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Arthur Jensen developed the Method of Correlated Vectors in the 1980s and presents a great explanation and analysis in his 1998 book THE g FACTOR: The Science of Mental Ability. Since IQ is correlated with g, it’s not presumable that the correlation between IQ and physical variable X does not involve g. More sufficient evidence would come from the correlation between X and the g factor’s scores. So Jensen proposed the method of correlated vectors that can determine whether there is a correlation between X (or any other factor other than X) and g. Still, it doesn’t tell us about the numerical correlations between g and X, but it can prove that there is a correlation between factor X and g and show if there are any other factors independent of g that are not correlated with X (pg 143).

When a significant correlation is observed between g factors and factor X using the method of correlated vectors, which Rushton (1999) calls the “Jensen Effect”, it demonstrates that the test’s g loading is the best predictor of that correlation with a given variable. Basically, a Jensen Effect arises when there is a correlation between a large number of biological and psychological variables and the g factor. Jensen did say in his interviews with Frank Miele for the Book Intelligence, Race, and Genetics: Conversations with Arthur Jensen:

. . . it involves what I have called “Spearman’s hypothesis.” In his book The Abilities of Man, Spearman made a casual observation that the size of the average W hite-Black difference on ten diverse tests was directly related to his subjective im pression of how much each test reflected the g factor— the more g, the greater the Black-White difference. I turned Spearmans offhand conjecture into an empirically testable hypothesis by calculating the average Black-W hite difference for a number of diverse mental tests, obtaining the g loading for each test (that is, how much each test measures g), and ranking the average W-B differences and the g loadings. If the rank order of the Black White differences and the g loadings are pretty much in the same order, Spearman’s hypothesis is confirmed.

I’ve now tested Spearmans hypothesis on 25 large independent samples and it has been confirmed on every one. It has held up for many different test batteries, and at every age level from three-year olds to middle-aged adults. Nor did matching Blacks and Whites for SES diminish the effect. It even shows up in reaction-time tests that have different g loadings but require no cultural knowledge and can be performed in less than one or two seconds by elementary school children. Based on all these studies, the overall probability that Spearmans hypothesis is false is less than one in a billion! (emphasis mine)

There is less than one in a billion chance that Spearman’s hypothesis is wrong. Which brings me to the Black-White IQ gap.

Using the MCV, Dragt (2010) had his prediction confirmed when the psychometric meta-analysis of IQ batteries showed a correlation of .91, based on a large N. Their study on language bias showed a small underestimate of 2.71 points. They conclude that Spearman’s hypothesis is an empirical fact:

Spearman’s hypothesis can now be considered to be an empirical fact. Mean differences in intelligence between ethnic groups can be largely explained by the complexity of the subtests in an IQ battery. So, the present study shows clearly that there is simply no support for cultural bias as an explanation of these ethnic group differences. Apart from subtests with a strong language component, IQ batteries appear to be excellent measures of intelligence for all groups studied in our meta-analysis.

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Conclusion: Mean group differences in scores on cognitive-loaded instruments are well documented over time and around the world. A meta-analytic test of Spearman’s hypothesis was carried out. Mean differences in intelligence between groups can be largely explained by cognitive complexity and the present study shows clearly that there is simply no support for cultural bias as an explanation of these group differences. Comparing groups, whether in the US or in Europe, produced highly similar outcomes. 

This proves the hereditarian hypothesis 100 percent. Since the black-white differences on subtests are greater the more the g factor is involved (complex tasks, etc), that shows that a magnitude of the black-white difference in IQ is genetic in origin. IQ tests are also not “flawed” or “biased“, as all of the variables that continually get brought up have been controlled for, and genetic confounding wins out every time. Since testing blacks and whites both in America and Europe produces the same outcome, there is a clear genetic component in IQ between blacks and whites.

However, as with most statements and theories by Rushton and Jensen, Jensen’s MCV doesn’t come without any detractors.

Ashton and Lee (2005) state that the MCV produces spurious results as well as non-g sources of variance, producing a vector correlation of 0, even when the item is strongly correlated with the g factor. However, Nijenhuis et al (2007) state that by performing a psychometric meta-analysis on the MCV would alleviate some of the limitations with MCV. Rushton and Jensen (2010) state:

For example, Dolan et al [59] and Ashton and Lee [60] argue that the method of correlated vectors (MCV) lacks specificity so that Jensen Effects might occur even when differences are not on g, and so more powerful statistics are needed, such as multi-group confirmatory factor analysis (MGCFA). However, this criticism misses the point because there is no absolute claim that the g effects have been proven, only that what is observed is what would have been expected if an underlying g did in fact exist (see Bartholomew [61] for the logic of g inferences). Thus, the onus is on the critics of g to identify whether some other factor is operating

Which the critics cannot do. That is because the g factor encompasses all mental abilities and the lower one’s g, the lower one’s overall intelligence. The MVC shows that the black-white IQ difference is largely biological in nature, seeing as the black-white IQ gap is 80 percent genetic and 20 percent environmental (Rushton and Jensen, 2005, p. 279):

. . . is that genetic and cultural factors carry the exact same weight in causing the mean Black–White difference in IQ as they do in causing individual differences in IQ, about 80% genetic–20% environmental by adulthood.

Blacks are ahead of whites at young childhood (around 4 years of age), but at around the age of 5, whites catch up and that’s when the significant racial differences in intelligence are noticed between the races. The fact that we have this debunked “blank slate” notion on the nature of not only race and intelligence but intelligence as a whole in this era of scientific knowledge is mind boggling. Changing all variables to where environments are as close as possible still produces the same occurrence: a clear 1 to 1.2 SD difference between blacks and whites.

Methods like factor analysis and the method of correlated vectors help us to understand the magnitude and heritability of the black-white difference in IQ. Since the differences are the highest on those subtests that are correlated with g, along with correlations from the MCV, with an 80/20 (Pareto Principle in action) genetic/environmental difference in black-white IQ, we can most definitively say that the 1 to 1.2 SD (the equivalent of 15 and 18 IQ points respectively) gap in IQ between whites and blacks is genetic in origin.