Friday, 28 October 2016

No sex differences in Romania




I am slowly learning the perverse art of headline writing, but retain an inherent allegiance to telling the truth: I am sure that there are the usual sex differences in Romanian men and women, as indicated in the traditional costumes above, but apparently no consistent differences in intelligence. A null result is as important as a positive result, so this finding must enter the mix for us to ponder about. Does it show something specific about one country, or something general about our methods, or both?

Dragos Iliescu, Alexandra Ilie, Dan Ispas, Anca Dobrean, Aurel Ion Clinciu. Sex differences in intelligence: A multi-measure approach using nationally representative samples from Romania. Intelligence Volume 58, September–October 2016, Pages 54–6

Interestingly, the intelligence tests standardised in Romania cover the full range: almost as if no intellectual measure had been left out. Whatever the finding, one cannot easily quibble that another test would have shown a different result.

However, the Lynn hypothesis is that boys are late to mature, so it is only at adult ages that male advantage shows itself. The SON test goes up to 8 years, so is not relevant. The WISC-IV goes up to 17 years so is partially relevant. The Raven test covers the full age range, so is relevant:


Romanian ravens adults

Sample sizes are small, which reduces the chance of “significance” but out of 13 age bands 10 show male advantage to some small degree. Advantage Lynn.

For the 12 adult groups on the MAB-II the story collapses. Overall IQ favours men for 10 out of 12, but only one is significant, the rest tiny. Performance IQ shows male advantage for 10 out of 12, but most are infinitesimal, so forgettable.

For GAMA there are 14 adult age groups, of which 11 show male advantage, but mostly tiny ones, only 3 being significant.

For IST there are 10 adult age groups, of which 2 show male advantage, and only the female advantage is significant.

Looking at the individual test results as a whole the picture is, as the authors imply, unconvincing on the male advantage hypothesis, even among those tests that cover adults.

However, almost all these tests do not report the raw scores, which is a considerable problem in ability testing. Why not? Well, many intelligence tests have idiosyncratic scoring systems according to the material used, number of items,  additions for quick completion, reductions for partial errors, and so on. So the real raw scores are changed into scaled scores, and those scaled scores may be drawn from different tables according to age. There is some scope for blurring reality. It should not affect sex differences, but the change from raw to scaled scores is not something easy to track down.  This certainly has an impact on Flynn effect calculations. Looking at the raw scores on coding tasks or digits forwards and backwards for each age (where the raw score is a real ratio scale) would be very interesting, which should knock on the head any residual doubts.

If you inspect the torrent of individual results in the paper, there is little evidence of any consistent pattern of sex differences. The sample sizes for each age band are respectable though not large, so it was with some relief that I turned to their overall meta-analysis of the results in Table 7, though that table is a little hard to read. A positive Cohen’s d score reveals a male advantage. The Q score is the Chi-square test result, with the degrees of freedom in brackets. The L squared test gives the chi-square results corrected for degrees of freedom and calculates the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error.

However, to test Lynn’s hypothesis we should have a Table 8 which restricts itself to the 17 year+ adults, up the whole age range. This would be interesting.


Romanian sex differences


The authors say: The only two scores with a significant (though small) effect are the Raven (d = 0.11, p < 0.01), and the Performance subscore of the SON-R (d = 0.12, p < 0.01), both in favor of males. In the case of the SON-R, medium heterogeneity is signalled by the data: Q(5) = 10.01, p < 0.10, I2 = 50.04, I.e. 50% of the total variability in this set of effect sizes are due to between-subsamples variability (true heterogeneity). In the case of the Raven scores, heterogeneity is not present: Q(22) = 21.34, ns., I2 = 0.00; I.e. all variability in effect size estimates is due to sampling error within subsamples.

Of course, as Richard Lynn found out, the Wechsler may have been fiddled with a bit to brush away some sex differences, but I doubt that can have been the case for all the other measures, particularly the Raven, designed long ago.

The authors do not bother to remark on something which caught my eye: the Wechsler Intelligence Scale for Children shows a lot of heterogeneity on Full Scale IQ, Verbal IQ and Perceptual Reasoning IQ. The Multidimensional Aptitude Battery and Intelligence Structure tests also show a fair amount of heterogeneity, compared with none for the Raven test. Of course, Richard Lynn might argue that the children’s scale does not prove anything, but that the adult form (not used here) would do so.

The authors conclude: The random and non-replicable pattern of differences observed in the current research seems to support the conclusion that any sex mean or variance differences are likely spurious and the result of sampling or measurement errors than substantive and stable effects. This conclusion is supported for both general intelligence and second-level (more specific) abilities (e.g. performance vs. reasoning, verbal vs. performance, fluid vs. crystallized).

Cautiously, they admit: The current study has a number of limitations. First, even though all the 6 samples on which we report data are carefully selected nationally representative samples, they are not comparable in volume to some of the samples on which data was reported in other studies, such as Deary et al. (2003), or Lohman and Lakin (2009). Therefore, while they make an important contribution for an understudied culture, they may only have a limited impact on the international state of knowledge. Second, some of the tests used in the current research were developed to be as sex neutral as possible. At least for the WISC-IV and SON-R, item bias was examined both by trained judges and through item analysis, and the GAMA and MAB-II were developed with the clear objective of minimizing adverse impact by gender. This may have affected the results and contributed to our null effect conclusion.

My comment: “sex neutral” sound impeccable, but the general drift of test construction is towards sex difference suppression.

Their final word: Research on group differences in intelligence is a politically charged topic with important societal consequences. Therefore, we strongly encourage researchers examining group differences in intelligence to pay close attention to the quality of the samples used and make efforts for increasing their representativeness.

In fact, I think the authors have done very well. They have set out results from many intelligence tests, not just one, on a good national sample. No, it is not the whole nation, as with the Scottish data. No, there was not a meta-analysis of the adult data separately (though it probably would not come up with much), but overall it certainly gives pause to the acceptance of the sex difference findings in other work.

Is it all down Romania, and some special sex-difference-annulling culture, as so sedulously sought by some people? Has Romania achieved what the Nordics strived for but could not attain? Although I believe in exceptional countries, as an outside observer I cannot find anything in Romania’s long and rich history which leads me to believe that sex differences were deliberately diminished. However, Romanian readers are invited to send me further and better particulars.


Thursday, 27 October 2016

Sex differences in trauma


Norway trauma



Despite having spent much of my professional life dealing with post-traumatic reactions,  I rarely blog about it. One interpretation is that it arouses painful memories, but in fact most of my memories are positive ones of patients recovering, even if for some they were only partial recoveries. Mostly I speak about it less because others have very ably taken over the task of researching the topic and explaining the results to the public.

What interests me enough to come back to the issue is a paper about sex differences in trauma responses. Psychiatric illness is more common in women (mostly anxiety and depression) as most of the evidence shows. However, this paper makes a stronger claim about sex differences, and since I have been covering sex differences from the perspective of intellectual ability, this publication comes at an apposite time.

Lassemo, E., Sandanger, I., Nygård, J.F. et al.  The epidemiology of post-traumatic stress disorder in Norway: trauma characteristics and pre-existing psychiatric disorders

Soc Psychiatry Psychiatr Epidemiol (2016). doi:10.1007/s00127-016-1295-3

In its favour, this paper comes from Norway, where they do proper epidemiology and keep track of people and their health and employment records. Against it, this paper comes from Norway, which lies on a warm bed of oil revenues, shields its citizens from major privations and always balances its budget. They also have an absurdly indented coastline and are prone to seasonal depression, skiing, and watching films about log fires. Are such people truly representative of the rest of humanity? Let us find out.

The methodology is good, the trauma measures fine, the assumptions on missing data reasonable, and the statistics almost easy enough for me to understand. I have some minor quibbles. The first two paragraphs of the results section are bewilderingly similar yet contradictory, as if they were setting us a “can you spot the difference” task.  Out of kindness to readers, the authors should re-write them. The frequent mention of one year and lifetime incidence and prevalence, probably corrected for age stratification confused me.

My other quibble is that they have somewhat under-displayed the sex differences, but I have tried to repair that by summarizing them for myself. First, here is their summary of their results:

The incidence for trauma was 466 and 641 per 100,000 PYs for women and men, respectively. The incidence of PTSD was 88 and 31 per 100,000 PYs. Twelve month and lifetime prevalence of PTSD was 1.7 and 4.3 %, respectively, for women, and 1.0 and 1.4 %, respectively, for men. Pre-existing psychiatric disorders were risk factors for PTSD, but only in women. Premeditated traumas were more harmful.

The authors have grouped all traumas into accidental and pre-meditated, a rough but useful distinction.

In the sample, more men (n = 203, 26.2 %) than women (n = 186, 21.7 %) were exposed to PTEs (potentially traumatic events)(p = 0.031). Of those exposed to trauma, more women (n = 38, 20.4 %) than men (n = 10, 4.9 %) filled diagnostic criteria for PTSD (p < 0.001), yielding a lifetime prevalence of 4.4 and 1.3 %, and a 12-month prevalence of 1.8 and 0.8 % for women and men, respectively. Given that PTEs are component causes for the PTSD diagnosis—necessary, but not sufficient, no individuals will have PTSD without having experienced a trauma. The sample had an underrepresentation of persons 18–34 years of age and an overrepresentation of persons 35–65 years of age, as compared with the Norwegian population.

I find the results section somewhat terse and hard to follow. So, I made some notes, as follows:

The sample of  1634 persons is composed of 859 women and 775 men.

Of the 859 women, 186 reported potentially traumatic events, of which 38 met the criteria for PTSD.

Of the 775, 203 men reported potentially traumatic events, of which 10 met the criteria for PTSD.

186/859 of women (21.7%) get a dose of bad events, and of those 38/186 (20.4%) get traumatized. Overall, facing the vicissitudes of life, 38/859 women (4.4 %) get traumatized.

203/775 of men (26.2%) get a dose of bad events, and of those 10/203 (5%) get traumatized. Overall, facing the vicissitudes of life, 10/775 men (1.3 %) get traumatized.

However, my summary is somewhat different from their tables, so by all means look through them, and try to make your own notes.  A little tree of frequencies would have been a help, particularly in understanding the all-important issue of dose-response relationships. (Perhaps this paper doesn’t get the Thompson prize for data analysis after all).

How much does previous psychiatric disorder contribute to trauma vulnerability?

The odds ratio (ORs) for conditional PTSD when suffering from pre-existing psychiatric disorders are presented, by gender, in Table 2. For women, any pre-existing depressive, anxiety or somatoform disorder was associated with an increased risk for PTSD [OR 3.6 (95 % CI 2.6–5.0)]. For women, pre-existing psychiatric disorder was associated with subsequent PTSD (p < 0.001). There were only a few men with pre-existing psychiatric disorders and traumatic exposure, and only one filled the diagnostic criteria for PTSD.


Premeditated trauma Norway

Notice the extraordinary impact on women of premeditated violence, mostly verbal threats, violence from relatives, sexual assault and rape.

In sum, slightly more men get exposed to bad events, but are much less likely to be traumatized by those events, particularly when they are older than 30. Even in tranquil and wealthy Norway, about a quarter of the population are exposed to troubling events. Of course we may wonder whether the self-reports are a function of the high standards all people in the wealthy world now expect from their lives. Would occupation by a foreign power increase the reporting rate, or re-set the scale as to what constitutes a potentially traumatic event? (I think the reporting rate would go up, but data on the bombardment of Beirut many years ago showed remarkable little change).

Even in Norway, the men are tougher, putting up with more slings and arrows of fate with less emotional injury. The supposed stereotype of tough men is validated, particularly those older than 30. The authors, however, note that the stressors are somewhat different, and have different incubation periods, with women ruminating in silence about past events. Of course, the selection of  what counts as upsetting is also sex-linked, so trauma will be a mixture of actual events and perceived injuries. Epictetus observed that: Men are disturbed not by things, but by the view which they take of them. To which I can only reply: To a certain extent, mate.

Women report more sexual abuse and rape, men more threats of physical violence.

There is lots more in the paper, so give it a look.

Finally, the authors make a point which has profound current interest: premeditation hurts people far more than true accidents. Some commentators tell us not to respond to0 much to terrorists because they don’t kill many people. Such commentators point out that rates of death from chronic disease are far higher, as are deaths from ordinary accidents and non-political criminal assaults. All this is statistically true yet misses the main point: it is the deliberate wish to kill people for who they are and how they choose to live which is rightly seen as an assault on freedom, and a source of dismay, incomprehension, and fear. Norway has experienced terrorism from the Right, but terrorism from any source raises a new category of existential threat, and requires us to cope with fresh pre-meditated hatred. Not nice, as my Granny used to say.


Monday, 24 October 2016

Do universities add value?


UCLAutumn2 image 


If you have anything to do with a university, you are probably above such childish things as university rankings. Just to explain to my esteemed readers what other less refined people get up to: university rankings attempt to assess universities according to the quality of their research and, if desperate, by the quality of their teaching. Research is assessed by publications in quality journals (quality being assessed by impact factors, which is a separate issue) and research grants obtained, or number of Nobel Prizes, or similar shibboleths of scholarship. Teaching is assessed by student surveys and, to scrape the barrel, by mentioning the quality of the student experience: whether they have ever met a member of staff, had an essay marked or been taught anything; silly stuff like that.

All this is good fun, and to save you looking it up,  University College London generally does well on international rankings.

QS World University Rankings 2016-17

  1. Massachusetts Institute of Technology (MIT)
  2. Stanford University
  3. Harvard University
  4. University of Cambridge
  5. California Institute of Technology (Caltech)
  6. University of Oxford
  7. University College London
  8. ETH Zurich
  9. Imperial College London
  10. University of Chicago

So what? The first three are in the US, which is very wealthy and can attract global talent and fund research. There is some first mover advantage, so Oxford and Cambridge do well, by attracting talent and legacies over 8 centuries. UCL and Imperial are in reasonably wealthy England. 9 of the top 10 were created by the English and their descendants. Not a bad legacy for one damp island.

However, whatever happened to Salamanca, Bologna, Padua, Naples, Siena, Valladolid, Macerata, Coimbra, and other first mover European centres of ancient learning,  who should be well ahead of the pack? Well, they weren’t English, for a start. They fell by the wayside for the lack of something: Italy lost its early start, and the common factor for all of them may have been the lack of an industrial revolution, or perhaps a prevention of curiosity under Catholicism, or fundamental deep disorders in the Mediterranean European psyche: too much time on the beach, lack of proper governments, or even the lack of the civilizing impact of sharp winters and Protestant bloody-minded rejection of authority, which engender the required Northern cunning and dedication to truth as an absolute value. Something was missing among the continentals.

The OECD have entered the fray, by asking what is for them a seditious question: how do university rankings relate to student quality? The BBC has ventured to summarise their answer: Not all that much.

Anyway, here is the BBC’s list of highest performing graduates, drawn from OECD data. Surreptitiously I have added another figure to the countries, which neither the OECD or the BBC mentioned.

The OECD's top 10 highest performing graduates

  1. Japan   105
  2. Finland   97
  3. Netherlands   100
  4. Australia  100
  5. Norway   100
  6. Belgium   99
  7. New Zealand   100
  8. England    100
  9. United States 98
  10. Czech Republic  98

Japan is out ahead, all the rest are Greenwich Mean Intelligence (100), with little variation. China is not on the list, but would rank close to Japan.

Student ability is a dangerous concept because the OECD does not mention intelligence. If you put “OECD” into the blog search bar you will get all the mentions I have made to their work, but here is a starter here below, with two others to give you a flavour of the way the OECD argues:

The BBC article, though interesting, raises an easily formulated explanation: the best universities now reflect a blend of early starters and mostly the English who kept the flame of the Enlightenment alive.

One can have very bright students, but still live in a country where universities have yet to generate sufficient research to draw in students from other countries. Eventually the new universities will reflect the global hinterland of talent. On intelligence alone then the cities of China will lead the way, so long as they are curious, disputative, and not led by any political party or over-weaning government.

Meantime, do universities add value? The BBC references a gargantuan OECD publication “Education at a glance 2016” which should bludgeon the average reader into submission. Just reading the contents list will make your head spin. This is what a large budget can bring you. Be warned: they have an explicit agenda which they list on page 15, and although these are noble aims, they include eliminating gender disparities and inculcating sustainable development and cultural diversity. All countries get graded on their progress to the correct curricula. Designing a Maths course to gain their approval might be tricky, particularly if the highest achievers are always Asian men. Perhaps education would be better with less cultural diversity: just imitate the Chinese.

The Report complains: More women than men are now tertiary graduates. But women are still less likely to enter and graduate from more advanced levels of tertiary education, such as doctoral or equivalent programmes. Women remain under-represented science and engineering, and over-represented in others, such as education and health. In 2014 there were, on average, three times more men than women who graduated with a degree in engineering and four times more women than men who graduated with a degree in the field of education. Graduates in engineering earn about 10% more than other tertiary-educated adults, on average, while graduates from teacher training and education science earn about 15% less.

Don’t dare to suggest that women may have different interests to men, and also different cognitive strengths and weaknesses.

Here are their summary comments on immigration:

Immigrants are less likely to participate at all levels of education. Immigrant students who reported that they had attended pre-primary education programmes score 49 points higher on the OECD Programme for International Student Assessment  (PISA) reading test than immigrant students who reported that they had not participated in such programmes. This difference corresponds to roughly one year of education. In most countries, however, participation in pre-primary programmes among immigrant students is considerably lower than it is among students without an immigrant background. In many countries immigrants lag behind their native-born peers in educational attainment. For example, the share of adults who have not completed upper secondary education is larger among those with an immigrant background.

The clear implication is that immigrants should be encouraged to get educated, and then they will be as able as the locals to contribute to their country’s economies. However, if brighter immigrant parents make sure their children children start learning early, some of 49 points apparently gained may be due to selection.

After those preliminaries, now let us turn to the issue highlighted by the BBC article in which the OECD's top 10 highest performing graduates are listed. I think (but cannot be sure) that it is taken from the Table A1.2  shown below:

OECD literacy proficiency

For some reason the BBC list drops Sweden. To my eye the Premier League of top students as regards “literacy” are to be found in Japan, Finland, Netherlands, Sweden and Australia. Norway to Slovakia are in the First Division, Korea to Italy in the Second Division, and Chile, Turkey and Indonesia lead the vast Third World.

What does “literacy” mean in this context? It means comprehension and the ability to handle concepts, something requiring verbal intelligence, a taboo concept for the OECD.

At level 4 tasks involve retrieving information which require the reader to locate and organize several pieces of embedded information. Some tasks at this level require interpreting the meaning of nuances of language in a section of text by taking into account the text as a whole. Other interpretative tasks require understanding and applying categories in an unfamiliar context. Reflective tasks at this level require readers to use formal or public knowledge to hypothesize about or critically evaluate a text. Readers must demonstrate an accurate understanding of long or complex texts whose content or form may be unfamiliar.

To really answer the question as to whether universities add value, here are some research possibilities. First, one could compare the national literacy measures against the rankings for all the national universities. This would be rough and ready, but informative. Second, and far better, one would look at the broad range of adult skills, and adult earnings, for students of high ability according to whether or not they went to university.

Here is the way I summarised whether secondary schools add value to primary schools when I discussed it in December 2013 . The same method should be applied to evaluating whether universities add value.

So, we know that the education systems of many countries ten years ago were not turning out uniformly capable citizens, and to the extent that today’s student results are roughly in line with the previous decade’s results, they will not be turning out uniformly capable citizens now. This is because there is a bell curve of ability and because social and because educational systems vary in their effectiveness. Discriminating the relative contributions of these two factors is well nigh impossible unless you take measures of cognitive ability, preferably pre-school, but certainly early in life and including ability at 11 years of age, and then you test their attainments at age 15/16. Basically, if you know what children can do at the end of primary school you are in a good position to see what benefit they get from secondary education. Without those facts, interpretation of educational interventions will be prone to considerable error.

At the moment the snapshot of education in the OECD 2016 report does not answer the question, though it is worth investigating. Enough of this. Go back to marking essays.

Monday, 17 October 2016

Differences in sex differences: US trends and India


Sex differences fascinate, but would be easier to understand if only they would stand still for a moment! Reported sex differences vary in magnitude, 3 to 1, or 4 to 1, or 7 to 1.

As usual, it depends on the representativeness of samples, the abilities being measured, and also how far out on the right hand side of the bell curve you go when you measure the man/woman ratio of high achievers. In the early 1980s on the SAT-Math the sex ratio was approximately 2 to 1 for scores ≥500 (top 0.5%) and roughly 13 to 1 for students scoring ≥700 (top 0.01%).

As Gigerenzer keeps pointing out, most people misunderstand the combination of decimal points and percentage signs. 0.5% means 1 in 200, or 5 in a thousand, or 50 in ten thousand.  0.01% is even more tricky: it is not a fifth but a fiftieth of 0.5%. It is 1 in 10,000. In sum, males are 2 to 1 at a level of ability reached by 50 out of 10,000 students, but at the very high levels achieved by 1 out of 10,000 students the male advantage is 13 to 1. At least, that was the picture in the 1980s. What are things like now?

Before that, here is some background:

Maths is a man thing. September 2013

Advice to men caught unawares. November 2014

The historical record is clear: eminent men predominate by at least 7 to 1 or, in Charles Murray’s “Human Accomplishment” 30 to 0 for the very top thinkers, people like Aristotle, Darwin, Galileo, Newton, Einstein (page 143) .  Women have the perfect alibi of motherhood, and as Larkin noted, sexual liberation did not come till after the Beatles’ first LP.

Sexual intercourse began.

In nineteen sixty-three

(which was rather late for me) -

Between the end of the "Chatterley" ban.

And the Beatles' first LP.

In fact, some liberation began in 1870 with the Education Act, more in 1914 with the First World War, more after the contraceptive pill in the early 1960s and more and more thereafter.  Although that is true about our own age, perhaps this story is wrong, a mere blip of epoch-centric bias, and denies the rights and the impact women had made centuries before.  Thirty wills survive today from the late Anglo-Saxon period and ten of those are the wills of women, each of whom was a significant property owner , with the same rights of ownership and bequeathal as any man.  Women were highly significant figures in Saxon history, and were admired for their power and nepotism, even if it involved the occasional murder. Interestingly, royal succession was not by primogeniture, but by classifying royal progeny as aethelings (throne-worthy) and from this gene pool the royal family would select the one who seemed best qualified for the job. Meritocracy within aristocracy. So, when pressure groups today want to force employers to appoint women to high offices, they should recall that, as a rule of thumb, in the year 1000 it  was already the case that about a third of the richest Saxons were women.

However, given the clamour for equality in modern times, surely the speed of women’s advance should be quickening?

The sex ratio in accomplishment depends on the skills being measured (harder subjects increase sex differences) and how accomplished you have to be to be judged accomplished (harder standards increase sex differences). So, if we go for Fields Medallists, the score is 55 to 1. Coming down slightly from those sorts of levels, how are young American men and women doing in Maths?

Matthew C. Makel, Jonathan Wai, Kristen Peairs, Martha PutallazSex differences in the right tail of cognitive abilities: An update and cross cultural extension. Intelligence Volume 59, November–December 2016, Pages 8–15

In the Abstract they say: Male–female ability differences in the right tail (at or above the 95th percentile) have been widely discussed for their potential role in achievement and occupational differences in adults. The present study provides updated male–female ability ratios from 320,000 7th grade students in the United States in the right tail (top 5%) through the extreme right tail (top 0.01%) from 2011 to 2015 using measures of math, verbal, and science reasoning. Additionally, the present study establishes male-female ability ratios in a sample of over 7000 7th grade students in the right tail from 2011 to 2015 in India. Results indicate that ratios in the extreme right tail of math ability in the U.S. have shrunk in the last 20 years (still favoring males) and remained relatively stable in the verbal domain (still favoring females). Similar patterns of male-female ratios in the extreme right tail were found in the Indian sample.

They plot out the main US results in a dramatic graph.

SAT 700 sex ratios


The detailed results are in Table 1, so see what you think:

SAT maths table sex ratio

Look like sometime between 1996 and 2000 a new score category of 800 was added. Why? 700+ was good enough before. That category shows the biggest male advantage compared to the 700+ column. Looks like either a) students got brighter or b) the test got easier.

However, the EXPLORE-Math score did not show a decline. Whether earlier changes on some tests and the on-going stability in other tests can be explained by potential ceiling effects in the measure in this sample (see Wai et
al., 2012) or other reasons — (e.g., lack of time for any intervention, the effects of test makers purposefully “juking” tests to reduce demographic differences as suggested by Loewen et al., 1988) — is currently unknown

Well, this leaves a lot unknown. The drop in the sex ratio between 1980 and 1990 is enormous. Something must have happened. Crack teams must have fanned out across America, treating Maths anxiety among girls, and giving them special tuition. There must have been summer schools for the brightest girls. I have never seen such a speedy change in a scholastic indicator, and that includes the rise in language ability of first generation immigrants. It is not clear to me whether the authors believe in the change or not, which is a pity, because this is apparently one of the best findings showing that a cultural intervention can overcome an apparently deep-seated biological difference between the sexes. To give the authors their due, they mention that the tests may have been tampered with, so as to reduce sex differences, but they are the ones closest to the data, so I am sure they could tell us a little more. For example, given that this particular period is so extraordinary, why not plot out the results for each year? Big oscillations in the sex ratio during those years would be suggestive of cultural changes coming in, and taking time to spread through all schools. A sharp fall in a single year would suggest that the test had been revised in a major way. Which is it? What did the test makers say about sex differences over the years? Did they ever mention working on items to make sure they were not sex biased?

At the moment all I can think of is that US Maths tests prior to 1991 had the following statement in the instructions: ALL THE QUESTIONS IN THIS EXAM RELATE TO SPARK PLUGS.

Despite all this, as late a 2010 boys outshone girls at 7 to to 1 (actually 6.58 to 1, but I have rounded up for effect). On the ASSET test top score of 35 the ratio is 8 to 1.

It is minor gripe, but having got some great data from India, it was difficult to find it in their table. Please label the Indian results India. Saves time.

Have we yet another result which shows a biologically based male/female difference, which is also subject to strong cultural forces? I cannot be sure. I don’t know enough about the test content, and what questions may have been dropped because of presumed sex bias. I don’t know if the tests have become easier overall, but suspect it, since during recent years GCSEs in the UK became much easier in terms of the overall pass rate, and are now becoming slightly harder again. Test constructors are under pressure to make sure that their tests are fair, and the concept of fair mitigates against finding sex differences, as well as the more familiar race differences.

Despite my uncertainties, this is a good paper, on a very sizeable population of test takers in the US and in India. In my view the authors have not mined the Indian material very much. Surely in these disparate US and Indian tests there must be some very similar test items which would allow a proper comparison between US and India. The authors do some comparisons which assume US intelligence is identical to India, which null hypothesis I think can be discarded. Time for them to team up with Richard Lynn and see if they can do more work on the sex ratios in different Indian provinces, which are extremely heterogeneous in terms of general ability. Not sure what my prediction about sex ratios would be: the brighter the province the higher the sex ratios?

Overall, an intriguing finding, strongly suggesting a change in the sex ratio for Maths, but with relevant points still unanswered. Some specific item analyses could be highly informative.

I have already hinted that I know of work which links intelligence to measured brain volumes of men and women, finding brain size to be a good predictor of sex differences, but that paper is only just now going before reviewers, so whereof one cannot speak one must remain silent.

Keep tuning in to Psychological Comments.

Thursday, 13 October 2016

Has Europe been enriched by contemporary immigration?


In a wish to show I am capable of building up dramatic tension, here is one slide from a talk by Prof Heiner Rindermann which shows the correlations between cognitive ability, institutions and the wealth of nations, arranged in a Structural Equation Model. The loadings have been removed just to make the picture clearer, but the fuller version can be found in the conference slides.  This is the trailer, to be gazed at while eating popcorn and waiting for the main feature.

Cognitive ability and wealth of nations


The main feature is in two formats, so split screen would probably be the best way to see things.

First, here is the link to the whole conference slide show “Has Europe been enriched by contemporary immigration?”

(Check out the SEM on page 12, to see whether you prefer the simplified version or the original)

Here is the link to the talk itself:

Wednesday, 12 October 2016

More markers, more differentiation, and people know what race they are anyway


Cultural lag is the polite term for habits and hypotheses that never die. They become immune to refutation by virtue of constant repetition.  One such meme, due to Lewontin (1972), asserts that there is more genetic variation within genetic groups than between them, and therefore that…… er, ….there is no difference between the groups/there is no genetic difference between genetic groups/any differences between groups cannot be due to genetic reasons/asserting that genetic group differences are discriminable by genetics would be arbitrary and wrong/genetic groups do not exist.

I had never been convinced by these arguments, on the simple basis that genetic groups are clearly visible, and sustain themselves by genetic means, and are usually halved by admixture. Also, it was only a vague thought, but it seemed to me that a t test could still be significant with relatively small mean differences if the sample size was high enough. Probably not relevant in genetics, I mused.

In fact, the ease with which you can separate two genetic groups depends, like all discriminations and all clustering, on the number of markers available for the discrimination and clustering techniques being used. With only a few markers, discrimination is difficult, and error prone. As you increase the number, allocation to different groups becomes progressively easier.

So, to counter the endless echo of the original hypothesis, I am trying to put together a list of papers which explain and test the issue.

Tim Bates explains that Lewontin based his claims on blood type markers: about as advanced as it was possible to be in 1972, but hopeless to identify genetic clustering, therefore doomed to render a false negative.  The 2005 paper by Neil Risch (now cited 400 times) shows how inadequate that procedure was by showing one can now predict race near perfectly with random sets of SNPs.

Hua Tang, Tom Quertermous, Beatriz Rodriguez, Sharon L. R. Kardia, Xiaofeng Zhu, Andrew Brown, James S. Pankow, Michael A. Province, Steven C. Hunt, Eric Boerwinkle, Nicholas J. Schork, and Neil J. Risch. (2005) Genetic Structure, Self-Identified Race/Ethnicity, and Confounding in Case-Control Association Studies.  Am J Hum Genet. 2005 Feb; 76(2): 268–275.

The authors say in their abstract:

We have analyzed genetic data for 326 microsatellite markers that were typed uniformly in a large multi-ethnic population-based sample of individuals as part of a study of the genetics of hypertension (Family Blood Pressure Program). Subjects identified themselves as belonging to one of four major racial/ethnic groups (white, African American, East Asian, and Hispanic) and were recruited from 15 different geographic locales within the United States and Taiwan. Genetic cluster analysis of the microsatellite markers produced four major clusters, which showed near-perfect correspondence with the four self-reported race/ethnicity categories. Of 3,636 subjects of varying race/ethnicity, only 5 (0.14%) showed genetic cluster membership different from their self-identified race/ethnicity. On the other hand, we detected only modest genetic differentiation between different current geographic locales within each race/ethnicity group. Thus, ancient geographic ancestry, which is highly correlated with self-identified race/ethnicity—as opposed to current residence—is the major determinant of genetic structure in the U.S. population. Implications of this genetic structure for case-control association studies are discussed.


In their discussion they say:

Attention has recently focused on genetic structure in the human population. Some have argued that the amount of genetic variation within populations dwarfs the variation between populations, suggesting that discrete genetic categories are not useful (Lewontin 1972; Cooper et al. 2003; Haga and Venter 2003). On the other hand, several studies have shown that individuals tend to cluster genetically with others of the same ancestral geographic origins (Mountain and Cavalli-Sforza 1997; Stephens et al. 2001; Bamshad et al. 2003). Prior studies have generally been performed on a relatively small number of individuals and/or markers. A recent study (Rosenberg et al. 2002) examined 377 autosomal microsatellite markers in 1,056 individuals from a global sample of 52 populations and found significant evidence of genetic clustering, largely along geographic (continental) lines. Consistent with prior studies, the major genetic clusters consisted of Europeans/West Asians (whites), sub-Saharan Africans, East Asians, Pacific Islanders, and Native Americans. It is clear that the ability to define distinct genetic clusters depends on the number and type of markers used (Risch et al. 2002). Reports that document inability to define distinct clusters generally used only a modest number of markers and, hence, had little power to detect clusters (Romualdi et al. 2002). Studies with larger numbers of markers appear to show strong evidence of clustering (Stephens et al. 2001; Rosenberg et al. 2002).

Another major point of discussion has been the correspondence between genetic clusters and commonly used racial/ethnic labels. Some have argued for poor correspondence between these two entities (Lewontin1972; Wilson et al. 2001), whereas others have suggested a strong correlation (Risch et al. 2002; Burchard et al. 2003). We have shown a nearly perfect correspondence between genetic cluster and SIRE for major ethnic groups living in the United States, with a discrepancy rate of only 0.14%.

In sum, you get a near perfect correspondence between genetic measures and the common racial labels, with a misclassification rate of a mere 14 per 10,000. Some of this is due to the admixed “other” category, and perhaps some existential confusion in the others, but 9,986 in 10,000 subjects can master the art of looking in a mirror and noting which race they most resemble, a task beyond the wit of some academics.

Tuesday, 11 October 2016

Scientist stabbed to death by mentally ill illegal immigrant


Murderer and victim

That is the striking headline in The Telegraph, with all the makings of a modern horror story.

The Daily Mail likewise:

The Guardian is more circumspect, but equally informative

Only a few weeks ago I was discussing the detection of violence in schizophrenic patients, and questioning the basis for saying that it was virtually impossible to do anything about it.

Although the poor widow of the murdered man has made her witness statement, it will have zero impact on sentencing, and it is unlikely to have any influence on the current academic wisdom, which is that it is impossible to prevent such murders without restricting large numbers of patients, say 35,000 of them.

As you will see in the above post, I have my doubts about this claim. The facts of this horrible murder are not in dispute: the man was known to be psychotic, to have stopped medication, to be carrying knives, and to have threatened a policeman. He was also a heavy cannabis user. What more does a person have to do to be rated as a risk to others?

In my post you will see that I had some difficulty understanding the “stranger murder” calculations, but all is much clearer if, instead of preventing a stranger murder, we try to prevent an assault. This is worth doing, because to be assaulted is a profoundly distressing event, and if injuries are caused, also a potentially life changing one.

Taking the very paper which provides the “35,000” figure for stranger murder, the figures for assault are shown below, and put things into a more manageable context. The annual rates for assault and violent crime are extraordinarily high, almost unbelievably so. Given the very high base rate, screening and monitoring are worth while.

Positive predictive value in schizophrenia


As the event becomes more rare, the positive predictive value of the risk-categorization becomes lower, and the error rate higher, with progressively more people needing to be monitored to prevent one rare event. However, to prevent an assault would require that 3 schizophrenic patients be monitored, calling them in to check they are taking their medication, and presumably (hardest part) searching for them if they failed to show up. Easier would be to link up with the Police, so that if a patient is brought in for violent behaviour of any sort there can be coordinated management of the offender. Devoutly to be wished, often denied, but in the manageable range given the will and the resources. It would provide a good service for the patients, reducing suicide attempts, improving the quality of their lives, and reducing threats to others. It would certainly be worth testing it out in a London Borough, and checking that the above figures, derived from the best sources, hold up on further examination.

None of the media coverage goes into the question which arises out of normal curiosity: is psychotic behaviour more common among Africans in the UK? The picture above shows murderer and victim, and is an all too common pairing. The answer to the African question is: 6 to 9 times higher.

Morgan et al. (2006) First episode psychosis and ethnicity: initial findings from the AESOP study. World Psychiatry. 2006 Feb; 5(1): 40–46.

We found the incidence of all psychoses to be significantly higher in African-Caribbean and Black African populations across all three centres compared with the baseline White British population [African-Caribbeans: IRR 6.7 (5.4-8.3); Black Africans: IRR 4.1 (3.2-5.3)]. These differences were most marked for narrowly defined schizophrenia (F20) and manic psychosis (F30-31). For example, after adjusting for age, the incidence of schizophrenia across the three study centres was nine times higher in the African- Caribbean population [IRR 9.1 (6.6-12.6)] and six times higher in the Black African population [IRR 5.8 (3.9-8.4)]. The incidence rates for schizophrenia in the African- Caribbean and Black African populations (71 per 100,000 person years, and 40 per 100,000 person years, respectively) are among the highest ever reported. A strikingly similar pattern was evident for manic psychosis (F30-31). After adjusting for age, the incidence of manic psychosis was eight times higher for African-Caribbeans [IRR 8.0 (4.3- 14.8)] and six times higher for Black Africans [IRR 6.2 (3.1- 12.1)] compared with the White British baseline group. The rates of depressive psychosis were also raised, but more modestly [African-Caribbeans: IRR 3.1 (1.5-3.6); Black Africans: IRR 2.1 (0.9-5.0)]. Intriguingly, the incidence rates for all psychoses were also raised for all other ethnic groups (other White, Asian, mixed, other) compared with the White British populations, albeit much more modestly (IRRs for all psychoses ranged from 1.5 to 2.7).

If screening and or monitoring was done on a rational basis, those of African descent would be given particular attention, because detection is easiest where the baseline rate is high. There is 10 year follow-up work, showing generally poor prognosis, but with some achievements.

So, screen Africans who are psychotic or manic, particularly those on cannabis or other drugs, unemployed, not compliant with treatment, and showing any threatening behaviours, and get them treated as quickly as possible.

As a historical note, and at the risk of confusing things by raising an idea since disproved, or at least called into question, it was argued in the 90s that the incidence of schizophrenia was the same the world over, but that has since been shown not to be the case, or at least subject to exceptions. Looking at the references in the above paper by Morgan et al. 2006 I don’t consider it a real refutation, but it would be good to repeat the WHO study again more extensively. However, in a case of cultural lag, since I knew the team at WHO who did the work, it lurks in me as a given and true fact, whatever the current concerns about it.

These studies in countries of origin are important. If the rates of serious mental illness are low, then a case can be made for the stress of migration, and stresses of living in Western society (a common interpretation) as being the causes of the disturbance. It still needs to be explained why other migrants are far less prone than Africans. That aside, I think we need better studies in the countries of origin before being sure about causation.





Sunday, 9 October 2016

Artificial general intelligence: A Von Neumann machine


Alpha Go team


Intelligence is the ability to perform well across a wide range of tasks.

Intuition is inexpressible implicit knowledge.

Creativity is synthesizing knowledge to produce novel ideas.

One day my daughter came back from school, very excited. Nothing particular in that: she enjoyed education. But this time it was more than a class discussion, a maths competition won, or the delights of Java programming. She had listened to a talk by an outside speaker and was inspired. So, the speaker became some-one we lived with, in the ethereal but instructive sense of hearing her discuss the ideas he had engendered. She managed to get a week with him and his game company as part of work experience later in her education, and we all followed his illustrious career with a sense of identification. Moral for researchers: give at least one talk at a school.

Yesterday, thanks to a recommendation from Dominic Cummings, I listened to the same guy and have come away inspired, despite the contact being through a YouTube recording of an MIT lecture, and not face to face in a small classroom.

In the taped lecture below he discusses how his general intelligence system beat the world champion Go player. That is astounding in itself, but to me the most interesting aspect of his talk is his enthralling enquiry into the nature of thinking and problem solving. Has he found a technique with very powerful and wide application that will change the way we solve difficult problems?

His company employs 200 researchers, and attempts to fuse Silicon Valley with academia: the blue sky thinking of the ivory tower with the focus and energy of a start-up. With commendable enthusiasm and naïve impudence (doesn’t he know that many clever academics find these issues complicated, have studied them, and left them even more complicated?) he frames the problem thus:

Step 1 fundamentally solve intelligence.

Step 2 use it to solve everything else.

Who does he think he is? OK, a master chess player at 13, flourishing game company boss that developed Theme Park   and Republic, double First in Computing at Cambridge, then PhD in cognitive neuroscience at UCL, lots of excellent publications, and all this without listening to wise advice that he was setting his sights too high.

He says: Artificial Intelligence is the most powerful technology we will ever invent.

What follows is my considerable simplification of his talk, from which the aphorisms at the very start are also my compressed renditions of his remarks and working principles.

More prosaically, the technology he has developed is based on general purpose learning algorithms which can learn automatically for themselves from raw inputs, and are not pre-programmed; and can operate across a broad range of tasks. Operationally, intelligence is the ability to perform well across a broad range of tasks. This artificial general intelligence is flexible, adaptive and inventive. It is built from the ground up to deal with the unexpected: things it has never seen before. Old style artificial intelligence was narrow: hand-crafted, specialist, single purpose, brittle. Deep Blue beat Kasparov, but could not play simpler games like tick-tack-toe.

Artificial general intelligence is based on a reinforcement learning framework, in which an agent operates in an environment and tries to achieve a goal: it can observe reality and obtain rewards. With only noisy, incomplete observations it must build a statistical model of the environment, and then decide what actions to take from the options available at any particular moment to achieve its goal. A machine that can really think has to be grounded in a rich sensorimotor reality. There should be no cheating, no getting to see the internal game code. (Cheating leaves the system superficial and dull). The thinking machine interacts with the world through its perception. Games are a good platform for developing and testing AI algorithms. There is unlimited training data, no testing bias (one side wins, the other loses), opportunities to carry out parallel testing, and measure progress accurately. End to end learning agents go from the very simplest sensory inputs to concrete actions.

Deep reinforcement learning is the extension of reinforcement learning (conditioning, it used to be called: making actions conditional upon outcomes) so that it works at scale. Deep Mind started its learning journey with Atari games from the 1980s. (How Douglas Adams would have loved this! It reminds me of showing him around the technology museum at Karlsruhe, and as I walked past what I assumed he would see as boring Atari kids games, he burbled with pleasure, and named every one of them and their characteristics. I digress.) The learning agents received nothing but the raw pixels (about 30,000 pixels per frame in the game), tried to learn how to maximise their scores, learnt everything from scratch, and developed ONE system to play ALL the different games. Hence, the systems were learning about the games at a very deep level. (Nature, Learning Curve, 2015 Mnih et al).

In a nod to neuroscience, systems can be considered to have a neurology at a very high computational level: algorithms, representations and architectures. Deep-reinforcement-trained machines can now cope with two-dimensional symbolic reasoning, similar to Tower of Hanoi problems, in which a start state is given and the device must follow the rules, but get to a specified Goal state. This is like (example comes from friends at lunch yesterday) trying to change round the furniture in their house and realising, late in the process, that the correct solution depended entirely on moving the small desk on the top landing.

“Go” is the perfect game to test the deep learning machine, previously trained up on the starter problem of all the Atari games. Go has 10 to the power 170 positions, 19 by 19 “squares” (interstices) and only two rules: stones are captured when they have no liberties (are surrounded and have no free vertices to move to); and a repeated board position is not allowed. It is the most complex, profound game, requires intuition and calculation, and pattern recognition plus long term planning: the pinnacle of information games. Brute force approaches don’t work, because the search space is really huge (branching factor of 200, compared to 20 in chess) and it is extremely hard to determine who is winning. A tiny change can transform the balance of power, a so called “divine” move can win the game, and change the history of the game. (See the pesky small desk at the top of the stairs).

To deep-learn the game of Go, the team downloaded 100,000 amateur games and trained a supervised learning “policy” network to predict and play the move the human player played. After a lot of work they got to 60% accuracy as to what a human would have done. They then made the system play itself millions of times, and rewarded it for wins, which made it slowly re-evaluate the value of each move. This got the win rate up to 80%. Then the system played itself another 30 million times. That meant for every position they knew the probability of winning the game, which gave them an evaluation function, previously thought an impossible achievement. They called this the value network, which allowed a calculation of who was winning, and by how much.

The Policy Network provides the input in terms of the probability of moves arising from a position, and the Value Network provides the game-winning value of a move. All this is great, but you still need a planning function. They used a Monte Carlo tree search, and instead of having to churn through 200 possibilities, they looked at the 2 or 3 moves most played by the amateurs. I have simplified this, but it made the search task manageable: a great breakthrough. Thus trained and maximized, AlphaGo could beat 494 out of 495 computer opponents. It then beat Fan Hui, a professional player 5-0. (Silver et al. Nature 2016)

Very interestingly, getting more computer power does not help AlphaGo all that much. Between the first match against the professional European Champion Fan Hui and then the test match against World Champion Lee Sedol, AlphaGo improved to a 99% win rate against the 6 month earlier version. Against the world champion Lee Sedol, AlphaGo played a divine move: a move with a human probability of only 1 in 1000, but a value move revealed 50 moves later to have been key to influencing power and territory in the centre of the board. (The team do not yet have techniques to show exactly why it made that move). Originally seen by commentators as a fat finger miss-click, it was the first indication of real creativity. Not a boring machine.

The creative capabilities of the deep knowledge system is only one aspect of this incredible achievement. More impressive is the rate at which it learnt the game, going up the playing hierarchy from nothing, 1 rank a month, to world champion in 18 months, and is nowhere near asymptote yet. It does not require the computer power to compute 200 million positions a second that IBMs Deep Blue required to beat Kasparov. Talk about a mechanical Turk! AlphaGo needed to look at only 100,000 positions a second for a game that was one order of magnitude more complicated than chess. It becomes more human, comparatively, the more you find out about it, yet what it does now is not rigid and handcrafted, but flexible, creative, deep and real.

Further, it is doing things which the creators cannot explain in detail. So intent were they in building a winner, they did not give it the capacity to give a running commentary. Now, post-win, they are going to build visualizers to show what is going on inside the Von Neumann mind. What will the system say? “Same stupid problem as Thursday?” “Don’t interrupt me while I am thinking?” Or just, every time: “comparing Policy with Network, considering the 3 most common moves, watching the clock and sometimes, just sometimes, finding a shortcut”.

What about us poor humans, of the squishy sort? Fan Hui found his defeat liberating, and it lifted his game. He has risen from 600th position to 300th position as a consequence of thinking about Go in a different way. Lee Sedol, at the very top of the mountain till he met AlphaGo, rated it the best experience of his life. The one game he won was based on a divine move of his own, another “less than 1 in 1000” moves. He will help overturn convention, and take the game to new heights.

All the commentary on the Singularity is that when machines become brighter than us they will take over, reducing us to irrelevant stupidity. I doubt it. They will drive us to new heights.

On that note, the program was created by humans, as shown in the picture at the top of the post. The AlphaGo team, who in my mind must rank high in the annals of creative enterprise, are a snapshot of bright people on whom the rest of us rely for real innovation.

All those years ago, my daughter was right to think that Demis Hassabis showed promise.

Promise me you will give at least one talk at a school.

Wednesday, 5 October 2016

Richard Lynn Intelligence database: Becker edition


Whereas there are many very well funded projects which study national and international scholastic ability without mentioning intelligence, there is one database for the national intelligence of the countries of the world, and that was put together by one person, unfunded, working in his study. Prof Richard Lynn gathered together the very disparate studies which mention the nationality of test takers, and assembled them into one database.

David Becker, who works with Prof Heiner Rindermann at the Technical University of Chemnitz, Germany, has taken on the task of going through all the results and tracking down the references, an enormous labour. We want a copy of each reference, so that everything can be checked. David is a research student studying for his Masters and has been concentrating on cross-national differences in ability, their consequences, and their possible origins in early human migration. Unusually for our effete profession, he had an honest trade before he entered Psychology. He is a fully trained butcher (three years of training) and worked in that profession for two years, so has skills which will stand him in good stead in research, as he cleaves fatty residues away from good meat.

Here are two references to introduce you to his work:

Becker, D., & Rindermann, H. (2016). The relationship between cross-national genetic distances and IQ-differences. Personality and Individual Differences, 98, 300-310.

Rindermann, H., Becker, D., & Coyle, Th. R. (2016). Survey of expert opinion on intelligence: Causes of international differences in cognitive ability tests. Frontiers in Psychology, 7, 399.

The database gives the Country, the age of the testees, the N, the test, the IQ, the short and the full reference, and then a column indicating whether we have an copy  of the reference (Y or N). Occasionally there are question marks where a reference has not been traced.

This is where you come in. Have a look at the list, and if you have a copy of the papers, send David Becker a scan of them.

Also, if you have extra papers which have not been included (we know that some of you have been extending the database considerably) please get in touch so that we can put everything, duly acknowledged, into one document. Here is how you email David Becker.  Write his name in lowercase, first and second name separated by a dot, then put in the eponymous at symbol, followed by “”

Now for the download. Use the following link, and you have the world at your fingertips. Every time you listen to the news, your can look up the IQ results for the relevant country, and draw your own conclusions.


Tuesday, 4 October 2016

A quick education in Edinburgh


One flight to Edinburgh and I could get an education:


R programming


R programming: so I could crunch data again without SPSS. It might drive me mad, but I am told that thereafter all is serene and pure, like Chapman’s Homer.


Cognitive genetics

Cognitive Genetics: so I could read results with more insight, and spot any errors or interesting connections as the genetic story unfolds.



Systematic reviews and meta-analysis: to check that these things are being done properly. On that general topic, I have already muttered a few suggestions about inclusion criteria in previous posts, suggesting they should be graded for two levels of methodological purity.

Cognitive testing and details

Cognitive testing: because, although I imagine I know about this, this will be the most recent stuff, and targeted at ageing research. Cognitive testing is advancing, particularly in internet driven research, and some assessments are now very fast and efficient.

After that, I could talk about almost everything of interest in psychometrics. The further particulars about applying are as shown above.

If I don’t make it, perhaps you would like to go along and then let me see your notes, sending them to me as a Christmas present. If I manage to get there, please sit with me at the back and explain things as they go along.

Monday, 3 October 2016

Cupid calls: research in progress


The received wisdom about lonely hearts ads is that men advertise their status and wealth, women their looks. It is a simple trade.

More nuanced approaches suggest that successful relationships will depend on similarities of character, interests and ambitions. More prosaically, that men and women will stay together when they do things together, because they like the hobbies and interests they have in common, and work together to build up those common interests.

Emil had collected publically available data about the questions people ask each other when looking for a partner on OK Cupid. We do not know who is talking to whom, or with what outcome, but we have the anonymous questions, which can be linked to the anonymous basic details given by the person. No one’s privacy is being invaded, but we are getting a look at the question American ask each other when looking for love.  This is very interesting and informative.

Here is the link to Emil’s website on OK Cupid.

The attached video is Emil’s talk. The subsequent discussion is an illustration of how research gets done.

Also informative is the way that researchers see connections, then test the generality and strength of those connections. You probably know all this, and have better examples, but the exchanges between researchers are, to me, very interesting to listen to.


Sunday, 2 October 2016

Sunday lecture: Ancestry in the Americas: a meta-analysis


Traditionally, British Sundays were a day of repose, dedicated to the minority who wished to go to church, on whose behalf the godless majority forswore pleasure, and dedicated themselves to uplifting literature and improving healthy walks. Mostly, it rained, and Monday was a relief.

For your proper entertainment, here is Emil himself, in full flow.

Biogeographic Ancestry and Socioeconomic Outcomes in the Americas: a Meta-analysis

Speaker: Emil O. W. Kirkegaard

Co-authors: John Fuerst

A meta-analysis of American studies reporting associations between socioeconomic outcomes (S outcomes) and biogeographic ancestry (BGA) was conducted. 41 studies yielded a total of 167 datapoints and 57 non-overlapping effect sizes. European BGA was found to be positively associated with S outcomes r = .16 [95% CI: .12 to .20, K=23, N=20,837], while both Amerindian and African BGA was negatively so, -.12 [-.18 to -.06, K=17, N=15,870] and -.10 [-.16 to -.04, K=17, N=24,142], respectively. There was considerable cross-study variation in effect sizes (mean I2=90%), but there were too few datapoints to permit credible moderator analysis. Implications for future studies are discussed.


Here is the full live version, in only 19 minutes, because Emil talks fast or, as I call it, “at normal speed”:

Much, much better than the box set you were thinking of watching.