It is generally agreed that the Wechsler tests are one of the best measures of intelligence, and can be considered the gold standard. That is hardly surprising, because they cover 10 subtests and take over an hour, sometimes an hour and a half, for a clinical psychologist to administer. This gives the examiner plenty of opportunity to see the fine grain of individual responses, to probe within the limits allowed by the manuals to make sure that the person has every chance to reveal what they know, and to observe the way in which the person handles objects on non-verbal tasks. Watching block design is a window into how a person thinks. The examiner can also notice when an explanation has been misunderstood and when attention is wandering, and can stop the test and continue after a break or on another occasion. The results are presented together with a written evaluation of how the person approached the individual tests, identifying strengths and weaknesses, and often suggesting areas where subsequent testing might show higher scores.
Wechsler put together the decathlon of tests on the pragmatic basis of having examined how particular tests functioned, and paid attention to the verbal versus non-verbal dichotomy, as well as complex and simple, speed versus untimed, thinking on the hoof versus testing for acquired mental skills. It does a pretty good job. More pragmatically, having 10 tests (can be up to 15 if subsidiary tests are included) gives both examiner and person something to ponder about. Originally the 5 verbal tests were added together to give a Verbal IQ, and the other 5 a Performance IQ estimate. Later that moved to 4 factors based on 2 or sometimes 3 tests each, which was less reliable, but allowed more discussion about different skills, and the supposed discrepancies between those skills. I think it is over-factored at the moment, and attempted new subtests often get dropped at the next revision.
Given that there is a lot of debate about the appropriateness of intelligence testing of Africans, it is particularly interesting to look at Wechsler results to see if their finer detail about different skills can cast light on the general pattern of African mental abilities.
A cross-cultural comparison between South African and British students on the Wechsler Adult Intelligence Scales Third Edition (WAIS-III). Kate Cockcroft, Tracy Alloway, Evan Copello and Robyn Milligan. Front. Psychol., 13 March 2015 | https://doi.org/10.3389/fpsyg.2015.00297
There is debate regarding the appropriate use of Western cognitive measures with individuals from very diverse backgrounds to that of the norm population. Given the dated research in this area and the considerable socio-economic changes that South Africa has witnessed over the past 20 years, this paper reports on the use of the Wechsler Adult Intelligence Scale Third Edition (WAIS-III), the most commonly used measure of intelligence, with an English second language, multilingual, low socio-economic group of black, South African university students. Their performance on the WAIS-III was compared to that of a predominantly white, British, monolingual, higher socio-economic group. A multi-group confirmatory factor analysis showed that the WAIS-III lacks measurement invariance between the two groups, suggesting that it may be tapping different constructs in each group. The UK group significantly outperformed the SA group on the knowledge-based verbal, and some non-verbal subtests, while the SA group performed significantly better on measures of Processing Speed (PS). The groups did not differ significantly on the Matrix Reasoning subtest and on those working memory subtests with minimal reliance on language, which appear to be the least culturally biased. Group differences were investigated further in a set of principal components analyses, which revealed that the WAIS-III scores loaded differently for the UK and SA groups. While the SA group appeared to treat the Processing Speed subtests differently to those measuring perceptual organization and non-verbal reasoning, the UK group seemed to approach all of these subtests similarly. These results have important implications for the cognitive assessment of individuals from culturally, linguistically, and socio-economically diverse circumstances.
The first thing to note is that the authors found that the factor structure of the WAIS results were different in the black South Africans compared to the white British. The caution is that the whites were not only white, but rich; and the black South Africans not only black, but poor. The sample sizes are rather small for factor analytic studies, but in the very strict interpretation of measurement invariance these two genetic groups should be seen by the authors as having an underlyingly different structure of intelligence. I think that the “measurement invariance” requirement is too harsh for all but large groups of subjects, and if we really apply it universally we end up being unable to discuss any group differences at all.
Also of importance is that there were few South Africans in the sample, only 107 as opposed to 349 for the British sample. Against that, far more data have been obtained on each person than is the case in group tests. There should be some bonus points for that, and for collecting Wechsler results in Africa, which are in short supply. Indeed, the authors gave 13 subtests, which is very good. However, factor studies on 107 people are not very likely to produce stable results.
The Africans were not rich:
All of the SA participants came from low socio-economic circumstances. The majority (82%) resided in rural areas, in a basic brick house with running water and electricity. Hardly any (98%) had washing machines, microwave ovens, or tumble-dryers. Less than 1% of families owned a motor vehicle or personal computer.
However, those conditions were similar to British life in the 1950s, at which time intelligence test scores were roughly IQ 100. On the other hand, educational provision then was probably much better than current day South Africa.
There is a 0.44 effect size on Performance IQ and a massive 1.53 effect size on Verbal IQ. These are big differences, given that both samples are university students. Another approach is to look at the results and list the South African subtest scores from strong to weak:
Matrix Reasoning 10.68
Digit Symbol Coding 9.75
Symbol Search 9.71
Digit Span 9.35
Letter-Number Sequencing 9.17
Block Design 8.67
Picture Completion 8.05
This is an interesting hierarchy, in that the very culture-loaded “Information” subtest (composed of very general, general knowledge questions) is a strength, not a weakness. Amusingly, in the US context it used to be considered too culture-loaded to be included in measures of group differences.
Continuing with the discussion about the results the authors say:
There was no evidence of cultural biases in the Matrix Reasoning subtest or in the WM subtests that had minimal reliance on language. (2) All of the verbal and most non-verbal subtests, as well as the PS subtests, showed evidence of cross-cultural differences. (3) The SA and UK samples’ scores revealed different factor structures.
My comment is that it would be more accurate to say that there are differences between the British and South African test-takers, which could be due to both genetic and environmental factors. There is no difference on Matrix reasoning in these two samples. This implies that once you match Europeans and Africans for university attendance, then they do not differ on ability as tested by Matrices. This strongly suggests that Matrices are a good cross-cultural test, and that we should accept Matrices scores as a valid measure of African intelligence.
So, what did Rushton and Jensen (2005) (not quoted by the authors of this paper) say about university intelligence test results in South Africa:
Black university students in South Africa also show relatively low mean test scores. Sixty-three undergraduates at the all-Black universities of Fort Hare, Zululand, the North, and the Medical University of South Africa had a full-scale IQ of 77 on the Wechsler Adult Intelligence Scale—Revised (Avenant, 1988,cited in Nell, 2000, pp. 26 –28). In a study at the University of Venda in South Africa’s Northern Province by Grieve and Viljoen (2000), 30 students in 4th-year law and commerce averaged a score of 37 out of 60 on the Standard Progressive Matrices, equivalent to an IQ of 78 on U.S. norms. A study at South Africa’s University of the North by Zaaiman, van der Flier, and Thijs (2001) found the highest scoring African sample to that date—147 first-year mathematics and science students who scored 52 out of 60 on the Standard Progressive Matrices, which is equivalent to an IQ of 100. This higher score may reflect the fact that they were mathematics and science students, specially selected for admission to the university from a pool of 700 applicants on the basis of a math-science selection test. At the University of the Witwatersrand in Johannesburg, South Africa, Rushton, Skuy, and colleagues gave the Raven’s Progressive Matrices in four separate studies under optimal testing conditions. Rushton and Skuy (2000) found 173 African 1st-year psychology students averaged an IQ equivalent of 84. Skuy et al. (2002) tested another 70 psychology students who averaged an IQ equivalent of 83. After receiving training on how to solve Matrices-type items, their mean score rose to an IQ equivalent of 96. Rushton, Skuy, and Fridjhon (2002, 2003) gave nearly 200 African 1st-year engineering students both the Standard and the Advanced version of the Raven’s test and found they averaged an IQ of 97 on the Standard and 103 on the Advanced, making them the highest scoring African sample on record. (The White university students in these four studies had IQs from 105 to 117; East Indian students had intermediate IQs, from 102 to 106.) Many critics claim that Western-developed IQ tests are not valid for groups as culturally different as sub-Saharan Africans (e.g., Nell, 2000). The main evidence to support a claim of external bias would be if the test failed to predict performance for Africans. Even if tests only under predicted performance for Africans compared with non-Africans, it would suggest that their test scores underestimated their “true” IQ scores. However, a review by Kendall, Verster,and von Mollendorf (1988) showed that test scores for Africans have about equal predictive validity as those for non-Africans (e.g., 0.20 to 0.50 for students’ school grades and for employees’ job performance). The review also showed that many of the factors that influence scores in Africans are the same as those for Whites (e.g., coming from an urban vs. a rural environment; being a science rather than an arts student; having had practice on the tests; and the well-documented curvilinear relationship with age). Similarly, Sternberg et al.’s (2001) study of Kenyan 12- to 15-year-olds found that IQ scores predicted school grades, with a mean r .40 (p .001; after controlling for age and socioeconomic status [SES], r .28,p .01). In Rushton et al.’s (2003) study of African and non-African engineering students at the University of the Witwatersrand, scores on the Advanced Progressive Matrices correlated with scores on the Standard Progressive Matrices measured 3 months earlier (.60 for Africans; .70 for non-Africans) and with end-of-year exam marks measured 3 months later (.34 for Africans; .28for non-Africans).
The only reliable example of bias so far discovered in this extensive literature is the rather obvious internal bias on the Vocabulary components of tests such as the Wechsler for groups that do not have English as their first language (e.g., Skuy et al., 2001). Even here, the language factor only accounts for about 0.5 of a standard deviation, out of the overall 2.0 standard deviation difference, between Africans and Whites.
The high score for Maths and Science South African university students is IQ 100. Such students in Britain, together with Engineering students, are usually brighter than the average run of undergraduates. Giving practice and training with matrices lifts the average from the 83 to 96, a very big gain, though it defeats some of the purpose of the test, which is that you come across puzzles for the first time, and have to use your wits to work out how to deal with them.
Of course, the main interest in the intelligence of university students is that they represent the brightest in the population. What ever they score, they are probably one to two standard deviations above the local mean, depending on what percentage of the young population get into university. So, the model is fairly simple: find the intelligence level of the undergraduates, and then impute the actual population intelligence level from which they were drawn.
If South African undergraduates in 2015 have an IQ of 93 what is South African intelligence as a whole? Crudely, if you assume a standard deviation of 15, and that being one standard deviation above the mean is enough to get you into university, then a local intelligence level of 78 is indicated. If you say that only those above 1.5 sd should go to university, then the underlying level will be IQ 70.
A problem with the findings of this paper is that the British results are so low as to make one question which universities they attended. Furthermore, many students are older than the usual university entrance age, which may have slowed their processing speeds though, as the authors note, that should have been coped with by the age related standardization scores. In 1992 all technical colleges were given university status, so the term is now far less meaningful than before. Equally, the extension of this generous categorisation of university status to include half of the student age population means that some students of IQ 100 are spending three years taking university courses. The overall result in this university sample of Full Scale IQ 106.95 sd 12.9 means that some students have been admitted with IQs of 94 or below, which is absurd. Only those who are one standard deviation above the mean of this sample would have a chance of getting into a well-rated UK university with an IQ of 120. So, in terms which most would understand it, only 35 of the 349 students in this study might have been considered university students at the Russell Group level. The standard deviation of 12.9 is also highly informative. Any strict entry criteria should reduce the standard deviation of the accepted candidates. It would be very interesting to know which universities these students attended, and what their entry criteria were.
The South African sample is of student age, and efforts were taken to recruit students who had attended poorer schools. Tertiary education in South Africa is only extended to a minority, roughly 5% of the student age of the population, and should be considered an elite, certainly as compared to Britain offering tertiary education to 50%. 5% tertiary education rates were the norm in Britain in the sixties, made up of 2% at universities, and the rest at technical and educational colleges. The Full scale IQ of black South Africans is 93.27 sd 8.94. The tighter standard deviation suggests more consistent entry requirements, but the overall level is very low for tertiary education. This is borne out by relatively low completion rates:
Breaking Stellenbosch University’s findings down by race, the completion rate for white students was vastly higher, at 71.6%. Black students had a 53.5% completion rate and coloured students a 53.8% completion rate. Asian students came in at 62.1%.
South African students who are one standard deviation above the South African university student mean are IQ 102. The crème de la crème who are at two standard deviations above the university mean (98th percentile, hence only 2 students) are IQ 111.15 and few of them would be likely to get into highly rated British universities.
In summary, this is an interesting study, in that Wechsler tests are considered the gold standard for intelligence testing, and university samples reflect the cognitive elite. The Matrices Reasoning subtest shows no difference between British university students (top 50% of the British population) and South African students (top 5% of the South African population). The implication of this sample is that if in South Africa university students are 1.5 standard deviations higher than the general population, then they are drawn from a population of IQ 70.
The latest Becker dataset 1.3.3 estimates South African intelligence (mostly on the basis of the Matrices test) at 69.80
It would seem we have an independent confirmation of the Becker estimate.