A correlation between the g factor and indices of heritability (h2) gives support for the genetic g hypothesis but, on the other hand, the interpretation may appear questionable if g correlates with shared (c2) and/or non shared (e2) environment to the same extent. The results from the present meta-analysis tend to support the hereditarian hypothesis.
Year: 2013 (Page 2 of 6)
Studies of the nature of the Flynn Effect are usually done in developed countries (e.g., Rushton, 1999; Wicherts, 2004; Nijenhuis, 2007; for an ‘Overview of the Flynn Effect’, see Williams, 2013). There are two recent data on two developing countries (Khaleefa, 2009; Liu, 2012). The reported numbers on subtests gains can be studied using either MCV or PC analysis. Next, we will see that shared (c²) and non-shared (e²) environments, as measured by Falconer’s formula, are unrelated to heritability (h²) of the WAIS and WISC subtests. Culture load, heritability, g-loadings, and black-white differences tend to form a common cluster (on PC1) that is different from the pattern of loadings shown by shared and non-shared environment.
The french adoption study, by Capron & Duyme (1989, Table 2; 1996, Table 3), attempted to show that IQ can be improved by adoption. Their numbers displayed an IQ gain of 15 or even 20 points (WISC-R). Jensen (1997) analyzed Capron and Duyme adoption data (1996) with N=38, a study often cited by environmentalists as evidence against the hereditarian hypothesis. Jensen showed that the IQ difference owing to the adoption of children from low-SES parents by high-SES families is not g-loaded while at the same time the IQ difference owing to low-SES versus high-SES biological families loaded in fact on the g factor or PC1. Plus, the SES-difference of adopted families correlated at only 0.099 with SES-difference of biological families.
In Bias in Mental Testing (1980, pp. 546-548), Arthur Jensen showed that a congruence coefficient test from a factor analysis of the within- (WF) and between-family (BF) correlations among blacks and whites could yield an identical g factor structure. A similarity in factorial structure for these four groups having been evidenced, he writes :
These correlations are statistically homogeneous; that is, they do not differ significantly from one another. Thus it appears that the g loadings of these seven tests show a very similar pattern regardless of whether they were extracted from the within-family correlations (which completely exclude cultural and socioeconomic effects in the factor analyzed variance) or from the between-families correlations, for either whites or blacks. … This outcome would seem unlikely if the largest source of variance in these tests, reflected by their g loadings, were strongly influenced by whatever cultural differences that might exist between families and between whites and blacks.
Jensen (1980, Table 4) has been replicated by Nagoshi and Johnson (1987, pp. 310-314). I will replicate those earlier tests using NLSY97 and NLSY79. As Jensen (1998, pp. 99-100) noted, the congruence coefficient (CC) can be interpreted as being an index of factor similarity.
At what age does the cognitive ability gap between blacks and whites first appear? At what age does the black-white ability gap stop growing?
Knowing the answers to these questions is vital to understanding the etiology of the black-white ability gap, especially if this gap has an environmental cause. However, the only scholarly work that attempts to investigate these issues is John Loehlin’s Race Differences in Intelligence (1975), which is nearly 40 years old. So I will update and expand upon that review here on Human Varieties by summarizing all available measurements of African American cognitive ability from early infancy to age 3; I will also discuss the relevance of this data to current debates in the social sciences.
In the NLSY97, a Jensen Effect of biracial blacks has been found, using self-reported white ancestry. In the NLSY79, some questionnaires (R00096.00, R00097.00) asked about the respondents’ first and second racial/ethnic origin. When the respondent reported being non-black or white in one of the questionnaires and black in the other, he was categorized as being a multiracial.
Bermuda is a tiny British Overseas Territory in the North Atlantic Ocean, some 600 miles from the East Coast of the United States (population: 64,700). Even though Bermuda is 1000 miles from the Caribbean Sea, there are a number of sociological similarities between Bermuda and the Caribbean island nations; it is an associate member of the Caribbean Community. Its economy, much like the Cayman Islands and The Bahamas, is largely based on finance and tourism, and it likewise enjoys one of the highest standards of living in the world.
According to the 2000 census, Bermuda is 54.8% black and 34.1% white. IQ and the Wealth of Nations (2002) did not include intelligence data for Bermuda, but IQ and Global Inequality (2006) reported an IQ of 90, as the average of two studies. In this post I discuss some overlooked data which suggest that Bermudian blacks have an IQ that is very close to 100, and that there is no IQ gap between black and white Bermudians. There is also some overlooked test data which suggest otherwise, and we are left with some uncertainty over the meaning of the conflicting research. Continue reading
In an earlier article, I have shown that the magnitude of sibling correlations among NLSY-ASVAB subtests correlates with the magnitude of g-loadings, but moderately with the magnitude of black-white IQ gaps in those subtests using Jensen’s method of correlated vectors, a possibly imperfect technique in some instances as explained in my previous article. In another post, it has been seen that US blacks having more (self-reported) white ancestry showed a higher IQ level, and that this effect is not mediated by skin color. Here, I will show that the magnitude of the score advantage for blacks with more white ancestry among subtests correlates with the above mentioned variables.
Number 4 in the social science’s top 10 list of “grand challenge questions that are both foundational and transformative” (Giles, 2010) is: “How do we reduce the ‘skill gap’ between black and white people in America?” Presumably, figuring out the cause of this psychometric intelligence differential would help when it comes to deciding how best to minimize it. If so, we can thank Meng Hu for his recent efforts focused on determining the cause. This includes his recent extensive exploration of differential regression.
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Recently, the GSS released the survey results for the year 2012. And a skin color variable has been included. But rather than using the SDA program, available here, I used the GSS cumulative datafile 1972-2012 for SPSS, available here. This allows more complex analyses to be performed than what is possible with the SDA.
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