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Educational Psychology Vol. 26, No. 6, December 2006, pp. 769 779 Self-Assessed Intelligence and Academic Performance Tomas Chamorro-Premuzic a * and Adrian Furnham b a Goldsmiths, University of London, UK; b University College London, UK CEDP_A_139075.sgm 10.1080/01443410500390921 Educational 0144-3410 Original 2006 Taylor 26 5000000October Dr pss02tc@gold.ac.uk TomasChamorro-Premuzic and & Article Francis (print)/1469-5820 Psychology 2006 Ltd Ltd (online) This paper reports the results of a two-year longitudinal study of the relationship between selfassessed intelligence (SAI) and academic performance (AP) in a sample of 184 British undergraduate students. Results showed significant correlations between SAI (both before and after taking an IQ test) and academic exam marks obtained two years later, even when IQ scores were partialled out. Several continuous assessment indicators (notably attendance, oral expression, and motivation) were also significantly correlated with SAI, even when IQ scores were controlled. A series of hierarchical regressions indicated that although exam grades were best predicted by IQ, SAI showed significant incremental validity in the prediction of AP, accounting for an additional 3% of exam, 9% of continuous assessment, and 2% of essay grades. Results are discussed with regard to current trends to integrate individual differences underlying AP. The idea that confidence may affect an individual s performance has existed so long that it seems irrelevant to date it. In psychology, however, this idea was first empirically supported by a series of NASA-funded studies, in the 1950 1970 period, which attempted to identify the determinants of scientific creativity. This program of research (e.g., Barron, 1969; Taylor & Barron, 1963) concluded that professional self-confidence was the strongest single predictor of excellence in a variety of occupations, such as among writers, architects, mathematicians, and even Everest climbers. Later, Bandura (1986) famously conceptualised self-efficacy the belief that one is capable of affecting a specific outcome as the most central and pervasive thought affecting action. Using regression analyses, Bandura showed that self-efficacy contributed to achievement beyond cognitive ability, arguing that a strong sense of efficacy deploy[s] [people s] attention and effort to the demands of the situation (p. 394), enabling them to produce their own future, rather than simply foretell it (p. 395). Self-efficacy theory deals with the independent effects of positive self-perception on *Department of Psychology, Goldsmiths, University of London, New Cross, London SE14 6NW, UK. Email: pss02tc@gold.ac.uk ISSN 0144-3410 (print)/issn 1469-5820 (online)/06/060769 11 2006 Taylor & Francis DOI: 10.1080/01443410500390921
770 T. Chamorro-Premuzic & A. Furnham real-life outcomes, irrespective of an individual s actual competence. Thus it considers confidence a cause, rather than a consequence, of high performance. Although the theory of self-efficacy generated great enthusiasm in many fields of psychology, extending from social psychology to other areas, notably educational psychology (Bempechat, London, & Dweck, 1991; Bouffard-Bouchard, Parent, & Larivee, 1991; Chemers, Hu, & Garcia, 2001; Dweck, 1986, 1999; Multon, Brown, & Lent, 1991), it has been largely ignored in individual differences research. In recent years, however, differential psychologists have increasingly examined what is arguably a psychometric variation of self-efficacy, namely self-assessed intelligence (SAI) or individual differences in self-estimates of cognitive ability (Furnham, 2001; Furnham, Kidwai, & Thomas, 2001; Paulhus, Lysy, & Yik, 1998). Instead of presenting the respondent with standardised problems (performance tasks), such as IQ test items, SAI inventories simply require an individual to rate his/her own intelligence, in terms of a general factor or multiple abilities, in comparison to the overall population (Furnham & Rawles, 1995, 1999; Rammstedt & Rammsayer, 2000, 2002). Thus there is a substantial theoretical overlap between the concepts of professional self-confidence, self-efficacy, and SAI, notably the emphasis on the selffulfilling effects of subjective evaluations regardless of their accuracy as determinants of actual performance. Due to the impressive validity of traditional IQ tests as predictors of school, university, and occupational success (Binet, 1903; Deary, 2001; Gottfredson, 2002; Schmidt & Hunter, 1998), there is little doubt that IQ scores represent an accurate indicator of a person s intellectual ability. This is probably why differential psychologists have mainly investigated SAI as a proxy or substitute measure of IQ. High correlations between SAI and IQ would suggest that people can accurately estimate their intelligence, while low correlations between SAI and psychometrically measured intelligence would suggests that individuals are largely unaware of their levels of intellectual ability. Although modest correlations (typically in the region of r =.30) between SAI and IQ led differential psychologists to conclude that SAI could not be considered an accurate measure of intelligence (Paulhus et al., 1998), direct paths from SAI to performance outcomes other than IQ tests have rarely been examined. However, as intelligence tests are validated against academic performance outcomes (the only reason one can claim that what IQ tests measure is intelligence is that IQ tests predict academic performance), it would seem natural to adopt the same approach to validating SAI. Furthermore, since IQ tests are not perfect predictors of academic performance, it is possible that SAI may account for unique variance in academic performance, which would support the idea that SAI has self-fulfilling effects on academic performance, regardless of whether it is an accurate indicator of IQ or not. In accordance with another salient theoretical and methodological line of differential research, which conceptualises self-assessed traits as in the realm of personality rather than intelligence (Cronbach, 1949; Eysenck & Eysenck, 1985), recent studies have shown that SAI is significantly related to an array of personality traits that have been consistently identified as beneficial for academic performance. People who
Intelligence and Academic Performance 771 award themselves higher intelligence tend to be more emotionally stable (less neurotic), more extraverted, more open/intellectual, and more conscientious/hardworking than those who award themselves lower intelligence levels (Chamorro- Premuzic & Furnham, 2005; Chamorro-Premuzic, Furnham, & Moutafi, 2004). Although these personality traits have been only marginally linked to psychometric intelligence measures (the only exception is the openness to experience trait, which has been found to correlate up to r =.30 with ability measures; see Ackerman & Heggestad, 1997; Costa & McCrae, 1992), they have been found to be significantly related to both academic and occupational performance (Busato, Prins, Elshout, & Hamaker, 2000; Chamorro-Premuzic & Furnham, 2002, 2003a, 2003b; Savage, 1962). Thus Chamorro-Premuzic and Furnham (2005) conceptualised SAI as an indicator of intellectual competence and hypothesised that SAI should be significantly correlated with performance outcomes, even when psychometrically measured intelligence (IQ) is taken into account. Like self-efficacy and professional self-confidence, then, SAI may be interpreted as the cause, rather than the consequence, of intellectual performance. To the extent that SAI may have a significant impact on AP, as well as partly determine the development of intellectual competence, we expect SAI to be significantly correlated with academic outcomes (exam grades, continuous assessment, and essay marks), even when IQ scores are taken into account. Thus, it is hypothesised that SAI will show incremental validity with regard to IQ in the prediction of AP. Method Participants Participants were 184 British undergraduate students (73% females). Their ages ranged from 18 to 34 (X= 20.10, SD = 5.42) years. All respondents completed the measures over a period of two weeks. Data were collected by the researchers themselves, who ensured debriefing and sincerity of responses. Although participants with missing values (e.g., essay marks, continuous assessment reports) were not removed from the data file mainly to enable us to analyse attendance levels they were excluded from the analyses of data whenever values were missing (see details below). Procedure and Materials First, students provided their self-estimates of intelligence (details of the SAI measure used can be found in Figure 1). This was done during an introductory lecture and participants were offered individual and confidential feedback. Next, students completed the Wonderlic Personnel Test of IQ. Four demonstrators were present to ensure proper administration according to the manual s requirements (see Wonderlic, 1992). This test is timed and takes 12 minutes to complete. Next, participants were asked to estimate their intellectual ability again (this was recorded on the same form provided to assess the initial SAI before the IQ test completion).
772 T. Chamorro-Premuzic & A. Furnham Academic performance data were collected from the university archives after two academic years, hence the longitudinal nature of the variables used as criteria. Details of all measures are provided below. Figure 1. Estimate your own intelligence (self-assessed intelligence chart) Academic performance (AP). AP was measured by three variables, namely: 1. Seminar performance/continuous assessment: every week (throughout two academic years) participants attended a compulsory one- to two-hour seminar as part of their degree. Six different seminar leaders (i.e., staff members) evaluated each student s seminar performance (presentation and discussion of diverse subjects) and wrote a final report upon conclusion of each seminar. Seminar performance was assessed using a standardised form which included a seven-point scale (from 1 = very poor to 7 = excellent ) to assess the student on six variables, namely attendance (no missing values), grasp of subject matter (12 missing values), motivation (eight missing values), written expression (seven missing values), oral expression (six missing values), and amount of participation (nine missing values). Attendance was calculated in percentages for each participant (number of seminar meetings attended / total number of seminar meetings scheduled 100). All seminar performance outcomes were found to have sufficient internal and longitudinal reliability (α >.71) and a total seminar performance factor was computed via simple addition. The mean seminar performance score for the sample was 350.54 (SD = 57.20), with a range of 181 485. 2. Overall essay marks: these were obtained by calculating the arithmetic mean for each participant (the internal reliability was α =.82). The number of essays submitted was held constant at three per term. Thus there were 12 essays per student in the two-year period (there are two terms per academic year). The topic of each essay was the same for every student, although students were permitted to choose how to approach each topic and what references/bibliographic information to use. The grading scale used to mark essays was 0 100, where 32 represents a pass, 40 50 a third, 50 60 a second, 60 70 and upper-second, and 70 or above a first or distinction. The overall mean for essay marks was 61.73 (SD= 6.16), with a range of 36 77. There were 12 missing values for overall essay marks. 3. Exam performance: this was measured throughout the two-year period using overall exam marks based on eight three-hour written exams (four after the first year, and four after the second year). The grading system for exams was the same as that for essay marks (see above). The overall mean for exam grades was 61.48 (SD = 6.32), with a range of 40 76. There were five missing values for overall exam marks. Although exam grades were found to be internally reliable (α =.79), exam grades were examined separately for the two years, as well as in aggregate. As exams represent the main indicator of AP (making up two-thirds of a student s final degree), it was deemed necessary to assess potential changes in exam performance between the two years.
Intelligence and Academic Performance 773 Psychometric intelligence (cognitive ability). The Wonderlic Personnel Test (Wonderlic, 1992) was used to measure individual differences in IQ. This 50-item test has been extensively used in differential and educational research (Chamorro-Premuzic & Furnham, 2005). It is administered in 12 minutes and scores can range from 0 to 50. Items include word and number comparisons, disarranged sentences, serial analysis of geometric figures, and story problems that require mathematical and logical solutions. The test has impressive norms and correlates very highly (r =.92) with the WAIS-R (Wechsler, 1958). In the present sample, the arithmetic mean (28.75) was slightly higher (about 1 SD) than the overall population norms (mean = 21.06, based on n = 118,549), but in line with the manual norms for U.S. college students (mean = 29.18). The SD (6.02) of our sample was also consistent with the manual norms for U.S. college students (6.66; see Wonderlic, 1992). Thus the present sample was representative of young (aged 18 28) college students, rather than the overall population, whereas the SD did not show any restrictions in range beyond the norms (in fact, the SD for young college students is only marginally lower than that for the overall population: 6.66 and 7.12, respectively). Self-assessed intelligence (SAI). Participants self-estimated intelligence was assessed with a two-time, one-item, self-report measure before and after they took the Wonderlic IQ test. In order to standardise SAI, the normal distribution of intelligence scores, including labels for mild retardation (IQ = 55), borderline retardation (IQ = 70), low average (IQ = 85), average (IQ = 100), above average (IQ = 115) superior (IQ = 130), and gifted (IQ = 145), was presented to the participants. Students were shown the bell curve (Figure 1), with standard deviation scores and their corresponding labels. Once the IQ test was completed, students were requested to estimate their IQ again, using the same range and figure. By assessing SAI both before and after completion of the IQ test, we attempted to estimate the degree to which SAI may be affected by an individual s level of insight into his/her performance on an IQ test, as well as comparing the predictive validity of a naïve general measure of SAI (obtained before completion of the WPT) with a second SAI measure that may have been influenced by actual intelligence test performance. Results Correlations Partial and bivariate correlations were then performed on the data in order to examine the relationship between SAI and AP. As mentioned above, two measures of SAI were obtained, namely before and after completion of the IQ test. Results showed there was a significant overlap between the two measures of SAI (r =.69, p <.001). The mean for SAI before completion of the WPT was 104.21 (SD = 18.27), whereas the average SAI after completion of the WPT was 103.15 (SD = 16.56). Thus SAI was only marginally lower after completion of the IQ test. ANOVA
774 T. Chamorro-Premuzic & A. Furnham The ìbell curveî figure below shows the normal distribution of IQ scores, which have a mean of 100 and a standard deviation of 15. Thus if your IQ = 100 you have ìaverageî intelligence, whereas an IQ = 130 shows superior intelligent, and an IQ = 70 signals borderline retardation. Using this information please rate your own IQ. SD -3-2 -1 0 +1 +2 +3 IQ 55 70 85 100 115 130 145 Figure 1. Mild Retardation Borderline Retardation Low Average Average Above Average Superior showed these differences were not statistically significant (F[1,360] =.326, p =.567), and the correlation of SAI with IQ was exactly the same before and after completion of the WPT, namely r =.414, p <.001. Table 1 reports the results from the bivariate and partial (controlling for IQ scores) correlations between SAI and different outcomes of AP. As can be seen, SAI was significantly and positively correlated with exam marks, even when IQ scores were partialled out. When indicators of continuous assessment were Table 1. Gifted Estimate your own intelligence (self-assessed intelligence chart) Bivariate and partial correlations (controlling for IQ) between AP, SAI, and IQ SAI1 (before IQ test) SAI2 (after IQ test) WPT (IQ test) Exams (Year 1).33** (.25**).32** (.21*).39** Exams (Year 2).17** (.11).26** (.17*).29** Exams aggregate.28** (.20*).33** (.23**).38** Essays aggregate.22** (.14).23** (.17*).29** Oral.23** (.24**).25** (.21*).27** Motivation.22** (.21**).22** (.15).28** Grasp.15 (.15).06 (.09).13 Participation.15 (.16*).10 (.14).12 Written expression.16* (.17*).12 (.14).23** Attendance.32** (.33**).34** (.32**).30** Seminar aggregate.29** (.28**).28** (.24**).34** n = 184; partial correlations, controlling for IQ, shown in brackets; *p <.05; **p <.01
Intelligence and Academic Performance 775 examined, SAI was shown to be significantly and positively correlated with essay marks (although the correlation between SAI before the IQ test and essay marks was not significant when IQ scores were partialled out), oral expression, motivation, written expression, and attendance, as assessed by a total of six independent raters (academic staff members). Further, the overall factor of seminar performance was significantly and positively correlated with SAI even when IQ scores were taken into account. There were no significant gender correlates for any of the criteria (AP measures). Next, a series of hierarchical regressions were computed to test the predictability of AP (exams, seminars, and essay marks) by SAI and cognitive ability. These are reported in Table 2. As can be seen, IQ accounted for a significant 15% of the variance in overall exam marks. When SAI1 (before taking WPT) and SAI2 (after taking WPT) were added as predictors, an additional 4% of the variance was explained, showing that SAI has incremental validity with regard to IQ scores in the prediction of examination marks. Gender did not account for any additional variance. A second hierarchical regression was then performed, with IQ, SAI, and gender as predictors of overall seminar results (i.e., continuous assessment marks). It was shown that IQ accounted for a significant 10% of the variance in the data. When SAI1 and SAI2 were included in the second block, the percentage of variance accounted for increased to 19%, showing that SAI accounted for an additional 9% of the variance in continuous assessment. In this model, SAI1 was the strongest significant predictor. Once again, gender did not account for any significant additional variance. Finally, the third hierarchical regression indicated that IQ accounted for 9% of the variance in essay marks, with SAI accounting for an additional 2%, and gender 0%. Cognitive ability and SAI1 were significant predictors of the model. Table 2. Hierarchical regressions predicting AP by WPT and SAI Exams (aggregate) Seminars (aggregate) Essays (aggregate) Std B t Std. B t Std. B t WPT (IQ).39 5.58**.35 4.36**.35 3.33** F (1, 170) = 31.09** (1, 137) = 16.98** (1, 146) = 13.24** AdjustedR 2.15.10.09 SAI1.10 1.00.34 2.78**.25 2.04* SAI2.16 1.60.02.13.07.63 F (3, 168) = 13.23** (3, 135) = 11.03** (3, 144) = 6.35** Adjusted R 2.19.19.11 Gender.05.60.07.81.08.97 F (4, 167) = 10.11** (4, 134) = 8.47** (4, 143) = 5.17** Adjusted R 2.19.19.11 n = 184; *p <.05; **p <.01; variations in degrees of freedom across different indicators of AP (i.e., exams, seminars, and essays) are a function of variations in sample size across different measures.
776 T. Chamorro-Premuzic & A. Furnham Discussion This paper attempted to investigate the idea that intellectual confidence, assessed through SAI, may have a significant impact on an individual s AP, even when the individual s cognitive ability is taken into consideration. Although this question has motivated much research in social cognition, particularly in terms of self-efficacy (Bandura, 1986), the overwhelming power of cognitive ability as predictor of educational performance (Binet, 1903; Deary, 2001; Gottfredson, 2002; Schmidt & Hunter, 1998), followed by a recent interest in personality traits (Chamorro- Premuzic & Furnham, 2004, 2005) shifted individual differences research towards examining traditional intelligence and personality measures, rather than subjective evaluations and performance-related beliefs. Nevertheless it has recently been hypothesised that SAI should be considered an indicator of intellectual competence and thus used to predict academic performance outcomes. Although the concept of SAI has been increasingly examined with regard to both intelligence and personality (Furnham, 2001; Furnham & Rawles, 1995, 1999; Paulhus et al, 1998), the question of whether SAI has incremental validity in the prediction of AP had not been explored prior to the present study. In the present study, SAI was examined with regard to different indicators of AP, namely examination grades, essay marks, and several indicators of continuous assessment (e.g., attendance, oral expression, participation in class). Most of these indicators were correlated significantly and positively with SAI, even when IQ scores were taken into account (this was true when SAI was measured before, as well as after, completion of the IQ test). Furthermore, correlations between SAI and AP were of similar size to those between AP and IQ scores. This confirmed the hypothesis of this study, which was that SAI would be significantly related to AP, even when IQ scores were taken into consideration. In order to test this hypothesis further, and examine the amount of variance in AP that may be predicted by SAI, a series of hierarchical regressions were conducted. Although cognitive ability (IQ) accounted for most of the variance in AP, notably exam marks and essays, SAI showed modest but significant incremental validity. Moreover, when continuous assessment indicators were analysed, the predictive power of SAI was comparable to that of IQ (i.e., only 1% less of unique variance was accounted for by SAI than by IQ). The present results suggest that subjective beliefs may have independent paths to performance in educational settings, affecting an individual s outcome. With regard to SAI specifically, this simple and quick measure of a person s beliefs about her/his intellectual ability seems to suggest that confidence in one s abilities is beneficial for AP, particularly with regard to participation in class and continuous assessment. What are the implications of the present findings for applied and educational settingsquest; First, it should be noted that the incremental validity of SAI is only modest and it would therefore be unwise to argue against the well-documented predictive power of standardised cognitive ability tests, such as the Wonderlic or any other established IQ measure. In fact, future studies could examine the validity of
Intelligence and Academic Performance 777 SAI over more comprehensive measures of IQ or the general intelligence factor g, such as the WAIS or a multiple battery of ability measures. Second, the present findings have clear limitations in terms of sample size and gender distribution, which undermine the representativeness of the sample. Moreover, in the present study participants SAI was collected for research purposes only and had no implications for the students. One may therefore infer that under real conditions, for example within university selection contexts, students may be more likely to fake good or lie about their SAI. In fact there are important ethical considerations in regard to students honesty when it comes to reporting low SAI, which would almost be equivalent to asking a man to hang himself. However, to those involved in the prediction of AP in university, the present results are a strong indicator that a simple self-report measure of a person s perceived own intelligence can add some additional information to the prediction of his/her future performance, even two years after the measure has been obtained. Moreover, particularly when AP is assessed through continuous assessment exercises (that is, on a daily basis), SAI may be an important predictor, even when compared to psychometric intelligence. This suggests that higher confidence may lead to better working habits and in turn higher AP, irrespective of level of IQ. In terms of the current efforts to integrate the individual differences underlying academic and work performance (see Ackerman & Heggestad, 1997; Chamorro- Premuzic & Furnham, 2004, 2005), the implications of the present results are of theoretical importance. They suggest that SAI, a variable found to relate to both cognitive ability tests and personality traits, may also have an impact on long-term academic outcomes, which are indicators of knowledge acquisition and adult learned skills. This clarifies the interpretation of the correlations between SAI and psychometric intelligence, which may be a sign either of other people s insight into one s intellectual abilities, or of the positive effects of high self-efficacy on performance on cognitive ability tests. With a third, higher-order measure such as AP, the predictive power of both SAI and intelligence as measured psychometrically has now been compared and it is likely that the incremental validity of SAI is an indicator of the effects of individuals perceived intelligence on real-world outcomes, which is consistent with the theories and constructs of self-efficacy and professional self-confidence. References Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121, 219 245. Bandura, A. (1986). Social foundations of thoughts and action: A social cognitive theory. New Jersey: Prentice-Hall. Barron, F. (1969). Creative person and creative process. New York: Holt, Rinehart & Winston. Bempechat, J., London, P., & Dweck, C. S. (1991). Children s conceptions of ability in major domains: An interview and experimental study. Child Study Journal, 21, 11 36. Binet, A. (1903). L etude experimental de l intelligence [Experimental study of intelligence]. Paris: Schleicher.
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