Next Article in Journal
Why Should We Study the Foreign Language Effect: Debiasing through Affecting Metacognition?
Previous Article in Journal
The Forward Effect of Delayed Judgments of Learning Is Influenced by Difficulty in Memory and Category Learning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
J. Intell.
Volume 11
Issue 6
10.3390/jintelligence11060102
jintelligence-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Intelligence, Personality and Tolerance of Ambiguity

by
Stephen Cuppello
1,2,
Luke Treglown
1,2 and
Adrian Furnham
3,*
1
Department of Psychology, City University of London, London WCIE 7HX, UK
2
Thomas International, Marlow SL7 1YG, UK
3
Department of Leadership and Organizational Behaviour, Norwegian Business School (BI), Nydalveien, 0484 Oslo, Norway
*
Author to whom correspondence should be addressed.
J. Intell. 2023, 11(6), 102; https://doi.org/10.3390/jintelligence11060102
Submission received: 20 February 2023 / Revised: 27 April 2023 / Accepted: 23 May 2023 / Published: 26 May 2023
Browse Figures
Versions Notes

Abstract

:
In this study, 3836 adults completed a personality test (the HPTI) and a multidimensional intelligence test (GIA). Two prominent theories that link personality traits to intelligence (compensation and investment) were tested. There were more sex differences in the personality traits than in the IQ scores. Correlational and regression analyses results provided little evidence for either theory but pointed to the role of tolerance of ambiguity as a consistently significant, positive correlate of IQ at both the facet and domain levels. The role of this neglected trait is discussed. Limitations of various aspects of this study and its implications are considered.

1. Introduction

There is longstanding interest in the relationship between personality and intelligence (Ackerman and Heggestad 1997; Bédard and Le Corff 2020; Eysenck 1998; Cattell 1971; Chamorro-Premuzic and Furnham 2004, 2006, 2008; DeYoung 2011, 2020; Kretzschmar et al. 2018; Major et al. 2014; Murray et al. 2014; Rammstedt et al. 2018; Reeve et al. 2006; Schermer et al. 2020; Zeidner and Matthews 2000; Ziegler et al. 2012). The relationship has been investigated in children and young adults (Johann and Karbach 2022) as well as in older adults (Gow et al. 2005). There is also an interesting and relevant literature linking intelligence with creativity (Corazza and Lubart 2021), dark-side traits (Lau et al. 2023), as well as examining how both personality and intelligence contribute to outcome measurements, such as work success (Macke et al. 2022).
Researchers have also developed theories regarding why certain traits (e.g., conscientiousness, openness) are related, albeit marginally, to intelligence (Chamorro-Premuzic and Furnham 2004; Rammstedt et al. 2018). The field has been reviewed (Anglim et al. 2022), and indeed there has been a special issue of this journal dedicated to the topic (Colom et al. 2019), as well as numerous other relevant papers (Demetriou et al. 2018; Willoughby et al. 2023).
There have been many differences in these studies with regard to the size and representativeness of the samples tested, as well as, perhaps more importantly, the nature of the tests used (Ackerman and Heggestad 1997; Furnham 2017a, 2017b; Furnham and Treglown 2018). It is not very easy to obtain a large, representative sample to test these hypotheses that can be traced over time, particularly using well known and robust intelligence tests, which often require up to an hour to complete (Beauducel et al. 2007; Gignac 2015; Johnson et al. 2008; Lohman and Lakin 2011). Further, as others have, Reeve and Blacksmith (2009) argued that an understanding of intelligence–personality associations requires the variance due to ‘g’ to be separated from the variance due to narrow cognitive abilities. Equally, it has been argued and demonstrated that it is important to examine the possibility of nonlinear relationships between the two variables.

1.1. Two Theories

There are two major theories in this area. First, compensation theory suggests that conscientiousness acts as a “coping/reimbursing strategy” for less intelligent, but ambitious and competitive, people in particular settings. Thus, relatively less intelligent individuals may become more methodical, organised, thorough, and persistent (i.e., conscientious) to compensate for their relative lack of intelligence in a highly competitive educational or work environment. That is, they can achieve as much as bright people by simply working harder and smarter. Alternatively, relatively more intelligent people may tend to succeed based on their cognitive efficiency, rather than strenuous effort or persistent effort and organisation.
There is some evidence for compensation theory. Moutafi et al. (2004) found conscientiousness to be more highly, but significantly negatively, correlated with fluid intelligence than crystallised intelligence, consistent with their theory. However, Wood and Englert (2009) found conscientious was negatively correlated with fluid and crystallised intelligence. Murray et al. (2014) argued and demonstrated that the true association between conscientiousness and IQ may be zero or positive at the population level but that the use of selected research samples has sometimes resulted in the appearance of a negative association. More recently, Harris-Watson et al. (2022) tested employee samples and found, in three of four samples, that the results supported a “nuanced compensatory mechanism”, showing that conscientiousness compensates for low to moderate GMA, but high conscientiousness may be detrimental to task performance in high-GMA individuals. Clearly, there remains much dispute over this theory.
von Stumm (2018) proposed an investment theory of adult intelligence, which posits that individual differences in knowledge attainment results from people’s differences in cognitive ability and their propensity to apply and invest that ability. These traits she referred to as investment personality traits. Thus, some traits, such as openness to experience, are related to IQ. von Stumm and Ackerman (2013) identified 34 trait constructs and corresponding scales that refer to intellectual investment, which were classified into different trait categories. These constructs included intellectual curiosity, abstract thinking, openness, absorption, ambiguity, novelty seeking, and social curiosity. In their meta-analysis of 112 studies with an overall sample of 60,097 participants, they found that investment traits were mostly positively associated with adult intellect markers ranging from 0 to .58, with an average estimate of .30. They concluded, however, that the strength of investment–intellect associations differs across trait scales and markers of intellect. In one study, Woods et al. (2019) found evidence for the theory using the California Personality Inventory (CPI).
Others have suggested that certain traits relate not so much to ability (IQ) but rather to the test-taking situation. For instance, it has been demonstrated that neuroticism is negatively correlated with both self-assessed and test-derived, IQ because test-anxiety influences performance (Furnham 2005). However, there have been a number of studies and theories to suggest that neuroticism is positively associated with academic success in highly selected groups (Austin et al. 2002; Leikas et al. 2009). However, this relationship is probably curvilinear since it is unlikely that high levels of anxiety could facilitate performance. Nevertheless, what is apparent is that the more “high stakes” that the test-taking situation is, the stronger that the relationship is between personality and intelligence. This point has been made by Major et al. (2014).

1.2. Other Traits

Although widely accepted, the Big Five do not encompass all personality traits that have been identified. Over the years, clinical, differential, and social psychologists have identified a large number of traits that could be logically and empirically related to intelligence. These traits include ones such as need for cognition and typical intellectual engagement (Furnham 2020). One recent study examined the relationship between strengths and fluid intelligence and found very little relationship except for the strength of “love of learning” (Kretzschmar et al. 2022).
This study explores two other traits that have been shown to relate to work performance.
The study used different, but validated, measures of both personality and intelligence. The High Potential Type Indicator (HPTI) was designed to assess personality in a work setting nearly 20 years ago. The idea was to assess those traits that had been demonstrated to be related to work performance. Initially, 12 traits were identified, but subsequent psychometric work identified six independent traits, which fulfilled the psychometric demands of a good test (Dissou 2003). A number of papers have used the HPTI (Furnham and Treglown 2018, 2021a, 2021b; Furnham and Impellizzeri 2021; Treglown and Furnham 2020, 2022). The psychometric properties of the measure have been reported (MacRae and Furnham 2020; Teodorescu et al. 2017). There is evidence of the construct, concurrent, and predictive validity of the measure, which has been used frequently in studies of businesspeople (Cuppello et al. 2023a, 2023b).
Four of the six HPTI scales are similar to components of the Big Five—conscientiousness, adjustment (low neuroticism), curiosity (openness), and competitiveness (low agreeableness). However, the HPTI does have two scales that measure concepts that have been part of the personality and individual difference literature for many years. Martinsen (2023) showed the concurrent validity of four dimensions when correlating HPTI and NEO-PI-R test scores in 1196 military people (correlation between adjustment and neuroticism were r = .62, that between curiosity and openness was r = .57, and that of both conscientiousness measures was r = .58.
The first, ambiguity acceptance (or tolerance of ambiguity; ToA) assesses how an individual or group processes and perceives unfamiliarity or incongruence. Those who are tolerant of ambiguity perform well in new or uncertain situations, adapt when duties or objectives are unclear, and are able to learn and function in unpredictable times or environments. The highly diverse literature has been reviewed by Furnham (2017a, 2017b) and Furnham and Marks (2013). Because studies have shown ToA to be correlated with openness (Caligiuri et al. 2000), as well as success in educational and work environments (Caulfield et al. 2014; Katsaros et al. 2014), we assumed that ToA would be positively correlated with intelligence.
The other variable was approach to risk or courage, which is the ability to combat or mitigate negative or threat-based emotions and broaden the potential range of responses. Unchecked fear restricts the potential range of responses and typically leads to behaviours such as avoidance or contrived ignorance, whereas courage is exhibited as the willingness to confront difficult situations and solve problems despite adversity. Courage is thought to be curvilinearly related to success in many work settings, with both high and low scorers being less successful. It is not clear whether courage would be related to intelligence.
We tested four hypotheses based on the previous literature: conscientiousness would be negatively correlated with overall intelligence (H1), curiosity (openness) would be positively correlated with overall intelligence (H2), adjustment (low neuroticism) would be positively correlated with intelligence (H3), and ToA would be positively correlated with overall intelligence (H4). We explored the relationships amongst the traits and the subscale scores on the IQ test but did not devise any specific hypotheses.

2. Method

2.1. Participants

There were 1380 women and 2456 men in our sample. Their average age was 38.57 years old (SD = 10.75). Nearly all were managers in various sectors, such as finance, technology, engineering, and human resources.

2.2. Measures

The High Potential Traits Inventory (HPTI) is a measure of normal, ‘bright’ personality traits, designed to ascertain how individuals think, prioritise, and act in the workplace (MacRae and Furnham 2020). The questionnaire comprises 78 items, with which participants decide the extent to which they agree on a 7-point Likert scale (1 = completely disagree to 7 = completely agree). Previous research has demonstrated that the HPTI assesses six dimensions of personality: conscientiousness, adjustment, curiosity, risk approach, ambiguity, and competitiveness.
General Intelligence Assessment (GIA): The GIA was used to investigate gender differences in fluid intelligence. The GIA assesses individuals’ cognitive abilities by measuring their speed and accuracy across five domains relevant to work contexts—verbal reasoning, perceptual speed, number speed, word meaning, and spatial visualisation—as detailed in Table 1 (Dann 2015; Furnham and Treglown 2018). Its aim is to primarily measure mental speed of processing (i.e., fluid intelligence and procedural knowledge), rather than depth (i.e., crystallised intelligence and declarative knowledge). It reflects individuals’ ability to quickly process novel information and to learn, develop, and apply new skills (Dann 2015). The assessment consists of five tests (described in Table 1), which are developed in real time, via computer-based item generation. This method enables the automatic production of numerous different tests of equivalent form (Irvine et al. 1990). Each test measures a particular cognitive function and involves one type of task, and all questions within a test are of equal difficulty. The response format is multiple choice, and no time limit is imposed (Dann 2015).

2.3. Procedure

Participants were tested by a UK-based psychological consultancy. All data for this study were gathered from high-stakes testing for genuine occupational test use, including to inform recruitment processes, as part of employee development and the identification of high-potential employees within businesses. The participants took these and other tests as part of an assessment exercise run under strict guidelines, and the data were logged. Each participant was given personal detailed feedback about his or her score. The participants were nearly all employed as middle to senior managers in British companies. They agreed to their anonymised data being analysed and the results reported to further the understanding of assessment and selection. However, we had no more data than their sex, age, and occupational sector and when they were tested.
Structural equation modelling (SEM) was conducted in the Lavaan package of R (version 3.3.0). Since the data were not normally distributed, maximum likelihood with robust standard errors was used for parameter estimation. Based on Kline’s (2005) recommendations, the following fit indices were applied: RMSEA, standardised root mean residual (SRMR), comparative fit index (CFI), and the Tucker–Lewis index (TLI). An excellent fit is indicated when RMSEA < .05 (MacCallum et al. 1996), SRMR > .08 (Hu and Bentler 1998), CFI > .95 (Hooper et al. 2008), and TLI > .90 (Hu and Bentler 1998). Where chi-square statistics are reported, they represent the minimum function statistic corrected for non-normality, scaled by Satorra–Bentler factors.

3. Results

Sex Differences

Because of the interest in sex differences, particularly in intelligence, a series of one-way ANOVAs were computed and are shown in Table 2. Considering results at the more conservative p < .001 level, there were four differences in personality with men scoring higher in conscientiousness, courage, ToA, and competitiveness. Similarly, half of the analyses for IQ showed a difference, with women scoring higher for reasoning but men scoring higher for numbers and spatial. However, the size of the differences was modest: e.g., risk approach (d = .36), competitiveness (d = .21), and numbers (d = .32).

4. Correlations

Table 3 shows the full correlation table, demonstrating that conscientiousness was only significantly correlated in one-sixth of the IQ scores and then negatively; curiosity was not significantly correlated with any of the IQ scores. Two of the correlations with neuroticism were significant, suggesting that those lower in adjustment performed better. However, all the correlations with ToA were significant, especially to the total score and words score (both r = .18).

5. Regressions

Next, a series of hierarchical regressions were performed with the total and subscale IQ scores being the criterion variables and demography and personality being the predictor variables. Age and sex were entered in the first step, followed by the six trait factors. The results are shown in Table 4.
In each regression, sex and age were significant and accounted for around 2% of the variance, with the exception of the number facet score, which accounted for 6% of the variance. Interestingly, the results were not fully consistent: in three analyses, men scored higher than women (total, numbers, spatial), but women performed better than men in two (reasoning, words). Similarly, younger people scored better than older people on all five analyses except for words.
There was a pattern also for the personality variables. For two, they were significant on all six analyses, showing that adjustment was negatively but ToA positively associated with the total IQ and subscores. Curiosity was a negative correlate in four and competitiveness in two, while courage was significant in one regressions and conscientiousness in none.

6. Structural Equation Model

SEM was used to explore the differentiating impact of tolerance to ambiguity on the relationship between personality and IQ. Three models were explored as a part of this analysis. The first was a holistic model in which IQ was entered as a latent variable, comprising five observed variables (i.e., the five subtests of the GIA: Reasoning, Perceptual Speed, Number Speed, Word Meaning, and Spatial Visualisation) and personality was entered as six observed variables (conscientiousness, adjustment, curiosity, risk approach, ambiguity acceptance, and competitiveness). In the second and third models, tolerance for ambiguity (ambiguity acceptance) was removed from the model and used to assess model fit for individuals with high (scores equal to or greater than the sample mean [49.64]) and low (scores less than the sample mean [49.64]).
Model 1 assessed the holistic impact of personality on IQ, the results of which can be seen in Figure 1. The model yielded a significant chi-square statistic (χ2(29) = 358.6, p < .001), indicating that the model deviates from the structure of the data. However, researchers have indicated that large sample sizes artificially inflate chi-square values, causing a rejection of the model. For this reason, other absolute fit indices were utilised, revealing an acceptable fit of the model: CFI = .94; TLI = .92; and RMSEA = .055 (90% CI upper limit = .060; 90% CI lower limit = .050). In model 1, ambiguity acceptance was the only significant, positive predictor (β = 0.25; p < .001), with adjustment (β = −.10; p < .001) and curiosity (β = −.04; p = .038) being significant, negative predictors of the latent IQ variable.
Model 2 assessed the impact of personality on IQ for individuals with high tolerance for ambiguity, the results of which can be seen in Figure 2. The model yielded a significant chi-square statistic (χ2(25) = 194.7, p < .001). Other absolute fit indices revealed a good fit of the model: CFI = .93; TLI = .90; and RMSEA = .060 (90% CI upper limit = .072; 90% CI lower limit = .055), showing similar fit indices to those in model 1. In model 2, adjustment (β = −.09; p = .004) and conscientiousness (β = −.06; p = .047) were significant predictors of the latent IQ variable, differing slightly from those in model 1.
Model 3 assessed the impact of personality on IQ for individuals with low tolerance for ambiguity, the results of which can be seen in Figure 3. The model yielded a significant chi-square statistic (χ2(35) = 146.2, p < .001). Other absolute fit indices revealed a good fit of the model: CFI = .95; TLI = .93; and RMSEA = .048 (90% CI upper limit = .056; 90% CI lower limit = .041), showing similar fit indices to those of models 1 and 2. In model 3, adjustment (β = −.08; p = .004) and curiosity (β = −.06; p = .003) were significant predictors of the latent IQ variable, showing similar predictors to those in model 1.

7. Discussion

The results of this study were particularly interesting for three reasons. First, there seemed to be no evidence for the compensation hypothesis, i.e., correlations between IQ and trait conscientiousness. Second, the results for the investment hypothesis went in the opposite direction: those who were high on curiosity/openness scored lower on IQ. Similarly, the two significant correlations with neuroticism went in the opposite direction, suggesting that those who were better adjusted performed worse overall on the IQ test. Third, the most clear, consistent, and significant correlate of the total IQ scale, as well as the subscales, was ToA.
The first issue consists of the nonsignificant or counterintuitive results. There may be a number of reasons for this outcome. The first is that neither the personality traits nor the intelligence test used here has been used to test this relationship. There is, however, evidence of concurrent validity with both tests (MacRae and Furnham 2020). It could be that the facets of conscientiousness, neuroticism, and openness, which are most closely related to intelligence, are not assessed by the HPTI, which does not have facet scores.
However, what are perhaps more important is to understand are the participants and the testing situation. Nearly all the participants were middle-aged, mid-level managers being assessed for promotion or selection. Although all the scores were relatively normally distributed, they were skewed towards a more “positive profile”: higher scores on all six factors. Thus, this sample would be higher in both conscientiousness and openness, which could affect the results, bearing in mind that the relationship between personality and intelligence is always weak.
In all regressions, the adjustment score was a significant correlate, indicating that those who were better adjusted performed less well. The explanation for the finding might lie not so much in the tests as in the particular participants and the conditions under which the tests were taken. The participants were successful businesspeople with overall high adjustment scores. Furthermore, they took the test in a high-stakes situation, knowing that it had implications for selection and promotion. The data from students suggest that, amongst highly selected and more talented groups, neuroticism is positively, not negatively, related to academic success and IQ tests because anxiety results in greater effort.
However, by far, the most interesting finding was the association of ToA and IQ, indicating that the more ToA that a person was, the better that he or she performed on a test. Accepting ambiguity infers that there can consider multiple, even contradictory or combinatorial, solutions to problems. People with this mind-set could develop more sophisticated problem-solving techniques and interests, developing in turn higher fluid and crystallised intelligence. This relationship is related to second- or third-level intelligence test abilities being most closely linked to ToA.
The results are seen more clearly in the SEM. Ambiguity acceptance was the only significant, positive predictor of the latent IQ variable, with adjustment and curiosity being significant, negative predictors.
In a recent comprehensive review of ToA, de Vries (2021) noted the many related concepts of ToA, which include “epistemic curiosity”, “curiosity and exploration inventory”, “need for cognition”, “openness for ideas”, “typical intellectual engagement”, “need for structure”, “need for precision”, and “need for evaluation”, as well as “dialectical” and “paradoxical” thinking. The idea is that the trait of ToA leads to behaviours such as the need to know more and to understand the environment, which logically seem related to intelligence. Many of these concepts have measures that have been shown to be related to ToA (Furnham 2020).
Certainly, this study has emphasized the role of a new trait as it relates to intelligence. Indeed, this emphasis might complement compensation and investment theory and be considered something akin to clarification theory: the idea that ToA encourages the act of trying to understand complexity, which relates to learning.
The results from the regression on total IQ in Table 3 confirm much of what we know about IQ. Thus, comparatively older people performed less well on most tests, particularly reasoning, but better on words/vocabulary, reflecting the well-established difference between fluid and crystallised intelligence. Similarly, the gender differences showed that men scored higher on spatial and number scales, but women scored better on reasoning tasks.
All researchers who have investigated the association between personality and intelligence have accepted that, whilst there are theoretical reasons to suppose relationships, they are not strong (i.e., have small effect sizes) and can be influenced by the particular tests used, as well as the testing situation (DeYoung 2011, 2020). In this study, we had a select group (mainly middle-aged businesspeople) completing less well-known tests in a selection environment. The results most of all have highlighted a variable that has been less explored in the personality-intelligence literature and that deserves greater attention.

Author Contributions

Conceptualization, S.C.; Resources, L.T.; Writing—original draft, A.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data available from the first author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ackerman, Phillip L., and Eric D. Heggestad. 1997. Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin 121: 219–45. [Google Scholar] [CrossRef] [PubMed]
  2. Anglim, Jeromy, Patrick D. Dunlop, Serena Wee, Sharon Horwood, Joshua K. Wood, and Andrew Marty. 2022. Personality and intelligence: A meta-analysis. Psychological Bulletin 148: 301–36. [Google Scholar] [CrossRef]
  3. Austin, Elizabeth J., Ian J. Deary, Martha C. Whiteman, F. Gerry R. Fowkes, Nancy L. Pedersen, Patrick Rabbitt, Nuala Bent, and Lynn McInnes. 2002. Relationships between ability and personality: Does intelligence contribute positively to personal and social adjustment? Personality and Individual Differences 32: 1391–411. [Google Scholar] [CrossRef]
  4. Beauducel, André, Detlev Liepmann, Jörg Felfe, and Wim Nettelnstroth. 2007. The Impact of Different Measurement Models for Fluid and Crystallized Intelligence on the Correlation with Personality Traits. European Journal of Psychological Assessment 23: 71–78. [Google Scholar] [CrossRef]
  5. Bédard, Marc-André, and Yann Le Corff. 2020. Intelligence and personality: A replication and extension study of the association between intelligence and personality aspects. Journal of Individual Differences 41: 124–32. [Google Scholar] [CrossRef]
  6. Caligiuri, Paula M., Rick R. Jacobs, and James L. Farr. 2000. The Attitudinal and Behavioral Openness Scale: Scale development and construct validation. International Journal of Intercultural Relations 24: 27–46. [Google Scholar] [CrossRef]
  7. Cattell, Raymond Bernard. 1971. Abilities: Their Structure, Growth, and Action. Washington, DC: Houghton Mifflin. [Google Scholar]
  8. Caulfield, Marie, Kathryn Andolsek, Douglas Grbic, and Lindsay Roskovensky. 2014. Ambiguity tolerance of students matriculating to US medical schools. Academic Medicine 89: 1526–32. [Google Scholar] [CrossRef]
  9. Chamorro-Premuzic, Tomas, and Adrian Furnham. 2004. A possible model for explaining the personality-intelligence interface. British Journal of Psychology 95: 249–64. [Google Scholar] [CrossRef]
  10. Chamorro-Premuzic, Tomas, and Adrian Furnham. 2006. Intellectual competence and the intelligent personality: A third way in differential psychology. Review of General Psychology 10: 251–67. [Google Scholar] [CrossRef]
  11. Chamorro-Premuzic, Tomas, and Adrian Furnham. 2008. Personality, intelligence and approaches to learning as predictors of academic performance. Personality and Individual Differences 44: 1596–603. [Google Scholar] [CrossRef]
  12. Colom, Roberto, Doreen Bensch, Kai T. Horstmann, Caroline Wehner, and Matthias Ziegler. 2019. Special Issue “The Ability–Personality Integration”. Journal of Intelligence 7: 13. [Google Scholar] [CrossRef] [PubMed]
  13. Corazza, Giovanni Emanuele, and Todd Lubart. 2021. Intelligence and Creativity: Mapping Constructs on the Space-Time Continuum. Journal of Intelligence 9: 1. [Google Scholar] [CrossRef]
  14. Cuppello, Stephen, Luke Treglown, and Adrian Furnham. 2023a. Are they sharper at the top? Intelligence, Personality and Management level. Acta Psychologia. [Google Scholar]
  15. Cuppello, Stephen, Luke Treglown, and Adrian Furnham. 2023b. Personality and Management Level: Traits that get you to the top. Personality and Individual Difference 206: 112108. [Google Scholar] [CrossRef]
  16. Dann, Peter. 2015. The General Intelligence Assessment (GIA) Technical Manual. London: Thomas International Limited. [Google Scholar]
  17. de Vries, Henderika Herie. 2021. Tolerance of Ambiguity. In The Palgrave Encyclopedia of the Possible. Edited by Vlad Petre Glaveanu. Cham: Palgrave Macmillan. [Google Scholar]
  18. Demetriou, Andreas, George Spanoudis, and Hudson Golino. 2018. gP for What is Common between Developing Intelligence and Personality: Response to the Commentators. Journal of Intelligence 6: 54. [Google Scholar] [CrossRef]
  19. DeYoung, Colin G. 2011. Intelligence and personality. In The Cambridge Handbook of Intelligence. Edited by Robert J. Sternberg and Scott B. Kaufman. Cambridge: Cambridge University Press, pp. 711–37. [Google Scholar]
  20. DeYoung, Colin G. 2020. Intelligence and personality. In The Cambridge Handbook of Intelligence, 2nd ed. Edited by Robert J. Sternberg. Cambridge: Cambridge University Press, pp. 1011–47. [Google Scholar]
  21. Dissou, G. 2003. The Identification of High Flyer Traits. Unpublished Doctoral thesis, University of London, London, UK. [Google Scholar]
  22. Eysenck, Hans J. 1998. Intelligence: A New Look. Piscataway: Transaction Publishers. [Google Scholar]
  23. Furnham, Adrian. 2005. Self-estimated intelligence, psychometric intelligence and personality. Psychologia: An International Journal of Psychology in the Orient 48: 182–92. [Google Scholar] [CrossRef]
  24. Furnham, Adrian. 2017a. Ambiguity Intolerance. In Oxford Encyclopedia of Health and Risk Message Design and Processing. Edited by Roxanne Parrott. Oxford: Oxford University Press, pp. 99–117. [Google Scholar]
  25. Furnham, Adrian. 2017b. Personality and Intelligence in a High Ability Sample. Psychology 8: 1355–62. [Google Scholar] [CrossRef]
  26. Furnham, Adrian. 2020. Typical Intellectual Engagement (TEI). In Encyclopaedia of Personality and Individual Differences. Edited by Virgil Zeigler-Hill and Todd K. Shackelford. Cham: Springer, pp. 3059–62. [Google Scholar]
  27. Furnham, Adrian, and Joseph Marks. 2013. Tolerance of Ambiguity: A review of the recent literature. Psychology 4: 717–28. [Google Scholar] [CrossRef]
  28. Furnham, Adrian, and Luke Treglown. 2018. High potential personality and intelligence. Personality and Individual Differences 128: 81–87. [Google Scholar] [CrossRef]
  29. Furnham, Adrian, and Luke Treglown. 2021a. Sex differences in personality scores on six scales: Many significant, but mostly small, differences. Current Psychology 42: 3449–59. [Google Scholar] [CrossRef]
  30. Furnham, Adrian, and Luke Treglown. 2021b. The Dark Side of High-Fliers: The Dark Triad, High-Flier Traits, Engagement, and Subjective Success. Frontiers in Psychology 12: 647676. [Google Scholar] [CrossRef] [PubMed]
  31. Furnham, Adrian, and Sylvia Impellizzeri. 2021. The Personality and Motivation of “Quants”: The Maths Geniuses of Wall Street. Journal of Financial Management, Markets and Institutions 9: 2150002. [Google Scholar] [CrossRef]
  32. Gignac, Gilles E. 2015. Raven’s is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g. Intelligence 52: 71–79. [Google Scholar] [CrossRef]
  33. Gow, Alan J., Martha C. Whiteman, Alison Pattie, and Ian J. Deary. 2005. The personality-intelligence interface: Insights from an ageing cohort. Personality and Individual Differences 39: 751–61. [Google Scholar] [CrossRef]
  34. Harris-Watson, Alexandra M., Mei-Chuan Kung, Michael C. Tocci, Anthony S. Boyce, Jeff A. Weekley, Nigel Guenole, and Nathan T. Carter. 2022. The Interaction Between Conscientiousness and General Mental Ability: Support for a Compensatory Interaction in Task Performance. Journal of Business and Psychology 37: 855–71. [Google Scholar] [CrossRef]
  35. Hooper, Daire, Joseph Coughlan, and Michael Mullen. 2008. Evaluating model fit: A synthesis of the structural equation modelling literature. Paper present at the 7th European Conference on Research Methodology for Business and Management Studies, London, UK, June 19–20; pp. 195–200. [Google Scholar]
  36. Hu, Litze, and Peter M. Bentler. 1998. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods 3: 424. [Google Scholar] [CrossRef]
  37. Irvine, S. H., P. L. Dann, and J. D. Anderson. 1990. Towards a theory of algorithm-determined cognitive test construction. British Journal of Psychology 81: 173–95. [Google Scholar] [CrossRef]
  38. Johann, Verena E., and Julia Karbach. 2022. The relations between personality, components of executive functions, and intelligence in children and young adults. Psychological Research 86: 1904–17. [Google Scholar] [CrossRef]
  39. Johnson, Wendy, Jan te Nijenhuis, and Thomas J. Bouchard Jr. 2008. Still just 1 g: Consistent results from five test batteries. Intelligence 36: 81–95. [Google Scholar] [CrossRef]
  40. Katsaros, Kleanthis K., Athanasios N. Tsirikas, and Christos S. Nicolaidis. 2014. Managers’ workplace attitudes, tolerance of ambiguity and firm performance: The case of Greek banking industry. Management Research Review 37: 442–65. [Google Scholar] [CrossRef]
  41. Kline, Theresa J. 2005. Psychological Testing: A Practical Approach to Design and Evaluation. Thousand Oaks: Sage Publications. [Google Scholar]
  42. Kretzschmar, André, Lisa Wagner, Fabian Gander, Jennifer Hofmann, René T. Proyer, and Willibald Ruch. 2022. Character strengths and fluid intelligence. Journal of Personality 90: 1057–69. [Google Scholar] [CrossRef] [PubMed]
  43. Kretzschmar, André, Marion Spengler, Anna-Lena Schubert, Ricarda Steinmayr, and Matthias Ziegler. 2018. The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us? Journal of Intelligence 6: 30. [Google Scholar] [CrossRef]
  44. Lau, Chloe, R. Michael Bagby, Bruce G. Pollock, and Lena Quilty. 2023. Five-Factor Model and DSM-5 Alternative Model of Personality Disorder Profile Construction: Associations with Cognitive Ability and Clinical Symptoms. Journal of Intelligence 11: 71. [Google Scholar] [CrossRef] [PubMed]
  45. Leikas, Sointu, Seppo Mäkinen, Jan-Erik Lönnqvist, and Markku Verkasalo. 2009. Cognitive ability×Emotional stability interactions on adjustment. European Journal of Personality 23: 329–42. [Google Scholar] [CrossRef]
  46. Lohman, David F., and Joni M. Lakin. 2011. Intelligence and Reasoning. In The Cambridge Handbook of Intelligence. Edited by Robert J. Sternberg and Scott Barry Kaufman. Cambridge: Cambridge University Press, pp. 419–41. [Google Scholar]
  47. MacCallum, Robert C., Michael W. Browne, and Hazuki M. Sugawara. 1996. Power analysis and determination of sample size for covariance structure modeling. Psychological Methods 1: 130. [Google Scholar] [CrossRef]
  48. Macke, Lindsey, Flor de León, Tobias Hermansson, and Petri Kajonius. 2022. An Investigation of the Relationship between Personality, Cognitive Ability, and Work Engagement in Intellectually Gifted Individuals. Journal of Intelligence 10: 100. [Google Scholar] [CrossRef]
  49. MacRae, Ian, and Adrian Furnham. 2020. A Psychometric Analysis of the High Potential Trait Inventory (HPTI). Psychology 11: 1125–40. [Google Scholar] [CrossRef]
  50. Major, Jason T., Wendy Johnson, and Ian J. Deary. 2014. Linear and Nonlinear Associations Between General Intelligence and Personality in Project TALENT. Journal of Personality and Social Psychology 106: 638–54. [Google Scholar] [CrossRef]
  51. Martinsen, O. 2023. Personality Traits as Predictors of Selection, Deselection, and Rejection of Leadership Roles in the Armed Forces. Unpublished paper. [Google Scholar]
  52. Moutafi, Joanna, Adrian Furnham, and Laurence Paltiel. 2004. Why is conscientiousness negatively correlated with intelligence? Personality and Individual Differences 37: 1013–22. [Google Scholar] [CrossRef]
  53. Murray, Aja L., Wendy Johnson, Matt McGue, and William G. Iacono. 2014. How are conscientiousness and cognitive ability related to one another? A re-examination of the intelligence compensation hypothesis. Personality and Individual Differences 70: 17–22. [Google Scholar] [CrossRef]
  54. Rammstedt, Beatrice, Clemens M. Lechner, and Daniel Danner. 2018. Relationships between personality and cognitive ability: A facet-level analysis. Journal of Intelligence 6: 28. [Google Scholar] [CrossRef] [PubMed]
  55. Reeve, Charlie L., and Nikki Blacksmith. 2009. Identifying g: A review of current factor analytic practices in the science of mental abilities. Intelligence 37: 487–94. [Google Scholar] [CrossRef]
  56. Reeve, Charlie L., Rustin D. Meyer, and Silvia Bonaccio. 2006. Intelligence–personality associations reconsidered: The importance of distinguishing between general and narrow dimensions of the intelligence. Intelligence 34: 387–402. [Google Scholar] [CrossRef]
  57. Schermer, Julie Aitken, Georg Krammer, Richard D. Goffin, and Michael D. Biderman. 2020. Using the 16PF to Test the Differentiation of Personality by Intelligence Hypothesis. Journal of Intelligence 8: 12. [Google Scholar] [CrossRef] [PubMed]
  58. Teodorescu, Alexandra, Adrian Furnham, and Ian MacRae. 2017. Trait correlates of success at work. International Journal of Selection and Assessment 25: 36–42. [Google Scholar] [CrossRef]
  59. Treglown, Luke, and Adrian Furnham. 2020. Birds of a feather work together: The role of emotional intelligence and cognitive ability in workplace interaction and advice networks. Personality and Individual Differences 158: 109833. [Google Scholar] [CrossRef]
  60. Treglown, Luke, and Adrian Furnham. 2022. Age, Sex, Education, EQ, IQ and Management Level: A Study from Great Britain. Journal of General Management 48: 320–29. [Google Scholar] [CrossRef]
  61. von Stumm, Sophie, and Phillip L. Ackerman. 2013. Investment and intelligence: A review and meta-analysis. Psychological Bulletin 139: 841–69. [Google Scholar] [CrossRef]
  62. von Stumm, Sophie. 2018. Better open than intellectual: The benefits of investment personality traits for learning. Personality and Social Psychology Bulletin 44: 562–73. [Google Scholar] [CrossRef]
  63. Willoughby, Emily A., Yuri Kim, James J. Lee, and Colin G. DeYoung. 2023. The Construct Validity of Intellect and Openness as Distinct Aspects of Personality through Differential Associations with Reaction Time. Journal of Intelligence 11: 30. [Google Scholar] [CrossRef]
  64. Wood, Paul, and Paul Englert. 2009. Intelligence compensation theory: A critical examination of the negative relationship between conscientiousness and fluid and crystallised intelligence. Australasian Journal of Organisational Psychology 2: 19–29. [Google Scholar] [CrossRef]
  65. Woods, Stephen A., Daniel P. Hinton, Sophie von Stumm, and James Bellman-Jeffreys. 2019. Personality and intelligence: Examining the associations of investment-related personality traits with general and specific intelligence. European Journal of Psychological Assessment 35: 206–16. [Google Scholar] [CrossRef]
  66. Zeidner, Moshe, and Gerald Matthews. 2000. Intelligence and personality. In Handbook of Intelligence. Edited by Robert Sternberg. Cambridge: Cambridge University Press, pp. 581–610. [Google Scholar] [CrossRef]
  67. Ziegler, Matthias, Erik Danay, Moritz Heene, Jens Asendorpf, and Markus Bühner. 2012. Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model. Journal of Research in Personality 46: 173–83. [Google Scholar] [CrossRef]
Jintelligence 11 00102 g001 550
Figure 1. SEM showing relationship between personality and IQ.
Figure 1. SEM showing relationship between personality and IQ.
Jintelligence 11 00102 g001
Jintelligence 11 00102 g002 550
Figure 2. SEM showing relationship between personality and IQ for high tolerance of ambiguity.
Figure 2. SEM showing relationship between personality and IQ for high tolerance of ambiguity.
Jintelligence 11 00102 g002
Jintelligence 11 00102 g003 550
Figure 3. SEM showing relationship between personality and IQ for low tolerance of ambiguity.
Figure 3. SEM showing relationship between personality and IQ for low tolerance of ambiguity.
Jintelligence 11 00102 g003
Table
Table 1. GIA Tests’ Descriptions.
Table 1. GIA Tests’ Descriptions.
TestDescriptionFormatLength (Minutes)Abilities Tested
VREvaluates problem-solving abilities (i.e., capacity to reason, make inferences, draw conclusions) by testing simple deductive verbal reasoning skills Problem-solving task: After reading a statement (e.g., Jack is taller than Jill), participants need to answer a related question (e.g., Who is shorter? Jack or Jill?).5Fluid and crystallised intelligence
PSMeasures visual checking skills (i.e., ability to identify and report on similarities/differences, details, and errors) by testing semantic perception and encodingLetter-matching task: Participants need to identify matching letters between rows of capital and lower case letters (e.g., ADGK/afgm). 4.5Broad cognitive speed
NSAssesses overall numeracy (i.e., capacity to process numerical information, perform mental calculations, and reason with quantitative concepts) Number task: Out of three numbers, participants need to identify which number is numerically farther from the others (e.g., 2, 9, 5).2Fluid intelligence and memory
WMEvaluates vocabulary and word-related knowledge (i.e., ability to comprehend large numbers of words and identify words with similar or opposite meanings)Semantic word task: Participants are shown three words (e.g., up, down, street) and need to specify which word is not related to the others (e.g., street).2.5Mainly crystallised intelligence
SVTests mental visualisation skills (i.e., ability to visualise concepts and objects and mentally rotate and manipulate shapes and symbols)Symbol task: Participants need to identify pairs of identical symbols (when symbols have been rotated and/or presented as mirror images of each other).2Fluid intelligence and visual perception
Note. VR = verbal reasoning; PS = perceptual speed; NS = number speed; WM = word meaning; SV = spatial visualisation.
Table
Table 2. Sex Differences in Personality and IQ Factors.
Table 2. Sex Differences in Personality and IQ Factors.
MSDFCohen’s d
ConscientiousnessFemale65.4615.6914.11−.13
Male67.4115.29
AdjustmentFemale64.8916.285.50−.08
Male66.1515.71
CuriosityFemale60.8413.161.12.04
Male60.3912.43
Risk ApproachFemale58.1513.35108.44−.35
Male62.6812.68
Ambiguity AcceptanceFemale48.4912.1020.20−.15
Male50.2911.85
CompetitivenessFemale46.9613.8067.84−.28
Male50.6813.18
Overall IQFemale51.0511.025.06−.08
Male51.9211.86
Reasoning Female40.778.7722.02.16
Male39.369.08
Perceptual Female43.026.552.20.05
Male42.696.59
Numbers Female13.365.5385.75−.32
Male15.266.42
Words Female30.315.611.65.04
Male30.075.68
Spatial Female9.575.1065.26−.27
Male10.985.22
Table
Table 3. Descriptive Statistics and Pearson’s Correlations between Demographic, Personality, and IQ.
Table 3. Descriptive Statistics and Pearson’s Correlations between Demographic, Personality, and IQ.
MSD1234567891011121314
1. Gender1.64.48
2. Age38.5710.75.19 ***
3. Conscient.66.7115.46.06 ***.05 **
4. Adjust.65.7015.93.04 *.07 ***.41 ***
5. Curiosity60.5512.70 −.02 −.05 **.30 ***.21 ***
6. Risk Appro.61.0513.11.17 ***.12 ***.54 ***.45 ***.40 ***
7. Amb. Accept.49.6411.97.07 ***.15 ***.15 ***.33 ***.31 ***.37 ***
8. Competitive.49.3413.52.13 ***−.21 ***.31 ***−.03 *.11 ***.27 ***.00
9. Overall IQ51.6111.58.04 *−.12 ***−.03−.04 *.01.02.18 ***.02
10. Reason39.879.00−.08 ***−.16 ***−.03−.02.00−.01.14 ***.00.79 ***
11. Percept42.816.58 −.02−.15 ***−.00.00.01.03 *.09 ***.03.77 ***.52 ***
12. Number14.586.18.15 ***−.16 ***−.01−.03−.02.02.14 ***.10 ***.73 ***.45 ***.42 ***
13. Words30.165.66−.02.09 ***−.05 **−.07 ***.02−.01.18 ***−.08 ***.74 ***.55 ***.46 ***.39 ***
14. Spatial10.475.22.13 ***−.03−.00−.02.02.04 **.11 ***.01.63 ***.34 ***.37 ***.41 ***.32 ***
*** p < .001, ** p < .01, * p < .05.
Table
Table 4. Results for Regression of IQ Scores on Demographics and Traits.
Table 4. Results for Regression of IQ Scores on Demographics and Traits.
Overall IQReasoning Numbers
BSEBetatBSEBetatBSEBetat
Gender1.27.39.053.23 **−.99.31−.05−3.23 **2.19.21.1710.60 ***
Age−.18.02−.17−9.92 ***−.16.01−.19−11.02 ***−.12.01−.21−12.88 ***
Conscient..00.02.000.04−.00.01−.00−0.10.00.01.010.33
Adjust.−.08.01−.11−5.60 ***−.04.01−.06−3.38 ***−.03.01−.07−3.84 ***
Curiosity−.05.02−.06−3.30 ***−.04.01−.06−3.40 ***−.04.01−.08−4.35 ***
Risk Appro..02.02.020.99.01.02.010.49−.00.01−.01−0.45
Amb. Accept..23.02.2413.67 ***.15.01.2011.48 ***.11.01.2111.87 ***
Competitive.−.02.02−.03−1.55−.02.01−.03−1.51.02.01.042.01
Adjusted R2.066.061.093
F34.81532.05850.131
p.000.000.000
WordsSpatial
BSEBetatBSEBetat
Gender−.40.19−.03−2.08 *1.49.18.148.26 ***
Age.03.01.063.59 ***−.04.01−.08−4.70 ***
Conscient.−.00.01−.00 −0.11.00.01.01 0.41
Adjust.−.05.01−.14−7.54 ***−.03.01−.08−4.18 ***
Curiosity−.01.01−.01−0.59−.01.01−.02−0.97
Risk Appro.−.00.01−.01−0.34.01.01.031.44
Amb. Accept..11.01.2312.90 ***.06.01.137.33 ***
Competitive.−.03.01−.06−3.37 ***−.02.01−.04−2.12 *
Adjusted R2.061.035
F31.91118.446
p.000.000
*** p < .001, ** p < .01, * p < .05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Cuppello, S.; Treglown, L.; Furnham, A. Intelligence, Personality and Tolerance of Ambiguity. J. Intell. 2023, 11, 102. https://doi.org/10.3390/jintelligence11060102

AMA Style

Cuppello S, Treglown L, Furnham A. Intelligence, Personality and Tolerance of Ambiguity. Journal of Intelligence. 2023; 11(6):102. https://doi.org/10.3390/jintelligence11060102

Chicago/Turabian Style

Cuppello, Stephen, Luke Treglown, and Adrian Furnham. 2023. "Intelligence, Personality and Tolerance of Ambiguity" Journal of Intelligence 11, no. 6: 102. https://doi.org/10.3390/jintelligence11060102

APA Style

Cuppello, S., Treglown, L., & Furnham, A. (2023). Intelligence, Personality and Tolerance of Ambiguity. Journal of Intelligence, 11(6), 102. https://doi.org/10.3390/jintelligence11060102

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop