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.
I present here some more evidence about the race*SES interaction concerning IQ from various survey data. The techniques are employed. Comparison of means among different SES strata, ANCOVA and multiple regression.
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.
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.
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.
I analyze two studies who provide the necessary data for studying the test-retest effects, namely, Watkins (2007), Schellenberg (2004, 2006). Both used the Wechsler’s subtests, and the correlations between the IQ changes among those subtests with g-loadings are negative, in line with earlier studies on this topic.
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.
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). To recall, Jensen (1997) analyzed Capron and Duyme adoption data (1996) with N=38, a study often cited by environmentalists as evidence against the hereditarian hypothesis. In Adoption Data and Two g-Related Hypotheses, Jensen shows that 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.