Year: 2013 (Page 1 of 6)

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

On the partial mediating role of processing speed between black-white differences, IQ and GPA

In the present article, I demonstrate that processing speed (using ASVAB speeded subtests) has a modest predictive validity over the g factor extracted from the ASVAB (non-speeded subtests) in predicting overall GPA in the NLSY97, within black, hispanic and the white sample. Next, I investigate the mediation of speed in the black-white difference in IQ (g). For both analyses, processing speed accounts for a modest portion of these associations. Nonetheless, some issues related with such ‘psychometric speed’ measures need to be clarified.

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Flynn contra Rushton on principal component analysis : A failed replication

While Rushton (1999) demonstrates, using PCA, that g and black-white differences were related, with Flynn Effect (FE) gains over time showing no relationship with the aforementioned variables, Flynn (2000) has challenged Rushton in arguing that Wechsler’s subtest loadings on the Raven test, an universally recognized measure of fluid g, showed positive correlations with both black-white differences and FE gains. Up to now, Flynn’s estimates of g fluid (Gf) has not been scrutinized. I will show presently that the Flynn’s g-fluid (call it, fluid reasoning) and Rushton’s g-crystallized (call it, consolidated knowledge) anomaly was solely due to a single statistical artifact, namely, g_Fluid vector unreliability. By adding additional samples, I created a new, updated Wechsler’s subtest Gf loadings. The present analysis comes to the conclusion that g_Fluid was not in fact correlated with FE gains. Furthermore, this Gf variable has been correlated with other variables as well, such as, heritability (h2), shared environment (c2), nonshared environment (e2), adoption IQ gains, inbreeding depression (ID), and mental retardation (MR). I will also discuss these findings in light of Kan’s (2011) thesis against the hereditarian hypothesis.

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An Analysis of the NLSY79 and NLSY97 Full Sibling Correlations by Race

In his classic work, Educability and Group Differences, Arthur Jensen presented a number of lines of evidence in defense of his thesis that the Negro-White difference in psychometric intelligence had a congenital component. On the basis of full sibling correlations and relations, Jensen offered the following arguments:

(a1) The full sibling correlations for Blacks and Whites are comparable; (a2) unshared environmental hypotheses, such as nutritional ones, would predict otherwise (pg. 338-339).

(b1) The full sibling correlations for Blacks and Whites are comparable; (b2) a shared environmental hypothesis of group differences would predict otherwise, assuming that the within population heritablities were the same (pg. 108-109).

(c1) The average absolute difference between full siblings is no greater for Blacks than for Whites; (c2) unshared environmental hypotheses, such as nutritional ones, would predict otherwise (pg. 338-339).

(d1) When matching Blacks and Whites on IQ, one sees differential sibling regression, a differential regression which does not decrease with increasing IQ; (d2) an environmental hypothesis of group differences would not predict this (pg. 118-119). Continue reading

Jensen effect on racial IQ differences and GPA controlling for SES in the NLSY79 and NLSY97

In The g Factor, Jensen (1998, pp. 384-385) states that because races differ in SES levels, the Spearman-Jensen effect (i.e., g-loading correlates) found in racial IQ differences (hispanics, denoted H; blacks, denoted B; whites, denoted W) could simply reflect this fact. One reason seems to be that SES correlates with g-loadings although he affirms that it was irrelevant to Spearman’s hypothesis (furthermore, this does not necessarily imply that IQ gain due to SES improvement is itself g-loaded; see Jensen 1997, or Metzen 2012). When testing this hypothesis anyway, it was shown that the WISC subtests’ correlation with SES is correlated with WISC g-loading in both the white and black samples. Also, when matching for SES, the BW difference still correlates strongly with g-loadings. Presently, I will try to replicate this result.

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A few New Analyses

Hu (2013, September, 5; 2013, July, 5; 2013, August, 18) has raised some interesting points. I will comment on a few of them here and present several new analyses.

Cultural Loading, Heritability, and the BW gap

As Meng Hu noted, Kan et al. (2011) showed that subtest cultural-loadings, as they estimated them, correlated both with the magnitude of the B/W subtest gaps and with subtest heritability estimates. The authors interpreted these associations as support for a GxE hypothesis of individual differences and offered a model similar to that proposed by Flynn and Dickens (2001). Moreover, Kan et al. (2011) saw the associations between cultural-load and heritability and between cultural-load and the magnitude of the BW gap as problematic for what they termed a biological g model. Below, I will show that g-loadings fully mediate the association between cultural loadings and the two other variables noted and therefore that what is in need of explanation is only the association between cultural-loadings and g-loadings. I will then proceed to offer an account for this.

First, I looked to see if g-loadings mediated the association between the BW gap and cultural loadings. They did. Then I looked to see if cultural-loadings mediated the association between the BW gap and g-loadings. They did not fully. The results are shown below. As reliability estimates were not presented for all subtests, I ran the analysis with and without reliability corrections. Continue reading

Investigation of the relationship between mental retardation with heritability and environmentality of the Wechsler subtests

The present analysis is an extension of Spitz’s earlier (1988) study on the relationship between mental retardation (MR) lower score and subtest heritability (h2) and g-loadings. These relationships were found to be positive. But Spitz himself haven’t tested the possibility that MR (lower) score could be related with shared (c2) or nonshared (e2) environment. I use the WAIS and WISC data given in my earlier post, and have found that MR is not related with c2 and e2 values. These findings nevertheless must be interpreted very carefully because the small number of subtests (e.g., 10 or 11) is a very critical limitation.

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