Content uploaded by Jan te Nijenhuis
Author content
All content in this area was uploaded by Jan te Nijenhuis on Apr 25, 2017
Content may be subject to copyright.
MANKIND QUARTERLY 2017 57:3 428-437
428
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)
MANKIND QUARTERLY 2017 57:3
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
MANKIND QUARTERLY 2017 57:3
432
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
433
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
d
8-9
457
14.5 ± 5.7
454
14.9 ± 6.7
-0.06
10
319
18.0 ± 7.8
390
16.4 ± 7.1
0.21***
11
586
18.9 ± 8.3
568
18.1 ± 8.1
0.10
12
664
20.7 ± 8.6
638
19.2 ± 8.2
0.18***
13
846
22.0 ± 9.0
820
20.1 ± 8.3
0.22***
14
763
23.4 ± 9.1
716
21.2 ± 8.9
0.24***
15
603
24.9 (9.2)
632
23.0 (9.1)
0.21***
16
577
25.8 (9.2)
567
22.7 (8.9)
0.34***
17
441
25.3 (8.7)
439
22.2 (8.4)
0.36***
18-19
327
25.0 (8.7)
357
21.2 (8.0)
0.46***
Average
22.0 (9.2)
20.1 (8.6)
0.23
MANKIND QUARTERLY 2017 57:3
434
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
435
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.
References
Bakhiet, S.F.A., Haseeb, B.-W.M., Seddieg, I.F., Cheng, H. & Lynn, R. (2015). Sex
differences on Raven's Standard Progressive Matrices among 6 to 18 year olds in Sudan.
Intelligence 50: 10-13.
Colom, R., Garcia, L.F., Juan-Espinoza, M. & Abad, F.J. (2002). Null sex differences in
general intelligence: Evidence from the WAIS–III. Spanish Journal of Psychology 5: 29-
35.
Deary, I.J., Irwing, P., Der, G. & Bates, T.C. (2007). Brother–sister differences in the g
factor in intelligence: Analysis of full, opposite-sex siblings from the NLSY1979.
Intelligence 35: 451-456.
Doppelmayr, M., Klimesch, W., Sauseng, P., Hodlmoser, K., Stadler, W. & Hanslmayr, S.
(2005). Intelligence related differences in EEG-band power. Neuroscience Letters 381:
309-313.
Dykiert, D., Gale, C.R. & Deary, I.J. (2009). Are apparent sex differences in mean IQ
scores created in part by sample restriction and increased male variance? Intelligence
37: 42-47.
Flynn, J.R. & Rossi-Casé, L. (2011). Modern women match men on Raven’s Progressive
Matrices. Personality and Individual Differences 50: 799-803.
MANKIND QUARTERLY 2017 57:3
436
Gur, R.C., Turetsky, B., Matsui, M., Yan, M., Bilker, W., Hughett, P. & Gur, R.E. (1999).
Sex differences in brain gray and white matter in healthy young adults: Correlations with
cognitive performance. Journal of Neuroscience 19: 4065-4072.
Haier, R.J., Jung, R.E., Yeo, R.A., Head, K. & Alkire, M.T. (2005). The neuroanatomy of
general intelligence: Sex matters. NeuroImage 25: 320–327.
Haier, R.J., Siegel, B.V., Nuechterlein, K.H., Hazlett, E., Wu, J.C., Paek, J., Browning,
H.L. & Buchsbaum, M.S. (1988). Cortical glucose metabolic rate correlates of abstract
reasoning and attention studied with positron emission tomography. Intelligence 12: 199-
217.
Halpern, D. (2012). Sex Differences in Cognitive Abilities, 4th ed. New York: Psychology
Press.
Hur, Y.-M. (2016). Assortative mating for educational level in parents of public school
children (N>7000 individuals) in the Lagos State, Nigeria. Behavior Genetics 46: 596-602.
Hur, Y.-M., Kim, J.W., Chung, K.W., Shin, J.S., Jeong, H.-U. & Auta, E. (2013). The
Nigerian twin and sibling registry. Twin Research and Human Genetics 16: 282-284.
Irwing, P. (2012). Sex differences in g: An analysis of the US standardization sample of
the WAIS-III. Personality and Individual Differences 53: 126-131.
Irwing, P. & Lynn, R. (2005). Sex differences in means and variability on the Progressive
Matrices in university students: A meta-analysis. British Journal of Psychology 96: 505-
524.
Jackson, D.N. & Rushton, J.P. (2006). Males have greater g: Sex differences in general
mental ability from 100,000 17- to 18-year-olds on the Scholastic Assessment Test.
Intelligence 34: 479-486.
Jensen, A.R. (1998). The g Factor: The Science of Mental Ability. Westport, CT: Praeger.
Keith, T.Z., Reynolds, M.R., Patel, P.G. & Ridley, K.P. (2008). Sex differences in latent
cognitive abilities ages 6 to 59: Evidence from the Woodcock Johnson III tests of cognitive
abilities. Intelligence 36: 502-525.
Kimura, D. & Hampson, E. (1994). Cognitive pattern in men and women is influenc ed by
fluctuations in sex hormones. Current Directions in Psychological Science 2: 57-61.
Liu, J. & Lynn, R. (2011). Factor structure and sex differences on the Wechsler Preschool
and Primary Scale of Intelligence in China, Japan and United States. Personality and
Individual Differences 50: 1222-1226.
Lynn, R. (1994). Sex differences in brain size and intelligence: A paradox resolved.
Personality and Individual Differences 17: 257-271.
Lynn, R. (1999). Sex differences in intelligence and brain size: A developmental theory.
Intelligence 27: 1-12.
HUR, Y-M., et al. SEX DIFFERENCES IN NIGERIA
437
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.
Lynn, R. & Irwing, P. (2004). Sex differences on the Progressive Matrices: A meta-
analysis. Intelligence 32: 481-498.
Lynn, R. & Kanazawa, S. (2011). A longitudinal study of sex differences in intelligence at
ages 7, 11 and 16 years. Personality and Individual Differences 51: 321-324.
Mackintosh, N.J. (1996). Sex differences and IQ. Journal of Biosocial Science 28: 559-
572.
Neubauer, A.C., Fink, A. & Schrausser, D.G. (2002). Intelligence and neural efficiency:
The influence of task content and sex on the brain–IQ relationship. Intelligence 30: 515-
536.
Neubauer, A.C., Grabner, R.H., Fink, A. & Neuper, C. (2005). Intelligence and neural
efficiency: Further evidence of the influence of task content and sex on the brain–IQ
relationship. Cognitive Brain Research 25: 217-225.
Nyborg, H. (2005). Sex-related differences in general intelligence g, brain size, and social
status. Personality and Individual Differences 39: 497-509.
Raven, J. (2008). Manual for Raven’s Progressive Matrices. London: Pearson.
Reynolds, M.R., Keith, T.Z., Ridley, K.P. & Patel, P.G. (2008). Sex differences in latent
general and broad cognitive abilities for children and youth: Evidence from higher-order
MG-MACS and MIMIC models. Intelligence 36: 236-260.
Rojahn, J. & Naglieri, J.A. (2006). Developmental sex differences on the Naglieri
Nonverbal Ability Test in a nationally normed sample of 5-17 year olds. Intelligence 34:
253-260.
Schmidt, F.L. (1992). What do data really mean? Research findings, meta-analysis, and
cumulative knowledge in psychology. American Psychologist 47: 1173-1181.
Schmithorst, V.J. (2009). Developmental sex differences in the relation of
neuroanatomical connectivity to intelligence. Intelligence 37: 164-173.
Spearman, C. (1923). The Nature of Intelligence and the Principles of Cognition. London:
Macmillan.
Terman, L. M. (1916). The Measurement of Intelligence. Boston, MA: Houghton Mifflin.
Wang, Y., Adamson, C., Weihong, Y., Mekibib, A., Akila, R., Byarsd, A.W. & Holland, S.K.
(2012). Sex differences in white matter development during adolescence: A DTI study.
Brain Research 1478: 1-15.
World Economic Forum (2015). Global Gender Gap Report. Geneva: WEF.