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.

I will use the same technique as was done in the NLSY97, namely, Jensen’s method of correlated vectors. I found a total of 55 mulattoes, and 156 multiracials. The ‘mulatto’ variable specified blacks with white (i.e., european) ancestry, while the ‘multiracial’ variable specified blacks with non-black ancestry, that is to say, blacks with some mexican ancestry, or asian ancestry, or indian (american) ancestry, or white ancestry, and so forth.

The magnitude of the BB(blacks)/BW(mulattoes) gap in the g and non-g sources in ASVAB subtests can be estimated first by factor analyzing (using PAF) the 10 ASVAB subtests and producing two unrotated factors, the first explaining 58% of the total variance, the second 11%. Using then the scores in g factor and non-g factor, derived from a comparison of means program (in SPSS), I produced the following table :

The pooled sd formula estimated a non trivial gap of around 0.43 SD or 6 points difference between blacks and mulattoes. The difference, as expected, was smaller between blacks and multiracials (0.36 SD). In any case, the non-g source of variance is trivial and the sign is even negative, which means that blacks outsmart multiracials in non-g sources. Another technique is the use of partial correlation between the multiracial dichotomized variable (1=black, 2=multiracial) and each ASVAB subtests, with PAF1 as a control variable. The correlations fell near zero, but this was not the case when PAF2 is used as control variable (syntax **here**). This means that g is the source of those differences. The point-biserial correlations (before partialling out g) were not very high as shown below :

Excel file **here**. One reason for these low correlations could be that the frequency distribution of the dichotomized variable is far from being optimal. As I pointed out before, the departure from an optimal 50/50 split of any dichotomized variable will reduce the obtained correlations. For instance, the frequency distribution for my mulatto variable is 0=3024, 1=55, and for my multiracial variable 0=2925, 1=156. The relevant formulas for correcting unequal sample sizes in point-biserial correlations are provided by Hunter & Schmidt (**2004**, p. 280). This explains why the r_{pbs} for multiracial are larger than r_{pbs} for mulatto even if the d gap is smaller using the multiracial variable. After correction for unequal sample size, using Hunter & Schmidt formulas (with Excel functions), I produced the following table :

As can be seen, the corrected rpbs for Mulatto variable seems a little bit higher than rpbs for Multiracial variable. For AFQT (2006 revised) the corrected rpb is around 0.22 (by way of comparison, the ASVAB 1999 BB-BW gap shows a corrected rpb of 0.28, and uncorrected rpb of 0.19). In any case, using either corrected or uncorrected rpbs, we can estimate the correlation between the magnitude of BB-BW difference and the magnitude of black-white d gap, B-W g loadings, B-W non-g loadings, as shown below.

For obtaining these correlations, I use the estimates of B-W g-loadings and non-g-loadings, as well as B-W d gap reported in my earlier post on IQ regression to the mean (Hu, **May.3.2013**). Given this, the Jensen effect is apparent, and to this can be added another Jensen effect test by Chuck (**May.9.2013**) in another article on the Scarr et al. (1977) admixture data among US blacks.