Friday, 31 July 2015

Phil Rushton 1943-2012

 

A book has just been published in memory of Phil Rushton, drawn from contributions to the special issue of Personality and Individual Differences in his honour, which became an obituary after his untimely death of Addison’s disease in 2012.

“The Life History approach to Human Differences” Ed Helmuth Nyborg, Ulster Institute for Social Research, 2015. www.ulsterinstitute.org will provide the eBook version for £5.

The book gathers together one of the final interviews Phil gave, and a subsequent obituary, both by Helmuth Nyborg; substantive contribution from Arthur Jensen on mental ability, Linda Gottfredson on race research, Jan te Nijenhuis on Flynn effect and g, Heiner Rinderman on African ability, Paul Irwing on general factor of personality, Donald Templar on personality theory, Yoon-Mi Hur on altruism, AJ Figueredo, Tomas Cabeza de Baca and Michael Woodley on Life History, Richard Lynn on r-K history, Helmuth Nyborg on migration, Gerhard Meisenberg and Michael Woodley on global behaviour variation and the same two authors on dysgenic fertility.

All this for two cups of coffee (at London prices).

For those of you who don’t drink coffee, here is a brief note, never published, which I put together in 2012 describing Phil Rushton’s work on intelligence and personality. At 1862 words it follows my usual stricture that explanations should be given in fewer than 2000 words. It is written in the present tense.

 

Phil Rushton is tough minded, and has needed to be. Scholarly enquiry often leads to surprising answers, and expounding unpopular views is no project for the faint hearted. His key achievement has been to gather together what would otherwise have been a rag tag of disparate findings and bind them into a coherent pattern of r-K evolutionary strategies. His approach is one more example of an Eysenkian gesamtkunstwerk, to which those of heriditarian persuasion seem drawn, in which an over-arching theory provides a sweet symphony that brings order to chaos. This has given the debate about behavioural differences between genetic groups a new rationale, and for that alone Rushton deserves praise.

In terms of his approach to the data he has shown doggedness in tracking down evidence and arguing his case. His 2005 review with Jensen sets out the hereditarian case as thoroughly and forcefully as has ever been achieved, and must be considered his shared magnum opus. In the best sense of the term he has been Jensen’s bulldog, taking on all comers with dogged persistence. Jensen and Rushton were able to draw together the main points of a complex argument and also retain the sense of challenge and flexibility as they invited their critics to grasp the gauntlet they had thrown down. By proposing to identify the 10 major fields of contention, and by rating their own progress in each of them they challenged others to reply.

At a time when his critics were denouncing his findings because of what they saw as unrealistically low scores in sub-Saharan Africans, Rushton was in fact arguing, on the basis of item analyses, that it was likely that all human beings shared a universal problem solving process. His very detailed studies of disparate racial and cultural groups showed that there were very few anomalies which required special explanation. The parsimonious explanation was that people do not differ in type when confronting intellectual puzzles, though they often differ in power. Interestingly, many of the criticisms of the low scores obtained in Africa revolved round the suggestion that Western tests did an injustice to African thought processes. Critics implied that there was an African way of thinking and of solving problems, and that special tests were required to reveal this potential. In modern parlance, Africans were conceived of as using a different computer operating system, which could not be fairly evaluated if one tested it with the wrong file format. However, there was never any detailed exposition as to what this African way of thinking consisted of, nor whether such continent-specific mental processes would be desirable. Real world observation shows that Africans use modern technology, understand Western culture and excel at Western games, despite these having Western rules and practices. In practical terms there seems to be no parallel universe of African thought. Reassuringly, Rushton’s detailed investigations in South Africa and in other parts of the world found no differences in kind, though differences in degree remain, and are important. So, one was left with a pleasing paradox. Rushton, excoriated by critics, has shown that humans are very alike and have a universal thought process as regards essential problem solving, though they differ in power, while the critical attack on him had implied that Africans were different in some profound cognitive way, which turns out to be false. Interestingly, defences mounted to argue against the observation of racial differences in intellect are often worse than the observations themselves. Differences in intellectual power can be due to mutable effects, whereas the suggestion that one thinks profoundly differently because of one’s culture or genetics, such that one is a different type, seems less mutable.

Among psychology researchers there has been general agreement that personality is best described by “The Big Five” dimensions (Neuroticism, Extraverstion, Openness to Experience, Agreeableness and Conscientiousness). Rushton considered whether it would be possible to reduce these 5 to 1 general factor. High scores on this General Factor of Personality indicate a “good” personality; low scores a “difficult” personality (someone who is hard to get along with). Individuals high on the General Factor of Personality are altruistic, agreeable, relaxed, conscientious, sociable, and open-minded, with high levels of well-being and self-esteem. Those with poorer personalities are at the other end of the descriptive spectrum. They would tend to be selfish, disagreeable, anxious, not dependable, unsociable, closed-minded or rigid thinkers, with high levels of distress and low self-esteem.

Rushton’s proposed general factor of personality (Rushton and Irving, 2011) has many attractive features, not least that it accords with everyday experience. Some people are hard to get on with, and there is often general agreement about this, particularly among employers, who are apt to describe them as “high maintenance”. Personality testing almost always begins with a lie: “There are no right or wrong answers”. It takes a tough minded person to reply “Why not?” There should be right and wrong answers because every day experience shows that some people are very difficult to get along with. Troublesome personalities exist, and extract a heavy cost on other people, most notably when their behaviour is criminal. In the tender minded perspective “it takes all sorts to make a world”. From that tender perspective all personalities are seen as equally valuable, against some unspecified criterion in which all personality types might conceivably be useful in some circumstances. From the tough minded perspective some personalities contribute and others detract. Maintenance costs are borne by others, and this becomes obvious in small companies but is diluted in society at large. The general factor of personality is far more functional, and better grounded in social observation. Measured against the usual social criterion of productive cooperation, some personalities may be considered failures, a others more successful. So, to encapsulate his insight into a revision of the usual platitude about all sorts being required, the riposte is: “it takes the best sorts to make a better world”.

One of the least well known of Rushton’s achievements is to have made a scholarly refutation of Stephen Jay Gould’s attack on the correlation of head size and intelligence. Joining others who had defended Morton and other early researchers, Rushton went further by sending Gould modern data from MRI studies substantiating the cortical size/intellect link, which Gould did not chose to acknowledge in later editions of his work. To those who have bothered to follow the exchange, it was clear that bulldog Rushton had won the tussle. Nonetheless, it remains a social fact that more people have read and remembered the beguiling prose of Gould’s “The mis-measure of man” than have noted Rushton’s scholarly refutation, a clear case of the mis-measure of reputation. This is yet another instance when access to the wider public has been denied to a radical thinker, and views which better fit the zeitgeist get credulous publicity.

Rushton has been able to respond to strong criticism by detailed refutations, and has been at his best when seeking to answer his critics and finding new supportive data. For example, rather than accept the low African IQs he deliberately set out to measure them again in more favourable circumstances. His work in Africa with local psychologists is illuminating because he worked hard to ensure that testing was done properly, and that impediments to good performance were removed. Also, his selection of university students for extra analysis has the merit of plotting out the bell curve, and deriving the underlying population means by inference from intellectually elite groups. This shows his capacity to think through the implications of individual findings, and grasp the big picture. It imposes an inherent reality-check on scores obtained from convenience samples, and allows not only comparisons of population means but also comparisons of elites. At heart, Rushton is willing to test out the implications of findings, and not accept them at face value.

Another example of a willingness to search for links is Rushton’s work on cousin marriage, in which the depression of subtest scores in cousin marriage is linked to racial differences on subtests. Using the method of correlated vectors, he was able to show that two culturally very different groups, African Americans and Japanese children of cousin marriages had something in common: depressed intelligence with some similarity of sub-test patterns, strengthening the case for a genetic component in both. Both the scope and the methods are of interest here.

Rushton has not recoiled from writing popular summaries of his work, doing all he can to disseminate his findings and his theorising. These have taken his views to a wider audience, all the more necessary when it is hard to get past reviewers with strong pre-conceptions as to what the results should look like.

Popularisation involves compromises, and his summaries of r-k life history tables mostly lack specific data. The mere repeating of general phrases such as “higher” or “greater” in the comparison columns is understandable, but potentially weakens the presentation of his case. It gives a general picture to the interested reader, but is too general to satisfy a sceptical critic.

On head circumference and cortical volume Rushton has been a redoubtable debater, and again his capacity to gather and integrate disparate data sources constitutes an incremental meta-analysis, showing the general finding and making a strong case.

His work on Raven Matrices, creating item-by-item heritability and “environmentality” estimates is subtle and initially slightly hard to grasp, but it provides a powerful analytic tool. By looking at each item in terms of heritability estimates derived from twin studies Rushton was able to use these as trace elements which he then followed through disparate samples, showing that a very broad range of cultural background did not generate any conspicuous differences in problem solving strategies. On the contrary, the individual items argue for a universal thought process, at least as far as higher mental functions are concerned.

What is most notable about Rushton is his intellectual resilience. He can grasp the big picture, and can assemble evidence in its favour. He has the capacity to understand the implications of individual findings, and to track down confirmatory or dis-confirmatory consequences. He can also link together entirely disparate publication networks, such as looking at cousin marriage in Japan to illuminate group differences in America. At every stage of discovery he believes he has done enough to convince his critics, but finds that the goal posts have been moved yet again. He has had to pick his way through a maze of imprecise hypotheses, as his critics reply to his specific proposals with a general portmanteau complaint that “these effects could be due to any number of things”. As he himself has observed, the hard-line environmentalist position is not progressive. It does not deign to specify environmental effects in any rigorous way, but tends to multiply ad hoc objections and demand standards never yet achieved in social science. It would be enough to discourage the strongest of constitutions, but despite reverses Rushton pushes on, tracking down weak arguments, studying the implications of research results so as to take them to further levels of examination, gathering new evidence, and as a consequence leaving well-constructed cairns of evidence along the trail-ways of exploration for other researchers to follow.

Wednesday, 29 July 2015

GCSE genes

As you will know, I am used to reporting on the long march of genetic science as it surges through the marshlands of blank-slate environmentalism. I expect we will be revising our understanding of human behaviour in the greatest change since the publication of Origins of Species. The telescope supplants naked-eye astronomy.

The story so far is that 58% of academic achievement can be explained by genetics. Now the same Plomin South London gang have taken a deeper look at individual school subjects, partialling out the common g factor of intelligence.

Kaili Rimfeld, Yulia Kovas, Philip S. Dale & Robert Plomin. Pleiotropy across academic subjects at the end of compulsory education. Scientific Reports 5, Article number: 11713 doi:10.1038/srep11713

http://www.nature.com/srep/2015/150723/srep11713/full/srep11713.html#closer

Pleiotropy is not the best word with which to start the title of a paper. Pleiotropy is the production by a single gene of two or more apparently unrelated effects. You knew that, but I just thought I’d tell you. Of course, I do not do headlines, but a typical British red top tabloid would scream: “GCSE genes found: for every one you get two exam passes free.”

Here is their abstract: Research has shown that genes play an important role in educational achievement. A key question is the extent to which the same genes affect different academic subjects before and after controlling for general intelligence. The present study investigated genetic and environmental influences on, and links between, the various subjects of the age-16 UK-wide standardized GCSE (General Certificate of Secondary Education) examination results for 12,632 twins. Using the twin method that compares identical and non-identical twins, we found that all GCSE subjects were substantially heritable, and that various academic subjects correlated substantially both phenotypically and genetically, even after controlling for intelligence. Further evidence for pleiotropy in academic achievement was found using a method based directly on DNA from unrelated individuals. We conclude that performance differences for all subjects are highly heritable at the end of compulsory education and that many of the same genes affect different subjects independent of intelligence.

In their introduction the authors say: Multivariate genetic analysis estimates the genetic contribution to the phenotypic correlation between traits and derives the genetic correlation, which corresponds to the correlation between genes that affect the two traits, independent of the heritabilities of the traits; the genetic correlation is an index of pleiotropy (the multiple effects of genes).

That sentence made me increase my coffee consumption. This is what I think it means: Correlations between traits are partly genetic, and multivariate genetic analysis estimates how much of those correlations are due to the same genes acting in common.

The authors used their very large twin sample to do the basic calculations, and also chose one twin at random to do the very different CGTA analysis of the influence of common SNPs on unrelated persons.

They start their results section by giving the old-fashioned statistics that I like, and then go on to the more complicated analyses of variance. Good stuff.

Here are the heritability estimates for intelligence and school subjects:

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Note that the shared variance (effects due to family and schooling which children share) is minimal for intelligence, and small for specific school subjects.

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When you control for intelligence (partial it out) then it would seem that Maths shows the biggest effect of schooling, the biggest of the small shared variance effects. English shows very small differential effects of schooling or family.

Summing up their results, the authors say: Our results demonstrate that educational achievement across a wide range of academic subjects from traditional core subjects of English, mathematics and science, to humanities, second language learning, business informatics and art at the end of compulsory education in the UK is highly heritable, with over half of the variance in children’s educational achievement explained by inherited differences in their DNA, rather than school, family and other environmental influences. These results are in line with our previous research at earlier school years and with results reported for core GCSE subjects. The slight difference in heritability estimates in core GCSE subjects results from including opposite sex twin pairs in the sample, which were not included in our previous study, resulting in more conservative heritability estimates. We have also demonstrated that this high heritability is not explained by intelligence alone, as the heritability remained high even after removing intelligence from the GCSE grades. This is consistent with our recent study that showed that high heritability of educational achievement is explained by many genetically influenced traits, not just intelligence.

In the most novel contribution of the present study, we showed that academic subjects at the end of compulsory education in the UK are to a large extent influenced by the same genes, even when intelligence is controlled. The genetic correlation between various academic achievement measures was substantial (.51–.88) and this includes traditional academic subjects of English, mathematics and science as well as art and language learning. The genetic overlap between GCSE scores and intelligence at age 16 was also substantial (.44–.69); however, genetic correlations were higher between GCSE scores than between GCSE scores and intelligence. Despite the genetic overlap between GCSE scores and intelligence, an intriguing finding is that pleiotropy among academic subjects is to a large extent independent of intelligence, as the genetic correlations were still substantial even after statistically removing intelligence from the GCSE scores (.49–.81).

It is possible that the strong genetic influence compared to the modest effects of shared family and school environments on academic achievement occurs because of the standardized curriculum in the UK. Environmental differences may be reduced in the UK because of its national standardized curriculum; heritability estimates could be high precisely because environmental differences are attenuated. In fact, it has been proposed that the heritability of educational achievement could be viewed as an index of equal educational opportunities. Empirical evidence provides some support for this hypothesis as the heritability of educational attainment has been reported to be higher and shared environmental influences lower in a centralized educational system as compared to a decentralized educational system, such as the United States.

This evidence for the highly pleiotropic nature of achievement in academic subjects and intelligence goes against the belief of specific learning abilities, such as mathematics ability versus ability in language.

This is a very good paper, making solid points. It points out that the calls for “a level playing field” are to be encouraged: they will boost overall ability, and increase heritability estimates. The authors also go through potential limitations of their study, and give possible answers and suggest possible research. Traditional papers have a lot to be said for them.

My O levels Maths teacher was excellent. Higher maths was another matter. It was taught by a bored physics teacher who made no concessions to the few students who went to his introductory lecture, secure in the knowledge that even fewer would sign up for the course. A pity.

Tuesday, 28 July 2015

Micronutrients in the first trimester of pregnancy

 

Here is an interesting study, which finds what looks like a big effect. Ethiopian Jews moved to Israel, some by a massive airlift which brought in women in various stages of pregnancy. This natural experiment allowed the researchers to look at the possible effect of vitamin supplements given to pregnant mothers. Amusing title, as well.

Victor Lavy  Analia Schlosser, and Adi Shany. Out of Africa: Human Capital Consequences of In Utero Conditions. Draft July 16, 2015.

http://conference.nber.org/confer//2015/SI2015/HE/Lavy_Schlosser_Shani.pdf

The authors say: Children who were in utero in Israel starting from the first trimester are about 12 percentage points more likely to obtain a Baccalaureate diploma than children who were in utero in Ethiopia during the first and second trimester but spent the rest of the pregnancy in Israel. This is a large effect since the average Baccalaureate rate of children who arrived at the second and third trimester is only 20 percent. Children who arrived to Israel during the first trimester also engage in more challenging study programs during high school relative to those who arrived at a later pregnancy stage. For example, they obtain 3.2 more credit units relative to those who arrived at the third trimester, an effect of about 33 percent. These individuals also attain 0.4 more credit units in Mathematics and 0.5 additional units in English, implying a gain of more than 50 percent. They are also 12 percentage points less likely to repeat a grade and 7 percentage points less likely to drop out of high school. 

We find that the effect of better environmental conditions in utero is larger and significant mostly among girls.

Try looking at Tables 3 onwards. The contrasting groups are in rows, not columns, which confused me when I looked at it as a welcome distraction after becoming punch drunk after posting on the “glass floor” LSE social exclusion report. For some reason, in the traditional brackets where researchers usually give the standard deviation these authors have put the standard error of the mean, which is much smaller. Surprisingly disorienting.

This study is of particular interest to me, because a PhD student of mine looked at whether the Iraqi invasion of Kuwait caused pregnant mothers to give birth to children with any behavioural disorders, and was unable to find any apparent disturbance. All the children were well nourished, and brought up in good material circumstances. However, health services were very poor at time of the invasion, and still no detectable effects (no long term look at adult outcomes).

It appears that vitamins might be doing something useful after all. Give it a close reading while I go off to bed.

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.

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/447575/Downward_mobility_opportunity_hoarding_and_the_glass_floor.pdf

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.

clip_image001

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?

http://www.migrationwatchuk.org/briefing-paper/1.42

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.”

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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.

 

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Pakistanis & Bangladeshis and Africans tend to persist in unemployment.

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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

http://archpedi.jamanetwork.com/article.aspx?articleid=2381542

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.

http://drjamesthompson.blogspot.co.uk/2014/11/adopt-child-but-discard-illusion.html

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,

http://drjamesthompson.blogspot.co.uk/2015/07/nobel-laureate-sir-tim-hunt-frs-gets.html

such a recording has now surfaced.

http://unfashionista.com/2015/07/20/will-the-new-york-times-correct-its-misreporting-on-tim-hunt/

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:

https://westhunt.wordpress.com/2015/07/16/brain-topography/

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.

http://ac.els-cdn.com/S0960982215006715/1-s2.0-S0960982215006715-main.pdf?_tid=08ae3226-2c83-11e5-9d7d-00000aacb35f&acdnat=1437137880_f9cc692b31d30e0a8a480fdf57bcb603

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.

http://emilkirkegaard.dk/understanding_statistics/?app=Jensens_method

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.

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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.

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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.