Sex differences in parents' estimations of their own and their children's intelligence

Intelligence 34 (2006) 1–14

Sex differences in parents’ estimations of their own and their

children’s intelligence

Adrian Furnham T, Katherine Bunclark

Department of Psychology, University College London, 26 Bedford Way, London WC1H OAP, Great Britain

Received 13 July 2003; received in revised form 15 May 2005; accepted 31 May 2005

Available online 2 August 2005

Abstract

In this study 141 British parents estimated their own, and one of their children’s IQ on their overall

intelligence as well as on Gardner’s (1983) [Gardner, H. (1983). Frames of the mind: The theory of multiple

intelligences. New York: Basic Books.] seven multiple bintelligencesQ. Replicating previous studies, fathers

gave higher self-estimates on overall, mathematical and spatial intelligence than did mothers. Factor analysis of

the seven self-estimates yielded two factors: cognitive and non-cognitive intelligence and there was a

significant difference on the former with fathers giving higher self-estimates than mothers. Parental estimates

of children’s overall intelligence were shown to significantly correlate with children’s actual IQ score (r =0.44),

derived from standardized tests of verbal, numerical and perceptual ability. The male advantage for overall

intelligence estimates, which was hypothesised, was shown for parental self-estimations but not for estimations

of children’s intelligence, which showed a female advantage perhaps because girls in this sample actually had

higher IQs.

D 2005 Elsevier Inc. All rights reserved.

1. Introduction

Controversy over the definition and measurement of intelligence has led to much lay and academic

debate (Carroll, 1993; Eysenck, 1998; Sternberg, 1990). One central debate concerns whether sex

differences are present in self-estimated (Furnham, 2001; Furnham, Clarke, & Bailey, 1999; Furnham &

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ress: [email protected] (A. Furnham).

A. Furnham, K. Bunclark / Intelligence 34 (2006) 1–142

Ward, 2001; Rammstedt & Rammsayer, 2002; Raty & Snellman, 1992) as well as psychometrically

assessed intelligence (Furnham & Rawles, 1999; Halpern, 1992, 2004; Lynn, 1996; Lynn & Mulhern,

1991). This study examines sex differences in parents’ self-perceived multiple bintelligencesQ, estimates

of their children’s bintelligencesQ and the correlations between estimates of children’s IQ and their actual

IQ scores. No other study in this area has looked at the relationship between parental estimates and

actual IQ scores (Furnham, 2000; Furnham & Gasson, 1998; Furnham & Mkhize, 2003; Furnham,

Rakow, & Mak, 2002; Furnham, Reeves, & Budhani, 2002; Furnham, Shahidi, & Baluch, 2002).

Hogan (1978) in the United States, and Beloff (1992) in Scotland, found male students give higher

self-estimated IQ scores than females. Studies by different research groups such as Betsworth (1999) in

the United States, Rammstedt and Rammsayer (2000, 2001, 2002) in Germany, Zhang and Gong (2001)

in China and Pallier (2003) in Australia have confirmed sex differences in self-estimated intelligence.

During the 1990s, Hogan’s (1978) study was replicated using a variety of relatives including siblings

(Byrd & Stacey, 1993; Furnham & Rawles, 1995), parents and grandparents (Furnham & Rawles, 1995).

All reported higher male overall IQ (g) estimates by raters of both sexes, that is, fathers are seen, by their

children, as brighter than mothers and grandfathers brighter than grandmothers. In other words, both

sexes routinely rate male family members as brighter than female family members, regardless of

generation of rater or ratee. Furnham, Wytykowska, and Petrides (2005) reported on twenty studies done

in nineteen countries (from Argentina to Zambia) using school children, students and working adults: all

but one of which showed males give higher overall IQ self-estimates than do females. No research to

date has systematically examined whether, when and how self- and other-ratings of intelligence may be

distorted by socially desirable responding, particularly as anonymity is no guarantee of it.

It has not been until relatively recently, though, that studies have investigated parental estimates of

their children’s overall IQ (Furnham, 2000; Furnham & Gasson, 1998; Furnham & Mkhize, 2003;

Furnham, Hosoe, & Tang, 2002; Furnham & Thomas, 2004). The evidence is equivocal on whether

parents rate their sons higher than their daughters.

Just as psychometric intelligence tests measure general intelligence (Weschler Adult Intelligence

Scales: Wechsler, 1958; Cognitive Ability Test 3: Thorndike, Hagen, & France, 2001), studies of self-

estimations have predominantly focused on a single, overall intelligence (g) score (Beloff, 1992;

Bennett, 1996; Byrd & Stacey, 1993; Furnham & Rawles, 1995; Hogan, 1978).

However, some researchers have advocated looking not just at general intelligence (as in past

studies) but a variety of more specific aptitudes which are essentially bbroad abilitiesQ that they

sometimes like to label bintelligencesQ (Gardner, 1983). However, some empirical research has been

unable to establish these as separate intelligences (Klein, 1997; Morgan, 1996). It can, however, be

very interesting to see to what extent people rate themselves and others (i.e. parents and their children)

differently on these various multiple bintelligencesQ regardless of whether they actually exist as

independent intelligences.

It has been suggested that sex differences derived from estimations of an overall intelligence notion

(g) may be inaccurate due to their male normative nature (Furnham, 2000). Sternberg, Conway, Ketron

and Bernstein (1981) and Raty and Snellman (1992) have suggested that the lay person perceives overall

IQ (g) as primarily based on reasoning or problem-solving abilities which psychometric testing suggests

is slightly superior in males (Born, Bleichrodt, & van der Flier, 1987; Stumpf & Jackson, 1994).

Rammstedt and Rammsayer (2000) reported on 105 German students, finding that male self-

estimates were significantly higher for logical–mathematical and spatial intelligences, while female

estimates were significantly higher for musical and interpersonal intelligences. More recently,

A. Furnham, K. Bunclark / Intelligence 34 (2006) 1–14 3

Rammstedt and Rammsayer (2001) tested 243 German high school pupils and found boys, in contrast

to girls, rated their abilities higher in mathematical and spatial intelligence, perceptual speed and

logical reasoning.

Half a dozen studies have compared self-estimates with psychometrically derived overall (actual) IQ

scores most, but not all, of which corrected for restriction of range. The majority of studies have shown

that individuals are relatively poor at estimating their own overall IQ (g) in terms of the correlation of

self-estimates with actual IQ score: Borkenau and Liebler (1993) (r=0.30, n=100); Reilly and Mulhern

(1995); Paulhus, Lysy and Yik (1998) (r=0.30, N=326); Furnham and Rawles (1999) (r=0.20,

n=193); Furnham and Chamorro-Premuzic (2004) (r=.30, n=184) Furnham, Fong and Martin (1999b)

(r=0.19, n=172); Furnham, Kidwai and Thomas (2001) (r=0.30, n=100). Chamorro-Premuzic,

Furnham and Moutafi (2004) looked at self-estimates and test scores on four IQ tests and found

Wonderlic Personnel Test r=.39; Baddeley Reasoning Test r=.49; Mental Rotation Test r=.40; AH5

r=.44 (N=83). Correlations are never the whole story, however, as it is important to consider whether

self-estimates tend to be over- or under-estimates. Furnham (2000) suggests that where there is a pattern,

males tend to over-estimate their score and females under-estimate it.

Studies exploring the presence of sex differences in the ability to predict ones own overall IQ (g) have

however revealed inconsistencies. Reilly and Mulhern (1995), Furnham and Rawles (1999) as well as

Furnham, Fong et al. (1999), found that correlations between estimated overall IQ (g) and overall

measured intelligence are higher for males compared to females while Borkenau and Liebler (1999)

found no sex differences in correlations.

Recently Halpern (2004) has reviewed recent data from 33 countries and concluded that compared to

females, males consistently score higher on standardised tests of mathematics and science (that are not

directly tied to their school curriculum) and show large differences on visuospatial tasks particularly

these involving judgements of velocity and navigation in three dimensional space. She proposed an

explanation for these differences primarily in the way test items/problems are presented and the type of

cognitive processes that are optimal for generating solutions. Whether or not lay people prefer socio-

biological or cognitive processing explanations the issue of interest in this study however, is whether

they recognise these sex differences in themselves and their children.

As yet no study has investigated the relationship between parents’ estimates of their children’s

intelligence and their actual IQ scores. The present study addressed this gap in the literature by asking

parents to estimate their own and their children’s overall IQ (g). Parents were then requested to repeat

this process for each of Gardner’s (1983) seven multiple bintelligencesQ (linguistic, mathematical,

musical, bodily–kinesthetic, spatial, intrapersonal and interpersonal). This study will concentrate on the

self-estimates of the overall scores and the estimates of verbal/linguistic, logical/mathematical and

spatial intelligence and less on the other four measures for which there are few known tests. Previous

studies have shown that when self-estimates of an overall measure of general intelligence is regressed on

Gardner’s seven multiple intelligence it is these three which are significant and not the less cognitive

bintelligencesQ (Furnham, Rakow et al., 2002).

Three main hypotheses were tested:

1. Fathers would self-estimate their own overall, mathematical and spatial intelligence higher than

mothers. This is based on the extensive, cross-culturally replicated male hubris, female humility

effect, which is that males tend to over-estimate their overall (g) intelligence relative to test derived

scores, while females tend to under-estimate their score (Furnham, 2001).


2. Parent’s estimations of their children’s overall IQ (g) and the mean of the seven multiple intelligence

estimations would correlate positively and significantly with children’s actual IQ score. This

hypothesis was based upon the findings of Borkenau and Liebler (1993), who demonstrated that

participants living with the to-be-estimated participant were more accurate (r=0.52) in their

estimations than self-estimators (r=0.30). Since the present study used a close relative in the

estimation process, a correlation of around r=0.50 was expected.

3. Parents would estimate their sons as being brighter than their daughters. This hypothesis is based on

results from self-estimate studies (Beloff, 1992; Bennett, 1996; Furnham & Rawles, 1995, 1999;

Reilly & Mulhern, 1995) and parental ratings of children’s overall IQ (Furnham & Gasson, 1998;

Furnham, Reeves et al., 2002).

2. Method

2.1. Participants

2.1.1. Parental participants

A total of 141 parental participants took part in the study with an age range of 30 to 74 (mean

age=42.43 years, SD=5.40). There were 59 fathers (mean age=44.75, SD=6.03, range=30–74

years) and 82 mothers (mean age=40.70, SD=4.16, range=30–51). 37.9% were educated up to

GCSE level (10th Grade), 24.8% to A-level or equivalent (12th grade), 5.5% to HND level (Higher

National Diploma, which is a post-school, pre-university qualification), 16.6% to undergraduate level,

3.4% to postgraduate level and 5.5% held professional qualifications. Using the 2001 National Office

for Statistics criteria for the socio-economic classification of occupations the following 10 categories:

managers and senior officials (3.4%); professional occupations (20.4%); associate professional and

technical (15%); administration and secretarial (10.2%); skilled trades (9.5%); personal services

(8.2%); sales and customer service (2.7%); process, plant and machine operative (1.4%); elementary

groups (9.5%) and unclassifiable (19.7%). From a social class perspective this sample seemed

slightly skewed to being middle class. The majority of the participants were married (85.9%)

followed by those cohabiting (8.1%), divorced/separated (4%), single (1.4%) and widowed (0.1%).

Data were not collected on whether any of the parents were step-fathers or mothers. It is possible

step-parents rate their children more harshly than their biological parents; step-fathers rate their step-

sons more harshly than their step-daughters, while step-mothers their step-daughters more harshly

than their step-sons. For the number of children, 2.7% had one child, 46.3% two children, 40.3%

three children, 8.7% four children and 2% had five children. Participants came from a wide variety of

social backgrounds as determined by their socio-economic skills. Less than 10% came from ethnic

minority backgrounds.

2.1.2. Child participants

A total of 141 child participants took part. There were 72 male children (mean age=142.38 months

(11 years 10 months), SD=27.62, range=64 to 184 months (5 years 4 months to 15 years 4 months) and

69 female children (mean age=132.88 months (11 years), SD=24.82, range=63 to 181 months (5 years

3 months to 15 years 1 month). Most of the children (63%) were between 120 and 160 months old. In all

fathers rated 29 sons and 30 daughters while mothers rated 43 sons and 39 daughters.


Pupils came from 2 different schools: primary and secondary. The primary school was located within

the Kent Local Education Authority in the boroughs of Dartford and Gravesham and was comprised of

201 pupils between the ages of 5 and 11. Questionnaires were distributed to all pupils present on the day

of issue (197 pupils). The comprehensive secondary school was located within the Surrey Local

Education Authority within the borough of Woking. The school was comprised of approximately 950

pupils between the ages of 11 and 18. Further correspondence with the school enabled the testing of all

pupils within Year 7 (ages 11 to 12) and Year 10 (ages 14 to 15). This resulted in the distribution of

questionnaires to 349 pupils (176 in Year Seven, 173 in Year Ten). Parents were asked to respond in 10

days. In many instances children returned only one form estimated by one parent. To ensure that this was

consistent across the sample where both parents had completed the estimations only one was used as

data. They were chosen randomly. In all 34.7% did so within that period but of these only 141 were

completed accurately. A preliminary analysis showed no differences in ability scores between those

children whose parents took part and those that did not.

2.2. Apparatus and materials

2.2.1. The questionnaire

The front sheet consisted of a cover letter to parents explaining the reason for the study’s conduct, the

requirements of the study and the date by which the completed questionnaire was to be returned by and

to whom. The next page showed a normal distribution curve with a mean and six standard deviations.

This questionnaire format has been used in about 10 studies and proved comprehensible to adults

(Furnham, 2001). Under each standard deviation there was an intelligence score and description (e.g. +2,

130, superior or �2, 70, retardation). As part of the instructions, participants were told that the average

or mean score was 100 and about two-thirds of the population score between 85 and 115, with very

bright people scoring around 130. Participants were presented with a grid with the eight intelligences

(overall and Gardner’s (1983) seven bintelligencesQ) labeled and described (e.g. linguistic intelligence:

the ability to speak fluently along with understanding of grammar (syntax) and meaning (semantics).

Using the normal distribution curve, participants were asked to rate their own and their specified child’s

overall and multiple intelligences. Participants were additionally requested to fill in details in reference

only to the child from whom they had received the questionnaire, stipulating their children’s name and

class. The next page of the questionnaire was used for the presentation of nine demographic questions.

Parents were asked to answer the questions in reference to themselves only.

2.2.2. Cognitive ability test scores

Cognitive ability test scores were derived from the schools’ own databases. Children had completed a

series of three cognitive ability tests designed by NFER-Nelson, a British based test publisher at the

beginning of the academic year. Each has established reliability and validity criteria (Thorndike, Hagen, &

France, 2001). The tests have been regularly re-standardized and updated and are widely used in British

schools. Kuder-Richardson reliability is over .90 in nearly all subscales. The test has been validated against

teacher ratings, school exam achievement grades and similar tests from this test publisher but not theWISC

or the WAIS. Schools would only allow a total score for the three tests to be used in this research.

2.2.2.1. The verbal test. The verbal ability test was comprised of three timed paper and pencil

subtests (8, 10 and 10 min, respectively); verbal classification, sentence completion and verbal


analogies. For the verbal classification test, three words were presented that were similar in some way

e.g. colour. The task being to pick a word for a list of five possibilities that matched the theme of the

other three e.g. blue. For sentence completion, the selection of an appropriate word to complete the

sentence was required from a list of possibilities. For example, for dapples _____ on treesT childrenwould be required to select dgrowT. For the verbal analogy test participants were presented with a pair

of words, such as dold and newT. Participants were then presented with another word, such as dwetTand were required to select a response from those presented that matched the theme, in this instance,

ddryT.

2.2.2.2. The numerical test. The numerical test was again comprised of three timed paper and pencil

subtests (12, 10 and 14 min, respectively); number analogies, number sequences and equation building.

For the number analogies test, participants were presented with two sets of two numbers linked together

by a rule, for example, d2–3T and d9–10T. Participants were then presented with the first number in the

third set, for example, d5T and were then required to select the appropriate response from a list of

possibilities to finish this set, in this instance, d6T. For the number sequences test participants were given

a sequence of numbers gained via a rule. Participants were required to report the next number by

selecting the appropriate response from a list. For the equation building task, participants were presented

with three numbers and two signs and were required to formulate possible answers from the

combinations possible. Only one of the combination answers was represented and this one had to be

selected.

2.2.2.3. The perceptual test. Again this test comprised of three timed subtests (10, 10 and 10 min,

respectively); figure classification, figure analogies and figure analysis. For the figure classification

test participants were presented with three shapes that were similar in some way, for example, all had

four sides and were then required to select from a selection of possibilities the shape that matched the

rule. For figure analogies participants were required to formulate the relationship between the first and

second shape presented, for example, the second shape was the same but smaller. Participants were

then required to use the same rule to select the second shape to complete the second set of two shapes.

For the figure analysis test, a hypothetical square of paper being folded into another shape was

depicted. In the final form a series of dholesT were dpunched throughT at various locations. Participantswere required to select the response that represented what the square looked like when it was

unfolded.

Administration of the tests was performed by each school individually at the start of the current

academic year. Raw scores on each of the three separate tests were held only by the secondary school

and thus only mean raw scores could be used for the purposes of the study. Parents received no feedback

on their children’s IQ score, nor did the children themselves.

2.3. Procedure

Children were issued with two questionnaires each (one for the father and one for the mother).

Children were instructed to give the questionnaires to their parents/guardians and return them by a

previously arranged date (approximately one week after initial distribution). Mean cognitive scores were

obtained for all pupils from all classes in the primary school and from all pupils in years 7 and 10 at the

secondary school. Raw scores were then standardized to z-scores for each year individually. After which


all standardized scores were re-standardized within each sample from each of the two schools for pupils

taking part in the study.

3. Results

3.1. Replication

3.1.1. Parents self-estimates

First a one-way ANCOVA on the overall parental self-estimate of intelligence (Co-varying age,

education, occupation) showed a significant difference: as in previous studies males gave significantly

higher estimates than females. This confirms the first hypothesis. Note that here, as in nearly all the

studies, people believe most of their own scores are above average (the Lake Woebegone effect).

Following this a MANCOVA over all multiple intelligences was computed and found significant

(F(7,122)=7.29, pb001; partial eta square= .29). However ANCOVAs yielded only two differences.

Males rated themselves higher on mathematical (F(1,140)=3.96, pb01) (males 111.46 vs females

100.48) and spatial (F(1,140)=4.57, pb .001) (males 110.97 vs females 100.36) intelligence (Table 1).

This analysis confirms previous studies on multiple bintelligencesQ.When the overall self-estimate was regressed onto the seven multiple intelligences only two were

significant (F(7,136)=12.83, pb .001; Adj R2= .37) (Constant: B=36.95 t=3.88, pb .001) namely

logical/mathematical (Beta= .41, t=5.15, pb .001) and linguistic/verbal (Beta= .16, t=1.97, pb .05).

These results give an indication of sex differences in the bases of self-estimated IQ. Following this an

orthogonal and oblique rotated factor analysis were computed on the seven multiple bintelligenceQparental self-estimates, however the results for both were very similar. The VARIMAX analysis yielded

two factors accounting for 60.34% of the variance. The two personal, non-cognitive bintelligencesQloaded on the second factor (Eigenvalue 1.25, Variance 17.95%) while the other five loaded on the first

factor (Eigenvalue 2.96, Variance 42.41%). A one way ANCOVA on both factors showed a sex

difference only on the first factor (F(1,140)=5.99, pb .01): males gave higher self-estimates. All these

analyses confirm various previous studies in the area (Furnham, 2000, 2001).

Table 1

Means and standard deviations for parent’s self-estimation of their overall and multiple intelligence IQ scores

Fathers Mothers

N = 61 N = 84

Overall intelligence 105.06 (9.92)1. Linguistic intelligence 104.02 (19.53) 105.01 (11.08)2. Logical–Mathematical intelligence 111.46 (16.86) 100.48 (14.94)3. Spatial–intelligence 110.97 (13.95) 100.36 (12.36)4. Musical intelligence 93.03 (15.68) 95.90 (11.00)5. Bodily–Kinesthetic intelligence 106.51 (17.21) 103.92 (13.09)6. Intrapersonal intelligence 107.74 (14.86) 109.14 (12.22)7. Interpersonal intelligence 108.56 (16.03) 111.62 (12.98)

111.41 (14.96)

Shaded cells indicate the higher IQ score on that intelligence.


3.2. Hypothesis testing

3.2.1. The relationship between estimation and psychometric intelligence score

A one-way analysis of variance was conducted with the children’s standardised actual IQ score as the

dependent variable and sex of the child as the independent variable. It was significant (F(1,120)=2.26,

pb0.01). This result showed that females attained higher scores in school-administered testing than

males. Analysis of actual IQ scores revealed a statistically significant female advantage for mean

cognitive ability scores. This finding, although contrary to the hypothesis, is in accordance with British

public examination results (Department for Education and Skills, 2001) which have shown currently that

at all levels of British public examination (SAT, GCSE, GNVQ, A-Level) females attain scores at least

five percentiles above male counterparts.

Table 2 shows the full correlational matrix of all the variables.

The pattern is fairly clear and shows the following. Parents’ estimates show their overall estimate

(i.e. general intelligence) is most highly correlated with the estimate of the mathematical bintelligenceQ(r=.60) and the lowest correlation is with interpersonal bintelligenceQ (r=.19). Most of the estimates of

the seven intelligences correlate around r=.30 (range r=.02 to r=.78). Parents’ estimates tended to

correlate highly with the estimates of their children. Parents overall self-estimates correlated r=.61 with

estimates of their child’s overall intelligence and r=.44 with the estimate of the composite score.

Correlations between the parental estimates of the child’s seven multiple intelligence showed inter-

Table 2

Correlational results

Parents Children

Po Pl Pmu Pma Ps Pbk Pia Pie Co Cl Cmu Cma Cs Cbk Cia Cie IQ PC

Parents Overall Po

Lingui Pl .42

Music Pmu .33 .35

Math Pma .60 .42 .24

Spatial Ps .48 .38 .34 .52

Body K Pbk .34 .34 .29 .24 .44

Intra Pia .20 .40 .15 .29 .40 .34

Inter Pie .19 .32 .02 .30 .32 .32 .78

Child Overall Co .61 .31 .37 .40 .48 .24 .20 .16

Lingui Cl .44 .40 .39 .36 .43 .25 .21 .12 .82

Music Cmu .33 .25 .31 .28 .46 .30 .26 .22 .66 .52

Math Cma .47 .33 .40 .47 .39 .20 .16 .17 .75 .66 .76

Spatial Cs .49 .33 .50 .42 .57 .33 .23 .21 .79 .74 .67 .76

Body K Cbk .39 .36 .34 .33 .45 .40 .18 .23 .69 .65 .62 .66 .76

Intra Cia .25 .30 .25 .30 .38. .30 .31 .29 .60 .65 .57 .54 .62 .74

Inter Cie .14 .24 .13 .22 .30 .18 .27 .34 .41 .45 .46 .33 .45 .57 .79

IQa Actual IQ .26 .01 .13 .18 .16 .01 .06 � .06 .44 .42 .36 .42 .37 .30 .28 .18

Parentalb Comp PC .63 .66 .52 .63 .69 .65 .70 .64 .40 .41 .43 .42 .50 .44 .44 .34 .25

Childc Comp CC .44 .38 .44 .41 .52 .34 .28 .28 .63 .63 .61 .63 .68 .71 .68 .54 .44 .57

a Child’s actual IQ.b This refers to parents’ composite self-estimate (the 7 multiple intelligences added together).c This refers to parents’ composite estimate for their child (the 7 multiple intelligences added together).


correlation around r=.60 (range r=.33 to r=.79) which was about twice the size of correlations for

parental self-estimates.

Parents can accurately estimate the intelligence of their children. A two-tailed bivariate correlation with

parental estimates of children’s overall IQ (g) and children’s standardised mean cognitive ability score as

the variables revealed a statistically significant positive relationship (r=0.44, p=b0.01). Further the higher

parents estimate themselves the higher they estimate their children. However, the correlations between

parental self-estimates and their children’s actual IQ score was just significant (r=.26 pb .01) (N=127).

A further correlation, involving calculated overall intelligence (the mean of the estimated Gardner’s

(1983) seven multiple intelligences) and children’s actual IQ score as the variables also returned a

statistically significant positive correlation (r=0.44, pb0.01). Correlations between the IQ test score and

the various multiple intelligences were: linguistic (r=.42), musical (r=.36), logical (r=.42), spatial

(r=.37), bodily–kinesthetic (r=.30), intrapersonal (r=.28), and interpersonal (r=.18). All were

significant (N=93 because of missing data) except the last one, namely interpersonal intelligence.

This confirms hypotheses two.

Various regressions were then calculated with the actual IQ score as the criterion variable. When the

actual score was regressed onto the estimates of the seven multiple intelligences, the regression was

significant (F(7,85)=3.61, pb .01; Adj R2= .17) (Constant: B=58.27, t=5.56, pb .001) with logical/

mathematical being the only significant predictor (Beta= .36, pb05). When the actual score was

regressed onto the parent’s and child’s sex and age the regression too was significant (F(7,85)=2.71,

pb .05; Adj R2= .07) (Constant: B=70.96, t=3.34, pb .001): Sex of child was the only significant

predictor (Beta= .35, pb .01), indicating that girls scored higher than boys.

3.2.2. Estimations of children’s intelligence

Parents of both sexes estimated female children (daughters) to have higher overall IQ than male

children (sons) (108.35 vs. 102.47). Fathers estimate daughters overall IQ to be, on average, 6.94 IQ

points above sons (109.73 vs. 102.79) while mothers estimate daughters to be higher by 5 IQ points

(107.26 vs. 102.26). This disconfirmed the fourth hypothesis.

Two-way ANOVAs showed a main effect for sex of the child was a statistically significant predictor

of the overall intelligence estimate given (F(1,134)=5.02, pV0.05) (effect size of d=0.60.) Neither a

statistically significant main effect for sex of the parent (F(1,134)=0.31) nor an interaction between the

two predictor variables was significant. A 2-way MANOVAwith sex of parent and sex of child as factors

and the seven estimates as independent variable showed a significant effect only for sex of child

(F(8,127)=5.49, pb .001). Subsequent ANOVAs on each of the seven estimates shown in Table 3

showed no significant effects for sex of parent or interaction between sex of parent or child. Five of the

main effects for sex of child were however significant: linguistic (F(1,138)=9.07, pb .01), musical

(F(1,138)=24.30, pb .01), bodily–kinesthetic (F(1,138)=7.92, pb .01), intrapersonal (F(1,138)=

15.07, pb .001) interpersonal (F(1,138)=11.23, pb .001). Repeating the analysis, co-varying out the

child’s actual IQ score, did not change the pattern of significance in the results.

The principal component analysis revealed only one component was extracted so that the solution

could not be rotated. These results indicate that with respect to their children (unlike themselves) these

parents did not differentiate in their ratings of the seven multiple bintelligencesQ.A multiple regression was then computed with the child’s overall estimates as the dependent

(criterion) variables and five independent (predictor) variables: child’s age and sex, parents age, sex and

education. This was not significant. Sons were not estimated as brighter than daughters so providing no

Table 3

Mean (standard deviation) estimates for the effect of parental sex on estimations of children’s multiple intelligences on

standardised bell curve (shaded cells indicate the higher estimate by each parent)

Fathers Mothers

Male children(N = 28)

Female children(N = 30)

Male children(N = 42)

Female children(N = 38)

Overall rating 102.78 (19.78) 109.73 (11.67) 109.73 (11.67) 107.26 (10.32)

1. Linguistic intelligence 98.04 (21.40) 107.73 (13.10) 97.71 (17.38) 104.74 (11.51)

2. Logical Mathematical intelligence 103.04 (21.92) 102.93 (9.11) 101.6 (17.39) 102.58 (11.95)

3. Spatial intelligence 99.29 (18.45) 103.73 (8.86) 98.1 (15.73) 101.79 (11.02)

4. Musical intelligence 93.57 (19.15) 107.67 (12.71) 94.88 (15.64) 106.21 (12.08)

5. Bodily Kinesthetic intelligence 97.50 (19.79) 107.14 (10.88) 100.64 (20) 106.97 (11.83)

6. Intrapersonal intelligence 97.86 (15.66) 108.50 (10.92) 98.02 (15.39) 106.61 (14.51)

7. Interpersonal intelligence 100.54 (13.56) 109.77 (15.9) 100.95 (14) 109.18 (16.65)

_

_


support for the second hypothesis. Then, as with parents the child’s overall general intelligence estimate

was regressed onto the seven estimated multiple intelligences (F(7,139)=70.71, pb .001, Adj R2 .77)

(Constant: B=6.29, t =1.19, ns). Three of the seven estimates were significant predictors: linguistic/

verbal intelligence (Beta= .45, t=6.50, pb .001); logical mathematical intelligence (Beta= .25, t=3.89,

pb .001) and spatial intelligence (Beta= .18, t=2.29, pb .05).

Following this, seven regressions were calculated with each of the child’s estimated multiple

intelligence as the dependent variable and child’s age and sex, and parents’ age and sex as the independent

variables as done in previous studies (Furnham, 2000). Four were significant but in each case only the

child’s sex was a significant predictor: linguistic intelligence (F(4,122)=2.53; pb .04; Adj R2= .05);

(Constant: B=81.25 t=4.74, pb001), child’s sex Beta= .23, pb .01; musical intelligence (F(4,122)=5.83,

pb .001; Adj R2= .13); (Constant: B=64.31, t=4.05, pb .001), child’s sex Beta= .39, pb .001. Intra-

personal intelligence (F(4,122)=3.84, pb .01; Adj R2= .08; (Constant: B=74.35, t=4.82, pb .001);

child’s sex Beta= .30, pb .001) and interpersonal intelligence (F(4,122)=3.15, pb .01; Adj R2= .06;

(Constant:B=68.92, t =4.26, pb .001); child’s sex Beta= .28, pb .01). In each case the estimated scores for

female children was higher than for male children.

As noted above the results of only the three most salient and established multiple intelligences will be

reported.

(a) Linguistic intelligence: both mothers and fathers estimate female children (daughters) to have

higher linguistic intelligence than male children (sons). Fathers estimate their daughters to be 9.69

IQ points higher than their sons (107.73 vs. 98.04) while for mothers this figure is 7.03 IQ points

(104.74 vs. 97.71). Partialing out parental sex, female children were estimated to have a linguistic

IQ score 8.22 points above that of male children (106.06 vs. 97.84). A two-way analysis of co-

variance (ANCOVA) co-varying the actual IQ score of the child with estimations of children’s

linguistic intelligence as the dependent variable and sex of the child and sex of the parent as the


independent variables returned statistically significant results for the main effects of sex of the

child (F(1,134)=4.91, p=0.05) and no significant difference for sex of the parent (F(1,134)=

1.84, p=0.18) or a significant interaction.

(b) Logical–mathematical intelligence: while fathers ascribed higher logical–mathematical intelligence

estimates to their sons (sons, 103.04 vs. daughters, 102.93), mothers ascribed higher logical–

mathematical intelligence estimates to their daughters (sons, 101.6 vs. daughters, 102.58).

Partialing out parental sex, male children’s logical–mathematical intelligence was estimated to be

0.57 IQ points less than female children’s (102.17 vs.102.74). A two-way ANCOVA (co-varying

the actual intelligence of the child) showed no main effects for sex of the child (F(1,134)=0.03,

p=0.87) or sex of the parent (F(1,134)=0.11, p=0.74).

(c) Spatial intelligence: fathers estimate their female child’s spatial intelligence to be 4.44 IQ points

higher than their male counterparts (103.73 vs. 99.29) while for mothers the figure is 3.69 IQ points

(101.79 vs. 98.1). This estimation goes clearly against the established data. A two-way ANCOVA

(co-varying the actual intelligence of the child) showed no statistically significant main effects for

sex of the child (F(1,134)=2.86, p=0.09) nor sex of the parent (F(1,134)=0.42, p=0.52).

4. Discussion

The results from fathers (hypothesis 1) are in line with various other studies in the area. (Furnham,

2001). The results from the multiple regression appear to explain sex differences in the bases of self-

estimated intelligence. Fathers’ mean intelligence self-estimates mirrors their self-estimates in logical and

spatial ability (their bstrong suitQ) while mothers’ mean IQ self-estimates mirror their ability in their bstrongsuitQ namely linguistic ability. If it is the male bstrong suitQ which is (generally) seen as the better indicatorof general intelligence, this may account for the consistent and pervasive sex differences in self-estimates.

Thus it seems that people judge their own, and anyoneTs overall intelligence according to their strongest

stratum II (cognitive ability) (Carroll, 1993). Females clearly think of themselves as superior in the

personal bintelligencesQ (Furnham and Petrides, 2004), but that seems not to affect their overall self-

estimated IQ because these abilities are not part of the cognitive manifold of perceived abilities.

The second hypothesis for the present study was that parents would be accurate in predicting their

children’s overall actual IQ. It was expected that this relationship would be around r=0.50 in accordance

with Borkenau and Liebler’s (1993) findings and that sex of the child would have a statistically

significant effect on the relationship. Results were in accordance with the hypothesis with parents being

accurate in predicting both male and female children’s overall intelligence. It is possible that parents are

more accurate at predicting their sons intelligence because the variance (as indicated by the standard

deviation) in ability test scores is higher for male children than for female children. Estimations of

children’s overall IQ also shows a higher level of variability for male children. Thus parents may not be

more accurate at predicting their own sons’ overall intelligence. It should be pointed out that significant

correlations can occur even if estimates are inaccurate in the sense that parents could consistently give

say 10 points higher or lower than those found from test scores. That is, high correlations can be

independent of the actual values ascribed if the relative rank of the individual is the same in both

distribution of scores. The data in this study were examined for this but this pattern was not occurring.

Additionally, the correlation between children’s estimated and bactualQ overall intelligence (r=0.44)

was slightly, though not statistically reliably, lower than that expected (r=0.50). Indeed the correlation is


in line with the accuracy of dstrangerT estimations (r=0.43) reported by Borkenau and Liebler (1993)

rather than the correlation derived with partner estimations (r=0.52), which was predicted would be

found in the present study. Parents may just as easily be poorer estimators because the ages of the child

sample (5 to 14 years) make it more difficult to accurately assess their child’s intelligence in relation to

their peers as their full cognitive abilities have not yet matured (Brody, 1992). Further, many parents may

not have had a good comparative sample of children with which to compare the ability of their own

child. Teachers may do, but parents are unlikely to.

In this study daughters were given significantly higher overall IQ (g) estimations than sons. The

result, although in line with the results derived from British public examination results and cognitive

ability results from the current study, is contrary to that found in previous research on estimations of

children’s overall IQ (Furnham, 2000; Furnham et al., 2002). In this sample girls were actually scored

higher than boys and the parents predicted it. Furnham, 2000; Furnham, Reeves et al., 2002, using a

random population of British adults have found male children to be attributed significantly higher overall

IQ (g) scores. These studies did not measure actual IQ of the children and thus it is not known if the male

children, attributed higher IQ scores by their parents, were actually more intelligent than the female

children.

Analysis of sex differences in estimations of children’s multiple intelligences partially supported

previous studies. Results derived showed that as predicted daughters were given higher estimates for

musical and intrapersonal intelligences, in line with the majority of previous studies (Bennett, 1996;

Furnham, Clarke et al., 1999; Furnham, Reeves et al., 2002; Rammstedt &Rammsayer, 2000, 2001; Zhang

& Gong, 2001). However, results also returned a significant female child advantage for bodily–kinesthetic

which has not been shown before while the expectation that females would be ascribed significantly higher

interpersonal scores was also demonstrated. The higher estimates for girls on kinaesthetic intelligence may

fit well with cultural norms that stress co-ordination, grace, deportment and dance. No male advantages

were found for logical–mathematical and spatial intelligences as found previously, however this may

involve this particular sample where, demonstrably, girls were more intelligent than boys.

The nature of this study means that it cannot answer various interesting and important questions. First

because the three IQ test scores were combined it is uncertain if parents may have been better at say

predicting their child’s verbal rather than mathematical intelligence. This seems like a reasonable

hypothesis because they possibly have more and better comparative data on a child’s vocabulary than

their mathematical skills. Second, it would have been desirable, but unfortunately not possible, to get

teacher estimates as well as parent and child estimates as done by Furnham and Budhani (2002). It seems

reasonable to assume that given the nature of their relatively disinterested judgement and their extensive

observations on the children’s abilities, their estimates would be the most accurate. Finally the study

cannot speak to the consequences of parents’ beliefs/estimates for the children themselves. The

bPygmalion EffectQ has been consistently challenged (Spitz, 1999) but it is probably the case that

consciously or unconsciously communicated parental beliefs can affect their children’s academic

achievement and self-confidence.

Acknowledgements

The authors would like to acknowledge the many detailed, helpful and critical comments, of four

named reviewers.


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Sex differences in parents' estimations of their own and their children's intelligence