and correlations of all study variables
are shown in Tables 1 and 2. None of the variables for inclusion in the higher-order analyses
were affected by skewness or kurtosis. All five of the investment-related personality traits
PERSONALITY AND INTELLIGENCE 12
(Achievement via Independence, Intellectual Efficiency, Psychological-mindedness,
Flexibility and Tolerance) were associated with the ability measures (ranging from r = .07 to
.37). To check for discriminant correlations, we computed the average of the correlations of
the ability tests separately with the investment-related scales and all other scales of the CPI.
The averages correlations with the PCT, W-GCTA, EAS 5 and EAS 6 were .28, .30, .11, and
.12 for the investment-related traits compared to .11, .11, .05 and .05 for the other scales of
the CPI. Investment-related personality traits measured by the CPI were markedly more
strongly correlated with the ability measures.
Notably, the investment-related traits were more strongly correlated with the W-
GCTA and PCT than with the EAS 5 and EAS 6. Both the W-GCTA and PCT are measures
of verbal reasoning, a point we return to in the discussion.
Examining Higher Order Personality and Intelligence Factors
The five investment-related personality traits were entered into principal axis
factoring (PAF) to extract the Originality factor. One factor was extracted, with an
eigenvalue of 3.049 explaining 61.0% of variance. The factor loadings for each of the CPI
scales onto this factor are shown in Table 3.
PAF was next conducted on the four ability test score variables to extract g. A one-
factor solution was extracted based on both eigenvalues and the resultant scree plot (first
extracted factor eigenvalue of 2.345 explaining 58.6% of the variance). Factor loadings are
shown in Table 4. The g factor score was saved via the regression method. Each ability test
score was then individually regressed onto these factor scores and standardised residuals
saved. These new variables represented unique variance for each test.
Table 5 reports correlations of the five investment-related traits, and higher-order
Originality with g. These were computed in two ways; first as the average of correlations
between each personality construct with the four ability tests, and second as correlations with
PERSONALITY AND INTELLIGENCE 13
extracted (i.e. regression-scored) factor g from the factor analysis of the four ability tests. We
computed these statistics for the full sample and for men and women in the sample
separately.
Examining the patterns of correlations with g shows consistent positive associations
of the facet-level investment-related traits with intelligence. Comparing the two methods of
computing these correlations shows that correlations were stronger with extracted g. This
confirms the observation of Reeve et al. (2006) that relying on individual observed test scores
as proxies for g in the study of personality-intelligence relations typically underestimates the
magnitude of the relationship.
Examining the association of the Originality factor with the observed ability test
scores and the unique variances after extracting factor g, our findings demonstrated three
notable points. First, Originality was associated positively with all ability test scores. Second,
associations were stronger for the measures of verbal ability, than quantitative and visuo-
spatial ability. Third, correlations of Originality with unique variances in the tests were
generally weaker than with the observed test scores, however like von Stumm et al. (2009),
we observed reversal of direction in two of the correlations (i.e. Originality was negatively
correlated with unique variance in the quantitative and visuo-spatial ability tests). This effect
is consistent with correlations of the unique variances with the facet-level traits (i.e.
personality scale scores). We return to this point in our discussion.
Comparing the results for men and women, we found significant differences in the
correlations of Originality, Achievement via Independence, and Originality with unique
variance in the EAS 6. The relationships were significantly more strongly negative for
women compared to men.
Finally, we examined the correlation between the latent g and Originality factors. To
estimate this, a CFA model was constructed in which the four ability measures were made to
PERSONALITY AND INTELLIGENCE 14
load onto a single latent factor to represent g (χ2 = 28.129; df = 2; p < .0001; CFI = .989; TLI
= .968; RMSEA = .052; SRMR = .015). The CPI scales were made to load onto a second
latent factor representing Originality (χ2 = 112.455; df = 5; p < .0001; CFI = .988; TLI = .977;
RMSEA = .067; SRMR = .017). These two latent factors were then correlated in the CFA
model (see Figure 1). We used this CFA approach because it avoids the potential problem of
factor score indeterminacy associated with this family of techniques (Grice, 2001). Model fit
was judged using a number fit indices. Though the χ2 test 2 = 773.877; df = 26; p < .0001)
indicated poor model fit, this must be interpreted in the context of this test’s tendency
towards Type I Error for large samples such as this one (Bentler & Bonnett, 1980).
Interpreting the other fit indices generated by the analysis, the CFI (CFI = .941), TLI (TLI =
.919), RMSEA (RMSEA = .078) and SRMR (SRMR = .057) all indicated acceptable model
fit by the criteria of Hu and Bentler, 1999. The standardised model correlation between the
latent factors representing g and Originality was found to be .37
Study Hypotheses
Interpreting the results in the context of our hypotheses, we may conclude that
hypothesis 1 was supported. Investment-related personality traits were consistently positively
associated with intelligence (factor g) in our data. Our findings partially support our
hypothesis 2 (that the associations would be positive across different levels of measurement
specificity). While most of the correlations we observed were positive, and stronger for
crystallized abilities, correlations of the investment-related personality variables with the
unique variances of the numerical and visuo-spatial ability tests were negative.
Discussion
In order to add to the body of existing empirical evidence on personality-intelligence
relations, in this study, we examined associations of investment-related personality traits of
PERSONALITY AND INTELLIGENCE 15
the CPI with a number of ability tests, and higher-order g, analysing the relations at different
levels of specificity.
The investment perspective proposes that certain personality traits lead people to
invest in intellectual and educational activity that improves intelligence over the course of the
lifespan. This mechanism is consistent with a long-term developmental and interactive view
of individual differences in personality and intelligence. Consistent with this perspective, we
hypothesized firstly that investment related traits of the CPI (Intellectual Efficiency,
Flexibility, Tolerance, Achievement via Independence, and Psychological-mindedness), and
an underlying factor extracted from them (Originality), would be positively associated with
intelligence, and secondly that the relations would be exhibited at different levels of
specificity.
Our first hypothesis was supported, and we found positive associations of the
Originality factor and its constituent facet-level scales with g. This was the case when
analysed as the average of correlations of personality across ability tests and as extracted g
from factor analysis. However, comparing these methods, we echo the conclusions of Reeve
et al. (2006), that reliance on individual test scores as proxies of g is likely to underestimate
the strength of personality-intelligence relations. We found that correlations of the personality
variables were stronger for extracted g than when taking the average of correlations across
the tests (such as might be done in meta-analyses).
The Originality factor in essence concerns independence, depth, and flexibility of
thinking. People high on the constituent scales of the factor are, among other related
characteristics, autonomous in the way they work on things, efficient intellectually, tend to
consider how others might perceive or think through experiences, permissive and tolerant of
others’ ideas, and are flexible in the way they problem solve. These characteristics are highly
consistent with the investment perspective. People with high Originality (and higher scores
PERSONALITY AND INTELLIGENCE 16
on the constituent scales) are more likely to engage in intellectually challenging activity, take
time to reason with information and ideas, be comfortable with competing ideas or beliefs,
yet sufficiently flexible in their thinking to arrive at their own conclusions. Engaging in these
behaviours could result in intellectual growth over time (e.g. Ziegler et al., 2012). It is also
possible that these traits and intelligence are linked reciprocally, with success in intellectual
pursuits attributable to intelligence, which in turn promotes greater future engagement with
such activity. We absolutely acknowledge that our data do not offer a test of this proposition,
which would rather require longitudinal data (De Young, 2012). However, our observed
correlations are consistent with accumulating research evidence for the associations of
investment-related traits and intelligence.
Turning to our hypothesis 2 (that the positive associations would hold across levels of
specificity), we noted broad supporting evidence, but some interesting contradictory findings
also. Whilst the associations of investment-related traits with g and observed test scores were
all positive, we observed changes to the direction of associations with unique variance in the
quantitative and visuo-spatial tests (once g was partialled from the scores). This is consistent
with previous studies (e.g. von Stumm et al., 2009). Our data do not permit us to determine
why this may be observed. However, it is notable that positive associations were maintained
with unique variance in verbal ability tests. One possible implication is that investment-
related traits exert effects differentially across facets of ability.
If a developmental perspective is adopted, this would indicate that such traits have
positive relations with the development of some ability facets, but negative relations with
others. Verbal ability is a central aspect of crystallized ability. Given that crystallized ability
represents acquired and learned abilities, the developmental explanation is conceptually
logical. That is, investment traits may relate most strongly to verbal and crystallized ability
because investment in verbal education and intellectual activity is likely to influence
PERSONALITY AND INTELLIGENCE 17
specifically those aspects of intelligence. What is not explainable easily by the developmental
argument is the consistent negative associations of the investment traits with unique variance
(i.e. with g variance partialled) of, in particular, the numerical test (EAS 6) in the study. If the
developmental explanation is similarly applied to this finding, it appears that investment in
intellectual activity may lead to higher verbal ability and g, but lower numerical ability after
the effects of g increases are controlled, especially so for women in our sample.
One possible explanation is that numerical reasoning ability has been argued to
comprise elements of both crystallized and fluid ability (Johnson & Bouchard, 2005), and
investment traits may develop the crystallized variance, but not the fluid component. Greater
reliance on crystallized numerical abilities could trade-off against a decline in fluid numerical
ability. Although such an explanation would run counter to developmental theory in the area
(e.g. Ziegler et al., 2015), which proposes rather that investment-related traits develop (i.e.
serve to increase) fluid ability, the possibility nevertheless remains an intriguing prospect to
address in future research, For example, note that our sample and analytic approach are
different from previous contributions, and indeed Ziegler et al (2015) proposed that in order
for the development to play out, people need sufficient degrees of freedom in their
environment to learn, which may be less so in our working age adult sample, than in
educational or early career samples.
Alternatively, the focus on analysing unique variance (residualized scores) in our
study might mean that the variance in the ability variables entered into our analyses may
represent something conceptually distinct from fluid ability, consequently influencing the
observed effects. Sex differences in numerical ability may also explain why the relationship
is more marked for women (Kimura, 1999; Halpern; 2000). In future studies, it would be
interesting to examine fluid and abstract abilities in future research to see if a similar result is
observed for other non-verbal and non-crystallised forms of ability. For example, we also
PERSONALITY AND INTELLIGENCE 18
found investment traits to be negatively correlated with unique variance in visuo-spatial
ability in our data. Clearly, future research should consider and test mechanisms by which the
interplay of various aspects of intelligence and investment related traits may unfold across the
whole lifespan.
Limitations and Strengths
One limitation of our study was its cross-sectional nature, which prevented testing
theoretical pathways of the investment perspectives that require longitudinal data. Secondly,
our extraction of factor g is restricted to the specific tests included in data we analysed. The
breadth and representativeness of the factor could be improved by addition of a greater
number of more varied assessment components. That said, our study also has notable
strengths, most notably a comparatively large dataset comprising working adults, addressing
common sampling limitations in this literature, which relies heavily on student samples.
Conclusions
Our findings add to the existing body of empirical evidence that demonstrated robust
associations between intelligence and personality traits. In particular, the current results lend
additional support to investment theories that suggest personality traits determine when,
where and how people apply their intelligence, thereby contributing to cognitive growth,
especially, it would appear in our data, for crystallized verbal abilities. In line with this, we
showed here that investment-related traits relating to Originality, a dimension associated with
flexible and independent thinking, were related to cognitive ability in various ways. This
result echoes previous research outcomes and highlights that intelligence-personality
associations replicate reliably across investment-related traits from different personality
models.
PERSONALITY AND INTELLIGENCE 19
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PERSONALITY AND INTELLIGENCE 24
Table 1
Descriptive statistics for all CPI scales and measures of cognitive ability.
Skewness
Kurtosis
Ability Measures
EAS 5
-0.21
-0.02
EAS 6
-0.52
0.68
PCT
-0.36
-0.23
W-GCTA
-0.88
0.69
CPI Folk Scales
Dominance
-1.01
1.41
Capacity for Status
-0.50
0.53
Sociability
-0.88
1.07
Social Presence
-0.41
0.35
Self-acceptance
-0.60
0.75
Independence
-0.66
1.27
Empathy
-0.43
0.23
Responsibility
-0.64
0.49
Socialization
-0.64
0.50
Self-control
-0.35
0.06
Good Impression
-0.14
-0.20
Sense of Well-being
-1.70
5.80
Tolerance
-0.76
0.74
Achievement via Conformance
-0.76
0.86
Achievement via Independence
-0.54
0.27
Intellectual Efficiency
-0.64
0.76
Psychological-mindedness
-0.35
0.35
Flexibility
-0.12
-0.33
Femininity/Masculinity
0.17
-0.19
Communality
-3.05
30.51
CPI Vector Scales
V1: Internality
0.37
0.04
V2: Norm-favouring
-0.27
-0.25
V3: Self-realization
-0.80
0.70
Note: S.E. (Skewness) = 0.04; S.E. (Kurtosis) = 0.07
PERSONALITY AND INTELLIGENCE 25
Table 2
Correlations of all CPI scales with measures of cognitive ability.
N=4705; r>0.03, p<0.05; r>0.04, p<0.01Note: DO = Dominance; CS = Capacity for Status; SY = Sociability; SP = Social Presence; SA = Self-
acceptance; IN = Independence; EM = Empathy; RE = Responsibility; SO = Socialization; SC = Self-control; GI = Good Impression; WB = Sense of
Well-being; TO = Tolerance; AC = Achievement via Conformance; AI = Achievement via Independence; IE = Intellectual Efficiency; PM =
Ability Measures
CPI Folk Scales
Vector Scales
1
2
3
4
DO
CS
SY
SP
SA
IN
EM
RE
SO
SC
GI
WB
TO
AC
AI
IE
PY
FX
M
F
CM
V1
V2
V3
1. EAS 5
42
38
33
04
04
-02
05
03
11
01
02
01
01
-01
12
08
-01
12
12
13
07
-24
07
-02
-03
09
2. EAS 6
47
44
05
04
00
08
08
08
04
02
02
-04
-09
04
12
00
13
14
08
12
-10
05
-04
-04
08
3. PCT
63
12
21
04
14
17
15
13
16
03
-06
-13
05
28
05
34
36
22
20
-05
06
-10
-07
19
4. W-GCTA
13
18
04
15
18
20
12
15
04
-04
-11
10
30
04
37
34
23
25
-06
06
-08
-09
23
DO
83
42
50
38
54
55
39
34
14
-12
10
23
10
34
16
23
21
-01
-24
16
-69
30
23
CS
72
58
51
48
43
62
32
08
00
17
25
36
26
48
48
46
28
-06
02
-44
08
49
SY
77
69
61
37
61
17
10
-21
10
20
18
22
25
30
21
18
-10
11
-61
17
32
SP
71
57
42
52
01
-07
-40
-12
16
19
-01
33
34
28
38
-16
08
-61
-09
32
SA
67
43
40
11
-06
-39
-20
04
09
10
24
25
15
20
-10
12
-61
-02
15
IN
74
30
19
-01
-03
11
32
19
16
32
35
38
27
-27
-05
-42
-02
36
EM
63
29
14
02
24
27
38
27
46
41
35
34
-02
06
-46
08
53
RE
77
38
44
44
39
52
53
37
42
37
01
08
04
-03
44
52
SO
78
43
39
37
20
49
07
16
16
-15
07
11
07
63
27
SC
83
77
48
34
45
19
20
26
-13
07
-10
51
35
47
GI
81
55
29
50
20
20
26
-14
-05
-15
17
46
57
WB
84
40
40
32
40
40
07
-24
13
00
29
60
TO
79
26
64
59
52
35
05
01
04
02
70
AC
78
19
29
26
-22
-01
14
-09
55
42
AI
80
65
59
49
01
-05
-07
-13
70
IE
79
51
34
-06
04
-12
00
64
PM
62
35
-12
-05
-07
00
61
FX
64
04
-13
-12
-49
39
MF
73
-05
24
-06
-06
CM
71
-09
14
-05
V1
-
-08
-05
V2
-
12
V3
-
PERSONALITY AND INTELLIGENCE 26
Psychological-mindedness; FX = Flexibility; MF = Femininity/Masculinity; CM = Communality. Reliability coefficients from Rushton and Irwing
(2009) in bold face in the CPI diagonal (note data not reported for the vector scales V1, V2 and V3).
PERSONALITY AND INTELLIGENCE 27
Table 3.
Loadings for each of the CPI Originality scales onto the first factor extracted by PAF.
CPI Scale
Loading on
Originality Factor
Achievement via
Independence
.880
Intellectual Efficiency
.749
Tolerance
.746
Psychological-mindedness
.685
Flexibility
.507
PERSONALITY AND INTELLIGENCE 28
Table 4.
Loadings for each measure of ability onto the first factor extracted by PAF.
Ability Test
Loading on factor g
Wesman PCT
.789
Watson-Glaser CTA
.727
EAS 6
.639
EAS 5
.522
PERSONALITY AND INTELLIGENCE 29
PERSONALITY AND INTELLIGENCE 30
Table 5
Correlations of CPI investment-related scales and Originality with unique variance in ability tests and factor g for male participants (M) female
participants (F), and full sample.
Tolerance
Achievement Via
Independence
Intellectual
Efficiency
Psychological-
mindedness
Flexibility
Originality
M
F
Full
M
F
Full
M
F
Full
M
F
Full
M
F
Full
M
F
Full
EAS 5
-.09
(.10)
-.07
(.12)
-.10
(.08)
-.11
(.13)
-.09
(.13)
-.11
(.12)
-.10
(.14)
-.11
(.11)
-.10
(.12)
-.00
(.13)
-.06
(.11)
-.01
(.13)
-.06
(.10)
-.07
(.08)
-.08
(.08)
-.10
(.15)
-.10
(.14)
-.10
(.13)
EAS 6
-.11
(.13)
-.13
(.12)
-.12
(.12)
-.14
(.16)
-.21
(.08)
-.16
(.13)
-.15
(.16)
-.18
(.10)
-.16
(.14)
-.12
(.07)
-.14
(.07)
-.12
(.08)
-.04
(.15)
-.12
(.08)
-.07
(.12)
-.16
(.17)
-.21
(.11)
-.17
(.15)
Wesman
PCT
.06
(.28)
.06
(.31)
.06
(.28)
.07
(.35)
.08
(.33)
.07
(.34)
.10
(.37)
.13
(.36)
.11
(.36)
.04
(.21)
.06
(.24)
.04
(.22)
-.02
(.20)
.02
(.21)
.00
(.20)
.07
(.37)
.09
(.37)
.08
(.37)
W-GCTA
.11
(.30)
.10
(.30)
.12
(.30)
.14
(.38)
.18
(.37)
.16
(.37)
.09
(.35)
.11
(.34)
.10
(.35)
.08
(.22)
.10
(.27)
.08
(.24)
.10
(.26)
.14
(.27)
.12
(.26)
.14
(.40)
.16
(.40)
.15
(.40)
Extracted g
.29
.30
.28
.36
.32
.35
.37
.33
.35
.22
.24
.23
.24
.23
.23
--
--
--
g (Average
correlation)
.20
.21
.19
.25
.23
.24
.26
.23
.24
.16
.17
.17
.18
.16
.16
.27
.26
.26
Correlations with observed ability test scores are presented in parentheses. N=4705; r>0.03, p<0.05; r>0.04, p<0.01. Note: Significant
differences in magnitude between correlations for male and female participants are flagged: bold = p < .05; bold italics = p < .01
PERSONALITY AND INTELLIGENCE 31
Figure 1. CFA model examining association of higher order g with Originality.
PERSONALITY AND INTELLIGENCE 32
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Explaining cognitive decline in late adulthood is a major research area. Models using personality traits as possible influential variables are rare. This study tested assumptions based on an adapted version of the Openness-Fluid-Crystallized-Intelligence (OFCI) model. The OFCI model adapted to late adulthood predicts that openness is related to the decline in fluid reasoning (Gf) through environmental enrichment. Gf should be related to the development of comprehension knowledge (Gc; investment theory). It was also assumed that Gf predicts changes in openness as suggested by the environmental success hypothesis. Finally, the OFCI model proposes that openness has an indirect influence on the decline in Gc through its effect on Gf (mediation hypothesis). Using data from the Berlin Aging Study (N = 516, 70-103 years at T1), these predictions were tested using latent change score and latent growth curve models with indicators of each trait. The current findings and prior research support environmental enrichment and success, investment theory, and partially the mediation hypotheses. Based on a summary of all findings, the OFCI model for late adulthood is suggested. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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In this study, we examine the structures of 10 personality inventories (PIs) widely used for personnel assessment by mapping the scales of PIs to the lexical Big Five circumplex model resulting in a Periodic Table of Personality. Correlations between 273 scales from 10 internationally popular PIs with independent markers of the lexical Big Five are reported, based on data from samples in 2 countries (United Kingdom, N = 286; United States, N = 1,046), permitting us to map these scales onto the Abridged Big Five Dimensional Circumplex model (Hofstee, de Raad, & Goldberg, 1992). Emerging from our findings we propose a common facet framework derived from the scales of the PIs in our study. These results provide important insights into the literature on criterion-related validity of personality traits, and enable researchers and practitioners to understand how different PI scales converge and diverge and how compound PI scales may be constructed or replicated. Implications for research and practice are considered. (PsycINFO Database Record