Author: Dalliard (page 2 of 3)

Bifactor Model Investigation of Spearman's Hypothesis

I’ve been catching up on recent research on psychometrics, behavioral genetics, race differences, and so on. I’ll be posting some comments on papers I found particularly interesting. The first is Frisby and Beaujean’s study of Spearman’s hypothesis. Continue reading

The Elusive X-Factor, or Why Jonathan Kaplan Is Wrong about Race and IQ

Philosopher Jonathan Kaplan recently published an article called Race, IQ, and the search for statistical signals associated with so-called “X”-factors: environments, racism, and the “hereditarian hypothesis,” which can be downloaded here. His thesis is that the black-white IQ gap could plausibly be due to racism and what he calls racialized environments. He presents simulations in support of this argument. He also argues that “given the actual state of the world there is no way to generate any reasonably strong evidence in favor of the hereditarian hypothesis.”

I have written a detailed critique of his claims. In short, he is wrong. Here’s the abstract of my article:

Jonathan Michael Kaplan recently published a challenge to the hereditarian account of the IQ gap between whites and blacks in the United States (Kaplan, 2014). He argues that racism and “racialized environments” constitute race-specific “X-factors” that could plausibly cause the gap, using simulations to support this contention. I show that Kaplan’s model suffers from vagueness and implausibilities that render it an unpromising approach to explaining the gap, while his simulations are misspecified and provide no support for his model. I describe the proper methodology for testing for X-factors, and conclude that Kaplan’s X-factors would almost certainly already have been discovered if they did in fact exist. I also argue that the hereditarian position is well-supported, and, importantly, is amenable to a definitive empirical test.

The PDF is available at Open Differential Psychology. You can also read the article below the cut. Continue reading

About That Gene-Environment Interaction Study by Turkheimer et al.

One of the more famous studies on the heritability of IQ is Eric Turkheimer and colleagues’ 2003 paper called Socioeconomic status modifies heritability of IQ in young children. According to Google Scholar, it has been cited more than 700 times. Based on a sample of 7-year-old twins, the study found that in impoverished families the shared environment accounted for about 60 percent of IQ variance while heritability was close to zero. In contrast, heritability was high and the effect of the shared environment nugatory in affluent families.

The literature on the interaction between socioeconomic status and IQ heritability is very mixed. Several studies besides Turkheimer’s find such interaction (although in no other study is it as extreme as in Turkheimer et al. 2003), but others, including some with the very best study designs, find none. I am not going to try to adjudicate between these contradictory findings at this time. Rather, I will show some interesting, hitherto unpublished (well, careful readers of Boetel and Fuerst’s The Nature of Race have seen them already) results pertaining to Turkheimer’s study and the question of race differences. Continue reading

Is IQ Heritability Moderated by Race? An Analysis of the CNLSY Sample

The strong heritability of IQ is well established for white populations in America, with dozens of studies confirming the basic findings. When it comes to heritability in non-whites, the handful of studies that exist (see Jensen 1998, p. 446ff.; Rowe et al. 1999; Guo & Stearns 2002; cf. John’s recent post) do not allow us to conclude that heritability is lower (or higher) in non-white Americans than it is among white Americans, but there is a sore need for more research.

To diminish this uncertainty, we compared the heritability of several different cognitive abilities in whites, blacks, and Hispanics in the CNLSY sample. The sample, which consists of the children of the mothers who are part of the NLSY79 study, includes the results of various ability tests administered between ages 3 and 13. Continue reading

Racial Differences on Digit Span Tests

In digit span tests, the respondents are asked to repeat a string of digits. There are two variants of the test, forward digit span (FDS) and backward digit span (BDS). In FDS, the digits are repeated in the order of their presentation, while in BDS they must be repeated in the reverse order. The largest number of digits that a person can repeat without error is his or her forward or backward digit span.

It is well-established that the black-white gap is substantially larger on BDS than FSD (see references in The g Factor by Jensen, p. 405, Note 22; see also my recent analysis of the DAS-II). However, replication is always good, so I analyzed black-white differences in the CNLSY sample, which contains FDS and BDS scores for relatively large samples of black and white children. Additionally, I compared the digit span performance of Hispanic American children to that of blacks and whites. Continue reading

Spearman's Hypothesis and Racial Differences on the DAS-II

According to Spearman’s hypothesis, the magnitude of the black-white gap on a given cognitive ability test is primarily determined by the test’s g loading. Tests that are better measures of g are associated with larger gaps.

The Differential Ability Scales, Second Edition, or the DAS-II, is an IQ test for assessing children and adolescents. It comprises a total of 21 subtests, although in the present analysis only 13 subtests are used, because not all tests are administered across age groups. I will use the method of correlated vectors (MCV) to test whether g loadings are correlated with mean racial differences on the DAS-II subtests. In addition to the black-white gap, I will also investigate if the test performance of Asians and Hispanics is predicted by g loadings. Continue reading

Some Further Notes on g and Shalizi

My defense of psychometric g has attracted more attention than I expected. It has been discussed on Metafilter, Noahpinion, Less Wrong, and iSteve, among other places. In this post, I will address some criticisms of my arguments and comment on a couple of issues I did not discuss earlier. Continue reading

Is Psychometric g a Myth?

As an online discussion about IQ or general intelligence grows longer, the probability of someone linking to statistician Cosma Shalizi’s essay g, a Statistical Myth approaches 1. Usually the link is accompanied by an assertion to the effect that Shalizi offers a definitive refutation of the concept of general mental ability, or psychometric g.

In this post, I will show that Shalizi’s case against g appears strong only because he misstates several key facts and because he omits all the best evidence that the other side has offered in support of g. His case hinges on three clearly erroneous arguments on which I will concentrate. Continue reading

Colorism in America?

Previously, we established that skin color and intelligence are correlated in the NLSY97 sample as predicted by hereditarian theory. Continuing this investigation, we looked into how these variables go together within and between African American families in the same sample. In other words, we wanted to know if lighter-skinned individuals tend to be smarter than their darker-skinned siblings just as the average light-skinned black in the general population is smarter than the average dark-skinned black. Continue reading

Spearman’s hypothesis and the NLSY97-ASVAB, part 2

Skin color is a (very imperfect) proxy for white ancestry in African Americans and Hispanics. If racial and ethnic gaps in intelligence have a genetic component, we would expect lighter skinned individuals to have higher IQs, on the average. Further, because g is the main heritable component of intelligence, tests with higher g loadings should show larger associations with skin color.

We investigated these hypotheses in the NLSY97 sample. It contains interviewer reports on facial skin tone of the respondents as measured on a scale of 1 (lightest) to 10 (darkest). The interviewers used a “color card” as a reference.

All the correlations below are significant at conventional levels unless otherwise indicated. Because of the way skin color is coded in this analysis, negative correlations between skin color and test performance are expected if the hereditarian hypothesis is correct.

I found that among blacks, the correlation between g scores and skin color (darkness) was -0.133 (N=1856), whereas T scores were unrelated to skin color (r=-0.012, ns; N=1856). Among Hispanics, g scores correlated with skin darkness at -0.123 (N=1051), while T scores were unrelated to skin color (r=0.062, ns; N=1051). Therefore the results are about as expected. (See the previous post for information about the T factor.)

Applying again the method of correlated vectors (MCV), we found that vectors of skin color-test gap correlations were strongly and significantly associated with g loadings within populations. In other words, lighter-skinned individuals tended to outscore darker-skinned coracials/coethnics more on tests with higher g loadings. Among blacks, the correlations were r=-0.84 and rho=-0.75, and among Hispanics r=-0.60 and rho=-0.59 (correcting for unreliability would make all these correlations somewhat stronger).

The MCV results could be interpreted in terms of genetic effects: tests with higher g loadings are more heritable, and skin color is a proxy for white ancestry and thus presumably better “IQ genes”. But why would these within-population color analyses produce the expected correlations between g loadings and race markers (i.e., skin tone) when the between-population MCV analysis, presented in the previous post, did not? It appears that on the ASVAB g is the major source of racial/ethnic differences, but the T factor also contributes to the gaps. (Cohen’s d’s on the g scale were B-W 1.124, B-H 0.368, and H-W 0.759, while on the T scale they were B-W 0.561, B-H 0.261, and H-W 0.306.) However, T is not associated with skin color within populations, which suggests that its heritability is low and it is linked to race and ethnicity for non-genetic reasons. This would explain why the MCV results from within- and between-population analyses differ.

In the NLSY97, higher g is associated with lighter skin among blacks and Hispanics. This is in accord with hereditarian theory, but nurturists would of course argue that these correlations are due to colorism. These competing hypotheses could be tested by comparing skin color-IQ associations within and between families, as was done here.

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