The present analysis, using the NLSY97, attempts to model the structural relationship between the latent second-order g factor extracted from the 12 ASVAB subtests, the parental SES latent factor from 3 indicators of parental SES, and the GPA latent factor from 5 domains of grade point averages. A structural equation modeling (SEM) bootstrapping approach combined with a Predictive Mean Matching (PMM) multiple imputation has been employed. The structural path from parental SES to GPA, independently of g, appears to be trivial in the black, hispanic, and white population. The analysis is repeated for the 3 ACT subtests, yielding an ACT-g latent factor. The same conclusion is observed. Most of the effect of SES on GPA appears to be mediated by g. Adding grade variable substantially increases the contribution of parental SES on the achievement factor, which was partially mediated by g. Missing data is handled with PMM multiple imputation. Univariate and multivariate normality tests are carried out in SPSS and AMOS, and through bootstrapping. Full result provided in EXCEL at the end of the article.
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.
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.
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.
After analyzing the NLSY97, it appears that religious and rich people reported to be more happy. But controlling for health reduces the influence of these variables. This holds true not only in the white sample, but also in the hispanic and black samples.
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.
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.
Some variables in the Add Health and the NLSY97 allow us to investigate the relationship of skin color with IQ and racial ancestry with IQ (AHPVT scores and ASVAB scores) among the US black population. Given the positive results, a question worth considering is whether or not skin color mediate the relationship between family ancestry and IQ.