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Testing Lynn's Theory of Sex Differences in Intelligence in a Large Sample of Nigerian School-Aged Children and Adolescents (N >11,000) using Raven's Standard Progressive Matrices Plus

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Abstract

Sex differences in intelligence have been much disputed for many decades. The present study examined the issues of whether sex differences in intelligence change during development. In total, 11,164 children (mean age = 13.5 years; SD = 2.6 years) completed the Standard Progressive Matrices Plus (SPM+). From age 8 to 19 years, sex differences in the total score of the SPM+ increased from-0.06d (favoring females) to 0.46d (favoring males), with an average of 0.23d. Our findings support Lynn's developmental theory of sex differences in cognitive abilities.
MANKIND QUARTERLY 2017 57:3 428-437
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Testing Lynn’s Theory of Sex Differences in Intelligence in
a Large Sample of Nigerian School-Aged Children and
Adolescents (N>11,000) using Raven’s Standard
Progressive Matrices Plus
Yoon-Mi Hur*
Mokpo National University, Jeonnam, South Korea
Jan te Nijenhuis
University of Amsterdam, The Netherlands
Hoe-UK Jeong
Mokpo National University, Jeonnam, South Korea
*Corresponding author: Yoon-Mi Hur, PhD, Department of Education,
Mokpo National University, Jeonnam, 61, Dorim-ri, Muan-gun, Jeonnam,
South Korea. Tel: +82614502176; Fax: +82614506476; email:
ymhur@mokpo.ac.kr
Sex differences in intelligence have been much disputed for
many decades. The present study examined the issues of whether
sex differences in intelligence change during development. In total,
11,164 children (mean age = 13.5 years; SD = 2.6 years)
completed the Standard Progressive Matrices Plus (SPM+). From
age 8 to 19 years, sex differences in the total score of the SPM+
increased from -0.06d(favoring females) to 0.46d(favoring males),
with an average of 0.23d. Our findings support Lynn’s
developmental theory of sex differences in cognitive abilities.
Key Words: Nigeria, SPM+, Intelligence, Sex differences,
Cognitive ability.
For approximately a century sex differences in intelligence have been
controversial among psychologists and the general public. The controversy was
originally kindled by Terman (1916) and Spearman (1923), who asserted that
HUR, Y-M., et al. SEX DIFFERENCES IN NIGERIA
429
there is no sex difference in general intelligence. Later research findings on this
issue were largely inconsistent, however. Some researchers reported a male
advantage (e.g. Irwing, 2012; Irwing & Lynn, 2005; Jackson & Rushton, 2006;
Lynn & Irwing, 2004; Nyborg, 2005), whereas others showed no differences
between the sexes (e.g. Colom et al., 2002; Deary et al., 2007), and still others
found a female advantage (e.g. Jensen, 1998; Keith et al., 2008; Reynolds et al.,
2008; see also Halpern, 2012, for a recent review of the literature). These
inconsistencies may partly be due to the variability in cognitive ability tests,
analytic methodologies (e.g., using a latent variable approach vs. an observed
test score approach), and diversity of samples, in particular with regard to age.
Halpern (2012) gives a detailed review of the various theories trying to
explain sex differences in intelligence. She describes biological hypotheses that
focus on genes, hormones, brains, evolutionary pressures, and brain-behavior
relationships. She also describes psychosocial hypotheses that focus on sex-role
stereotypes and a large number of other psychosocial hypotheses. Surprisingly,
of the large number of papers testing Lynn’s developmental theory of sex
differences only one is mentioned in passing. In the present paper, however, we
focus on Lynn’s theory using data collected from Nigeria.
Lynn (1994, 1999) argued that sex differences in cognitive abilities were due
to sex differences in maturational rates. According to Lynn’s developmental
theory, girls mature earlier than boys both physically and mentally prior to puberty,
but from the age of approximately 15 years onwards the growth of girls
decelerates while that of boys continues. For this reason, girls outperform boys
up to the age of about 14 years but males begin to outperform females around
puberty and this male advantage continues through adulthood. To date, however,
there is no consensus on the age at which sex differences emerge. For example,
Lynn and Irwing (2004) showed in a meta-analysis of 54 studies of sex differences
on the Standard Progressive Matrices (SPM) that the advantage of boys emerges
at the age of 15, increases in late adolescence, and remains stable for the whole
age range of 20–29 through 80–89 years. However, Liu and Lynn (2011)
observed a consistent male advantage in the Full Scale IQ scores at ages as
young as 5 to 6 years in a Chinese sample, and on spatial ability tests of the
Wechsler Preschool and Primary Scale of Intelligence (WPPSI) among children
aged four and five years in China, Japan, and US. The magnitudes of sex
differences were inconsistent as well. While Lynn and Irwing’s meta-analysis
demonstrated an average sex difference of 0.33d, two large-scale studies (Lynn
& Kanazawa, 2011; Rojahn & Naglieri, 2006) converged to indicate that although
sex differences followed the developmental pattern as Lynn (1994, 1999)
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430
suggested, the differences after puberty were less than 0.12dand thus concluded
that the differences were practically insignificant.
While sex differences in cognitive abilities have been extensively studied in
Europeans, Americans, and Asians, there are only a few reports on sex
differences in cognitive abilities among Africans. Lynn (2002) administered the
Raven’s SPM to 3,979 15- to 16-year-olds in secondary schools in South Africa
and found that males obtained a higher mean equivalent to 0.16damong 15-year-
olds and to 0.31damong 16-year-olds, suggesting that the sex difference
increases with age. However, these differences were not consistent across ethnic
groups in the study sample. More recently, Bakhiet et al. (2015) analyzed scores
of the SPM in 7226 students aged from 6 to 18 years in Sudan. Females tended
to perform slightly better than males on the total score up to age 11 years, with a
highest dof -.12. From the age of 12 years onwards, however, a male advantage
began to appear even though the magnitudes of sex differences were generally
moderate ranging from d= .10 to d= .20 with an exception of d= .66 for 17-year-
olds.
Jensen (1998, ch. 13) gives an excellent review of sex differences in specific
tests and in first-order factors. Males excel in spatial-visualization tests, and
especially on tests in which spatial ability is combined with types of specific
knowledge content with which males are typically more familiar, such as
information about electronics. Males also have a small advantage in math ability.
Women have an advantage on tests of verbal fluency, and on scholastic-type
achievement tests involving verbal content such as reading, writing, grammar,
and spelling. Moreover, women have an advantage on tests of perceptual speed,
short-term memory, and speed and accuracy. However, a fundamental question
is whether there are sex differences on the gfactor. The use of Raven’s SPM
would allow a strong test of sex differences in the gfactor, because the Raven’s
SPM is a test of reasoning ability known to be one of the best measures of general
intelligence (Jensen, 1998; Mackintosh, 1996).
In view of the findings to date, it is evident that more data are needed to
resolve the issue of sex differences in intelligence. Using the Standard
Progressive Matrices Plus (SPM+; Raven, 2008), the present study addresses
the question of whether the developmental theory of sex differences in
intelligence can be confirmed among Nigerians.
Method
1. Sample and Procedure
The present study consisted of 11,164 students (mean age = 13.5, SD = 2.6
years) drawn from three separate samples in the Nigerian twin-and-sibling studies
HUR, Y-M., et al. SEX DIFFERENCES IN NIGERIA
431
(Hur et al., 2013). Students who skipped the question on sex (N= 124) or younger
than eight years (N= 75) or older than 19 years (N= 119) were not included in
data analysis. There were approximately equal numbers of males and females
aged between 8 and 19 years. The first sample consisted of twins and siblings
collected from 45 public junior and senior secondary schools covering all six
administrative areas in Abuja Federal Capital Territory (FCT). Note that as twins
and siblings share segregating genes and rearing-family environment, only one
member of each twin or sibling pair was used for data analysis (N= 905). This
procedure enables all of the subjects in the study sample to be independent from
each other, avoiding violation of the assumption of independent data points. Mean
age of this sample was 14.9 years (standard deviation SD 2.0 years). The 45
schools were chosen for their large size of enrollment (typically N> 500) as more
twins were available in larger schools.
The second sample comprised 8979 students collected from 17 public primary
and 13 public junior and senior secondary schools across all six school districts
in Lagos State. The 30 schools were selected to obtain a sample maximally
representative of the students attending public schools covering very rural to very
urban areas in Lagos State. In primary schools, only students higher than grade
three were allowed to participate in the study. The average age of these students
was 13.1 (SD = 2.6) years. A detailed description of this sample can be found in
Hur (2016).
The third sample was composed of 1280 individual twins from 212 public
junior and senior secondary schools across six school districts in Lagos State.
Again, only one member of each pair was selected. The mean age of these twins
was 14.5 (SD = 1.9) years. Data collection procedures were very similar in all
three samples except that when we tested twins, there were smaller numbers of
subjects in the testing room. Staff members in the Ministry of Education and
Education Boards were consulted when we chose schools in each district.
Bringing letters of approval from the Education Boards and the Ministry of
Education, the first author visited schools and gave tests to twins and sibling pairs
in a library or classroom in the school. Research assistants and teachers were
available in the testing room to give instructions and monitor the tests. We
encouraged students to try all items of the SPM+, asking them to make their best
guess when they felt items were very difficult. We did not limit the testing time.
2. Measure
The SPM+ consists of 60 matrix items divided into five sets (A, B, C, D, & E)
constructed to become progressively more difficult moving from set A to E. Validity
and reliabilities of the SPM+ have been well established (Raven, 2008). As the
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SPM+ is a non-verbal test, it has commonly been used to assess sex differences
in cognitive abilities in diverse populations with different languages and cultural
backgrounds.
3. Data Analysis
Three steps were taken in data analysis. First, we computed means and SDs
by sex and by age. Second, we performed ANOVA and tested the main effects
of sex and age, and their interaction for the total score of the SPM+. Statistically
significant sex by age interaction would serve as evidence for the developmental
change in sex differences in cognitive abilities, indicating that sex differences in
the main effects vary across the age groups. Finally, to determine the magnitudes
of sex differences on the total score of the SPM+, we computed the standardized
effect size of difference (d) on SPM+ between males and females for each age
group. As dwas calculated as males’ mean minus females’ mean divided by a
pooled estimate of the standard deviation, a positive value indicated a higher
score in males and a negative value, a higher score in females.
2. Results
Table 1 shows means and SDs for the total score of the SPM+ by age and
sex, and Figure 1 presents a graphic representation of the magnitudes of sex
differences (d) by age. ANOVAs for the total score of the SPM+ produced
significant main effects for age, F= 127.06, p<.001, and sex, F= 76.09, p<.001.
Except for the youngest group, ages 8 to 9 years, males were consistently higher
than females in the total SPM+ score. These sex differences attained statistical
significance at p< .001 at every age except 8 to 9 years and 11 years. With
increasing age, both sexes showed gradual increases in their mean scores on
SPM+ peaking at ages around 15 years for girls and around 16 years for boys.
The effect of age x sex interaction was also significant for the total score of the
SPM+, F= 4.25, p<.001, indicating that the magnitude of sex differences
becomes significantly larger with increasing age (Figure 1). In support of Lynn’s
developmental theory, a notable increase in sex differences began to occur at
around age 16 years. The estimates of dstarted from -.06 at ages 8-9 years,
reached .34 at age 16 years and then increased quite sharply to .46 at ages 18-
19 years, with an average of .23 across the whole age range.
HUR, Y-M., et al. SEX DIFFERENCES IN NIGERIA
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Table 1. Mean ± standard deviation (SD) for the total score of the SPM+ for
males and females by age, and standardized sex difference d.
Note. *** Sex difference significant at p< .001.
Figure 1. Standardized effect size (d) of sex differences in the total score of the
SPM+ from age 8 to 19 years.
-0.1
0
0.1
0.2
0.3
0.4
0.5
Effect Size (d)
Age in years
Male
Female
Age (years)
N
Mean ± SD
N
Mean ± SD
8-9
457
14.5 ± 5.7
454
14.9 ± 6.7
10
319
18.0 ± 7.8
390
16.4 ± 7.1
11
586
18.9 ± 8.3
568
18.1 ± 8.1
12
664
20.7 ± 8.6
638
19.2 ± 8.2
13
846
22.0 ± 9.0
820
20.1 ± 8.3
14
763
23.4 ± 9.1
716
21.2 ± 8.9
15
603
24.9 (9.2)
632
23.0 (9.1)
16
577
25.8 (9.2)
567
22.7 (8.9)
17
441
25.3 (8.7)
439
22.2 (8.4)
18-19
327
25.0 (8.7)
357
21.2 (8.0)
Average
22.0 (9.2)
20.1 (8.6)
MANKIND QUARTERLY 2017 57:3
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Discussion
The present study partially confirms the developmental theory of sex
differences in cognitive abilities among African children and adolescents in
Nigeria. From ages 8 to 19 years, sex differences in the total score of the SPM+
increased steadily from -.06dto .46d, with an average of .23d. A sharp sex
difference emerged at age 16 when most children passed puberty. These results
are consistent with the findings from the meta-analysis by Lynn and Irwing (2004)
in that a large sex difference emerges at the end of puberty. However, unlike
other studies, we show that boys perform better than girls at almost all ages
although the magnitudes of the sex differences before age 16 were much smaller
than those found from age 16 onward.
In support of sex differences in cognitive abilities, Kimura and Hampson
(1994) suggested that sex hormones such as testosterone and estradiol influence
sex differences in cognitive abilities, especially in spatial rotation. More recently,
many neuroimaging studies yielded evidence for sex differences in structural and
functional characteristics of the brain. For example, Gur et al. (1999) and Haier
et al. (2005) showed that global white matter volume correlated more strongly
with intelligence in adult women, while global gray matter volume correlated more
strongly in adult men. In support of these results, Schmithorst (2009) found in a
longitudinal study based on children aged from 5 to 18 years that girls developed
a positive correlation of fractional anisotropy (FA; a marker for white matter
organization) with IQ with increasing age in frontal and fronto-parietal regions in
white matter, while boys developed a negative correlation of FA in these regions.
In another developmental study Wang et al. (2012) found that adolescent boys
(13-18 years) continued to demonstrate white matter maturation, whereas girls
reached the mature state earlier. Taken together, these studies suggest that sex
differences in intelligence may be due to different developmental trajectories of
brain structures between the sexes.
Limitations
The use of student samples for studies of sex differences in cognitive abilities
has been criticized because students are not necessarily representative of the
general population (Dykiert, Gale & Deary, 2009; Flynn & Rossi-Casé, 2011).
Although the present sample was recruited from many public schools across all
administrative areas in Lagos State and Abuja, FCT in Nigeria, adolescents not
enrolled in schools or students in private schools were not included in the present
sample, indicating that children and adolescents at both high and low ends of the
cognitive ability distribution among Africans in Nigeria are likely to be under-
represented in the present sample. Still, the very large size of the sample and the
HUR, Y-M., et al. SEX DIFFERENCES IN NIGERIA
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careful sampling allow us to conclude that our sample is highly representative of
the public schools in a specific part of Nigeria.
Suggestions for Future Research
Our finding of gender differences in cognitive abilities in Nigeria renders
support for Lynn’s developmental theory of sex differences. Although our sample
is a large one, this study is the only one in Nigeria conducted as far as we
understand. More studies need to be carried out so that a meta-analysis can be
performed (Schmidt, 1992), which will allow us to make strong conclusions and
examine whether sex differences in cognitive abilities are moderated by ethnic or
racial groups. The World Economic Forum (2011) determined that in Nigeria,
gender gaps in education, economic empowerment and political participation
remain. Moreover, cultural and religious influences foster the maintenance of a
'son preference' within the country, which may also influences teachers' attitudes
and behaviors towards boys versus girls. These influences should also be taken
into account in future studies.
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... One of the methods used for the exclusion of women in the workplace and university was psychometric assessment, which created the segregation of women because of theorised intelligence deficits. Globally, women were discriminated from men through the application of psychometric instruments, which indicated that they were cognitively inferior to men and were thus not capable of occupying certain positions (Camarata & Woodcock, 2006;Hur, te Nijenhuis, & Jeong, 2017;Hyde, 1981;Miller & Halpern, 2014;Palejwala & Fine, 2015;Toivainen, Papageorgiou, Tosto, & Kovas, 2017). This discrimination was very powerful as it appeared to be scientifically proven that women were less intelligent than men in male-dominated spaces. ...
... This discrimination was very powerful as it appeared to be scientifically proven that women were less intelligent than men in male-dominated spaces. It was found in several studies that men tended to score better in verbal analogies and spatial relations tasks (Camarata & Woodcock, 2006;Hur et al., 2017;Hyde, 1981;Miller & Halpern, 2014;Palejwala & Fine, 2015;Toivainen et al., 2017), whilst women were found to be cognitively stronger in certain domains than men, such as verbal ability (reasoning) and other verbal-related cognitive assessments, such as word memory, anagrams, reading, writing, general and mixed verbal ability assessments (Griskevica & Rascevska, 2009;Hur et al., 2017;Hyde, 1981;Miller & Halpern, 2014;Palejwala & Fine, 2015;Strand, Deary, & Smith, 2006;Toivainen et al., 2017;Wai, Hodges, & Makel, 2018;Wilsenach & Makaure, 2018). These gender differences were explained as biological differences between the male and female sexes because of hormones or genetic differences between men and women (Hur et al., 2017;Miller & Halpern, 2014;Toivainen et al., 2017;Wilsenach & Makaure, 2018). ...
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... . SeeFeingold (1992) for a landmark literature review on the topic. The finding of greater male variability is now widely accepted, with many different tests usually showing a 5-15% greater standard deviation on male cognitive test scores (e.g.,Deary, Thorpe, Wilson, Starr, & Whalley, 2003;Hur, te Nijenhuis, & Jeong, 2017;Lakin, 2013;Strand, Deary, & Smith, 2006). ...
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The past 30 years of research in intelligence has produced a wealth of knowledge about the causes and consequences of differences in intelligence between individuals, and today mainstream opinion is that individual differences in intelligence are caused by both genetic and environmental influences. Much more contentious is the discussion over the cause of mean intelligence differences between racial or ethnic groups. In contrast to the general consensus that interindividual differences are both genetic and environmental in origin, some claim that mean intelligence differences between racial groups are completely environmental in origin, whereas others postulate a mix of genetic and environmental causes. In this article I discuss 5 lines of research that provide evidence that mean differences in intelligence between racial and ethnic groups are partially genetic. These lines of evidence are findings in support of Spearman’s hypothesis, consistent results from tests of measurement invariance across American racial groups, the mathematical relationship that exists for between-group and within-group sources of heritability, genomic data derived from genome-wide association studies of intelligence and polygenic scores applied to diverse samples, and admixture studies. I also discuss future potential lines of evidence regarding the causes of average group differences across racial groups. However, the data are not fully conclusive, and the exact degree to which genes influence intergroup mean differences in intelligence is not known. This discussion applies only to native English speakers born in the United States and not necessarily to any other human populations.
... However, many outcomes reveal similar results in the NTSR and Western or East Asian twin samples. For example, coefficients of assortative mating for educational achievement (Hur, 2016) are comparable, as are developmental changes in sex differences in cognitive abilities (Hur, te Nijenhuis et al., 2017), heritability of prosocial behavior (Hur & Rushton, 2007;Hur, Taylor et al., 2017) and measures of family environment (Hur, Taylor et al., 2017;Kendler & Baker, 2007). Furthermore, the finding of Hur et al. (2019) that religious attendance shows a large shared environmental influence with negligible amount of genetic effects was also consistent with reports from many western twin studies (e.g., Kirk et al., 1999). ...
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Here we provide an update of the 2013 report on the Nigerian Twin and Sibling Registry (NTSR). The major aim of the NTSR is to understand genetic and environmental influences and their interplay in psychological and mental health development in Nigerian children and adolescents. Africans have the highest twin birth rates among all human populations, and Nigeria is the most populous country in Africa. Due to its combination of large population and high twin birth rates, Nigeria has one of the largest twin populations in the world. In this article, we provide current updates on the NTSR samples recruited, recruitment procedures, zygosity assessment and findings emerging from the NTSR.
... Indeed, based on a linear regression with just IQ, the standardized residual for Nigeria is only -0.16, meaning that the country performs very slightly worse at mental sports in general than one would expect based on its mean national intelligence. The predicted IQ of Nigeria was 73.1 based on a nonlinear model with just game performance as the predictor, Lynn and Vanhanen's (2012) estimate was 71.2, and a recent large-scale study (n ≈ 11k) using Raven's Standard Progressive Matrices found a mean IQ of 65.5 (Hur, Nijenhuis & Jeong, 2017). 11 The anomalously high Scrabble performance is not plausibly interpreted as hidden ability, but rather as a culture-specific preference for a specific sport. ...
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Research at the individual level shows strong positive relationships between performance in video games and on intelligence tests. Together with evidence of above average IQs of players of traditional mental sports such as chess, this suggests that national IQs should be strongly related to national performance on mental sports. To investigate this, lists of top players for 12 different electronic sports (e-sports) and traditional mental sports were collected from a variety of sources (total n = 36k). Using a log count approach to control for population size, national cognitive ability/IQ was found to be a predictor (p<.05) of the relative representation of countries among the top players for every game except Go. When an overall mental sports score was calculated using a factor analytic approach, the factor scores correlated r = .79 with Lynn and Vanhanen's (2012) published national IQs. The pattern was somewhat nonlinear such that national IQs below 85 seemed to have no relationship. The games that related most strongly with the general factor of mental sport ability also correlated more strongly with national IQs (r = .94). The relationship was fairly robust to controls for geographical region (coefficient 74% of the original in chosen model specification).
... Once again, modern research can shed light on the issue. The variability hypothesis has been supported in large, representative samples in several countries (e.g., Deary, Thorpe, Wilson, Starr, & Whalley, 2003;Hur, te Nijenhuis, & Jeong, 2017;Lakin, 2013;Strand, Deary, & Smith, 2006), usually with males having a 5% to 10% larger standard deviation for IQ scores. Assuming equal means and normally distributed variables within each group, a 5% difference in standard deviation between two groups would produce a ratio of 1.47:1 in the top 1% of individualsmore than enough to cause Terman's imbalanced sex ratio. ...
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Lewis Terman is widely seen as the “father of gifted education,” yet his work is controversial. Terman’s “mixed legacy” includes the pioneering work in the creation of intelligence tests, the first large-scale longitudinal study, and the earliest discussions of gifted identification, curriculum, ability grouping, acceleration, and more. However, since the 1950s, Terman has been viewed as a sloppy thinker at best and a racist, sexist, and/or classist at worst. This article explores the most common criticisms of Terman’s legacy: an overemphasis on IQ, support for the meritocracy, and emphasizing genetic explanations for the origin of intelligence differences over environmental ones. Each of these criticisms is justified to some extent by the historical record, and each is relevant today. Frequently overlooked, however, is Terman’s willingness to form a strong opinion based on weak data. The article concludes with a discussion of the important lessons that Terman’s work has for modern educators and psychologists, including his contributions to psychometrics and gifted education, his willingness to modify his opinions in the face of new evidence, and his inventiveness and inclination to experiment. Terman’s legacy is complex, but one that provides insights that can enrich modern researchers and practitioners in these areas.
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The SPM+ was administered on a sample of 5189 school children from Sudan in 2016. Data about age, sex, locality, school type and stage, parental education and profession, family size and birth order were collected. Results for intelligence are congruent with the literature, giving the sample a mean IQ of ≈80 on British norms. Sex-differences are largely negligible. Differences in intelligence were found between three locations and are consistent with differences in parental education and income. Family income is a better predictor than parental education for children's intelligence. Children in private schools outperform children in public schools with mean IQs of 84–78 but path analysis points to a possible negative effect of private education. IQ-differences between age-groups and school-stages were found but no Simber-effect. The effects of selective processes along the educational pathway are shown and discussed with reference to the need for samples more representative for total populations.
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The area of sex differences is one of the most controversial subjects in social sciences, especially when significant differences are detected in the field of intelligence. In this chapter, we offer results from several studies conducted at specific and general levels of intelligence using a variety of different cognitive measures. Finally, we present results obtained from our SLATINT Project.
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How should data be interpreted to optimize the possibilities for cumulative scientific knowledge? Many believe that traditional data interpretation procedures based on statistical significance tests reduce the impact of sampling error on scientific inference. Meta-analysis shows that the significance test actually obscures underlying regularities and processes in individual studies and in research literatures, leading to systematically erroneous conclusions. Meta-analysis methods can solve these problems-and have done so in some areas. However, mela-analysis represents more than merely a change in methods of data analysis. It requires major changes in the way psychologists view the general research process. Views of the scientific value of the individual empirical study, the current reward structure in research, and even the fundamental nature of scientific discovery may change.
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Raven’s Progressive Matrices data of high quality from five advanced nations show that females matched males both below and above the age of 14. This counts against hypotheses that genetic factors cause general intelligence differences between the genders. Evidence unfriendly to gender parity at mature ages is based on suspect samples. At ages 15–18, more males than females are school dropouts. At ages 18–24, female deficits among university students may be caused by an IQ/academic achievement gap.
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In 1992, it was reported by Ankney and Rushton that males have larger average brain size than females even when allowance is made for body size. It is known that brain size is associated with intelligence, and it would therefore be expected that males would have higher intelligence than females. Yet it has been universally maintained that there is no difference in intelligence between the sexes. It is proposed that this anomaly can be resolved by a developmental theory of sex differences in intelligence which states that girls mature more rapidly in brain size and neurological development than boys up to the age of 15 years. The faster maturation of girls up to this age compensates for their smaller brain size with the result that sex differences in intelligence are very small, except for some of the spatial abilities. From the age of 16 years onwards, the growth rate of girls decelerates relative to that of boys. The effect of this is that a discernible male advantage of about 4 IQ points develops from the age of 16 into adulthood, consistent with the larger average male brain size. This paper presents new evidence on the developmental theory of sex differences in intelligence and discusses alternative attempts to deal with the anomaly by Ankney (1995), Mackintosh (1996), and Jensen (1998).
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Assortative mating for educational level is a widespread phenomenon in Western industrialized societies. However, whether or not the results from Western samples can be generalizable to populations in developing countries in Africa remains to be seen. The present study investigated assortative mating for educational level in parents of public school children (N > 7000) in the Lagos State in Nigeria. Approximately 61.5 % of the parents had spouses at the same level of education. More mothers than fathers married upward in educational level. The assortative mating coefficients for educational level were .52-.61 across respondents' classes, .51-.62 across six school districts, and .57 (.55-.59) in the total sample. Overall, these results were very similar to the findings from Western or Asian samples, providing evidence to support the robustness of human mating pattern in educational attainment across different cultures and ethnic groups. The present findings should be incorporated in future quantitative and molecular genetic studies on Africans.
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Developed by British psychologist J. C. Raven and first published in 1938, the progressive matrices (PM) tests measure the ability to deduce relationships within geometric patterns or among figural elements contained in a matrix. Items employ either a complete pattern from which a piece has been removed, or figural elements placed in discrete rows and columns, with one element missing. The missing element must be selected from six or eight answer choices presented. These tests reportedly assess the ability to impose meaning on confusion, to formulate nonverbal constructs to explain complex relationships, and to minimize the influences of verbal communication and past experience (J. Raven, J. C. Raven, & Court, 1998, p. 1).Keywords:intelligence measures;nonverbal intelligence tests;cross-cultural tests;culture-fair tests;general intelligence tests
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This study employed both hierarchical and Bi-factor multi-group confirmatory factor analysis with mean structures (MGCFA) to investigate the question of whether sex differences are present in the US standardization sample of the WAIS-III. The data consisted of age scaled scores from 2450 individuals aged from 16 to 89years. The findings were more or less uniform across both analyses, showing a sex difference favoring men in g (0.19–0.22d), Information (0.40d), Arithmetic (0.37–0.39d) and Symbol Search (0.40–0.30d), and a sex difference favoring women in Processing Speed (0.72–1.30d).
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Lynn [Lynn, R., 2002. Sex differences on the progressive matrices among 15–16 year olds: some data from South Africa. Personality and Individual Differences 33, 669–673.] proposed that biologically based developmental sex differences produce different IQ trajectories across childhood and adolescence. To test this theory we analyzed the Naglieri Nonverbal Ability Test (NNA; [Naglieri, J. A., 1997. Naglieri Nonverbal Ability Test-Multilevel Form. San Antonio: Harcourt Assessment Company.]) standardization sample of 79,780 children and adolescents in grades K-12, which was representative of the US census on several critical demographic variables. NNAT data were consistent with Lynn's developmental theory of gender differences insofar as (a) there were no gender differences between 6 and 9 years; (b) females scored slightly higher between 10 and 13 years; and (c) males were ahead of females between the ages of 15 and 16. However, the discrepancies between the genders were smaller than predicted by Lynn. In fact they were so small that they have little or no practical importance. In other words, the NNAT did not reveal meaningful gender differences at any stage between the ages of 6 and 17 years.