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Ife PsychologIA 2014, 22(2), 27-35 Copyright © 2014 Ife Centre for Psychological Studies/Services, Ile-Ife, Nigeria ISSN: 1117-1421
27
A Standardisation study of the Raven’s Coloured Progressive Matrices in Ghana
Adote Anum
Department of Psychology, University of Ghana, Legon
Email: [email protected]
The Raven’s Progressive Matrices test was developed as a test of Spearman’s concept of general intelligence or index of g which measures an ability that is not influenced by
external factors. The purpose of this study was to develop local norms for children in
Ghana and to test the hypothesis that test scores on the progressive matrices are not
influenced by socio- cultural factors. Seven hundred and sixty-three children selected
from both urban and rural locations were administered with the Raven’s Colored
Progressive Matrices. We found expected gradual developmental change in scores
associated with increase in age. This increase was different for children from urban and rural populations. Children from rural areas consistently lagged behind in test scores
and this difference got bigger between nine and eleven years. We associate the
difference between urban and rural children to differences in socio-economic
opportunities and conclude that these are two different populations and therefore need
to have different comparative norms. The findings also challenge the perceived notion that the progressive matrices measures ability that are not influenced by education and
cultural factors.
Keywords: Children, cognitive assessment, general intelligence, Ghana, RCPM
Intelligence and its measurement is one of the most complex concepts in psychology.
To simplify the construct of intelligence,
psychologists divided intelligence into two
broad domains – general and specific
mental abilities (Jensen, 1998). This classification was inspired by Charles
Spearman’s assertion in 1904 that a
common factor underlies all mental
abilities. The general factor of intelligence (g) is the ability that is reflected in all tests
while the specific component is unique only to that test or a limited number of tests. Spearman postulated the g factor to explain
correlations he found to exist among
diverse tests of perceiving, reasoning, and
thinking.
General intelligence is usually measured by the performance subtests or non-verbal
measures of cognitive ability. They are also
measured by matrix reasoning or tests of
abstract reasoning which have typically
been designed to assess general intelligence
tests. An example of this is the Raven’s Progressive Matrices (RPM). There are three
versions of the progressive matrices. The
Standard Progressive Matrices (SPM) which
was the first to be published in 1938 was
designed to test analytical reasoning through visual analogies. There are five sets
and 12 items within a set with latter items becoming increasingly difficult. The
Advanced Progressive Matrices was
designed for individuals of above-average
intelligence. The third version, the Colored
Progressive Matrices, was designed for children aged 5 through 11 years old, the
elderly, and mentally and physically
impaired individuals. Unlike the two
previous versions, most of the items are
presented on a colored background to make
the test visually stimulating for young participants.
The Progressive Matrices is widely regarded
as the best or one of the best tests of
general intelligence (Court, 1983; Jensen,
1998). The test has therefore been used in a variety of situations that include but not
limited clinical assessment, educational
placement, and predicting job performance
(Raven, 2000; Vincent & Cox, 1974) thus,
making the progressive matrices one of the
most widely used matrix reasoning test in different countries.
In Ghana the progressive matrices is used
widely primarily for clinical purposes and
for job selection. The progressive matrices
is useful in Ghana for a number of reasons. There are very few standardized tests that
have been developed in Ghana to measure
Ife PsychologIA, 22(2), 2014
28
intellectual functioning in both children
and adults. Therefore a quick and
‘unbiased’ measure of intelligence becomes particularly useful. The other advantage
that the RPM provides is that the RPM is
non-verbal. Ghanaian children grow up
speaking two or more languages (i.e., one or
more local dialects and English). Most
children, however, do not start communicating in the English language
until they are in school especially in the
rural areas. Since the progressive matrices
has minimal verbal instructions and verbal
response it avoids the difficulties and confounds associated with translating
English instructions into local languages.
Finally, the RPM is intended to measure an
ability that is not affected by social or
educational variations. The test
requirement does not depend on acquired knowledge and being non-verbal it has been
considered one of the most culture-fair
instruments. This makes it a measure of
choice to assess individual differences
between groups that vary greatly along socioeconomic characteristics which is a
reflection of differences between urban and
rural populations. Poverty levels are higher
in rural areas and the quality of education
is also lower. The progressive matrices is
therefore particularly useful for measuring individual differences in cognitive ability
between rural and urban children where
the effects of these external differences are
very noticeable.
The progressive matrices has been used as a measure of g and because of its predictive
ability in a variety of situations. However,
available evidence seems to suggest that
the test cannot be used without adequate
local standardization norms (e.g., Lynn,
Abdallah, & Al-Shahome, 2008; Lynn, Backhoff, & Contreras, 2005;
Constenbader, & Ngari, 2001; Pind,
Gunnarsdóttir, Jóhannesson, 2003;
Pullmann, Allik, Lynn, 2004). Findings from
these studies show that children, particularly in sub-Saharan Africa perform
poorly on foreign psychometric tests and
that they would need locally based norms
or a modification of the test for a
meaningful interpretation of their scores
(e.g., Kitsao-Wekulo, Holding, Taylor, Abubakar & Connolly, 2012). In the
current study, the primary objective was to
develop local norms for the children’s
version of the progressive matrices, Raven’s Colored Progressive Matrices (RCPM) among
school children in Ghana.
Group Differences on Progressive Matrices
Cross-cultural studies in Africa and Asia
have shown that there are group differences
in scores that may be attributed to socio-cultural factors. For example, Kaniel and
Fisherman (1991) demonstrated that
children from impoverished backgrounds
had significantly lower scores on the
Progressive Matrices when compared with children of the same age but of upper or
middle class backgrounds. In their study
Kaniel and Fisherman compared the non-
verbal intelligence test scores of Ethiopian
and Israeli Jews using the RPM among a
sample of 14- and 15-year old boys and girls who had immigrated to Israel one year
earlier with native Israeli children between
9 and 15 years. The Ethiopian children
demonstrated a delay of 4 to 5 years
placing them between the 5th and 10th percentile rank while most of the Israeli
children fell within Raven's normative scale.
There have been very few studies done in
Africa in which the performance on the
RPM has been compared across different
socio-cultural and economic groups. These have usually been in South Africa, where
most of these comparisons have been
between Black and White South Africans
(e.g., Lynn & Owen, 1994; Rushton & Skuy,
2000). The researchers have consistently reported lower scores for Black Africans on
the Raven’s progressive matrices with IQ
equivalents between 60 and 75. In Ghana,
Glewwe and Jacoby (1992) found that
scores of adolescent children on the
Raven’s progressive matrices was significantly lower when compared to the
British published norms. However, the
authors did not compare performance
between different socioeconomic groups.
The children used in the study were selected from the public school system
(which is usually associated with lower
income families). The test scores from this
study may therefore be a reflection of the
performance of children from only a specific
socioeconomic group.
Adote Anum: Raven’s Coloured Progressive Matrices in Ghana
29
The Present Study
The primary objective of the present study
was to standardize the Raven’s Colored Progressive Matrices (RCPM) among the
Ghanaian students between six and twelve
years selected from public and private
schools and from urban and rural
communities. With the knowledge that this
dichotomy is closely associated with socioeconomic factors, one would expect
that if socioeconomic factors had effect on
test scores it should reflect in differences
between children from public and private
schools and between urban and rural children. If not, there should be differences
between these groups of children,
particularly on a test that is not influenced
that is not influenced by external factors.
As indicated, the goal of the current study
was standardize the Raven’s Colored Progressive Matrices on a representative
sample of children in Ghana taking
cognizance of the different socioeconomic
and cultural identities that might affect
performance on a cognitive test. Standardization of the RCPM would enable
a more accurate assessment of children for
school placement and for evaluation of
children with cognitive disabilities. There
have been very few standardizations done
in Ghana where currently, there is no published data on the Progressive Matrices
and any of the major psychological
assessment instruments. This presents a
major obstacle in assessment and
evaluation since comparison invariably is made on norms developed from other
populations.
Method
Sample and Participant Selection
To achieve a reasonable representative
sample for the country’s standardization study, participants for the study were
selected from both private and public
schools and from both urban and rural
areas within the Greater Accra Region
which is the administrative region in which the capital city is located. The sampling
procedure comprised a multi-stage random
sampling method to obtain urban and rural
sample of 16 schools from the Regional
capital. In Ghana, private schools are rated
A to D based on availability of resources
such as library, teacher-student ratio, and
teaching and learning resources. Better
resourced schools are rated A and less endowed schools rated D. Stratified
sampling technique was used for selection
of six private schools. Public schools which
are funded by the government are not
rated. The selection in the rural
communities focused on public schools. There are very few private schools in the
rural communities and the resources
available in private schools are not different
from those in publicly funded schools. The
selection of the public schools was based on convenience. We looked for schools that
had space for testing and could provide a
congenial environment for testing purposes.
Four and six schools were selected from
urban and rural locations respectively.
In schools where the class sizes were large (usually in the urban schools) sample
selection was systematic. For example,
every second or third student listed in the
class register was selected. In the schools’
register males separately from females are listed separately and therefore selection is
done separately for each group. In some
rural schools, all children in particular
classes were selected because of the low
enrolment. There were no children with
cognitive disability. Usually, children who have any identifiable cognitive challenges
are educated in special schools.
A total of 763 subjects were selected for the
entire study (see Table 1). However, due to
some incompletely tested subjects, the results of 734 (96%) were used for the
analysis. The ages of the children ranged
from six years 0 months to 11 years and 11
months (based on school records). Children
start school at about six years and
therefore the average age in the first grade (Class 1) is six and the average in the sixth
grade (Class 6) is about 11.5 to 12 years.
The population of Ghana is multiethnic and
multicultural. In the education system
however, there is no distinguishing ethnic or cultural factors that uniquely affect
education. The medium of instruction in all
schools is English. The level of proficiency
in English is however varied with children
in urban and private schools having higher
levels of proficiency. The medium of
Ife PsychologIA, 22(2), 2014
30
instruction for the study was English for all
children. The instructions were repeated
several times for younger children to ensure they understood the requirements of the
tasks.
Table 1: Distribution of participants by
location and sex Location/Sex 6-7 7-8 8-9 9-10 10-11 Total Urban 71 78 79 62 102 392
Rural 53 49 57 70 113 342 Boys 45 62 68 62 100 337
Girls 79 65 68 70 115 397 Total 124 127 136 132 215 734
Measures The Raven's Colored Progressive Matrices (RCPM).The RCPM is a nonverbal and
untimed test just as other versions of the
progressive matrices. There are three
sections, Section A, Ab, and B and each
section has 12 items. Each item contains a
matrix with one missing part. Children are
expected to select the missing part from an array of six options to make the matrix
complete. The highest possible score on the
test is 36. It can be administered in a group
or individually.
The test was administered in English following the directions for individual
administration suggested in the test
manual (Raven, Court, & Raven, 1998). The
RCPM was administered by one assessor
with extensive experience in assessment
procedures.
Results
Descriptive Analyses
The total score on the RCPM is based on
aggregate scores for each section (A, Ab,
and B). The mean and standard deviation by age and sex are presented in Table 2.
The mean scores are computed for the total
sample. In subsequent analyses
computations were done for the aggregated
sample and for urban and rural sample
separately. The overall means showed a gradual increase in total score from 6 years
to 12 years which suggested a
developmental trend although very small
changes were observed between some age
groups.
Table 2: Descriptive statistics for RCPM by sex, age (years)
Sex N Mean Standard deviation SE
Male 337 17.47 5.321 0.290
Female 397 16.41 4.867 0.244
Total 734 16.90 5.104 0.188
Analyses of Normative Data
For purposes of norming the RCPM
participants were grouped into eleven age
bands between 6.5 and 11.5 years based on six-month interval. The first group
comprised of all children between six and
six and half years and the last group
comprised of all children between 11 and
half years and 11 years 11 months. The age
classification is consistent with the age grouping for the published norms (Raven,
1998). Percentile scores based on the 6-
month age interval between 6 years and
11.5 years are presented in Table 3. The
norms were obtained by calculating percentile ranks for each age group in
SPSS. Seven percentile ranks were selected
to match the ranks used in the 1998
standardization reported in the manual
(Raven, 1989) and to allow for comparison
with the British norms. These are 5th, 10th, 25th, 50th, 75th, 90th, and 95th.
The percentile ranks calculated from the
raw scores showed that as expected there
was a gradual increase in scores with age.
For example, the average score for a six
year old is 13 (50th percentile rank). This increases to 15 for a nine-year old and to
19 for children who are 11.5 years old
(Table 3). Similar trends were observed for
scores at higher percentile ranks.
When compared to the UK published norms, the range of scores for the
Adote Anum: Raven’s Coloured Progressive Matrices in Ghana
31
Ghanaian norms is much lower (Raven,
1989) (Table 3). Comparison is made for
scores at the 50th, 75th, and 95th percentile ranks. High performing children on the
Ghana data (95th percentile) was
comparable to scores at the 50th percentile
on the British standardization data which is
an indication of superior performance for British children when compared to
Ghanaian children.
Table 3: RCPM Percentile scores for age groups1 (Ghana and British norms)
Percentiles
6 ½
7
7 ½
8
8 ½
9
9 ½
10
10 ½
11
11½
95
16
17
18
20
21
22
24
25
27
29
31
90
16
16
16
17
17
20
21
22
23
25
28
75
14
15
15
16
16
17
18
20
22
22
24
50
13
13
14
15
15
15
17
17
18
18
19
25
12
12
12
13
13
13
14
14
15
15
16
10
11
11
11
12
12
12
13
13
13
13
14
5
10
10
10
11
11
12
12
12
12
13
13
N
33
45
48
53
62
73
69
56
80
87
128
British norms
95 23 24 25 26 28 30 32 33 33 35 75 18 19 20 21 23 26 28 28 29 31 50 15 16 17 18 20 22 24 24 26 28
1 Smoothed percentile scores
When the data are disaggregated into urban
and rural children a different trend of
results for each group was observed. The
range of scores of children in the urban
group was superior to those in the rural group (Tables 4 and 5). Children in the
urban group obtained higher scores for
each age level and respective percentile
ranking. For example, scores that
correspond to 95th percentile rank for 9
year old children in the rural group was equivalent to 75th percentile among urban
children. Among 11 and 11.5 year old year
children in the rural sample, scores that
correspond to the 95th percentile rank was
comparable to scores at the 50th percentile
among urban children. This indicated that
the gap between the two groups increased
among older children in the study. For
rural children, the change in scores from
six years to nine years was quite imperceptible. Even then, high functioning
children (95th percentile) do not score
higher than urban children who score at
the 50th percentile.
It was observed that the difference in
performance between the urban children in this study was similar to and the published
norms especially between 7 and 11 years
showing two to three point difference at
each level.
Ife PsychologIA, 22(2), 2014
32
Table 4: RCPM Percentile scores for age groups for urban school children1
Percentiles
6½
7
7 ½
8
8½
9
9½
10
10 ½
11
11½
95
17
18
20
22
23
24
25
28
30
33
34
90
16
17
18
19
20
22
24
27
29
30
32
75
14
15
15
16
17
18
20
23
25
27
28
50
13
14
14
15
15
17
19
20
22
24
25
25
12
12
12
13
13
14
15
16
17
18
21
10
11
11
11
12
12
13
13
14
15
16
18
5
10
11
11
11
11
12
12
13
14
15
16
N
22
30
21
34
33
49
36
23
43
40
61
1 Smoothed percentile scores
Table 5: RCPM Percentile scores for age groups for rural school children1
Percentiles
6½
7
7½
8
8½
9
9½
10
10½
11
11½
95
16
17
17
18
19
19
20
21
22
22
23
90
15
16
16
17
18
18
19
20
20
20
21
75
14
15
15
16
16
17
17
18
18
18
19
50
13
13
14
15
15
15
16
16
16
16
17
25
11
11
12
12
13
13
14
14
14
14
15
10
10
10
10
12
12
13
13
13
13
13
13
5
8
10
10
10
10
11
11
11
11
12
12
N
11
15
27
19
29
24
33
33
37
47
67
1 Smoothed percentile scores
Comparing age groups, location, and sex
To test for age group differences, age groups
were reclassified into seven groups instead
of the 11 used for the percentile ranking. The classification is based on 12-month
intervals rather than 6 month intervals
(starting from 6 to 6.5 years to 11 years 11
months labeled as 12 years). It was
expected that we were more likely to observe cognitive changes significant
enough to be detected in statistical
analyses on 12-month interval than on six
months. Analysis of Variance (ANOVA) was
computed to test for statistical difference
between age groups, location (rural and
urban), sex (males and females). The
ANOVA results showed significant main
effects for age group (F(6, 734)=34.60, .001)
and for location (F(1, 734) = 71.53, .001), but not for sex. We did not find significant
mean differences among children who are
6.5 years and 8.5 year and between 8.5 and
9.5 years. Similarly, there was no difference
between children in the three highest age groups. We did however find significant
mean differences between 6.5 years and
9.5, 10.5, 11.5, and 12 years. We also
found significant differences between 8.5
years and 10.5, 11.5, and 12 years.
Adote Anum: Raven’s Coloured Progressive Matrices in Ghana
33
Table 6: Post hoc Analyses for age group on the RCPM (total sample) Age Group Age group Mean SD 7.5 8.5 9.5 10.5 11.5 12
6.5 13.12 1.93 -0.36 -1.98 -3.04* -5.53* -5.92* -6.75* 7.5 13.48 2.49 -1.62 -2.68* -5.16* -5.56* -6.39* 8.5 15.10 3.69 -1.06 -3.54* -3.94* -4.77* 9.5 16.16 3.73 -2.49* -2.88* -3.71*
10.5 18.65 5.85 -.40 -1.23 11.5 19.05 5.73 -.83 12 19.87 5.13 Total 16.90 5.10
Discussion
The primary objective of this study was to collect data for reference norms on the
RCPM among school children for clinical
and educational purposes in Ghana. The
results showed that there were substantial
differences in percentile scores between this study and scores from the published norms
(Raven, 1989). The biggest differences were
observed among the older children
(especially between 9 and 11 year olds).
Overall, the Ghana sample still lagged
behind by at least 4 points. It appears that high performing Ghanaian children (95th
percentile) are comparable to performance
on 50th percentile. This is the strongest
indication for the development of
appropriate reference local norms for Ghana for the RCPM.
There are three principal issues that are of
interest in this study. First, there was the
revelation that scores obtained from the
Ghana sample were lower at all age levels
when compared to the British standardization sample (Table 3). Similar
results have been reported in previous
normative studies in Libya, Kenya, and
South Africa on both the children’s and
adults’ version of the progressive matrices (Al-Shahome, 2012; Costenbader & Ngari,
2001; Lynn et al, 2008). Basically, in all
these studies, the average scores from
Africans of North African and Black South
African decent were lower than obtained in
the normative data published in the manual and obtained from Western
countries such as Britain, Australia, and
North American. Lynn et al (2008) reported
that the average IQ of Libyan children was
86 compared to British IQ of 100. Again, in a similar study using the Standard
Progressive Matrices for adults in Libya, Al-
Shahomee (2012) found in a review of 21
normative studies in North Africa that the
median IQ based on British norms was 84. These two studies seem to suggest that the
performance of North African children on
the Raven’s progressive matrices is about 1
standard deviation lower than that of the
British children. The findings from the North African studies are consistent with
previous findings from Kenya and South
Africa (Constenbader & Ngari, 2001;
Rushton & Skuy, 2000). In South Africa,
results from Black South Africans have
been significantly lower than other groups (see for example, Knoetze, Bass & Steele,
2005; Rushton & Skuy, 2000). Some
researchers have attributed the superior
performance to the vast difference in
educational and socio-economic opportunities that exist in the Western
countries (for example, Wicherts, Dolana,
van der Maas 2010). Constenbader & Ngari
(2001) made the point that comparison
between scores of children from different
socio-cultural background is not intellectually useful. The variance in
performance within cultures may provide
more useful information about the validity
of the test than comparisons with scores
from other countries.
The second principal issue of interest in
this study was the revelation that the
variance in performance was high resulting
in significant disparity in the scores of
urban and children. Scores of high
functioning children from the rural group (95th percentile ranking) were comparable to
the 50th percentile ranking among the
urban school sample, especially among
children who are between nine years and
11.5 years. This means that children in urban locations develop superior abstract
reasoning, a skill required for the
progressive matrices, and maintain this
advantage over children in rural areas. On
Ife PsychologIA, 22(2), 2014
34
one hand, this is not very surprising
because urban areas, compared to rural
areas have better socio-economic indicators such as improved educational facilities and
lower prevalence of poverty. In Ghana, most
teachers do not accept appointment into
rural areas because of poor infrastructure
and very high teacher-student ratios
(Akyeampong, Djangmah, Oduro, Seidu, & Hunt, 2007). School enrolment rates are
higher in urban areas than in rural areas
across all ages. Malnutrition, disease, and
poor quality of life are prevalent in rural
areas than in urban areas. In Ghana, children in rural areas are more likely to be
stunted because of poor nutritional status.
These factors can affect a child’s cognitive
development and likely to reflect poorly on
intelligence test scores (Duncan & Brooks-
Gunn, 2004; Farah, et al. 2006).
Finally, we examined sex difference in this
study but did not find it to be significant,
contributing to the controversial yet
interesting debate on sex differences on the
progressive matrices. There is no clear consensus on this debate (Savage-McGlynn,
2011; Yang, Liu, Wei, Hitchman, Li, Qui, &
Zhang, 2014). Generally, males have scored
higher on Raven’s Progressive Matrices
(Lynn & 2004; Lynn & Irwing, 2004). Lynn
and Irwing (2004) for example have reported that boys have advantage of 3.2 IQ
points on the RCPM. This advantage on the
progressive matrices, they claim increases
in adolescence. Our finding however is
similar to others that have not reported sex differences (for example, Colom & Garcıa-
Lopez, 2002). Other studies have reported
differences favoring females (for example,
Khaleefa & Lynn, 2008). We may need to
explore this further in Ghana with the other
versions of the progressive matrices.
Conclusions
This study was the first attempt to
standardize the RCPM in Ghana. The
results provided strong support in two
areas. First, it supports the need to standardize all tests on the local population
before making assumptions about
performance. Secondly, it challenges the
notion that the progressive matrices and
other matrix reasoning tests measure an
ability that is not influenced by socio-cultural factors.
Our findings have supported previous
findings in Kenya, South Africa, and Libya
as well. The findings from the Ghanaian standardization are particularly very
interesting because we directly explored
socioeconomic differences among
participants selected from the same genetic
pool. For this reason, any difference in
scores between these groups was going to be attributed to socioeconomic factors that
are determined not only by income
disparities but by exposure, quality of
education, opportunities, and general
quality of life. All of these factors vary significantly between urban and rural
locations in most African countries.
There are two issues that come to the fore.
First, we can conclude quite firmly that
development of children’s fluid ability may
not follow a singular developmental path for all children and therefore we need to be
careful in our assessment of children from
varied socioeconomic background. As our
data have shown, different norms are
needed to have a more accurate picture of children with lower socioeconomic statuses.
Secondly, the findings seem to challenge
the widely held notion the progressive
matrices measure an ability that is not
influenced by external factors. Our position
has been supported quite frequently and it is therefore essential to begin a systematic
examination of the different factors it might
measure the mechanisms involved in the
development of general intelligence.
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