www.elsevier.com/locate/paid

Personality and Individual Differences 43 (2007) 959–968

‘‘I think I can, I think I can . . .’’: The interrelationshipsamong self-assessed intelligence, self-concept, self-efficacyand the personality trait intellect in university students in

Scotland and New Zealand

Elizabeth R. Peterson a,*, Martha C. Whiteman b,1

a Department of Psychology, University of Auckland, Auckland, New Zealandb Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, United Kingdom

Received 16 June 2006; received in revised form 9 February 2007Available online 30 April 2007

Abstract

The constructs and measures of self-assessed intelligence (SAI), the personality trait Intellect, self-effi-cacy and academic self-beliefs by definition appear to be distinct, but at the item level there appears tobe considerable content overlap. It is possible that researchers working largely in isolation from oneanother have developed different construct names for the same underlying construct or trait. This articleexamines the overlap between these constructs in a sample of 301 university students. Structural equationmodelling indicated that SAI, academic self-concept, academic self-efficacy and the personality trait Intel-lect all contain an underlying component of academic self-belief, but that the overlap was not as high asexpected. These findings suggest that the constructs are distinct from one another and should be usedindependently.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Self-assessed intelligence; Self-concept; Self-efficacy; Intellect; Personality

0191-8869/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.paid.2007.02.019

* Corresponding author. Tel.: +64 9 373 7599; fax: +64 9 373 7450.E-mail addresses: e.peterson@auckland.ac.nz (E.R. Peterson), M.Whiteman@ed.ac.uk (M.C. Whiteman).

1 Tel.: +44 0131 650 3317; fax: +44 0131 650 3461.

960 E.R. Peterson, M.C. Whiteman / Personality and Individual Differences 43 (2007) 959–968

1. Introduction

For some time, researchers have been hinting at the conceptual overlap between measures ofself-assessed intelligence and the personality trait intellect or openness to experience (Ackerman& Heggestad, 1997; Bates & Sheiles, 2003; Chamorro-Premuzic, 2005; Chamorro-Premuzic &Furnham, 2004; Chamorro-Premuzic, Furnham, & Moutafi, 2004; Gignac, Stough, & Loukom-itis, 2004) and less overtly the overlap between self-assessed intelligence (SAI) and academicself-concept and academic self-efficacy (Ackerman & Heggestad, 1997; Chamorro-Premuzic,2005; Pietsch, Walker, & Chapman, 2003; Skaalvik & Skaalvik, 2002). While by definition theseconstructs are separate, at face value there is overlap at both the item level and at the applied level.That is, all four constructs contain self-report type evaluations of cognitive ability and they haveall been identified as having important social and educational consequences, especially for work-place and educational behaviour and performance (Ablard & Mills, 1996; Byrne, 2002; Chamo-rro-Premuzic, 2005; Chamorro-Premuzic et al., 2004). Despite their potential overlap,researchers in the fields of education and psychology have largely worked in isolation. Only re-cently have researchers, particularly personality researchers, started to investigate some of theseinter-relationships, but to date, no research has directly compared all four constructs.

Ackerman and Heggestad (1997) argue it is important to investigate the overlap between traitconstructs for three main reasons. Firstly, significant contributions to science have often comefrom putting together constructs that have been treated as independent. Secondly, behaviour israrely determined by any one single trait construct and thirdly, we may have a jangle fallacy sit-uation, where researchers have unknowingly and unnecessarily developed two or more constructnames for the same trait.

The present study was therefore designed to examine the relationships among four trait con-cepts: SAI; the personality trait intellect; academic self-concept and general self-efficacy, to seethe degree to which these constructs overlap. Table 1 gives examples of typical items used to mea-sure these constructs.

On inspection of Table 1, all the constructs can be argued to have in common an underlyingself-rating of some form of cognitive ability or self-rating on a task that requires cognitive ability.In this paper we use the terms cognitive or academic ability, or general intelligence (g), to refer tothe general ability to learn, reason, solve problems and think abstractly. Self-ratings of cognitiveability are important because they are believed to have an effect (most likely an indirect effect) onperformance outcomes. That is, realistic and positive self-ratings of ability are more likely to leadto the adoption of self-fulfilling behaviours such as increased motivation, greater task persistenceand more self-regulated learning, which in turn result in enhanced performance outcomes (Beyer,1999; Furnham & Chamorro-Premuzic, 2004; Marsh, Walker, & Debus, 1991).

The constructs in Table 1, with the exception of SAI, are typically measured using a Likert-typeresponse scale. These scales ask participants to rate themselves on each item within a given range, forexample ‘definitely false’ to ‘definitely true’ or ‘very inaccurate’ to ‘very accurate’. Self-assessedintelligence is typically measured by presenting participants with a graph of normally distributedintelligence scores with the average IQ (Mean = 100) and standard deviations clearly marked andexplained. Using this graph as a reference, participants are asked to estimate their own intelligence.

While the content of these items and their measurement have many broad similarities, there arealso differences, especially theoretical differences. The question is, are the differences between them

Table 1Table comparing items from the constructs SAI, academic self-concept, self-efficacy and the trait intellect

Construct Sample items

SAI (general) My overall intelligence is . . .SAI (verbal) My verbal intelligence is . . .SAI (mathematical) My logical or mathematical intelligence is . . .

Academic self-concept (verbal) I have a poor vocabularyI have good reading comprehensionRelative to most other people, my verbal skills are quite goodI can write effectivelyI am an avid readerI am good at expressing myselfI have to read things several times before I understand them

General academic self-concept I like most academic subjectsI am not particularly interested in most academic subjectsI am good at most academic subjectsI hate studying for many academic subjectsI get good marks in most academic subjectsI enjoy doing work for most academic subjects

General academic self-efficacy I am certain I can master the skills being taught in this courseI am confident I can do an excellent job on assignments and testsI am certain I can understand the most difficult material presented in texts

Intellect I have a rich vocabularyI use difficult wordsI am quick to understand thingsI have difficulty understanding abstract ideasI have a vivid imaginationI spend time reflecting on thingsI am full of ideas

Academic self-concept items taken from Self-Description Questionnaire III (Marsh, 1999); general academic self-efficacy items taken from the PISA Indices (Adams & Wu, 2002); and Intellect items taken from the InternationalPersonality Item Pool (Goldberg, 1999). SAI is measured with reference to a normally distributed population with aMean of 100. The remaining measures use a Likert scale.

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big enough that they can be considered unique constructs, do typical items pick up on the theo-retical differences between the constructs, or do we have item promiscuity where the same itemcontent is common to two or more constructs?

Of the four constructs in Table 1, SAI is the most general with respect to assessing cognitiveability. For example, verbal SAI asks for an overall evaluation of verbal intelligence without spec-ifying any particular verbal domain (spelling, reading, comprehension, etc.).

In contrast, academic self-concept (what one believes about one’s academic ability), especiallydomain specific academic self-concept, is the most specific with respect to evaluations of cognitiveability. Questions typically contain evaluative questions about specific content areas (‘‘I am goodat X’’) and they also usually contain a descriptive component (‘‘I like X’’) (Byrne, 1996; Strein,1993). Theoretically, academic self-concept should also capture not only an individual’s senseof their academic ability, but also a more rounded sense of the individual’s academic beliefs

962 E.R. Peterson, M.C. Whiteman / Personality and Individual Differences 43 (2007) 959–968

and pride (Song & Hattie, 1984). It is unclear to what extent items such as those presented in Ta-ble 1 capture this broader notion of an academic self-concept or whether they could more accu-rately be described as academic self-evaluations.

Self-efficacy is a measure of self-confidence in an individual’s ability to perform or master a par-ticular task against some criterion (depending on the specificity of the self-construct measure)(Bandura, 1994). Measures of self-efficacy typically involve ‘‘I think I can . . .’’ items. Conceptu-ally, asking someone to look into the future and assess if they think they can do something is dif-ferent from asking whether they can do something (which implies drawing on past experience).Any estimation of confidence in future performance is likely to be based on relevant past experi-ences, unless the task is completely novel (Marsh et al., 1991).

The personality trait Intellect or Imagination from Goldberg’s (1999) International PersonalityItem Pool NEO (IPIP-NEO) is also known as Openness to Experience in Costa and McCrae’s(1992) NEO-PI-R personality inventory. Although these traits are argued to be measuring thesame underlying construct, recent research has suggested that the IPIP intellect items focus moreon ideas and imagination whereas the NEO openness items are broader and include willingness totry new experiences. Either way, the personality trait intellect or openness is conceptually the mostdifferent of the four constructs in Table 1. Note that as a personality trait, by definition it does notattempt to evaluate any given behaviour like SAI does, but rather it assesses typical behaviour(Ackerman & Heggestad, 1997). In this sense, intellect or openness can be argued to be quite dis-tinct from SAI, but at the item level, the IPIP-NEO and the NEO-PI-R appear to have a strongevaluative cognitive ability component e.g. ‘‘I have a rich vocabulary’’ and ‘‘I use difficult words’’are items from the IPIP-NEO. Similarly, the NEO-PI-R contains items about getting enjoymentout of playing with abstract ideas and theories and being intellectually curious. It is this cognitivecomponent that has led researchers to investigate the overlap between openness and psychometricintelligence, especially crystallised intelligence (e.g. Information and Comprehension) (Ackerman& Heggestad, 1997; Ashton, Lee, Vernon, & Jang, 2000; Holland, Dollinger, Holland, & Mac-Donald, 1995). Findings suggest that the overlap between general intelligence and openness issmall (according to Cohen’s criteria), between r = .11 (Gignac, 2005) and r = .30 (Ackerman &Heggestad, 1997). While it has been implied that this overlap is due to openness acting as a proxySAI measure, or to SAI moderating the relationship between openness and crystallised intelli-gence (Gignac et al., 2004), researchers have not investigated the relationship directly.

The purpose of the present study was therefore to clarify the concepts of SAI, academic self-concept, self-efficacy and the personality trait intellect. The aim was not to suggest a common def-inition for these constructs, but to investigate the extent to which they overlap.

2. Method

2.1. Participants

A total of 306 participants participated in this study. Sixty-four were taking a New ZealandUniversity third year Psychology course, 85 were taking a New Zealand University first year Edu-cation course and 157 were taking a Scottish University first year Psychology course. The majorityof participants (79%) were female with an age range of 16–51 years (M = 21, SD = 5.6) and the

E.R. Peterson, M.C. Whiteman / Personality and Individual Differences 43 (2007) 959–968 963

remaining males’ ages ranged from 17 to 40 years (M = 21, SD = 4.6). The New Zealand basedparticipants identified themselves as being of Pakeha/European descent (51%), Asian (33%),Maori (10%) or Pacifika (6%). The majority of Scottish based participants identified themselvesas Scottish (41%), English (41%) or Irish (11%).

2.2. Materials

Participants were asked to complete a number of items on self-assessed general intelligence anddomain-specific intelligences, academic self-concept and academic self-efficacy. The measures werechosen for their relevance to the age group of the sample; moreover, they have been tested on NewZealand and Scottish samples and they have good scale reliability.

Self-assessed intelligence was measured with a questionnaire which displayed a normal distribu-tion curve at the top of the page as a reference point. The mean and three standard deviationseither side of the mean were marked on the curve. Above the mean was written the score 100and the 15 point standard deviations increments were also marked. The area under the curvewas shaded and marked to show the percentage of the population who could be expected to fallinto each standard deviation. The participants were told that the distribution showed the spreadof intelligence in the population with a mean IQ of 100. Two thirds of the population could beexpected to have an IQ of between 85–115 and about 2% would score above 130 or below 70.The questionnaire asked participants to estimate their own overall intelligence along with theirverbal, spatial and logical/mathematical intelligence.

The trait intellect from Goldberg’s (1999) lexical ‘Big Five’ International Personality Item Pool(IPIP-NEO) was used. This trait is measured by 10 items on a five-point Likert scale, ranging fromvery inaccurate (1) to very accurate (5). The scale has been validated against the NEO-FFI in aScottish sample, with Intellect correlating with Openness at r = 0.59 (Gow, Whiteman, Pattie,& Deary, 2005).

General academic self-concept was taken from the well established and validated Self-Descrip-tion Questionnaire III (Marsh, 1999) for late adolescents. Due to administration time constraints,the four self-concept items with the highest item-total correlations were used (these are the firstfour items given in Table 1). These self-concept scales were all measured on an eight-point scaleranging from (1) definitely false to 8 (definitely true). The scale’s internal reliability is r = .93 andit has been used in the 16–25 age group (Marsh, 1999).

General academic self-efficacy was measured using the three items from the PISA Indices(Adams & Wu, 2002) which have been validated in both New Zealand and Scottish samples wherethe scale’s internal consistency was found to be r = .71 and r = .70, respectively (see Table 1). The4-point Likert scale was however extended to be the same as that of the Self-Description Ques-tionnaire to keep the format of the items similar across the two inventories.

2.3. Hypothesised model

Given the apparent overlap between the constructs above, the hypothesised model presented inFig. 1 was proposed. This model indicates that SAI is defined by four indicator variables: VerbalSAI, Spatial SAI and Logic SAI and the trait Intellect. Intellect was included in this latent traitas previous research has suggested that intellect may be a proxy for SAI (e.g. Gignac, 2005).

Self-Efficacy

Intellect

Ability Self-Beliefs

SAI

Logic SAI

Spatial SAI

Verbal SAI

Academic SC

0,Error 1

1

0,Error 2

1

0,Error 3

1

0,Error 4

1

0,Error 5

1

0,Error 6

1

0,Error d2

1

0,Error d11

Fig. 1. The hypothesised model of the relationship between SAI, the trait Intellect (openness), general academic self-efficacy and general academic self-concept. The first- and second-order latent factors (ellipses) are extracted from themeasured variables (rectangles).

964 E.R. Peterson, M.C. Whiteman / Personality and Individual Differences 43 (2007) 959–968

Importantly, the model also shows that the latent trait SAI along with Academic Self-Conceptand Academic Self-Efficacy define a higher-order latent trait of Ability Self-beliefs.

In Fig. 1, each observed variable also has a unique measurement error. This is shown by a smallellipse and an arrow pointing to each variable. This error indicates that each of these variables isalso measuring something other than the hypothesised variable. The straight arrow leading fromthe latent variables (ellipse) to the dependent variables (rectangles) indicates that a structure coef-ficient needs to be estimated. Each latent variable (SAI and Ability Self-beliefs) has three or fourstructural coefficients to be estimated as well as a unique prediction error. This prediction errorindicates that the latent variables SAI and Academic Self-beliefs are not entirely predicted bythe latent or dependent variables.

2.4. Procedure

Before the questionnaire was given out, the lecturer explained to the participants how to interpreta normal curve and how it related to the distribution of intelligence in the population. This lectureformed part of a series of lectures about intelligence. Following this, the participants were given thequestionnaires to fill in during class time. The questionnaires were the same for all participantgroups, with the exception that degree and ethnicity information was changed to pick up the differ-ences most likely to exist in the United Kingdom (UK) and in New Zealand (NZ). The question-naires were anonymous and took approximately 20 min to complete. All questionnaires werearranged in the same order, with SAI first, followed by self-concept items and then personality.Analysis of the results was fed back to the students in a subsequent lecture. Fourteen subjects wereremoved from the analysis because of improbably high (>200) or low (<50) self-reported IQ scores

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which suggests that they either did not take the task seriously or they had not understood the dis-tribution of intelligence. A further 17 were removed due to having more than 5% of their dataincomplete. The two NZ groups were combined into one NZ group for analysis: the educationand psychology students showed no significant differences on personality, self-concept or SAI.

3. Results

A 2 · 4 MANOVA was conducted with independent variables of gender and country of study.The dependent variables comprised the personality trait (intellect), 4 SAI measures (overall esti-mated intelligence, verbal, spatial and logical/mathematical estimated intelligence), general aca-demic self-concept and general academic self-efficacy. A significant multivariate main effect wasfound for gender with males rating themselves higher on spatial SAI (F(1,276) = 4.9, p = .027,g2

p ¼ :018). The power of the calculation was low (60%) and the effect size small, the F was, nev-ertheless, statistically significant. Although caution is needed when interpreting this statistic, thissignificant sex difference is in keeping with the perceived gender stereotype that men are betterthan women on tasks that involve spatial abilities (Coluccia & Louse, 2004).

Significant small-to-moderate multivariate main effects were also found for country of studywith the UK participants rating themselves with higher verbal SAI and higher logic SAI(F(1,276) = 5.73, p = .017, g2

p ¼ :020; F(1,276) = 5.12, p = .024, g2p ¼ :018, respectively), but no

differences were found for overall SAI. The main effects found may reflect the different ethnicbackgrounds of the samples from the two countries. The NZ sample had a high proportion ofparticipants (49%) who identified themselves as being of Maori, Polynesian or Asian descent:these are ethnic groups that are stereotypically perceived as being more shy and reserved thanEuropean populations. The verbal SAI and self-concept differences may also reflect the partici-pants’ linguistic backgrounds, which could lead to higher verbal SAI and self-concept in groupswith more experience of English.

Main effects were also found for self-efficacy with the New Zealand sample having higher gen-eral self-efficacy (F(1,272) = �7.92, p = .005, g2

p ¼ :03). This finding is in keeping with a growingconcern about Scottish low self-confidence (Craig, 2003). However, while all our UK participantswhere studying at a Scottish University, only 41% of them identified themselves as Scottish.

Correlations, means and standard deviations for the measures used are presented in Table 2. Aconfirmatory factor analysis using maximum likelihood estimation was conducted using AMOS5.0. The hypothesised model is presented in Fig. 1.

The initial model used was based on the assumptions that SAI, self-efficacy and academic self-concept may be part of a single higher-order ability self-belief trait. Examination of the initialmodel showed unacceptable fit. However, examination of the modification indices and the ex-pected parameter changes indicated that a substantial improvement in model fit could be gainedif the trait intellect had a direct path with the latent factor ability self-beliefs rather than with over-all SAI and with the addition of three correlated measurement errors (see Fig. 2). The statistical fitfor the re-specified post hoc model was acceptable and the final model is illustrated in Fig. 2. Theroot mean square error of approximation = .034 (CI = 0–.095), which was in keeping with therecommended maximum .06 required for a good fit (Hu & Bentler, 1999). The v2 = (5,N = 284) = 6.67, p = .246 was non-significant which was also in keeping with a good fit. The

Table 2Correlations and descriptive statistics for the variables assessed in the study

1 2 3 4 5 6 Mean SD Skewness Kurtosis

1 Overall SAI 111.27 11.67 .11 .922 Verbal SAI .757** 110.67 13.18 .08 �.093 Logic SAI .752** .558** 105.50 15.85 .41 .984 Spatial SAI .545** .539** .547** 108.14 13.63 �.15 .235 Academic SC .240** .167* .171** .150* 20.75 6.13 �.52 .566 Intellect .161* .173** .125 .148* .147* 33.73 10.51 .16 �.277 Self-efficacy .180** .159* .111 .064 .433** 259** 16.37 3.27 .37 .68

Note: SAI = Self-assessed intelligence, SC = self-concept.* p < .05.

** p < .001.

Self Efficacy

Intellect

Ability Self-Beliefs

SAI

Logic SAI.77

Spatial SAI.74

Verbal SAI.71

Academic SC

.45

Error 1

Error 2

Error 3

Error 4

Error 5

Error 6

Error d2

Error d1

.36

.54

.84

.28

.44

.02

Fig. 2. The re-specified path model of the relationship between SAI, Intellect, general academic self-efficacy and generalacademic self-concept. The first- and second-order latent factors (ellipses) are extracted from the measured variables(rectangles). All parameters are significant at (p < .05). See Section 3 for model fit indices.

966 E.R. Peterson, M.C. Whiteman / Personality and Individual Differences 43 (2007) 959–968

comparative fit indices were as follows: Bentler–Bonett Normed Fit Index = .985; Bentler–BonettNon-Normed Fit Index = .988; Comparative Fit Index = .996, all exceeding the recommendation.90 or .95 (Hu & Bentler, 1999).

The correlated measurement errors among the trait intellect and the self-assessed intelligencemeasures suggest that while there is no path from intellect to overall SAI, the two constructsdo share error variance.

4. Discussion

This study has shown that the constructs of SAI, trait intellect, academic self-efficacy and aca-demic self-concept are reliably associated within the context of this model. While this overlap may

E.R. Peterson, M.C. Whiteman / Personality and Individual Differences 43 (2007) 959–968 967

come as no surprise to some researchers, it is the first study to bring these constructs together andempirically demonstrate and start to quantify the overlap.

This finding is important because it helps clarify the relationship between these constructs andshows that while these constructs are related (they all seem to have an underlying ability self-beliefcomponent), by and large they are sufficiently unique to continue to warrant separate consider-ation in empirical studies by both educationalists and psychologists.

Specifically, our data suggest that self-efficacy measures capture how well an individual may beable to capitalise on their general abilities and, if the measures are used well, could help identifywhere a student is likely to benefit from targeted intervention or tutoring to build confidence.However, these findings also suggest that even though intellect and SAI correlate, intellect shouldnot be used as a proxy for SAI. This indicates that the correlation between these two constructsmay be accounted for by both constructs being embedded in a higher-order structure such as intel-ligence. It appears that even with self-reported intelligence and trait intellect items which might bethought to capture the same concept (e.g. ‘‘I have a poor vocabulary’’), the overlap is small(around 0.30). Thus, respondents are keeping their academic ability self-assessments separatefrom their personality assessments and the two measures do not yield the same information. Thismay, in part, reflect differences between IQ-type test measures, which capture ‘maximum’ perfor-mance, versus personality measures, which assess ‘typical’ performance/behaviour.

Although this study demonstrates for the first time the relative independence of these constructsfrom each other, there are a number of study limitations that need to be considered. Firstly, thesmall number of male participants means the findings will need to be repeated using a larger morerepresentative sample. Secondly, the model presented in Fig. 1 is a saturated model and as suchthere are a number of alternative models that could yield equivalent fit and potentially otherimportant variables not included in the analysis (such as psychometric intelligence) may play arole. However, examination of plausible alternative models (such as a path from openness to over-all SAI) indicated poor fit which gives further weight to the re-specified model presented in thisstudy. In addition, the current model is in keeping with the theoretical distinctions between theconstructs suggested in the literature.

Overall, these limitations should not distract from our novel finding that we have empiricallydemonstrated for the first time the extent to which the constructs of SAI, intellect, academicself-efficacy and academic self-concept overlap. Our findings suggest that while there is a degreeof overlap, educators and psychologists can learn more about behaviour by treating these con-structs as separate measures. Finally, it appears that despite item level overlap and conceptualoverlap, we do not have a jangle fallacy where one higher-order trait can be used instead of multi-ple individual measures.

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