Tuesday, 28 July 2015

The Glass Floor and Frosted Glass Statistics


UK newspapers are full of a new study purporting to show that middle class parents arrange things for their children so that they get better jobs than their cognitive abilities merit, and don’t fall as far as they ought to, thus being sustained by a “glass floor”. The assertion is that middle class children do better in life than working class children of the same level of ability, because of some manipulated advantage. 

The BBC: A study by the Social Mobility and Child Poverty Commission has found that bright children from poor backgrounds are not getting highly paid jobs because of less able middle-class children. The group said a so-called "glass floor" had been created by better-off families, which stops their children falling down the social scale.

The Guardian: Children from wealthier families but with less academic ability are 35% more likely to become high earners than their more gifted counterparts from poor families, according to findings from the Social Mobility and Child Poverty Commission.

All this is based on a study: Downward mobility, opportunity hoarding and the ‘glass floor’. Research report June 2015. Abigail McKnight, Centre for Analysis of Social Exclusion (CASE), London School of Economics. Produced for the Social Mobility and Child Poverty Commission.


Anyone working at the “Centre for Analysis of Social Exclusion” is up against a moral dilemma. If they find, after due analysis, that there is no evidence for social exclusion, should they resign? I do not wish to boost their income stream from the production of government reports, but surely members of this group might be seen as more credible if they entitled it something more neutral : “Centre for Analysis of Social Class”?

By the way, I cannot find anything in the report to substantiate The Guardian’s 35% claim. Try searching for “35” in the report and see if you can produce their conclusion.

The report nails its colours to the mast immediately: “A society in which the success or failure of children with equal ability rests on the social and economic status of their parents is not a fair one” and there is a temptation to conclude that they are producing a result the Commission wants to hear. However, it is being reported as if it was an independent report, and that is how I intend to treat it. In order to meet the requirements of their findings, the authors need to show the outcomes for children of equal ability at several points in the ability range. For example IQs 70, 80, 90, 100,110, 120, 130. I could not find any results plotted out in that way.

They say: We begin by examining bivariate relationships between three variables: family background, low cognitive skills in early childhood and labour market success in adulthood. These simple statistics provide estimates of the raw correlations between family income or parental social class and children’s early cognitive test scores, the relationship between these test scores and position in the wage distribution at age 42 and the relationship between family background and position in the age 42 wage distribution.

However, they do not give those correlations. Worse, they do not give the overall plots of the data, which would allow us to judge whether the bivariate division made any sense. On the matter of making a composite score of the cognitive tests the author says she follows the work of Parsons (2014) but that turns out to be an unpublished “data note”. There is another Parsons working paper which I eventually found. It makes one general reference to composite scores, but I could not find the Principal Components Analysis referred to in the report.

The first finding is: In the raw data we find that, on average, children from lower income families or those with less advantaged social class backgrounds do not perform as well in a series of cognitive tests taken at age 5 as children from higher income families or those from advantaged social class backgrounds.


Figure 1 is a projective test. You and I know that there are two likely causes: different genetics and different circumstances. The depicted results could be due to parents’ intelligence (passed on in their genes) allowing their children to get better paid jobs; or unfairly obtained money and power allowing parents to buy their children educational toys and other advantages. The report makes zero references to “genetic” or “genetics” or “heredity”. However, there is one use of the word “inherited”:

It has been suggested that the reason success runs in families is that ability is inherited: the only reason that poorer children don’t succeed is because they are not as bright or don’t work as hard, or are not academic. This is simply not the case.

The authors go on to quote the work of Feinstein (2003). I had long and friendly exchanges with Feinstein at the time of this publication, saying that a genetic explanation for his results was equally plausible, but he reiterated that he thought it was due to the way in which middle class parents encouraged and taught their children. We could not resolve the issue since no measures of parental ability were collected, but his surmise about environmental effects was widely accepted as a basis for government policy.

McKnight sees Feinstein as supportive evidence, whereas I see it as having obvious genetic confounding. McKnight does quote concerns about measurement error and regression to the mean. She says that having a variety of cognitive measures and taking a common factor reduces such error.

We attempt to minimise the impact of ‘regression to the mean’ in three ways. (1) In our research we define ability groups based on cognitive test scores at age 5 and define family background at a different age (age 10). (2) We use the results from five separate cognitive skill tests taken at age 5 to create a composite measure of attainment. This minimises the chance that the score from one test, driven by good luck or bad, results in a child being allocated to a high or low attaining group in error. (3) We avoid looking at the extremes of the ‘ability’ distribution.

Method 1 merely introduces another source of error, in that social status at age 10 may be different from social status at age 5. Method 2 is better, so long as we can see the loadings of each test. It might have been even better to use the Peabody Picture Vocabulary Test which is the best of the bunch, or that and the Goodenough Draw A Man test. We need to see the data on that, and it is not available in the internal reports mentioned. Method 3 loses data, and assumes that measurement error will be more evident at the extremes, whereas it will be present in every test score.

A very large number of the references in this report are to “working papers”, not peer-reviewed papers published in journals. Reeves and Howard (2013) is given significant mentions 4 times in the text, but not shown in the reference list. I have found a paper “The Parenting Gap” of theirs that year, but on examination the main finding is work done by Waldfogel and Washbrook in 2011 saying that parenting is the major source of income related gaps in children’s cognitive outcomes. It is probably another paper “The Glass Floor” which is more substantive, and seems to have provided many of the catch-phrases and data analytic techniques.

The unpublished work of Joshi is mentioned 8 times, the published paper of Sophie Stumm (with Ian Deary and others) 6 times, the very many published papers on the genetics of academic achievement by Robert Plomin 0 times. People are right to disparage social sciences because too many of them cluster into confirmatory tribes, averting their gaze from contrary findings. We are all tempted to into this self-serving research apartheid, but must learn to avoid it. If we cannot achieve linkage between research projects such that educationalists use twin studies where possible, and if findings in any research area do not make other researchers review their methods and interpretations, we will never progress from a cottage industry of pamphleteers. Genetic research is driving a coach and horses into the territory of old explanations. Telescopes supplant naked-eye astronomy.

I expected that the report would show the basic data, that is to say, the means and standard deviations of each of the cognitive tests taken at each age, and then a figure showing those scores (or a composite) plotted out by social class of origin. Plots of early cognitive ability against achievements in adulthood for each social class would also be interesting. In this way the reader could be oriented properly into the basics of the data set. There is no link to supplementary tables. There is a footnote saying: “The tables containing the complete sets of regression results can be downloaded from the Social Mobility and Child Poverty Commission website” but I cannot find them on that site. I would like to see the correlations between all the cognitive measures and academic measures taken at all stages. It is very hard to judge the end results without being able to look at those important details.

The report, having given its conclusions, then sets out its method: the two lower quintiles of cognitive ability at age 5 will be measured against the upper two quintiles in binary comparisons. This is a massive data loss. Why not plot the all children against the social mobility measures so that we can see the shape of the relationship?

I want to see through the glass clearly, not be confused by frosted glass, and have prepared assumptions forced on me.

Talking about high and low attaining children the author says: There remains an unexplained additional advantage associated with high income or advantaged social class background. High attaining children from disadvantaged family backgrounds appear to be less successful at or less able to convert early high attainment into later labour market success.

This might be a genetic advantage, or middle class encouragement, or both. How does the report go about investigating this?

Quintiles, quintiles quintiles. No data plots or correlation coefficients. Time and space are wasted. The report plods through the findings that middle class people do better, but says of these effects that they are “reinforcing patterns of advantage”. Well, yes, or sustained evidence that bright and diligent families get ahead because of some genetic advantage.

Essentially, all I can find after reading the entire report several times is that it finds “unexplained additional advantage” for higher social classes. Determining why this is the case is left for another day. The results would be consistent with higher class children being somewhat slower to mature, and rising slowly to better achievements, as befits a slow lifestyle. By the way, any difference is described as an instance of things being “unequal”. If you have read this far, while others haven’t, that is an instance of unequal chances.

Here are the report’s conclusions:

Why should parental education contribute to children’s chances of career success? Parents’ education can indirectly affect this likelihood through the extent to which education equips parents to: help their children develop cognitive and non-cognitive skills, choose the best primary and secondary schools for their children, assist them with their homework, help them with exam preparation, help guide them through the process of making further and higher education choices, assist them with career choices and interviews. It is natural for parents to want to do the best they can to help their children do well and this should not be discouraged. If parental education is directly related to children’s skills, affecting social mobility, then policy should be directed at trying to redress educational inequality among adults in the UK. Many attempts have been made and they have been largely unsuccessful but this does not mean that a solution should not be sought.

Notice there is no mention of the possibility that brighter and more diligent parents have children who eventually mature into brighter and more diligent adults for reasons which are partly genetic.

Some of the correlation between parental education and children’s career success could be driven by unmeritocratic factors. If highly educated parents are using their connections to help their children find good jobs. This amounts to opportunity hoarding and results in fewer opportunities available for equally able but less connected children.

Apart from innuendo, the report has no data on these “un-meritocratic” factors.

Parents’ education could also be giving children an unfair advantage in the selection of primary and secondary schools. Focusing on increasing choice can simply result in parents who are in a better position to make informed choices and able to exercise that choice sending their children to the best performing schools, thereby hoarding these school places at the expense of less-advantaged children. Could reducing choice actually increase outcomes if instead these parents are limited to working with schools to drive up standards? The question remains unanswered.

We find a clear advantage for children who attend a Grammar or a private secondary school.

There is an obvious artefact here: we need to know whether the cognitive abilities of children sent to private schools are any different from those sent to state schools. Simple statistics are the royal road to understanding data sets.

I cannot substantiate the newspaper headlines with anything in this report. The report does not plot out all the data in a way I can understand. It does not use a multiple regression model or SEM approach on all the available data. Instead it goes for binary categories, and then plods through the quintiles until most readers will have lost the will to live. Finally, without giving us the proper comparison, it jumps to innuendo to account for residuals. You read it, and tell me if I have missed the crucial (non-existent) page where they give me a simple Chi square of adult outcomes by class versus age 5 ability.

The report gives some of the data, explains some of the approaches, and is supported by some of the literature. It is not a proper paper, and does not let the reader into the fundamentals of the data. It is written to be read by policy makers, which is not always a bad thing, because it makes the writing clear. The downside is that the methods are not as clear as they should be. The report does not give anything like the full picture, and does not substantiate the conclusions that have been drawn from it. Authors cannot be held responsible for occasional misreporting, but if the BBC and all the press cover it in the same mistaken way then the authors and their university publicity departments have something to explain.

I repeat: if you can find the killer result, showing that bright working class children are being held back by less bright middle class children, in a proper, balanced, like-for-like comparison, direct me to the page and paragraph in the report.

A more world-weary view is that the report has probably achieved its purpose, which is to imply that middle class people cheat. Their wish to educate their children and make them productive is “opportunity hoarding”. Hoarding is mentioned 65 times. They base this conclusion on an imperfect model which in their view means that outcomes must have been manipulated. Yet the alternate hypothesis, which is that the behaviour of parent and child are half driven by the same genetics, carrying similar genes for ability and effort, has had no chance to be evaluated. The report casts middle class helping as unfair, but working class lack of helping is judged to be entirely due to lack of material resources, not lack of interest.

In the same week that this report was given such glowing coverage Robert Plomin has published a paper in Nature looking at the genetics of GCSE achievement. I will try to post about that shortly, but it appears to confirm that academic success has genetic elements over and above those which account for general intelligence.

I shouldn’t get the least bit agitated about this report and its credulous reception, but I do feel a stirring of dismay, so I am writing my despairing words in this little blog, and will then watch the wild West wind blowing rain through the garden.

Friday, 24 July 2015

Do immigrants contribute?

A year or so after the London bombings a document was leaked which had all the appearance of a confidential Home Office study on whether immigrants felt a primary attachment to the United Kingdom and how much they contributed to the economy and to society. I say “appearance” because as far as I know it was never confirmed to be an official document, but was put together in the Whitehall style, with plentiful use of government statistics and restrained interpretations of the findings. I imagined that some civil servant felt that this should be known to the public, and anonymously made public some of the findings, if only selected pages.

Naturally, the focus of interest at that time was British Muslims, since it was from that particular demographic that the bombers had been drawn, but the findings had general relevance as regards immigrant contributions, and the final conclusion could be summarised in one word: variable. Some immigrants contributed more than others. Looking at the figures for employment by religion, Muslims contributed least of all, presumably because few women worked outside the house. The highest contributors were atheists. As regards Muslims, the report conceded that this covered a wide spectrum from wealthy Gulf Arabs to poor Pakistanis, but the end result in terms of unemployment and benefits was low economic contribution overall.

Now to a study published a few days ago. Migration Watch is a campaigning group who act as a thorn in the side of governments on the issue of immigration. They specialise in detailed studies of official immigration figures, and often prove closer to the mark than official estimates. Mainstream UK political opinion is that immigrants contribute “immensely”, and now Migration Watch (opposed to mass migration) has conducted an analysis of the UK Labour Force Survey data 2014, the most complete data set on employment to test that proposition. Their conclusions? The contribution of immigrants is variable. Some contribute more than the natives, some less. It is almost as if humans showed individual and group differences. Could any of these variations in economic contribution be due to intelligence and diligence?


The report says the UK Labour Force Study “is currently the most complete data source for examining the impacts of migration on the UK labour market. This paper noted that much previous research was based on periods prior to the economic recession starting in 2008. While the most recently published academic paper in this area[1] is based on data up to and including the 2011/12 fiscal year, its reporting of most recent key economic characteristics (and hence calculated outcomes) are based on averages over a period stretching back to 2001, and these are not necessarily any guide to what is happening now. The Migration Observatory at Oxford University publishes a helpful and regularly updated series of briefings about migrants in the UK [2], and this report complements these with a more detailed  analysis of some key aspects.”

For guidance:

Prior to 2004 Europe EU15 comprised the following 15 countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom.

In 2004 Europe took in the following "A10" countries: Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia.

In 2007 Bulgaria and Romania joined.

Adult migrants in the UK are predominantly in age bands between 25-44 and thus should be compared with the local working population of that age. Immigrants look more beneficial if they arrive as working adults (the usual way the benefits of immigration are presented), and but less beneficial as they age and require pensions and health care or leave the labour market to have children who require education and benefits (less often presented in studies of immigration). A host nation potentially benefits by getting immigrants only in the adult earning phase. Taken in the round and across the lifespan, immigrants only benefit the local population if they are better than the local average in ability and character, and make greater contributions than locals.

Looking at the number of adults (16+) in the UK by country of birth, Africa, the Indian sub-continent, Western Europe and Eastern Europe each account for over a million, and Rest of World for a further two million. By historical standards, recent immigration has been massive. It is a permanent change in the genetics of the UK, such that by current estimates the indigenous population will be a minority by 2066.

Ed West in “The Diversity Illusion” writes:

“The latest projections suggest that white Britons will become a minority sometime around 2066, in a population of 80 million, which means that within little over a century Britain will have gone from an almost entirely homogenous society to one where the native ethnic group is a minority. That is, historically, an astonishing transformation. No people in history have become a minority of the citizenry in their own country except through conquest, yet the English, always known for their reticence, may actually achieve this through embarrassment.”



It is not clear whether the relatively small number of South Africans include those of European and Indian origins, but there may be data on that somewhere.



Pakistanis & Bangladeshis and Africans tend to persist in unemployment.



Of course, immigrants to the UK may not be representative of their country of origin. The elite of one country (the smart fraction) may leave because they can obtain even better wages and conditions for their scarce skills, while the least able of another country may leave because more generous benefits more than make up for their inability to get jobs. However, looking at the average for each country of origin is a fair approximation, and a good starting point for analysis. Just looking by eye at the groups shows a rough concordance with national intelligence scores at the lower end of the spectrum, but far from a perfect match. Selective migration is probably the cause, but far more detail would be required to be sure about it.

Finally, the irony cannot be lost on any UK citizen that the group who contribute least in employment and wages and take most in benefits are also those most likely to harbour militants planning acts of violence so as to savagely bite the hand that continues to feed them.

Wednesday, 22 July 2015

Poverty of brain: poverty of hypothesis testing


It should be easy to understand that people differ in ancestry, and that their genetics may influence their behaviour. However, some researchers regard that possibility as a distant cloud which should not disturb their picnic. They lay out their favourite hypotheses on a familiar rug, namely that the slings and arrows of outrageous fortune determine our lives, and then sit down to examine these notions to the exclusion of others. This is a pity, because finding the truth requires that all hypotheses should contend against each other.

Here is an example, from the “poverty shrinks your brain” school of thought. First of all, it is possible that poverty might shrink brains, particularly where poverty leads to starvation. Secondly, it is also possible that innate ability and character determine a major portion of achievement in life, such that poverty is not imposed from the outside, but created by inner failings perhaps associated with slightly smaller brains. Third, it could be a blend of both competing hypotheses.

Association of Child Poverty, Brain Development, and Academic Achievement. Nicole L. Hair; Jamie L. Hanson; Barbara L. Wolfe; Seth D. Pollak. JAMA Pediatr. Published online July 20, 2015. doi:10.1001/jamapediatrics.2015.1475


The authors have scanned 389 children aged 4 to 22. They say: Low-income students are now a majority of schoolchildren attending public schools in the United States. Data collected by the National Center for Education Statistics show that 51% of students across US public schools were from low-income families in 2013. If so, low income is the norm in the US, which suggests a sloppy criterion, and of doubtful status given that incomes of that magnitude are certainly well above the world standard. Few nations of the world can supply their citizens with so much food at so little cost.

The sample has an average Full Scale IQ of 112, which is almost one standard deviation above the mean, well above average in the US. Higher social classes refused to participate more often. Lower social classes were excluded almost twice more often. All this may have had some influence on the results.

In defence of their supposition that socio-economic status can boost IQ (and presumably brain size) the authors quote a very interesting study by Duyme (1999) which shows greater gains for borderline abused children when they are adopted age 4-6 into high SES families than low SES families, suggesting a compensatory effect of the better home environment ehrn measured in adolescence. It is possible the gains do not continue when leaving home, but it seems prima facie evidence of a successful intervention, and is an important finding. Incidentally, they describe the study in a misleading way. They say: In that study the IQs of more than 5000 children were assessed prior to adoption and again in adolescence. This is a sleight of hand, intended to make readers think that 5000 children were involved in the study of SES effects. That is not so, and the authors should issue a correction. This is what Duyme et al say: From 5,003 files of adopted children, 65 deprived children, defined as abused and/or neglected during infancy, were strictly selected with particular reference to two criteria: (i) They were adopted between 4 and 6 years of age, and (ii) they had an IQ <86 (mean = 77, SD = 6.3) before adoption. So, for 5000 read 65. Duyme et al. selected an extreme sub-sample for their analysis. There are only 22 low ability children, 24 middle ability and 19 higher ability.

A far better study on 286 adoptees, which also used MZ and DZ twins as controls, does not find any effect of adoption on intelligence.


The Duyme finding is hard to understand, other than it being based on initial testing when children were in a particularly bad state (not able to give of their actual best) and then on non-random allocation of small samples to adoptive families. It is not quite the compelling finding I had imagined.

Methodologically sound studies do not find an adoption effect on intelligence.

The authors give their results as follows:

Results Poverty is tied to structural differences in several areas of the brain associated with school readiness skills, with the largest influence observed among children from the poorest households. Regional gray matter volumes of children below 1.5 times the federal poverty level were 3 to 4 percentage points below the developmental norm (P < .05). A larger gap of 8 to 10 percentage points was observed for children below the federal poverty level (P < .05). These developmental differences had consequences for children’s academic achievement. On average, children from low-income households scored 4 to 7 points lower on standardized tests (P < .05). As much as 20% of the gap in test scores could be explained by maturational lags in the frontal and temporal lobes.

Comment: The phrase “poverty is tied to structural differences in several areas of the brain” is unwarranted. It is an inference, and subject to confounding by genetic effects.

Conclusions and Relevance The influence of poverty on children’s learning and achievement is mediated by structural brain development. To avoid long-term costs of impaired academic functioning, households below 150% of the federal poverty level should be targeted for additional resources aimed at remediating early childhood environments.

Comment: We cannot be sure from this study that poverty has a direct influence on learning and achievement, nor that it has a direct influence on brain development. It would be prudent to do further testing of the parents. Giving additional resources to US-classified-poor households may help them, but most of China is poorer and brighter, so a big effect is unlikely.

The authors doubt that genetics played a part in the results they obtained:

First, it is possible that reported differences across socioeconomic groups could have been caused by a third factor tied both to family poverty and smaller regional gray matter volumes, such as a genetic predisposition that might have led an individual to become poor. Our analyses mitigated concerns related to this competing explanation. We focused on regions of the brain known to undergo a protracted period of postnatal development (most likely to be influenced by environmental conditions), specifically, the brain’s gray matter tissue, which previous work suggests is likely affected by early environment and less heritable than other brain tissues. Second, the National Institutes of Health study was designed specifically to study typical development; therefore, children were screened based on factors thought to adversely affect brain development. However, such adversities are disproportionately represented among impoverished children, meaning that this study examined a sample of children who were likely doing better than most children living in poverty. Our analyses likely understated the full effects of poverty on children’s development. The strict exclusionary criteria were beneficial in that they allowed us to rule out a number of potentially confounding factors, particularly a child’s early or initial health status, as influencing reported associations with family income or socioeconomic status and mitigated the potential for adverse selection of sample families based on unobserved factors (eg, families who may volunteer out of concern for a child’s health or developmental progress). However, a true representative sample of children in poverty is likely to reveal even greater deficiencies than those reported in this relatively healthy sample of impoverished children, who, despite meeting the study’s inclusionary criteria, still evinced striking neurocognitive delays.

Comment: Of course, they do not know that the “striking neurocognitive delays” are delays. They may be striking differences of the sort also shown by their parents. I think that the author’s attempts to mitigate the genetic explanation by looking at gray matter tissue is oblique and uncertain. The better technique would have been to scan the brains of the parents and test their abilities.

As regards their second argument, about the exclusion of poorer children I was initially confused by it. The authors say: “children were screened based on factors thought to adversely affect brain development”. I think they mean “screened out” that is to say, excluded, and their methods section confirms a long list of exclusionary criteria: risky pregnancy, birth, and neonatal histories; physical/medical histories (eg, lead treatment or maternal medications during breastfeeding); family psychiatric history; and behavioral/psychiatric measures, including low IQ.

I agree that remaining differences are likely to be under-estimates of SES differences, but at the greater cost of slanting the study towards an unrepresentative brighter sample.

In sum, to do further work on these findings does not get round the confounding which lies at the heart of the method. At the very least testing the abilities of the parents would have illuminated inherited ability as exhibited by their children.

Tuesday, 21 July 2015

Sir Tim Hunt, an audiotape and a Curriculum Vitae


Just as I complete a post bemoaning the fact that some people have rushed to judge Sir Tim Hunt’s lunchtime talk at a conference for women scientists without there being any recording of the event,


such a recording has now surfaced.


I have not found a way of listening to it, (send me a link if you can find it) but it is claimed that the recording confirms that Sir Tim was joking, and that his comments were followed by general laughter. The recording also confirms that he made laudatory remarks about women scientists in the main body of his speech.

Any scientist would regard this development as taking us nearer to the truth, that elusive and beguiling fundamental state we seek above all others: a state of affairs that remains detectable even when we stop believing in it. It should hasten the day when those who were too quick to judge reflect on their error, and make the necessary correction to their mistaken world view. One would expect that UCL and the Royal Society would at least seek out the newly discovered recording, listen to it, and make their own judgment. As far as I know, that has not happened. The correct procedure would be for those two institutions to make a fresh statement, saying that new facts have come to light which they will be investigating.

Meanwhile, an academic misdemeanour at another academic institution has gone uncorrected. Connie St Louis, who has an academic post at City University, was the person who tweeted her views about Sir Tim’s lunchtime remarks. Since they were at variance with other accounts,  journalists have looked at her CV and found many claims about achievements which were either exaggerated or very out of date. She described herself as “an award-winning freelance broadcaster, journalist, writer and scientist” but this has proved to be too generous a self-evaluation. She said she had an ‘upper second’ BSc (Hons) in applied biology” but did not state which institution had awarded her this degree, which is an essential part of understanding the quality of her education. An Alma Mater is usually a source of pride and affection, not something to be brushed out of the record.

Currently, her webpage at City University  “ is in the process of being updated”. It has remained in that state for some time, despite the fact that updating a CV should take no more than an hour or two at most. For the benefit of City University, here is a guideline as to what is required:

Give the details of your degrees stating the University, the degree course, the grading of the degree and the date of graduation.

Give the details of your academic posts.

Give the details of all your peer reviewed journal publications. In a separate section mention selected other relevant publications.

Give a list of grants obtained, the total amount, and the grant giving body.

If pushed to find something else to say, mention contributions to teaching and professional societies. Only mention media appearances if you think the quality of the work is greater than the loathing it will generate in your colleagues.

Could someone please let me know when the new CV appears on the City University website?

Friday, 17 July 2015

Racial brain differences


You may recall a debate I had with Prof Noble about the interpretation of brain volume differences between rich and poor children in the US. I argued that a genetic interpretation was at least as plausible as an environmental one. It appeared from Prof Noble’s paper that the Pediatric Imaging, Neurocognition, and Genetics (PING) study had identified cortical differences between the African American children and the rest, but the matter was not pursued further.

Greg Cochran now comments on another paper arising from this research, which as he points out calls into question the extreme environmentalist position regarding racial differences in ability and behaviour:


Chun Chieh Fan, Hauke Bartsch, Andrew J. Schork, Chi-Hua Chen, Yunpeng Wang, Min-Tzu Lo, Timothy T. Brown, Joshua M. Kuperman, Donald J. Hagler Jr.,Nicholas J. Schork, Terry L. Jernigan, Anders M. Dale

Modelling the 3D Geometry of the Cortical Surface with Genetic Ancestry.  Current Biology 9 July 2015 In Press.


The authors are a San Diego team, and have worked on 562 individuals scanned when older than 12 years, by which time brain development is fairly stable. Overall, can explain about 50% of surface variability, though up to 66% for the African group.

Cortical surface by ancestry




For example, as the proportion of the African component increases, the temporal surfaces move posteriorly and inward. The proportion of the European component is associated with protrusion of the occipital and frontal surfaces. Increases in the proportion of the East Asian component are accompanied by variations in temporal-parietal regions. The Native American component is associated with flattening of the frontal and occipital surfaces.

The authors say: Our data indicate that the unique folding patterns of gyri and sulci are closely aligned with genetic ancestry. The geometry robustly predicts each individual’s genetic background even though the population has been shaped by waves of migration and admixtures. A previous study, using only facial features, achieved 64% explained variance in YRI ancestry among African Americans. Our 3D representation of cortical surface geometry performs similarly in predicting YRI ancestry and also performs well for the other three continental ancestries. As data in Table 1 show, the explanatory power is not due to the differences in total brain volumes, nor to the differences in areal expansion of the cortical surface. Instead, regional folding patterns characterize each ancestral lineage.

On the other hand, the global shapes of the reconstructed cortical surface geometry match W.W. Howells’ description of craniometry of 2,524 ancient human crania from 28 populations [20]. Crania of African ancestry tended to have a narrower cranial base, and those of Northern European ancestry had elongated occipital and frontal regions. Crania of East Asian ancestry had a high cranial vault, and crania of Native American ancestry were flatter. Regarding the morphing differences of YRI, EA, and NA, all had high magnitude and variations in the posterior-temporal regions (Figure 3).These findings are consistent with the notion that temporal bones contain more variations across ancestral groups [6].

I would have expected the authors to show the total brain volumes for each group, but I cannot find those in the paper, nor in the supplemental materials. A pity, because it would help resolve some debates about brain sizes.

It may be being held over for the next paper, but of course I would like to see to what extent the 3D model predicts the intelligence measures for the children.

The authors point out that brain studies will have to bear in mind that individuals of different racial groups can affect the overall results. Of larger importance is that these brain differences in folding patterns could have links to intelligence, personality and other behaviours.

So, you can spot the difference between races in adolescence by looking at the cortical surface of the brain . When discussing this, just remember to call it “population stratification”, or you will be troubled by fools getting in the way of your research.

Thursday, 16 July 2015

Are your vectors correlated?

Jensen’s method of correlated vectors is explained on page 143 of “The g factor” and also in a full Appendix B on page 589.

In an imprecise way, I always though of this as a way of estimating whether a correlation between an intelligence score and some outcome variable could be considered to show a relationship between that variable and g.

Jensen said: “If the degree to which each of the various tests is loaded on g significantly predicts the relative magnitudes of the various tests’ correlations with the external variable X, it is concluded that variable X is related to g (independently of whether or not it is related to other factors or test specificity).”

I remember reading this twice, and then nodding to myself that it seemed reasonable and wondering how one tested the match for significance. In fact,Jensen goes into this in his explanation in Appendix B, and gives his own cautionary notes.

Now along comes our Grand Visualizer, Emil Kirekgaard, to give a working example, and to warn that the Jensen method has some limitations, and can be perturbed in a repeated measures design.


Lots of other visualizations on Emil’s site, including one for the Dunning-Kruger effect.

Wednesday, 15 July 2015

Intelligence, crime and getting caught

Quips encapsulate an observation, and the well-established association between low ability and crime provokes the dismissive observation that duller minds are not more criminal, just less able to avoid capture. Perhaps so.

Capture/recapture methodologies allow us to estimate the size of populations on the basis of two or three random samples having been tagged. The overlap between samples (showing up in the second sample as having already been tagged on the first occasion) makes close estimation of population totals possible.

Measures of intelligence taken in school aged children provide a great insight into their achievements and difficulties in adult life. These prior measures often invalidate other contemporary measures and explanations, showing that habitual traits predominate over situational variables.

Having set the scene, what if we “tag” children who were in middle or high school in 1994-5, administer various tests including IQ tests (a short version of the Peabody Picture Vocabulary), and then interview them about their behaviour, including violence, as young adults in 2001-2.

Kevin M. Beaver, Matt DeLisi, John Paul Wright, Brian B. Boutwell, J.C. Barnes, and Michael G. Vaughn. No evidence of racial discrimination in criminal justice processing: Results from the National Longitudinal Study of Adolescent Health. http://dx.doi.org/10.1016/j.paid.2013.01.020 Personality and Individual Differences 55 (2013) 29–34

Overall, the final analytic sample size ranged between N = 1308 and 3506 and varied as a function of missing data and the unique restrictions placed on the data for some of the statistical models. Respondents were asked to indicate whether they had ever been arrested (0 = no, 1 = yes) and whether they had ever been incarcerated (0 = no, 1 = yes). In addition, respondents who indicated that they had been arrested were asked to report the length of their sentence in total months. To assess frequency of antisocial behavior, a self-reported life-time violent behavior scale was created. For each wave, items were identified that measured involvement in acts of serious physical violence and then summed to develop a lifetime violence scale that consisted of twenty-two items across all four waves of data (a = .81). Respondents completed an abbreviated version of the Peabody Picture Vocabulary Test-Revised (PPVT-R), known as the Picture Vocabulary Test (PVT) during waves 1 and 3 of data collection. The PVT measures verbal abilities and has been used extensively as a measure of IQ among researchers using the Add Health data.

Table 1 demonstrates that, as expected, African-American males are more likely to be arrested, incarcerated, and receive longer criminal sentences than White males. Importantly, however, the results of the t-tests in Table 1 also reveal significant racial differences with African Americans self-reporting more violent behavior over their life course and Whites scoring significantly higher on the composite IQ measure.


It should be pointed out that the lifetime violence figures come from self report at interview, not capture by Police. African American males were 43% more likely to be arrested than Whites, so at first sight this is confirmation of race-based bias against young black men.

After controlling for life-time violence and verbal IQ, however, the effect of race on the probability of being arrested dropped from statistical significance, though it is still different. Fig. 1 further illustrates the finding in that the predicted probability of being arrested in the baseline model for Whites was 0.41 and for African Americans was 0.49. After controlling for self-reported lifetime violence and verbal IQ, however, the difference was not statistically significant with the White predicted probability being 0.41 and the African American predicted probability being 0.44.



The same reductions when using the control model are found for incarceration. Is it appropriate to apply these controls? I think so. Self-reported lifetime violence is unlikely to be exaggerated, since it is given in confidence to a health survey, not a law enforcement agency. It is a relevant prior behaviour which might lead to being violent, and thus arrested in future. Verbal IQ is a good measure of general ability, which has been showing to be related to offending behaviour, in that duller people may not understand the need for regulations or why they apply to them.

The authors say: Without including control variables for potential alternative explanations, the results were consistent with previous research indicating that African American males are more likely to be arrested and incarcerated compared to their White counterparts. After introducing control variables for self-reported lifetime violence and verbal IQ (to rule out alternative explanations), the association between race and being processed through the criminal justice system was reduced to non-significance. Taken together, analysis of data from the Add Health strongly suggest that research examining racial disparities in the criminal justice system must include covariates for self-reported criminal involvement and perhaps even for verbal IQ or they are likely misspecified. The most likely result of this misspecification is an upwardly biased race effect that purportedly indicates that African American males are treated more harshly than White males due to a biased criminal justice system.

The authors ask for a replication, so as to test the soundness of their results.

Here is another approach, using a different capture method. Suppose in a city or some other defined location you collect victim and bystander descriptions of assailants, and limit yourself to those in which there is agreement about the race of the perpetrator. (You can also look at the race of the victim, but that is not the main point here). Then compare the racial rates of assault with the proportions of the relevant races in that population. That gives you a quick indication whether rates of assault are in proportion to population numbers. Then look at the proportions of the witnessed assailants who get arrested, tried and if convicted, for how long. These will be relatively raw data, uncorrected for previous violence and mental ability, but will provided a rule of thumb check of how the numbers come out as accused persons go through the legal system.

Monday, 13 July 2015

Nobel Laureate Sir Tim Hunt, FRS; gets Watson’d


It may be far too late to make any useful comment on the case of Nobel Laureate and Fellow of the Royal Society Sir Tim Hunt, but I was waiting for “better and further particulars” as the lawyers say, though those have been slow in coming. Astoundingly, much has been decided very quickly, with scant basis for judgment. Judgment requires time in which the facts can be gathered and evaluated. “News” is fast moving, but academia ought to have time for thought, and even time for wisdom.

The facts so far are that Sir Tim Hunt gave a lunchtime talk at a conference. There appears to be no recording of this talk. One conference guest reported that Sir Tim had made offensively sexist remarks which stunned the audience of women scientists. At least one other conference guest seemed to agree with this interpretation. Two other attendees gave very different interpretations: that his introduction had been a light hearted and self-mocking joke about his chauvinist tendencies, but his subsequent talk had been about lauding women scientists’ contribution to science, all of which had been accepted warmly by the female audience. In a subsequent radio interview Sir Tim appeared to confirm his remarks and said that male scientists found it difficult to give necessary criticism to women lab scientists. Sir Tim resigned from his honorary post at UCL and from his honorary duties at the Royal Society. Both resignations were accepted, and UCL made a statement which accepted and approved of his resignation. The Royal Society President also approved of his resignation.

What processes were followed by UCL and the Royal Society? I would have expected that in each institution some appointed person or committee would have asked for details about the conference remarks, and contacted Sir Tim to get his side of the story. Even if these noble institutions had received his resignation apparently out of the blue, I assume they would have taken steps to find out why a Nobel Laureate should suddenly want to sever connections with them. So far as I know, none of this has happened. If the remarks were confirmed to have been of the reported form: “keep women and men scientists in separate labs” I assume the named person or committee would have then gone on to check up whether Sir Tim was known to have behaved in a biased, unfair and offensive way to women scientists and to women job applicants. Many scientists have in fact said that this was not the case, and that he has been supportive and encouraging of women in science, his attendance at the women’s conference being part of this positive pattern.

I would have assumed that, after considering all these facts, the committee would have come to a judgment and made a statement, saying what the facts were, and how they intended to respond. As far as I know, none of this has happened. Instead, the procedure has been reminiscent of the Soviet terror: the esteemed scientist is seen on the podium in Red Square with all his medals pinned on his overcoat waving to the cheering crowds; some months later he makes a tearful confession about sundry seditious crimes, including economic sabotage; he is stripped of his medals, responsibilities and privileges; becomes an Un-person, and is mentioned no more. Miscreants are not even accorded a show trial. They are given verdict without process. Even to ask about them, on this fearsome model, becomes a risky business.

The consequence for individual academics of judgment without trial is that if they say something which others take badly they may be damaged where it hurts most: their reputation as a scientist is trashed and their influence as a teacher reduced to nothing. The consequence for academia is a fetish for form over function: curiosity, observation, and conjecture will take second place to a circumspect acquiescence to political policies. Scientists will become missionaries rather than actuaries, selling a line rather than examining claims. Researchers will have to develop the skills of politicians, and favour missionary zeal over the mundane routine of collecting data and examining possible causal links.

Professional societies are often unprofessional when they deal with what they regard as disciplinary matters. They do not understand due process, and fail to resolve their conflict of interest: they have to uphold the good name of their societies and also deal fairly with the interests of their members. Very few provide their members with independent legal advice and representation. Members who do not get a fair hearing have a probable case in law, but rarely fight to get redress. Academics and health professionals are easily crushed. They work in an atmosphere of trust, and once that trust is lost, even on the flimsiest of evidence, they are broken. In recent times I worked to give support to professionals who had been ruined by newspaper accounts, and it is very hard to get them to assemble their defences. They are usually on the tender-minded end of the personality spectrum, and more likely to accept the ministrations of a psychologist than the advice of a hard-nosed lawyer (though I always recommend additional help from the latter). Retirement generally looks a good option, rather than fighting the case. Although I understand why they retreat, every time they withdraw from the field they leave their colleagues more open to new hostile attacks.

What saddens me is the speed with which a remark is judged sufficient to end a career. As far as I know, no-one has brought a validated case against Sir Tim Hunt showing him to have damaged the career of a woman scientist, or to have turned down well qualified candidates because they were women. To my great surprise Nobel Laureate Sir Paul Nurse, the President of the Royal Society, has been quoted in The Telegraph (I assume they have a recording) thus: “Tim is a lovely man and I have known him a long time,” he said. “But there is no question about it, he did say some stupid things which cannot be supported and they had to be condemned. He said he was a chauvinist and that is not acceptable.”

Notice that what was not acceptable (if the report is accurate) was that Sir Tim Hunt said he was a chauvinist, not that he has been proved to be a chauvinist in the way he actually treats women scientists. You do not need to know much about psychology to know that people who say they have prejudices may often act without prejudice in their professional lives, and that people who say they have no prejudices whatsoever sometimes act very prejudicially. The match between self-assigned attitudes and actual behaviour is often weak. An honest person’s self-examination may lead to more confessions than the few admitted by a self-satisfied prig, which is yet another example of the ubiquitous Dunning-Kruger effect.

It really does surprise me that any President of the Royal Society should regard Sir Tim Hunt’s comments as if they were an incantation which requires immediate ex-communication. It has a very religious flavour to it, which I do not associate with a scientific society. Talk about Totem and Taboo, and magical thinking about human sacrifices! Sir Tim has been judged to have made comments which (despite whether he meant them in the way they were interpreted) the President finds unhelpful to the cause of getting more women into science and into the Royal Society, and so Sir Tim has to be lustrated. Words are a mortal sin, and a blameless life helping women scientists is no defence. He must be banished, for the purity of the congregation. In the bestiary of punishments, if a joking reference to love affairs in the lab destroys a career, what punishment should be meted out to academics who grossly exaggerate their CVs?

Sir Tim’s observation that some male bosses might avoid their usual management criticisms of juniors if those juniors are women is a possible problem worth discussing. Here is another topic, one which Sir Tim did not raise, but one I have an interest in. What do women scientists think of women scientist bosses? How do they rate them, compared to male scientist bosses? Are women scientists any better than male scientists when managing their lab workers, both male and female? Indeed, as a general rule, are women Professors better leaders and managers than male Professors? According to some researchers women cry more often and more copiously. Is that of any significance in how they perform their jobs and how they are managed? Do women excel in science, or are they too normal? Should one especially encourage women into science and, come to that, should one especially encourage anyone into science? (Doing well in research requires great talent and lots of hard work, for relatively low rates of pay. Funding is never guaranteed, and you must dance to the tune of grant giving bodies. Researchers should be a community of scholars, but can sometimes be a pack of wolves. School teaching might be more fun than lab work, and with far better holidays). Are there any occupations in which same sex organisations are preferable? Front line troops, for example? Should women be allowed to cluster in biology while men do Physics? On Ricardo’s theory of comparative advantage, it may be the best policy, even if only on motivational grounds.

I do not expect these questions to be answered, but I would love them to be asked, at lunch time conference talks and in open debate. Disinterested curiosity is the lifeblood of true civilisation, etc.

Back to the wolf pack, where the story usually ends. The Un-person shuffles off to a country retreat, and the questions he raised are avoided, as newcomers learn to avoid the topics which led to his downfall. This time an unusual thing has happened: the ritual shaming process has been questioned by notable scientists who have spoken up in Sir Tim’s defence. Many of them question the speed of judgment, the lack of procedure, and ask that his remarks be seen in the light of his well-known kindness and helpfulness to all scientists, both men and women. They want him judged in the round, for all his achievements and actions, and not as just a sound bite. Few of his friends think that he went about introducing his lunchtime speech in a way which meets modern sensibilities, but they want this good scientist brought back to his honorary duties, to continue the very teaching which took him to the women’s conference in the first place.

What do his pursuers want? I do not know, but I surmise that they want to punish anyone who uses words of which they do not approve and anyone who expresses ideas, however ironically, they find “unacceptable”. They have raised the reaction of being offended into a weapon of culture war, secure in the knowledge that it has always worked. Their trump card is to dare you to question them, for fear of being given the same treatment.

They have broken a butterfly on a wheel. Sir Tim Hunt is bright enough for a Nobel, but not crafty enough to understand what modern academia has become.

Wednesday, 8 July 2015

Not tonight, cousin

Although psychology is still a cottage industry, Edinburgh seems to be leading the scholarly equivalent of a global corporate behemoth, churning out blockbuster productions with author lists longer than the average paper, and sample sizes greater than many principalities.

With the enigmatic title: Directional dominance on stature and cognition in diverse human populations the paper just sits there, like a new jet engine for a plane which has yet to be built. Let me walk you round this gleaming contraption in awe, and tell you what I can discern of its surface characteristics.


Directional dominance means that all the genes show dominance in the same direction. If you marry close relatives, such as cousins, you increase the Bad Bingo chance that a bad mutation in the father will line up exactly with a bad mutation in the mother to give the poor child a double dose of trouble. (Of course, the stultifying conventions of academic publication do not allow anyone to say anything as plain as that).



The authors, whose name is Legion, say:  Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is
required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory
lung volume in one second, general cognitive ability and educational attainment. In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months’ less education.

In plain language, runs of homozygosity caused by inbreeding have significant deleterious effects on body and mind.

Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance.

The effect is solid across races and different degrees of inbreeding.

Contrary to earlier reports in substantially smaller samples, no evidence was
seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio- metabolic traits.

Cardio-vascular traits are not affected by inbreeding.

Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important
risk factors for late-onset complex diseases may not have been.

For many generations taller and brighter persons have been chosen as better mates.

(Perhaps, late in life, I could get a job as a translator of scientific papers).

Here are some more findings: Isolated populations have a higher burden of homozygosity, Africans least homozygosity. The Amish are among the most inbred, with other small communities in the US and elsewhere close to those levels.




Here are their findings on intelligence:  Our genomic confirmation of directional dominance for g and discovery of genome-wide homozygosity effects on educational attainment in a wide range of human populations adds to our knowledge of the genetic underpinnings of cognitive differences, which are currently thought to be largely due to additive genetic effects. Our findings go beyond earlier pedigree-based analyses of recent consanguinity to demonstrate that the observed effect of genome-wide homozygosity is not a result of confounding and influences demographically diverse populations across the globe. The estimated effect size is consistent with pedigree data (a 0.01 increase in F decreases g by 0.046 s.d. in our analysis and 0.029–0.048 s.d. in pedigree-based studies)20. It is germane to note that one extreme of cognitive function, early onset cognitive impairment, is strongly influenced by deleterious recessive loci, so we can speculate that an accumulation of recessive variants of weaker effect may influence normal variation in cognitive function.

Here is their last word:

We have demonstrated the existence of directional dominance on four complex traits (stature, lung function, cognitive ability and educational attainment), while showing any effect on another 12 health-related traits is at least almost an order of magnitude smaller, non-linear or non-existent. This directional dominance implies that size and cognition (like schizophrenia protective alleles) have been positively selected in human history – or at least that some variants increasing these traits contribute to fitness.

Breeding with closely related persons reduces the height, intelligence and lung capacity of your children. Other stuff, like cardio-vascular health, is probably unaffected. 

What fills me with admiration is the way that these very large data sets are being assembled, enabling the signal to stand out from small sample noise. Looking at the genetics of intelligence has become commonplace, and I hope that comparisons across continents will also become habitual, thus putting genetic hypotheses to the test.

There is more here for the sharp-eyed reader, but the general picture is clear: Keep your reproductive distance from cousins and other related persons, if you value intelligence, that is.

Tuesday, 7 July 2015

Chanda Chisala: An African Hereditarian?


It is good when an article giving data on African scholastic attainment and intelligence draws attention and comment. Data is what we thrive on.


There is much in this article to be discussed. I left an immediate comment which, for ease of reference, I copy below.

Thanks for your article. I think that the problem which bedevils much research on intelligence is the difficulty of getting representative samples. It is for that reason that birth cohort studies and other full population studies are so important. Even small rates of selective attrition can affect the overall means, and where samples are small and unrepresentative less reliance can be placed on results. For example, looking at the UK cognitive data you show above, the sample size for White British is 145,000 and the immigrant groups are at best 4000 or far less. Although one can have reasonable confidence about the error terms for the White British assessment, one would have to accept larger error terms for the smaller groups. Those latter groups could only be seen as representative of their genetic and cultural societies of origin if we can obtain proper measures on representative samples in their countries of origin. PISA and TIMMS have some data (though few African countries have ever participated in such studies) and the general pattern is for immigrant groups to reflect the same averages as in their countries of origin. As a general rule, single country immigrant histories are only indicators and not always representative, and currently the UK is a magnet for immigrant groups, particularly elites.
There is certainly great merit in looking at elite genetic groups in Africa, but so far it has been hard to get many reliable representative studies (mostly they tend to be nation based). If genetic based intelligence research can be done in Africa that would go a long way to elucidating gene-intelligence effects.

I went back to the article and I thought it worthwhile to make further comments, purely because it raises interesting questions about African intelligence. I have tried to find the original sources, though that has sometimes been difficult. There are other matters about which I have reservations, like the interpretation of regression to the mean, which I will skip for the time being. Most of what I am going to say has been covered in previous posts, so if you have already read those, please bear with me while I make some basic points.


I will look at the claims Chanda Chisala makes about African performance in the UK because it is a big part of his argument, and also because it is easier for me to comment on the educational system here.

Chisala observes: “The first report that caused some consternation in the IQ blogosphere indicated that black African pupils were apparently catching up with British white pupils on their GCSE tests and that in fact, they had already overtaken them at the lower end: the poor black kids were now performing better than poor white kids (The Guardian, 2010).”

The reference is slightly dated (true of some of the other references) and there are results for 2013 showing further African improvement, so it makes more sense to use the most up-to-date figures. The most recent in 2015 carries a caution that it is not comparable to previous years, so I might deal with it separately later. Instead I will use the recent but comparable account from the Department for Education First Statistical Release for January 2014.


Here is the relevant highlight in the executive summary:

“Pupils of any black background have seen a large improvement. The percentage of black pupils achieving 5 or more GCSEs at grade A* to C or equivalent including English and mathematics has increased by 3.5 percentage points since 2011/12. The gap compared to the national average has narrowed by 1.7 percentage points since 2011/12.”

First of all, what does it mean to achieve 5 or more GCSEs at grade A* to C? The GCSE is the standard UK scholastic examination at age 16. A* is the highest mark and the C is the lowest mark which still counts as a pass. Students of very low ability do not usually take the test, but the coverage is otherwise good. Here are the scores achieved in the 2008/9 to 2012/13 period.


“FSM eligibility” refers to poorer children who get Free School Meals (shown in blue) with the majority of the sample shown in red. On the left are the scholastic results which include general “softer” subjects. 85% of the school population get a pass mark, which by some process has gone up from 73% in 2008/9. That means that currently students with an IQ of 84 and above get the qualification. This is not of itself highly informative about scholastic ability. The C grade is very low and raw scores giving the actual percentage scores would be much better.

On the right are the slightly more demanding scores which include English and Maths (normally the core of any educational system). This shows that 65% of the school population achieve the required level, which has also risen from 54% in 2008/9. The present results mean that reading, writing and arithmetic can be achieved by students of IQ 94 and above. UK results on international tests like PISA have not gone up over this same period. It appears likely that the GCSE test has been made easier. The pass rate does not reveal the raw scores. I do not know why they suppress these essential data points. For example, giving pass rates denies us a look at the real score distributions. Do any of my readers have influence in these official statistical publication matters, and are they able to get the raw results?

Here are the racial comparisons:


This histogram nobly shows the pass rates for the 3 R’s from 2008 to 2012, thus giving the game away. There has been significant grade inflation. Pass rates are open to “gaming” as described in my previous post. We need to see the distributions for the raw scores. However, the racial comparisons are still instructive. Several groups have higher pass rates than the White British. This is certainly worth attending to. (A not-very-good measure can still contribute knowledge, particularly if no other data are available, though that is not the case here).

This is what the Government document says:

Chinese pupils remain the highest attaining ethnic group. The percentage of Chinese pupils achieving 5 or more GCSEs at grade A* to C or equivalent including English and mathematics GCSEs or iGCSEs is 17.5 percentage points above the national average. The percentage of Chinese pupils achieving 5 or more GCSEs at grade A* to C or equivalent including English and mathematics GCSEs or iGCSEs increased by 1.6 percentage points in 2012/13, having decreased by 2.1 percentage points in the previous year. Despite this, the overall attainment gap for this indicator between Chinese pupils and the national average continues to remain at its lowest level, and has narrowed by 3.4 percentage points since 2008/09.

Pupils from a black background remain the lowest performing group, although they have shown the largest improvement. The percentage of black pupils achieving 5 or more GCSEs at grade A* to C or equivalent including English and mathematics GCSEs or iGCSEs is 2.5 percentage points below the national average. This gap has narrowed by 1.7 percentage points since 2011/12 but over the longer term has narrowed by 3.7 percentage points since 2008/09.”

“Gap narrowing” is obviously the focus of interest, but the metric used is misleading, because the size of the apparent gap is highly sensitive to the pass rate. Make the pass rate go up and the “gap” artificially seems to reduce as a consequence of the cut off used. Displaying the two distributions makes this clear. Try using Emil’s visualiser for tail effects to see how a pass rate can lead to different areas under each population distribution, and thus different gaps, whilst the mean differences remain the same.


The original and more detailed explanation of how to close the racial learning gap is given by Griffe du Lion here:


What the Chisala article does not give is sample sizes, which would allow us to work out confidence levels for the individual racial group results. However, the article then goes on to give sample sizes from the GL report on the Cognitive Assessment Test, which is for a very comparable sample. This assessment using cognitive tests is much better, and the sample sizes, means and standard deviations can be used for hypothesis testing.



Although representation for the White British is of course very good, the numbers for immigrants are understandably far smaller. Indian and Pakistani are over 3000, Black African at 2,197 students are more numerous than Black Caribbean at 1,978 which seems to indicate that the last decade of African migration has surpassed that of West Indians over 60 years. “The 2011 UK Census recorded 1,904,684 residents who identified as "Black/African/Caribbean/Black British", accounting for 3 per cent of the total UK population.[1] This was the first UK census where the number of self-reported Black African residents exceeded that of Black Caribbeans”. It would be good to know which Africans are in the Black African category, and particularly what proportion is sub-Saharan.

I do not see how these figures could be considered representative of their countries of origin without further comparison with good local samples. However, the figures are informative. Sub-Sarahan African intelligence test results have been much debated by intelligence researchers, and the estimates range from about IQ 70 from Richard Lynn to IQ 80 from Jelte Wicherts. The key argument is about the representativeness of samples. The tests seem to be OK, much to popular surprise. Humans in all continents appear to solve basic problems in the same way. Africans have the same cognitive operating system as other continental groups. There are power differences, but not operating system incompatibilities. The most favourable comparison for immigrants in these data would be to concentrate on non-verbal intelligence, least affected by language and probably least affected by education. Here are three results drawn from the above Table 4.

White British Non-verbal IQ 101.4 (14.4)

Black Caribbean Non-verbal IQ 94.6 (12.9)

Black African Nov-verbal IQ 94.8 (12.1)

These scores are almost a projective test, in that they invite different interpretations. The Africans and Caribbeans have the same mean score and almost the same standard deviation, although the Africans have an even more restricted range than the Caribbeans. This restriction suggests some selection for the African group, but both Black groups have a more restricted range of ability than the larger White population. This phenomenon was noted for African Americans by Jensen, who said that the sd for African American intelligence was “approximately 12” which is borne out in this case. As you know, this has big implications for expected numbers at the highest levels of intellect, in that there should be even fewer than predicted on the basis of the lower mean. This is another testable finding.

If we take African IQ 75 as a temporary compromise between Lynn and Wicherts, the findings suggest that recent African migrants to the UK at IQ 94.1 are 1.27 standard deviations above the mean of their populations (assuming sd 15 in the local population, but if local population had a sd of 12.1 they would be even more high selected at 1.57 sd above their home mean) which might be plausible if the brighter ones were more likely to migrate. If one knew how they had been selected (say by looking at family backgrounds or prior scholastic attainments) it might be possible to do some calculations to test the hypothesis that brighter African students have migrated to the UK. However, the standard deviation for Africans is narrower than for White British (see later for some social class results).

The other approach would be to look at the racial composition of winners of national tests of higher achievements, say A* in Mathematics or Maths Olympiads or Physics prizes or whatever. By looking at how well various groups are represented calculations can be made about the mean ability of the populations from which they are drawn. That method depends on knowing the base population, and it would be unfair to count all Africans world-wide as the base population. UK census figures for immigrants would be the best alternative, but could be biased by selective migration of elites. (In general, international comparisons of representative samples of the scholastic abilities of immigrant groups do not show such elite effects, and the national average is a good estimate).

Griffe du Lion explains how to calculate the mean IQ of a sub-group without testing all of them with an intelligence test:


Even in the midst of this African intelligence debate, I cannot resist a digression. Have a look at the standard deviations for boys and girls on all three cognitive tests. Boys are more various than girls as per the usual findings on sexual dimorphism.

Now to the estimates of the abilities of particular groups of Africans. Much of this in the Chisala article comes from a 2006 government report, and the one table selected (actually Fig 4) gives the numbers of Africans by nation of origin, showing Somali, Nigerian and Ghanaian in descending order. Not shown in the Chisala paper is that the social profile of African immigrants is probably bimodal. They have almost as many parents in the professional ranks as the UK average, but also a very large number of unemployed persons. It is an odd distribution, suggestive of at least two different sources of immigrants as regards social status.


I cannot find a direct reference for the next figure in the article, about free school meals, but it is not crucial to Chisala’s argument. The “poverty” argument complicates cause and effect, and is particularly problematic for recent immigrants.

The next table Chisala shows is from “Back to basics: Towards a successful and cost-effective integration policy”, a publication from the “Institute for Public Policy Research, the UK's leading progressive thinktank”. (I do not know if it is a good thing to lead in these matters).


It takes some searching to find that Chisala has picked Table 6.1 on page 43. They use the familiar “5 A*-C GCSE grades” which have shown considerable grade inflation over that period. Here is the full description of how the table was put together “Table 6.1 presents new analysis for England showing educational performance at 16 (in GCSE examinations) by extended ethnicity code.” It would be good to have a little more detail, and at least to have sample sizes for each ethnicity for each year. The authors give the results as “mean percentage difference from England mean”. Without a methods statement I am not sure what this means. Since the table is about pass rates, the actual pass rates would suffice.

At face value these are interesting results. However, on a matter of this importance we need more than face value. Reference to a specific publication with methodological details is required.

The next table is about pass rates by language spoken at home, and comes from a Lambeth Borough publication.


The methods description is much better (page 8 with a good map of African languages). They are quite clear they picked the schools with the best scholastic results. “This research is an ethnographic study of outstanding schools in an inner London Local Authority. Two complementary methodological approaches were therefore adopted, each contributing a particular set of data to the study.” These approaches were GCSE results for all students in the Borough (which provides a good comparison) and case study research which is interesting but not standardised, so of marginal importance to the main debate.

So, we have one London Borough (with many African students) from which the best schools have been chosen, and the results are given as pass rates. Incredibly, although the publication is credited to the Borough’s Research and Statistics Unit, there is no mention of sample sizes anywhere that I can find. If they have better, more detailed results, they do not make it evident. We simply do not know how representative these children are of their populations of origin.

My summary of this publication is that there are some very bright African kids in Lambeth (and everywhere else in the world), but it is hard to drawn many conclusions from that statement.

Then a statement which drew a lot of interested comment: The superior Igbo achievement on GCSEs is not new and has been noted in studies that came before the recent media discovery of African performance. A 2007report on “case study” model schools in Lambeth also included a rare disclosure of specified Igbo performance (recorded as Ibo in the table below) and it confirms that Igbos have been performing exceptionally well for a long time (5 + A*-C GCSEs); in fact, it is difficult to find a time when they ever performed below British whites.

This bold claim is followed by a set of bright red and blue histograms showing that “African Ibo Africans” are the best students with a 2007 pass rate of 88%. Strong stuff. The link to this report is a Powerpoint presentation which indeed shows that very same figure. However, there is no hint of its provenance. I am sure it exists somewhere, but the link is not to a primary source, as would be the usual academic convention. An earlier slide in the same Powerpoint presentation shows that the Lambeth GCSE pass rate rose from 29% in 1998 to 62% in 2008. Either teachers doubled in effectiveness, or some brighter children arrived, or the test got much easier, perhaps by the C grade being given more generously.

So, that is the slide which launched a thousand website comments about the Ibos. The Ibos may very well be Africa’s finest, but an article should allow interested readers to get to the original source without wasting time guessing where it might be.

I deduce it may be Demie, Feyisa; McLean, Christabel EDUCATIONAL STUDIES 33(4):415-434 · DECEMBER 2007.

Essentially, they chose high performing schools, saying: Selection of high-performing schools was a prerequisite because the research concerns itself with educational success. So, it is a deliberately non-representative sample.

Here is Table 1 from that paper, showing that the pass rate for the case-study schools is higher than that for the rest of the Borough. More important, the African pass rate for all schools is the same as the national average.


Here is Figure 1 giving the results for their chosen case-study schools.


Remember, this measures whether students can get a C grade. Absolutely no sample sizes are given in this paper, nor in the research note from which it is drawn. It is hard to draw any conclusions from this report as it stands. I cannot find the figure Chisala gives in his article, but by now I am getting weary.

It is hard to track down the selected article slides to an actual publication, from which one can get the methods, selection criteria, sample sizes and descriptive statistics. There is material there, no doubt, but it is far from the clear signposting required in research. Academic papers may be boring, and the peer review process tedious, but they have some merits when you wish to check the facts.

Chisala mentions Prof Simon Burgess’s work. Here is my comment on that paper:


You will see that “progress” measures tend to favour those who start with low scores.

Prof Burgess has been offered the right of reply, as always, and has acknowledged the offer.

This is a curate’s egg of an article. The Cognitive Assessment Test data are interesting, informative, and worthy of further study. The GCSE pass rate results are poor by comparison: crude and less informative. The pass rates by African language, and by implication by African genetic sub-groups are hard to track down, but at the same time, they are tantalising.

If evolution applies to all of us, that includes Africans. It is perfectly possible that some African sub-groups are brighter than others. Any geographic group which takes care to encourage bright persons to marry one another could see positive effects after 20-30 generations of consistently strong selection, if those brighter parents have more surviving offspring, which is likely. The hereditarian position (ie genetic probably accounting for 50% of the variance) would be strengthened by finding reliable evidence of genetically brighter groups in all populations including Africa. The Al-Rashidis used to tell me “We are the Jews of the Arab world”. If such groups exist on any continent they will work hard, have commanding positions in commerce and learning, and will be very picky about marriage choices.

One cannot argue that a whole population is bright on the basis of a few prize winners, however emotionally appealing the impact of the individual case. However, if a particular genetic or cultural group is over-represented among the winners of a tough intellectual competition (out of proportion to their population numbers), then that can be good evidence that they are drawn from a group which has high ability. Potentially, there are undiscovered tribes and even extended families out there with outstanding intellectual abilities, and they are worth searching for. There should be no impediment to Africans championing African ancestry as the cause of African intellectual superiority, and supplying the supportive evidence.

Perhaps I have spent too long on this particular article, but I do not think that the tables and figures presented by Chisala are sufficient proof that particular African groups are among the brightest persons on the earth. They may be, but it would be prudent to require the usual sorts of evidence, evidence which is hard to obtain from the article. As the lawyers say “We need further and better particulars” and only then can the case proceed.