Transracially adopted intermediate IQ : Hereditarian nonsense
A bad, invalid argument commonly used by hereditarians is the research concerning transracial adoption. The claim usually made is that black, black-white mixed-race, white, asian-white mixed-race adoptees had IQs following the well-established genetic hierarchy seemingly B < BW < W < AW.
(I’m disappointed by the readers who cite this blog article everywhere, thinking that my article refutes the hereditarian hypothesis. It doesn’t. Disregard the title and read the entire thing. This will avoid you looking incredibly silly.)
The evidence from transracial adoption data is highly controversial, some having found results consistent with hereditarian model (Scarr & Weinberg, 1976, 1983; Scarr et al., 1993; see also the comments on the Minnesota Transracial Adoption Study, Levin, 1994; Lynn, 1994; Waldman et al., 1994) while some did not (Willerman et al., 1974; Tizard et al., 1972, 1974; Eyferth, 1961; Moore, 1986). Those studies have been discussed at length elsewhere (Chuck, Feb. 20, 2011). Whatever the final conclusion one would make, or would like to make depending on ideological inclinations, the samples are very small and most of the relevant information on adoptees and adoptive/biological parents is not available. None of the aforementioned studies provide full longitudinal information on adoptees and adoptive families. And yet, ignorant hereditarians cite this research as an established proof of racial genetic hierarchy. On the other side, however, I usually see that environmentalists have been trapped into the same fallacy as well. They cite transracial adoption data in support of their views without any care about 1) longitudinal data 2) biological parents’ characteristics. If adoption gain is empty in regard to g as was the case for educational intervention programs, we should expect vanishing gains over time. Besides, if shared environmental (c2) effects decrease over time, we may also expect vanishing gains. Hence the importance of follow-up data.
Concerning the above cited research, one obvious missing parent data is parental background/IQ. For instance, Willerman et al. (1974) report that mixed race (BW) children have higher IQ if the mother’s race was white. They state, obviously, that white mothers provide better environments for the children. But Rushton & Jensen (2005, p. 262) mentioned that white mothers average one more year of schooling than black mothers. Nisbett (2005) dismissed the argument on the grounds that one year of schooling is not a big deal. Yet in the NLSY97 and NLSY79, I found that blacks and whites differ by 1 SD in IQ while the difference in parental education was about 1 year, not more. While this could mean that “years of education” variable is unlikely to explain much of the current BW 1 SD gap, it also means there is a BW parent education gap of only 1 year despite a BW parent IQ gap of 1 SD (under the assumption that parent IQ gaps and offspring IQ gaps are equivalent). This tells us the importance of having both adoptive and biological parents’ IQ.
Discussed in Nisbett (1995, 2005), there was the strange Tizard et al. study (1972), reviewed in Tizard (1974). Their result can’t be trusted because no one has succeeded to replicate it: they found that both blacks and biracials have higher scores than whites. Given the sheer number of fraudulent adoption studies, caution is indeed warranted. Regarding the study, the SD of the adopted blacks’ IQ was much larger than those stayed in the institution. This could indicate that the intervention produced very heterogeneous effects (some IQs went up, or down, or did not move). This large variation is concerning but is not explained. Furthermore, there is no statistical test for selective placement bias and no report on the magnitude of difference in the occupation or education level of the biological parents.
Two other controversial studies cited were usually cited by environmentalists. The Eyferth (1961) and Willerman (1974). We can easily distrust Eyferth. That study shows nearly no difference between the BW and WW children. Biracials scored equally well with white adoptees. When looking at the gender groups, something unexpected happens (Jensen, 1998, p. 482). There was an extremely large male-female gap in the white group. The white girls had a 8-point deficit with regard to the white boys. No such difference was found in the biracial groups. But generally, there is no difference in IQ between males and females in childhood (Rushton & Jensen, 2010, pp. 24-25), thus calling into question the Eyferth sample (Mackenzie, 1984, p. 1229). In adulthood, there might be an advantage for the males, but it is not clear whether or not this advantage is g-loaded (Jensen, 1998, pp. 536-540; Flores-Mendoza et al., 2013, Table 1). In reality, the BW-WW difference is null only because the white girls scored extremely low; when comparing the boys however, the BW-WW difference is consistent with the hereditarian hypothesis (HH). Willerman (1974) also displayed a similar curious pattern. It has been found that 4 years-old mixed race (BW) from W mothers with B fathers couples have a higher IQ than mixed race (BW) from B mothers with W fathers. If we take a look at their Table V, however, the 9 points difference is driven by the extremely low scores of black males. Besides, the sex difference is huge between BW males and BW females (6 points for the white mothered and ~20 points for the black mothered). Even the Eyferth study does not show any sex gap for the biracials. Furthermore, the BW-W IQ gap lacks consistency. There is virtually no difference between BW females of married black mothers and BW females of married white mothers, while the gap between BW males of married black mothers and BW males of married white mothers is about 17 points. The authors have nothing to say about this.
Concerning the Moore (1986) study, who reported a null IQ difference between black and mixed-race raised by white families, Chuck affirmed (Dec.13.2012) that those numbers do not depart significantly from the hereditarian predictions. Perhaps more concerning is the IQ mean of either the blacks and biracials being so much larger than the white mean, raising serious questions about how the sample was collected.
Along with transracial adoption studies, the environmentalists often mentioned that admixture studies typically failed to establish a relationship between african ancestry and IQ. While they claimed that the old research supported their views, these studies were methodologically flawed (Lee, 2010; Reed, 1997; Jensen, 1998, pp. 478-481). It seems that these two direct tests did not provide any evidence for either environmental or genetic hypothesis, mostly due to methodological limitations (e.g., unreliability).
On the other hand, the most cited study by hereditarians is surely the Scarr & Weinberg (1976) longitudinal Minnesota transracial study. As Locurto (1990) pointed out, there was also a lot of important missing data, e.g., biological parents’ IQ, rendering interpretation rather difficult. Having the biological parents’ SES data does not make it easy to estimate what the IQ of adopted children would have been if they were not adopted.
Overall, one can even argue that whatever the IQs of interracial children would be, they are probably depressed by psychological disturbance related to self-identity. This is what Nisbett mentioned (2009) but Lee replied (2010) that both blacks and mixed-race have raised their IQ when adopted in a comfortable white home in the Moore (1986) study. One should not over-generalize that minor study. But even if Lee was right, there is still another issue. Adoptive parents may put more investment in (BW) children with 1 black, 1 white parent and less investment in (BB) children with 2 black parents. Scarr & Weinberg (1976) tested this hypothesis and found no evidence of such (parental) expectancy effect. The IQ of the 12 children wrongly believed by their adoptive parents to have 2 black parents was similar to the IQ of the 56 children correctly classified by their adoptive parents as having 1 black, 1 white parent. Like all other studies, the first obvious problem is the small sample size.
Even so, it does not necessarily rule out the colorism effect, which states that regardless of racial group, gender group, age group and all, darker skinned people tend to face more discrimination, not only at school, at the job, but also within the family. The colorism effect must be universal, affecting all people. One may even argue that parents favor lighter skinned children. Skin color variation among siblings (i.e., within family) is probably small, or at least for sure, much smaller than skin color difference between families. One possible and neat test of colorism is to control for family effect. Because it is supposed to be universal, colorism would not predict the absence of skin color-IQ relationship at the within family level. In other words, differences in skin color between full siblings must be correlated with IQ. When such analysis is done in the NLSY97, that correlation was too weak, and much weaker than skin color differences between groups of siblings between different families. These results were more supportive of the genetic prediction because skin color is an index of parental ancestry or admixture. Obviously, admixture effect must have been statistically controlled when analyzing pairs of full siblings, hence the near absence of skin color-IQ correlation. But of course, that was something these transracial adoption studies did not reveal. Generally, if darker skinned people were discriminated against on the basis of their physical appearance, there must have been a correlation between skin color and wage or education even when demographic and socio-economic factors are held constant. However, the regression coefficient for skin color as predictor is usually close to zero in the Add Health and GSS sample.
On other possible moderators of adoption gains, van IJzendoorn (2005, pp. 308-309) meta-analysis is informative in this regard. They report several findings : that age of adoption (before versus after 12 months old) and type of adoption (domestic versus international) have no significant impact on IQ. They did find however a large effect size for environmentally deprived adopted children. They were only four studies, and not six as the authors affirmed (Colombo et al., 1992, N=27; Dennis, 1973, N=136; Schiff et al., 1978, N=52; Tizard & Hodges, 1978, N=39) all having small sample sizes (total N=254). Among them the Schiff study is probably fraudulent. Locurto (1990) discusses this study. A small group of children had been adopted by high-SES families, while the siblings of these children had been raised by their biological parents, the adopted children having an advantage of 16 IQ points. The problem was that those siblings were not full siblings in reality but mostly half-siblings, which would cause even less IQ resemblance between the two groups and even more so due to the fact that some of them were born illegitimate, and that they were not raised exclusively by their biological mothers but instead by nurses or grandparents, with the result that only a very few of these children were raised by their biological parents (mother + father). As would be expected, the non-adopted “siblings” have no family stability, unlike the adopted children. All those factors, according to environmentalists, are susceptible to lower children’s IQ. The control group was not adequate. Finally, if the fittest babies were likely to have been selectively adopted, the IQ difference between adopted and non-adopted children due to adoption gains is probably over-estimated.
Even in the US black population, the portion of abused children, or those dying from malnutrition, must be incredibly small. These large gains should not be over-generalized even to the blacks. Unless they participate in an experiment, the adoptive parents do select the children they want in the real world, generally the healthier infants and children, so that at the end there will be a positive correlation between the home quality of these adoptive families and the IQ of the biological mother of these children.
But this assumes first that the IQ gains must be g-loaded. If not, all the tenets of this research collapse. When Jensen (1997) analyzed Capron & Duyme adoption data (1989, 1996) no evidence of g-gains had been found. Although the sample was very small, citing this study is a much better move than citing transracial adoption (useless) data. More recently, Jongeneel-Grimen & te Nijenhuis (2007, unpublished) meta-analyzed a collection of 4 adoption (data) IQ gains, totalling 691 subjects, with a true correlation of -0.95 (211% of variance explained by artifactual errors) which jumped at -1.05 after a final correlation for deviation from perfect construct validity; while the correlation is outside the normal range, this phenomenon can happen when applying artifact corrections (te Nijenhuis et al., 2007). Generally, as Jensen (1998, pp. 476-477) summarized, there is no good evidence that environmental factors have large impact on IQ in adolescence/adulthood, except for the extreme, non-generalizable cases :
There is simply no good evidence that social environmental factors have a large effect on IQ, particularly in adolescence and beyond, except in cases of extreme environmental deprivation. In the Texas Adoption Study, [54] for example, adoptees whose biological mothers had IQs of ninety-five or below were compared with adoptees whose biological mothers had IQs of 120 or above. Although these children were given up by their mothers in infancy and all were adopted into good homes, the two groups differed by 15.7 IQ points at age 7 years and by 19 IQ points at age 17. These mean differences, which are about one-half of the mean difference between the low-IQ and high-IQ biological mothers of these children, are close to what one would predict from a simple genetic model according to which the standardized regression of offspring on biological parents is .50.
In still another study, Turkheimer [55] used a quite clever adoption design in which each of the adoptee probands was compared against two nonadopted children, one who was reared in the same social class as the adopted proband’s biological mother, the other who was reared in the same social class as the proband’s adoptive mother. (In all cases, the proband’s biological mother was of lower SES than the adoptive mother.) This design would answer the question of whether a child born to a mother of lower SES background and adopted into a family of higher SES background would have an IQ that is closer to children who were born and reared in a lower SES background than to children born and reared in a higher SES background. The result: the proband adoptees’ mean IQ was nearly the same as the mean IQ of the nonadopted children of mothers of lower SES background but differed significantly (by more than 0.5σ) from the mean IQ of the nonadopted children of mothers of higher SES background. In other words, the adopted probands, although reared by adoptive mothers of higher SES than that of the probands’ biological mothers, turned out about the same with respect to IQ as if they had been reared by their biological mothers, who were of lower SES. Again, it appears that the family social environment has a surprisingly weak influence on IQ. This broad factor therefore would seem to carry little explanatory weight for the IQ differences between the WW, BW, and BB groups in the transracial adoption study.
An informative study is that of Scarr & Weinberg in the Minnesota (1978). They analyzed samples of children raised by adoptive parents (N=104) or by their biological parents (N=120). Based on this representative sample, with incomes ranged from under $10,000 to more than $40,000 (1978 dollars), they conducted multiple regression, child IQ as dependent var., parents’ education, father’s occupation, family income, birth rank, number of childs, parents’ IQ, and natural mother’s education, age, and occupation. The high R² (0.309) in regression models in biological families compared to the weak R² (0.075) of the adoptive families points out that SES and IQ of adoptive parents are not of great importance compared to the SES and IQ of biological parents. But as noted elsewhere, the R² is not an appropriate measure of effect size. The R² must always be square rooted to obtain the correlation (r), and in doing so, their respective r would be 0.55 and 0.27. This seems to exaggerate the effect of adoptive home however. The last column of their Table 3, which controls for all variables, including the natural mother’s background, suggests weak effects of adoptive home. The unstandardized coefficient of mother’s year of education is 0.282. If the dependent variable is the children IQ as presented in their Table 2, that implies one year of maternal education would cause 0.28 IQ point, or 10 years education for only 2.8 IQ points. The unstandardized coefficient of the father’s IQ is 0.091, which means a jump of 10 IQ points in the father would cause an increase of 0.9 IQ point in the child. The effect of mother’s IQ and father’s education are both zero. So, one would wonder whether it may be due to sampling error or measurement error or both. In any case, each of these effects is small. Perhaps the more revealing information is given in Table 5, where we see that the child-child correlation in biological families was 0.35 but -0.03 in adoptive families.
By way of regressions, Sacerdote (2002, Table 1) finds that the percentage of adoptees (N=157) with SAT-Math>500 is not correlated with adoptive parental income or population of the adoptee’s hometown (both entered as independent var.) with a model R² as low as 0.00. On the other hand, doubling the parental mean income produces a decrease in 10% of the probability for not being in college (N=290). Sacerdote (2000, Tables 4-6) also studied the outcomes of the adopted children (N=170, mean age 17.465, SD=2.192) and their parents in the NLSY79. One additional year of education of the adopted mother (N=170) and adopted father (N=151) is positively associated with AFQT score of the adopted children, with coefficients of 1.893 and 1.264, respectively. Their mean IQ was 49.32 (versus 44.4 for control children), with SD of 27.05. The coefficients (each from a separate regression) for adoptive mother’s and father’s education are 1.893 and 1.264. Taking the largest effect (i.e., mother’s effect) the SD gain is ((1.893+49.32)-49.32)/27.05 =0.069 or 1 IQ point by 1 year of education, or =((1.893*5+49.32)-49.32)/27.05 =0.349, equivalent to 0.349*15 =5 IQ points by 5 additional years of education. The same calculation using the father’s coefficient yields an effect of 3.5 IQ points. This is equivalent to calculating the Y-standardized coefficient : Coefficient(X)/SD(Y). Sacerdote noted that college graduation was more malleable by way of family environments than is the AFQT. This, again, suggests IQ has a more heritable component. Among problems, however, is that no information about random placement was available. Sacerdote (2000) also looks at the NCDS (with N about 110-120) and finds that 1 SD increase in adoptive parent SES (N=128) produces 0.16 SD and 0.29 SD increase in reading (age 8) and math (age 16) score. On the other hand, the comparable figure in control families is not appreciably larger, except for reading test at age 16 where 1 SD of adoptive and control family SES produced, respectively, 0.18 and 0.31 SD gains. The condition of random assignment is supported (p. 8). IQ was measured at age 11, the unstandardized coefficient of adoptive family SES is 1.163, the mean (and SD) of adoptive family SES amount to 6.78 (and 2.91), the mean IQ (and SD) of adopted children amount to 46.086 (and 15.551) thus the SD gain in IQ is equal to =2.91*(((1.163+46.086)-46.086)/15.551) =2.91*0.07 =0.217. That is, 3 IQ points by SD increase in the SES scale. And by using the coefficient of 1.473 for adoptive father’s education (obtained from a separate regression) yields a Y-standardized coefficient of 0.095 or 0.095 SD gain in IQ for one additional year of the adoptive father’s education, and 0.095*5 =0.475 SD gain in IQ for 5 additional years of father’s education (i.e., 2.375 IQ points). Finally, in the CAP data, 1 SD increase in birth family SES produces 0.16 SD gain in child verbal Wisconsin IQ test, and the corresponding figure was 0.17 SD for control group (N=203), and 0.04 SD for adoptive family (N=117), using the same calculation. Furthermore, adoptive mother’s education plays no role in any of the child IQ measurements whereas biological mother’s education was important.
A study of abused children has been conducted by Duyme et al. (1999). The IQ gains were meaningful. The mildly retarded (N=10) had an IQ of 65.7 and 80.2 before and after adoption, whereas the upper borderline (N=27) had an IQ of 83.0 and 95.1 before and after adoption, with respective IQ changes of 14.5 and 12.1. The first problem is that a sample of neglected children is hardly generalizable to the population at large. The second problem is that the SD of adopted children after adoption has become much larger, regardless of SES subgroup level, which suggests that the effect is extremely heterogeneous (e.g., some IQs went up, or down, or did not move) yet there is no causal mechanism proposed to explain this pattern. The adoption effect is conclusive only if the heterogeneity can be explained by better nurturing (e.g., better parenting must be associated with larger gains).
Even if the informations about the (biological and adoptive) parents and adopted children were so plentiful that we shouldn’t worry about missing anything crucial to assess parents and children’s characteristics in a longitudinal way, the problem of the hypothetical dual hypothesis, which states that within-group (WG) differences and between-group (BG) differences have different independent causes, had to be dealt with.
This is because it could be argued that what constitutes a good environment for whites is not necessarily a good environment for blacks, that is, blacks and whites are affected by the same environment in different ways. But when confronted with empirical data, no such racism effect emerges. Perhaps the most sophisticated method for assessing the effect of racism on minorities is by way of structural equation modeling (SEM) analyses. These have been performed by Rowe and his colleagues (1994, 1995; & Cleveland, 1996). Rowe (2005) discusses this research later :
The first research question was whether the covariance structures (i.e., the correlations among variables) were quantitatively the same in Blacks and Whites. For example, if Blacks had special causes of variation in mathematics that did not exist in Whites, then the total variance of math scores would be greater in Blacks than in Whites. Or if shared environmental effects were twice as strong in Blacks than in Whites, tests scores would correlate more highly within sibling pairs in Blacks than in Whites. Equal covariance matrices in the two populations, however, would imply a similarity of influences on academic achievement.
… The correlations among the three tests were nearly identical in the four groups (2 races x 2 sibling types), with the two verbal tests correlating approximately .80, and the math test correlating with each verbal test approximately .60. Sibling correlations were also on the same order of magnitude in equivalent groups. Hence, a striking similarity of the two races was observed: They were nearly identical in the association of the variables (Rowe, Vazsonyi, & Flannery, 1994; see also Jensen, 1998, pp. 350–530). As expected under a genetic hypothesis, correlations were greater for full siblings than for half-siblings. For instance, the sibling correlations on reading comprehension were .36 and .42 in White and Black full siblings, respectively, compared with .09 and .22 in White and Black half-siblings, respectively. The correlation pattern, however, did not always support a genetic hypothesis; but the Black sample was small, and thus its correlations had large standard errors. Because the method of maximum likelihood was employed, the structural equations’ fit used all the statistical information in the covariance matrices. In the best-fitting model, both the genetic and shared environmental latent variables were retained.
Once the equivalence of correlation matrices between Blacks and Whites has been established, a second step is fitting the racial means. In the model, the latent genetic and shared environmental factors were permitted to have a racial mean difference. The product of factor loadings of a test and this mean difference should reproduce the observed PIAT mean. Because the PIAT racial differences must be proportional to factor loadings for the model to be correct, where mean differences belong in a model of within-group variation can be tested statistically. A good fit increases one’s confidence in the explanation of mean differences.
On the PIAT subtests, racial mean differences ranged from 0.3 to 0.5 standard deviation units. This relatively small racial difference may reflect the sampling bias noted earlier (i.e., that the siblings were the offspring of young mothers). It is possible to calculate from the factor loadings and a factor’s mean difference the percentage of a test’s mean difference due to shared environment and to genes. In the best-fit SEM, the genetic factor accounted for 66%–74% of the racial mean difference in reading comprehension and reading recognition and 36% of the racial mean difference in mathematics, which was the test most strongly loaded on the shared environment factor. The shared environmental latent factor accounted for the remainder of the mean differences.
In sum, when minority and majority children attain a similar level of achievement/intellectual ability through a different development process, there will be a statistically significant between-race difference in the correlation of, say, IQ with achievement or any kind of familial environmental measures. But no difference between these correlations had been observed at the between-race level (blacks, hispanics, whites, asians). Whatever factors and relationships were important at the within-race level were also important at the between-race level, meaning that the environmental factors responsible for race differences originate from environmental factors operating at the individual level. This completely rules out the ‘discrimination argument’. With regard to psychometrics, strictly speaking, Dolan (2000, 2001) showed that measurement bias was not present when comparing lower black IQ and higher white IQ in the USA. Lubke et al. (2003) confirmed these findings in an old sample and explained that the evidence of measurement equivalence can be taken for granted that the factors responsible for individual IQ differences and those responsible for racial IQ differences are of the same nature. This research has been replicated by Trundt et al. (2013). Transracial adoption data can’t rule out this argument however. Like correlational analyses, it provides absolutely no insight on causal pathways. Such data therefore is totally useless.
As seen above, a lot of questions are not answered directly by transracial adoption data. The only interest in those transracial adoption and mixed-race data is to show it does not contradict the hereditarian hypothesis. If we manage to establish a non-significant racial IQ difference between black adoptees and white adoptees from white parents, this contradicts indeed the hereditarian model but in no way a significant IQ difference would provide any support for a causal genetic model. It is not a bad move for environmentalists to cite those studies when they do not support the hereditarian hypothesis, but to think that when consistent with the said hypothesis, a causal pathway has been established is a foolish idea. This is bad move.
References.
Capron, C., & Duyme, M. (1989). Assessment of effects of socio-economic status on IQ in a full cross-fostering study. Nature, 340, 552–554.
Capron, C., & Duyme, M. (1996). Effect of socioeconomic status of biological and adoptive parents on WISC–R subtest scores of their French adopted children. Intelligence, 22, 259–275.
Dolan, C. V. (2000). Investigating Spearman’s hypothesis by means of multi-group confirmatory factor analysis. Multivariate Behavioral Research, 35, 21–50.
Dolan, C. V., & Hamaker, E. L. (2001). Investigating Black–White differences in psychometric IQ: Multi-group confirmatory factor analyses of WISC–R and K–ABC and a critique of the method of correlated vectors. In F. Columbus (Ed.), Advances in psychology research (Vol. 6, pp. 30–59). Huntington, NY: Nova Science.
Duyme, M., Dumaret, A., & Tomkiewicz, S. (1999). How can we boost IQs of “dull” children? A late adoption study. Proceedings of the National Academy of Sciences, USA, 96, 8790–8794.
Eyferth, K., Brandt, U., & Hawel, W. (1960). Farbige kinder in Deutschland [Colored children in Germany]. Munich: Juventa Verlag.
Eyferth, K. (1961). Leistungen verscheidener Gruppen von Besatzungskindern in Hamburg-Wechsler Intelligenztest für Kinder (HAWIK) [Achievement of children on the Hamburg-Wechsler Intelligence Scale for Children]. Archiv für die gesamte Psychologie, 113, 222–241.
Jensen, A. R. (1997). Adoption data and two g-related hypotheses. Intelligence, 25, 1–6.
Jensen, A. R. (1998). The g factor. Westport, CT: Praeger.
Lee, James J. (2010). Review of intelligence and how to get it: Why schools and cultures count, R.E. Nisbett, Norton, New York, NY (2009). Personality and Individual Differences, 48, 247–255.
Levin, M. (1994). Comment on Minnesota Transracial Adoption Study. Intelligence, 19, 13–20.
Lucurto, C. (1990). The malleability of IQ as judged from adoption studies. Intelligence, 14, 275-292.
Lubke, G. H., Dolan, C. V., Kelderman, H., & Mellenbergh, G. J. (2003). On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model. Intelligence, 31, 543–566.
Lynn, R. (1994). Some reinterpretations of the Minnesota Transracial Adoption Study. Intelligence, 19, 21–27.
Mackenzie, B. (1984). Explaining race differences in IQ. American Psychologist, 39, 1214-1233.
Moore, E. G. J. (1986). Family socialization and the IQ test performance of traditionally and transracially adopted Black children. Developmental Psychology, 22, 317–326.
Nisbett, R. E. (1998). Race, genetics, and IQ. In C. Jencks & M. Phillips (Eds.), The Black–White test score gap (pp. 86–102). Washington, DC: Brookings Institute.
Nisbett, R. E. (2005). Heredity, environment, and race differences in IQ: A commentary on Rushton and Jensen. Psychology, Public Policy, and Law, 11, 302–310.
Reed, T.E. (1997). The genetic hypothesis; it was not tested but it could have been. American Psychologist, 52, 77-78.
Rowe, D. C., Vazsonyi, A. T., & Flannery, D. J. (1994). No more than skin deep: Ethnic and racial similarity in developmental process. Psychological Review, 101, 396–413.
Rowe, D. C., Vazsonyi, A. T., & Flannery, D. J. (1995). Ethnic and racial similarity in developmental process: A study of academic achievement. Psychological Science, 6, 33–38.
Rowe, D. C., & Cleveland, H. H. (1996). Academic achievement in Blacks and Whites: Are the developmental processes similar? Intelligence, 23, 205–228.
Rowe, D. C. (2005). Under the skin: On the impartial treatment of genetic and environmental hypotheses of racial differences. American Psychologist, 60, 60–70.
Rushton, J. P., & Jensen, A. R. (2005). Thirty years of research on race differences in cognitive ability. Psychology, Public Policy, and Law, 11, 235–294.
Sacerdote, Bruce (2000). The Nature and Nurture of Economic Outcomes. National Bureau of Economic Research (Cambridge, MA) Working Paper No. 7949.
Sacerdote, Bruce (2002). The Nature and Nurture of Economic Outcomes. American Economic Review, 92(2): 344-348.
Scarr, S., & Weinberg, R. A. (1976). IQ test performance of Black children adopted by White families. American Psychologist, 31, 726–739.
Scarr, S., & Weinberg, R. A. (1978). The influence of “family background” on intellectual attainment. American Sociological Review, 43, 674-692.
Scarr, S., & Weinberg, R. A. (1983). The Minnesota adoption studies: Genetic differences and malleability. Child Development, 54, 260-267.
Scarr, S., Weinberg, R. A., & Waldman, I. D. (1993). IQ correlations in transracial adoptive families. Intelligence, 17, 541-555.
te Nijenhuis, J., van Vianen, A. E., & van der Flier, H. (2007). Score gains on g-loaded tests: No g. Intelligence, 35, 283–300.
Tizard, B. (1974, February 1). IQ and race. Nature, 247, 316.
Trundt Marie (2013). Construct bias in the differential ability scales, (DAS-II): A comparison among African American, Asian, Hispanic, and White ethnic groups.
van IJzendoorn, M. H., Juffer, F., & Poelhuis, C. W. K. (2005). Adoption and cognitive development: A meta-analytic comparison of adopted and nonadopted children’s IQ and school performance.
Waldman, I. D., Weinberg, R. A., & Scarr, S. (1994). Racial-group differences in IQ in the Minnesota Transracial Adoption Study: A reply to Levin and Lynn. Intelligence, 19, 29–44.
Willerman, L., Naylor, A. F., & Myrianthopoulos, N. C. (1974). Intellectual development of children from interracial matings: Performance in infancy and at 4 years. Behavioral Genetics, 4, 84–88.