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Nations have personalities, too.

April 28, 2011 4 comments

Prior, I posted on personality differences between US racial groups in the big 5 . Here’s the global pattern. (I’m pretty busy so I don’t have time to comment on this now).

Schmitt et al., 2007. THE GEOGRAPHIC DISTRIBUTION OF BIG FIVE PERSONALITY TRAITS Patterns and Profiles of Human Self-Description Across 56 Nations

Look at the number of co-authors on this. (Over 120).

For previous research on this, Refer to: Allik and McCrae, 2004. TOWARD A GEOGRAPHY OF PERSONALITY TRAITS Patterns of Profiles Across 36 Cultures

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Articles of Interest

April 28, 2011 Leave a comment

Jensen, 2011. The theory of intelligence and its measurement

At a time of increasing attention to IQ variation among subpopulations, the FE promised to absolve the onus of unfavorable social attitudes engendered by these results. The seeming benevolent promise of the FE is that if samples of entire populations in various countries showed secular gains in IQ scores, the lower-scoring subpopulations within these regions would also gain in average IQ. Since the gradual rise in test scores is assumed to approach a saturation (i.e., peak) level, the subpopulation differences in mean IQ should eventually diminish to nonsignificance. Although Flynn did not explicitly make this hopeful surmise, the popular appeal of the FE attracted the interest of experts in psychometrics and statistics. It is through their agency that the greater significance of Flynn’s contribution will finally be realized.

The critical point about the FE, however, is the singular fact that both the whole phenomenon and the massive data relating to it are scientifically incapable of answering the essential questions it raises. The central issue is that methodology by which the dependent variable (viz., secular gains in IQ scores) has been measured, fails to meet the standard of the advanced sciences on an absolutely critical point! Despite the popular inference drawn from all the IQ data collected, this research can neither confirm nor reject the existence of the FE. Doubling the amount of the already massive data (other conditions being unaltered) could not resolve the issue. But whatever the outcome of a proper investigation of the FE, the gentleman– scholar philosopher James Flynn deserves recognition as an important figure in the history of psychometrics. The term Flynn Effect, however, will go down in history as a blind alley in psychometrics, viz., trying to answer a basic, nontrivial factual question using wholly inappropriate data.

See also: The Jensen Mental Chronometer in the ISIR 2010 Abstracts page 58.

Beaver and Wright, 2011. School-level genetic variation predicts school-level verbal IQ scores: Results from a sample of American middle and high schools

To our knowledge, this is the first study to aggregate DNA markers to a unit of analysis higher than the individual. Moreover, this is the first study to our knowledge that has revealed that variation in aggregate IQ scores is associated with variation in aggregate DNA markers. These results are in line with Lynn and Vanhanen’s (2002, 2006) (see also Hart, 2007; Rushton, 1997) thesis that the average IQ of nations is the result of genetic differences across those nations. Of course, the current study used schools, not nations, as the unit of analysis, meaning that the results reported here may not generalize to other levels of aggregation, including the nation level. There is good reason to believe, however, that the association between DNA and IQ would be even stronger at the nation level in comparison with the school level. There is much more variation in both genetic markers and IQ scores cross-nationally than there is across schools. Schools in the current study were all drawn from the same country (i.e., the United States) creating more genetic homogeneity among schools than there is among nations. Given that nations can vary quite drastically in terms of the allelic distributions of certain genes (Cavalli-Sforza, Menozzi, & Piazza, 1994), it stands to reason that this increased genetic variation would be able to explain more of the variance in IQ scores. Future research is needed to address this issue more fully and examine whether the link between DNA markers and IQ scores would be detected at other levels of aggregation

(Kevin Beaver also coauthored: Beaver et al., 2010. Three dopaminergic polymorphisms are associated with academic achievement in middle and high school and Beaver et al., 2010. Genetic risk, parent–child relations, and antisocial phenotypes in a sample of African-American males; Beaver and Wright recently authored The association between county-level IQ and county-level crime rates (2011).)

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Stereotype Accuracy

April 26, 2011 Leave a comment

Do stereotypes contain a grain of truth? According to Satoshi Kanazawa [1], yes:

“Stereotypes” have a bad name, and everybody hates stereotypes. But what exactly is a stereotype?

What people call “stereotypes” are what scientists call “empirical generalizations,” and they are the foundation of scientific theory. That’s what scientists do; they make generalizations. Many stereotypes are empirical generalizations with a statistical basis and thus on average tend to be true. If they are not true, they wouldn’t be stereotypes.

But how accurate are they, anyways? In a review of the literature, Jussim, et al. (2009) found that stereotypes tend to be highly accurate, especially when judged in terms of the standards adopted in the behavioral sciences:

Table 4 compares the frequency with which social psychological research produces effects exceeding correlations of r = .30 and r = .50, with the frequency with which the correlations reflecting the extent to which people’s stereotypes correspond to criteria exceed r = .30 and r = .50. Only 24% of social psychological effects exceed correlations of r = .30 and only 5% exceed r = .50. In contrast, all 18 of the aggregate/consensual stereotype accuracy correlations shown in Table 1 and Table 2 exceed r = .30, and all but two exceed r =.50. Furthermore, nine of eleven personal stereotype accuracy correlations exceeded r = .30, and four of eleven exceed r = .50.

[1] Kanazawa, 2008. All stereotypes are true, except… I: What are stereotypes? Psychology Today, April 24. http://www.psychologytoday.com/blog/the-scientific-fundamentalist/200804/all-stereotypes-are-true-except-i-what-are-stereotypes
[2] Jussim, et al., 2009. The Unbearable Accuracy of Stereotypes

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Macroeconomics and genetics

April 25, 2011 Leave a comment

Spolaore and Waczair, 2006. THE DIFFUSION OF DEVELOPMENT

In this paper we have documented the following facts: First, differences in income per capita across countries are positively correlated with measures of genetic distance between populations. Second, genetic distance, an overall measure of differences in vertically transmitted characteristics across populations, bears an effect on income differences even when a large set of geographical and other variables are controlled for. Third, the patterns of relationships between income differences and measures of genetic and geographical distances hold not only for current worldwide data but also for estimates of income per capita and genetic distance since 1500, as well as in a sample of European countries. Finally, similar patterns hold when the dependent variable is differences in human capital, institutional quality, population growth and investment rates.

These results strongly suggest that characteristics transmitted from parents to children over long historical spans play a key role in the process of development. In particular, the results are consistent with the view that the diffusion of technology, institutions and norms of behavior conducive to higher incomes, is affected by differences in vertically transmitted characteristics associated with genealogical relatedness: populations that are genetically far apart are more likely to differ in those characteristics, and thus less likely to adopt each other’s innovations over time. The pattern of the effects of genetic distance in space and time, and the interaction with geographical distance, suggest that genetic distance is associated with important barriers to the diffusion of development Some evidence, particularly the results for European countries, also suggests that these differences may stem in substantial part from cultural (rather than purely genetic) transmission of characteristics across generations.

This should be read along with: Gelade, 2008. IQ, cultural values, and the technological achievement of nations

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The General Mental Ability (GMA) of Black British

April 25, 2011 2 comments

I’ve been going through the literature to double-check Lynn’s Global Bell Curve data. While there is some debate concerning the various magnitudes, the data is robust when it comes to African/White differences, at least, within or between the following countries/regions:

1. US
2. Caribbean
3. SS. Africa
4. Netherlands

I was unsure of the British data. Based on Lynn’s data there is a .9 SD UK B/W IQ gap [2].

I double-checked the IOP literature. According to Evers et al (2005) there is a 1.69 standardized GMA gap. (The predictive validity of GMA in the UK is comparable to that in the US [3]).

A parallel, though much smaller gap — about .28 SD [6] to .5 SD [4, 5] as compared to .7 SD in the US — exists in education [4]. According to Stevens (2007):

“Finally, the largest differences in achievement between various categories of social class or social deprivation can be observed among the highest achieving racial/ethnic groups (White British, Irish, and Indian pupils) and less among Bangladeshi, Pakistani, or Black pupils (Demack et al., 2000; Department for Education and Skills, 2005a). The only exception appears to be the group of Chinese pupils, for which even those from deprived backgrounds outperform almost any other racial/ethnic group, irrespective of their social backgrounds (Department for Education and Skills, 2005a).

5. Britain
(I’ll have to look into the education versus GMA gap some more.)

[1] Lynn, 2008. The Global Bell Curve. pg. 88
[2] Evers, te Nijenhuis, van der Flier, 2005. Ethnic Bias and Fairness in Personal Selection: Evidence and Consequences pg. 309. Source: Scott and Anderson, 2003. Ethnic and gender differences in GMA test scores: Findings from the UK
[3] Bertua et al., 2005. The predictive validity of cognitive ability tests a meta analysis
[4] Stevens, 2007. Researching Race/Ethnicity and Educational Inequality in English Secondary Schools: A Critical Review of the Research Literature Between 1980 and 2005
[5] Strand, (in press). The White British-Black Caribbean achievement gap: Tests, tiers and teacher expectations * Use normsinv(%)-normsinv(%) to change % into SD
[6] Strand, 2010. Do some schools narrow the gap? Differential school effectiveness by ethnicity, gender, poverty and prior achievement. ** I averaged the Black differences

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Waiting for Flynn

April 24, 2011 5 comments

[For further discussion on African IQ, see: AfrIQ-Notes]

Wicherts et al. (2010) contend that the African IQ will markedly increase in the near-term due to the processes underlying the Flynn effect [1]. They show that there has been a steep score increase in the samples of African IQs that they deem representative.

Is the African IQ really skyrocketing? Is the Congo student IQ really now around 120-something? Using the complete IQ data bank, I plotted the increase in African IQs over time. There is disagreement about several of the sample scores. The graph on the left is based on a minimalistic exclusion criteria with judgement calls made by me. The graph on the right is based on a minimalistic exclusion criteria with judgement calls made by Wicherts et al. The rationale for using the complete data bank is that the complete bank presents a better picture of “the African IQ”; sampling bias at either ends (e.g., university students and tribal children) will cancel each other out. The overlap of the African Average calculated using the complete bank and the average derived from international assessments supports this contention. (Note: I used the un-weighted means, so the average IQs are slightly inflated in these graphs. The weighted means were 73.1 and 74.3 for the first and second graph, respectively.)

The regression lines show that the African IQs have been fairly constant across time relative to UK norms. If an accelerated Flynn effect was occurring in Africa, such that the African and Western averages were due to intercept in the near-term, we would expect a positive slope of more than negligible magnitude. If the Flynn effect had yet to hit Africa, we would expect a negative slope, as the Western scores should have risen over time relative to the African scores. The Flynn effect seems to have occurred in African in tandem with the West. The massive African Flynn effect found by Wicherts et al. appears to have been a result of sampling bias.

For further confirmation of this, I plotted the international test score equivalents (calculated using equalization of the means) over time (K = 17). Were Wicherts et al. correct, see the figure at the top of the page, the African student scores should show an increase corresponding to their supposed IQ increase. They don’t. Rather, the African student scores show a decrease in tandem with increased enrollment and increased test sophistication.

[1] Wicherts, 2010. Raven’s test performance of sub-Saharan Africans: Average performance,
psychometric properties, and the Flynn Effect

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Getting information out there

April 20, 2011 2 comments

OD just posted about Richwine’s recent study “The Myth of Racial Disparities in Public School Funding.” Richwine demonstrates that differences in school funding are not the cause of “the gap.”

Nationwide, raw per-pupil spending is similar across racial and ethnic groups. The small differences that do exist favor non-white students. After breaking down the data by region, the non-white funding advantage becomes more pronounced. In the Northeast, for example, blacks receive over $2,000 more than whites in per-pupil funding per year. The region with the smallest differences is the South, where spending on black and Hispanic students is only slightly higher than on whites.

But who really argues that funding is the cause? After all, Jensen debunked that explanation 4 decades ago (Educability and group differences pg. 243-253). Melissa over at Racism Review does:

What this story has finally done is highlight the central cause of racial disparities in test scores and graduation rates – school funding, the one factor that seems to go ignored in much of the debate regarding “what’s wrong with our nation’s schools.” For the last six months, since the release of the Davis Guggenheim’s documentary, Waiting for Superman, TV, radio, and print news have interrogated the reasons for low minority performance. But only very rarely, have the ways in which we fund our nation’s schools mentioned. Instead, blame is usually placed on the usual suspects, those with the least power within the system – teachers , parents, and the children themselves. The racist school system, the one that has consigned minority students to inferior education… Exposing the Real Guilty Party: School Funding and Racial Disparities, Feb 12, 2011.

But so what? Now people know better. No they don’t. They don’t because the information is not well disseminated. And, more importantly, the information has not been forcefully disseminated. As a result, (white) people, even when they are aware of the facts, are prone to fall for clamshell games:

Still, some analysts now argue that education funding is not equitable unless far more money is spent on minority students compared to white students. Indeed, a 1998 NCES report used a student-needs adjustment that made school funding “equitable” only if poor students (usually defined as qualifying for free or reduced-fee lunch) received 20 percent more per-pupil funding than non-poor students.[19]

The justification is that poor and minority students face greater socioeconomic problems outside the classroom, necessitating greater education spending as a kind of remediation. This revised view of school funding is very different from the one espoused by the NAACP, Kozol, and others quoted earlier. The original argument made by equalization advocates identified the alleged disparity in school funding as the cause of lower minority achievement. Under the revised view, the cause must be problems outside the classroom, and spending is considered equitable only if it is high enough to remediate those problems.

People do not think logically. They have a live belief imprinted in their heads — “The Racial disparity is caused by funding” — it’s alive even if they have been superficially convinced otherwise. It’s because they have this belief, on some level, that they can be convinced to do what would only make sense if they had this belief. People don’t think “if A then B, not A, then not B.” They have A as an schema in their head. B still follows if A has logically been shown to be false even if they agree with the logic. After all, A still exists subliminally. A needs to be demolished and replaced with Z. This can only be done by submitting (white) people to an unrelenting barrage of facts (to counter the myths propagated by our Leftist, socialist, antirace-ist friends).

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The other general factor – GFP

April 20, 2011 Leave a comment

The existence of a GFP which divides the world into Alphas and Betas would be of no surprise to our game theorist friends.

Just, 2011. A review of literature on the general factor of personality

The position of a general factor of personality atop the hierarchal structure of personality has broad implications for the field of personality theory. It suggests that natural selection has shaped our social fitness in addition to our intellectual prowess. This was first suggested by Darwin (1871) and further investigated by Galton (1887). Rushton’s (1985) differential K theory suggested that variability exists in humans in the extent to which they utilize the K-strategy. While the idea of a general factor of intelligence has received much attention in psychology, a similar solution for personality has not been thoroughly investigated until recently (Musek, 2007). A number of studies have used behavioral–genetic analyses to quantify the genetic basis of the GFP (e.g. Rushton et al., 2008; Rushton et al., 2009; Veselka et al., 2009b). Further research will hopefully use a large enough sample to provide more insight into the existence of non-additive genetic effects. The GFP has been found to correlate positively with intelligence (Schermer & Vernon, 2010). As well, neurobiological foundations of the GFP have been proposed (Erdle & Rushton, 2010). Data from a number of highly diverse measures of personality have revealed the position of the GFP atop the hierarchy of personality structure (e.g. Rushton & Irwing, 2009a, 2009b, 2009c, 2009d; Rushton et al., 2010). The statistical artifact explanation of the GFP presents a real concern for the GFP’s validity and future research should address this issue in light of the recent, highly varied evidence supporting the existence of the GFP. Additionally, gender differences in the general factor of personality present an area of future research that could be quite fruitful. Males and females fulfill distinct social roles, and it is highly likely that evolution has selected for different personality traits in males and females due to different environmental pressures. In a similar vein, an investigation of whether the GFP accounts for more variance in male or female samples could provide interesting results. A stable, measurable GFP would possess tremendous predictive validity as a measure for socially desirable traits. This is exemplified by the recent trend in GFP research towards examining the relationship between the GFP and job-performance, employee selection and assessment, and social status (Van der Linden & Bakker, submitted for publication; Van der Linden, & te Nijenhuis, et al., 2010; Van der Linden, & Scholte, et al., 2010). It is hoped that this trend will continue, and that it will be complemented by additional research regarding what exactly lies at the heart of the GFP.

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Ethnoracial differences in personality

April 19, 2011 4 comments

A reader inquired about research on ethnoracial differences in personality. There actually is a growing body of research on this. Why? Given IQ differences, to avoid unequal proportionality (i.e. disparate impact), a number of industrial psychologists have advocated using personality measures in place of cognitive measures. As it has turned out, though, there are average differences in personality too. So now there is ongoing research on the unequal proportionality caused by using personality tests (1,2 ). Could the differences partly be due to nature? The heritability of personality factors range for .33 to .65 and several genes have already been identified which code for individual personality differences [3] (in some populations), so it’s possible — if you want, you can enter some of the associated polymophisms found by de Moor et al. (2010) into Hapmap and see if it turns up anything. I can’t imagine a large genetic effect, but who knows.

Anyways, I edited the tables from a recent meta analysis (4) which compared Asians, Blacks, Indians, Hispanics, and Whites on the Big 5. The only large differences are between Asians and Blacks. These, however, move in the opposite direction of the cognitive differences, leading to a Scylla and Charybdis for IO
psychologists. [d means standardized difference; a d of .1 would be equivalent to 1.5 points if translated into white IQ metrics (SD=15)].



References

[1] Hausdorf and Risav, 2010. Decision Making Using Personality Assessment: Implications for Adverse Impact and Hiring Rates
[2] Hausdorf and Risav, 2011. Personality Testing in Personnel Selection: Adverse impact and differential hiring rates
[3] de Moor, et al., 2010. Meta-analysis of genome-wide association studies for personality
[4] Foldes et al., 2008. Group differences in personality: meta-analyses comparing five U.S. racial groups

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Articles of Interest: Cognitive Epidemiology

April 19, 2011 Leave a comment

Deary, 2009. Introduction to the special issue on cognitive epidemiology

Gale, et al., 2009. Intelligence in childhood and risk of psychological distress in adulthood The 1958 National Child Development Survey and the 1970 British Cohort Study

Roberts, et al, 2009. Reaction time and established risk factors for total and cardiovascular disease mortality Comparison of effect estimates in the follow-up of a large, UK-wide, general-population based survey

Singh-Manoux et al., 2009. Cognition and incident coronary heart disease in late midlife The Whitehall II study

Lubinski. 2009. Cognitive epidemiology With emphasis on untangling cognitive ability and socioeconomic status

Leon, 2009. The association of childhood intelligence with mortality risk from adolescence to middle age Findings from the Aberdeen Children of the 1950s cohort study

Gale, et al., 2009. Intelligence in childhood and risk of psychological distress in adulthood The 1958 National Child Development Survey and the 1970 British Cohort Study

Deary, et al., 2009. Intelligence and persisting with medication for two years Analysis in a randomised controlled trial

Anstey, et al., 2009. Level of cognitive performance as a correlate and predictor of health behaviors that protect against cognitive decline in late life The path through life study

Wilson et al, 2009. Cognition and survival in a biracial urban population of old people

Reeve and Basalik, 2010. Average state IQ, state wealth and racial composition as predictors of state health statistics Partial support for ‘g’ as a fundamental cause of health disparities

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