This is a continuation of The facts that need to be explained.
We can classify potential causes of a mean difference according to the neuropsychological pathways by which they are proposed to work. The most general classes are cultural and biological causes, where the former refers to influences which act through sensory-informational pathways and the latter refers to influences which act through non-sensory physiological pathways. The say that the Black-White difference has an environmental-biological origin is to say that the cause lies in environmental influences which affect intelligence though the latter pathways. A near exhaustive list of such possible influences is given by Wiessen (2009); they include: Prenatal exposure to pollutants; Prenatal experiences leading to low birth weight; Fetal alcohol syndrome; Maternal Iron deficiency; Hunger; Organic disorders; Iron deficiency; Lead poisoning; Severe dehydration; Exposure to drugs; Postnatal exposure to pollutants; Postnatal exposure to heat; Poor health; Hypertension; Mercury exposure; Inequality in health and dental care; Inequality in immunizations, parasite infections, and rates of breast feeding. The commonality, again, is that the effects of these influences on mental ability are not mediated through sensation and perception.
These influences can be decomposed into Prenatal and Postnatal ones. One theoretical consideration with Prenatal influences, that needs to be born in mind, is that the net effect on a population mean is not clear, even when there are clear negative effects on the individual level. Specifically, insofar as these factors lead to fetal death and there is selection for physiologically healthier offspring, given that general intelligence is related to general fitness (Gottfredson et al, 2009), such influences by pruning the physically and mentally weak can, in principle, raise a population’s IQ.
On theoretical grounds, both Prenatal and Postnatal influences are problematic explanations for the mean differences, as they tend to be unshared environmental influences within populations. That is, they decrease the correlation between siblings. This is problematic as:
(a) Differential sibling regression to the mean studies imply that the causes of the B-W difference have a relatively uniform effect within and across families. (Refer to section H. Sibling and offspring regression towards the mean and Points G, H, and I together).
(b) The Black and White sibling correlations are equivalent (Jensen, 1974).
(c) The variance in IQ explained by unshared environmental factors (factors which make sibs different) is approximately the same in the Black and White population. (See: Gou and Stearns 2002, for example).
This problem exists so long as the influences in single or sum are not relatively uniformly distributed between family members of the populations affected. And frequently the types of environmental-biological influences cited are not (e.g., nutrition and infectious disease; see: Corruccini, 1983).
Many specific explanations are untenable as:
(d) The exposure to the influences decreases with SES and yet the differences are larger, or at least not smaller, at higher SES levels (Jensen, 1998). (For an important class of environmental-biological influences (e.g., Lead pain exposure, malnutrition, etc.), the rate of exposure is conditioned on SES. These types of influences, then, are poor candidates for explaining the gap at the higher SES levels; and obviously, if, in aggregate, they explained a substantial portion of the gap, then in proportion, the gap should be smaller at higher SES levels, which it is not.)
Prenatal factors are problematic specifically as:
(e) IQ differences increase with age; according to Flynn and Dickens (2005), Fryer and Levitt (2004), and Rippeyoung, (2006), they are quite small at young ages. Indeed, Flynn make this a point:
It has also emerged that they steadily lose ground on white people with age. At just 10 months old, the average score is only one point behind; by the age of 4, it is 4.6 points behind, and by the age of 24, the gap is 16.6 points. This could be due to genes, but the steady rate after the age of 4 (about 0.6 IQ points lost every year) suggests otherwise, since genetically driven differences such as height differences between males and females tend to kick in at a certain age (Flynn, 2008. A tough call
While this is a poor argument against genetic influences for reasons discussed elsewhere, this is a fair one against prenatal ones, as such influences should affect performance, between populations, no less at young ages than at old. This is, after all, what is found within populations for many of these influences. This point about the magnitude of the gap, age, and biological influences is accentuated when one controls for cultural influences (see below). Cultural influences likely causally explain a substantial portion of the young age gap Were biological-environmental influences to substantially causally explain the older age gap, one would have to propose either that the gap at young ages was large and negative or maintain that the influence of these factors increase with age. Both are implausible.
Both prenatal and postnatal factors are problematic as:
(f) Differences are g-selective. Blacks are depressed in neither weight nor height; Nor in rote memory; nor in psychomotor ability (e.g., reactivity to sensory stimulation and coordination; see: Haiback et al., 2011). And yet, it is empirically established that many of these influences (e.g., lead poisoning, mercury poisoning, malnutrition, etc.) affect memory and psychomotor ability.
Some of these causally biological-influences can be classified as maternal factors (e.g., rates of breast feeding and birth weight).To the extent that they are said to affect Black mothers more frequently than White mothers, they are problematic explanations as:
(g) The scores of mixed race, Black and White, individuals are, at least at older ages, intermediate to those of monoracial Black or White individuals. And yet the vast majority of mixed race individual have White mothers.
We can turn to specific proposed influences to see how our points above apply. In “Health Disparities and Gaps in School readiness,” Janet Currie discussed numerous possibilities and makes that case that environmental-biological factors can explain 2 IQ points. She tells us:
Three common chronic conditions—dental caries, allergies, and ear infections—are potentially implicated in cognitive and behavior problems in children, but research is not yet far enough along to make it possible to estimate how large those effects might be.
Dental caries (tooth decay) is the most common childhood chronic condition. Chronic pain from dental disease can affect both children’s cognitive attainment and their behavior.
Here, it is either being argued that (1) there is a direct biological effect on latent intelligence, (2) that there is an indirect effect on latent intelligence, such that these influences lead to a differential developments of IQ, or (3) that these influences lead to different IQ tests scores (e.g., via distractions.) (3) can be dismissed as the IQ gap is a true latent intelligence gap . (2) represents causally cultural influences, not biological – which will be discussed latter. (1) is problematic, by point (d) above, in addition to others, because the exposure to these influences is conditioned on SES, as Currie notes. Currie continues:
The literature on asthma strongly suggests that its greater prevalence among impoverished children could be due in part to characteristics of their housing. The degree of segregation by race, ethnicity, and income in American cities suggests that some groups are more likely than others to be exposed to environmental hazards. Moreover, to the extent that known environmental hazards are capitalized into housing prices, pollution will lower rents, making hazardous areas more attractive to poor people than to rich ones. Conversely, low land prices in poor neighborhoods may draw in new hazards. One environmental hazard whose effect on children’s health has been studied extensively is lead…A calculation similar to those made for ADHD and asthma suggests that differing exposure to lead might be responsible for 0.2 point of the average eight-point racial gap in scores assumed above. If racial disparities in exposure to other environmental hazards have also grown, exposure to such hazards could be an increasingly important cause of disparities in school readiness.
Again, we run into problems with our point (d) above (in addition to (a) through (c). And, as lead poisoning affects memory and psychomotor abilities, our point (f). Currie continues:
Iron deficiency is much more common among poor and black children than among
other children. Twice as many black children as white children are iron deficient (16 percent versus 8 percent for toddlers), while poor children are more than 50 percent more likely to be deficient than nonpoor children. If iron deficiency impairs cognitive functioning, it could well be responsible for part of the test score disparities between blacks and whites and between poor and nonpoor children. Sally Grantham-McGregor and Cornelius Ani reviewed observational studies that followed a group of children over time and found that conditional on measures of social background, gender, and birth weight, low hemoglobin levels in children aged two or younger are strongly linked to poor schooling achievement, cognitive development, and motor development in middle childhood. These studies, however, do not establish a causal relationship, given the strong association between iron deficiency and other factors that could affect development, such as poverty.
In addition to points (a) through (c), (d) and (f), this explanation is problematic due to point (e). Turing to maternal factors, Currie continues:
Typically they find IQ gains of two to five points for healthy infants and up to eight points for low birth weight babies. Once again, however, given the strong relationship between breast feeding and various measures of socioeconomic status, it is unclear whether the association between breast feeding and cognition is causal….
[..]If, however, breast feeding does affect IQ scores, then the racial differences in prevalence are large enough to explain a significant part of the gap in the generic test score that I have been considering. Suppose, for example, that breast feeding for six months raises IQ by five points, or about one-third of a standard deviation. Then the fact that 29 percent of white infants, but only 9 percent of black infants, are breast fed for six months would generate a one point difference in average scores.
Within populations, controlling for maternal IQ substantially attenuates the correlation between breast feeding and offspring IQ. Since within populations, the relationship between breast feeding and IQ is only partially causal, between populations the relationship could be partially causal, fully causal, or fully non-causal. The reason for supposing that, between populations, it is less causal than not, is our point (g) in addition to (a) through (e). Whatever the case, the amount of difference explainable is not very large.
This exhausts the environmental biological explanations that Currie has to offer. In the same special journal issue, Nancy Reichman discusses the impact of birth weight in “Low Birth Weight and School Readiness”:
Only two studies of which I am aware have presented data indicating the potential effect of low birth weight on racial test score gaps. Yolanda Padilla and her coauthors, in a study using = National Longitudinal Survey of Youth (NLSY) child data and focusing on the effects of the Mexican American birth weight advantage on early childhood development, found that low birth weight explains less than 1 percent of the (unadjusted) black-white gap in scores on the Peabody Picture Vocabulary Test-Revised (PPVT-R) among three- and four-year-olds in the late 1980s and early 1990s.52 Jeanne Brooks-Gunn and her coauthors presented a similar estimate in a recent analysis of the contributions of family and test characteristics to the black-white test score gap.53 Also using NLSY child data, they found that low birth weight and gender together explain less than 2 percent of the unadjusted racial gap in PPVT-R scores at age five.
My own estimate of the potential impact of birth weight on the racial gap in one test of cognitive ability—full-scale IQ score—is similar, though somewhat higher. My subject is all black and white infant survivors born in 2000, including multiples. In contrast to Padilla and Brooks-Gunn I do not use the NLSY data, because although that data set has actual test scores, it may underrepresent the very lightest babies. Instead I use vital statistics data, which provide exact race-specific birth weight distributions for surviving infants in the United States, though test scores must be imputed. I assigned an IQ score to each survivor, based on the infant’s birth weight. I then computed the racial gap in imputed IQ scores and divided this figure by the total observed racial gap in IQ scores, to compute the maximum proportion of the overall gap that can be explained by birth weight. Using various distributions of IQ scores based on past research and a range of assumptions, I found that birth weight explains a maximum of 3 to 4 percent of the racial gap in IQ scores, or one-half a point in IQ.
One major problem here is that the birth weight differences between races are substantially conditioned by genes (e.g., Anum et al., 2009.) To the extent that these differences condition IQ differences, the IQ differences between races, can be said to be partially indirectly genetic. (To note, the fact that birth weight differences predict African ancestry calls into question the practice of statistically controlling for birth weight when attempting to environmentally explain the gap). Another is that we run into the problem of determining the population level effect. As discussed above, higher rates of prenatal causalities may increase a populations mean IQ.
Whatever the case, based on both author’s best estimates, added together the influences mentioned could statistically explain only 2.5 points of a 16.5 point adult gap. But these influences co-vary to some extent, so their effects can not simply be added. Taking into account co-variation, the amount statistically explainable is surely less than 2.5 points. And this would be the effect statistically explainable. To some extent the association between these influences and IQ will be mediated through parental IQ (e.g.., Breast feeding) and genetics (e.g., reproductive casualties). Taking this into account, the upper bounds of the causal effect of the influences listed is likely closer to 1 point. Counting environmental influences, of course, is also problematic since the influences focused on are typically the ones thought to depress Black IQ relative to White IQ. Yet there are bound to be influences that run the other way — for example, the effect of older age of reproduction on IQ, which is a problem more for Whites.
It could be argued, nonetheless, that there are numerous undiscovered environmental-biological influences which possibly contribute to the IQ gap, but as we said we have a number of theoretical reasons for concluding that, in sum, these are not causing at most more then a small part of the IQ gap. If they were, we would see this in terms of: (a) either reduced differential sibling regression at higher IQ levels or non-linear regression, (b) substantially different sibling correlations between Blacks and Whites, (c) a noticeable differences in unshared variance between Blacks and Whites, (d) a decreased magnitude of the gap at higher SES levels relative to lower SES levels, (e) larger unexplained gaps at young ages, and (f) depressed sensory-motor and memory capacities in Blacks relative to Whites
Overall, causally biological explanations seem to make for poor explanations of the gap.
This leaves the more promising causal cultural explanations. As we said earlier, these are problematic as:
(1) These tend to be shared family influences (e.g., SES) and the shared environmental influences are low at older ages and especially at upper SES levels.
(2) These have to cause the robust correlation between the magnitude of the gap, g, and heritability. (Refer to section C, E, and F.)
(3) These have to cause the robustly biological based (e.g., whole brain size) gap discussed prior. (Refer to section B and D)
(4) Early childhood intervention has little enduring impact on IQ, calling into question shared environmental cultural-influences as causes of the gap. (See: N. Failure of Intervention programs to produce an enduring Effect).
Of these, point (2) is the most significant. The B-Wgap correlates with g (a Spearman effect) and with heritability (a Jensen effect). And yet many gaps which are the product of cultural influence do not. Some have argued that a Spearman and Jensen Effect could result from gene-environment co-variation. For example, Dickens (2005) explains:
Those cognitive abilities for which multiplier processes are most important will be the ones that show the largest heritability, because of the environmental augmentation of the genetic differences. But they will also be the ones on which a persistent change in environmentwill have the biggest influence. Thus we might expect that persistent environmental differences between blacks and whites, as well as between generations, could cause a positive correlation between test score heritabilities and test differences.
But such an explanation fails to account for the cases, including between generational differences, in which no non-trivial effects are found. While we could suppose that “environmental augmentation” leads to a Spearman/Jensen Effect in the case of the Black-White differences, it stands to reason that the type of environmental influences that cause the Black-White difference must be unlike those that cause the differences in cases were no Spearman/Jensen effects are found. From this we can infer that: the cultural causes of the Black-White gap must be unlike the causes of the Flynn Effect and the Protestant Effect (causes unknown, but which both probably have to do with a increased emphasis on learning). It must be unlike the cause of the Deaf/Dumb-unimpaired gap, which is probably due to partial sensory deprivation. It must be unlike the test trained-untrained gap, which is due to immediate sensory-informational exposure. And it must be unlike the between SES gap, when the SES measured is that of adopted, not biological parents, which is due to prolonged sensory-informational exposure.
Point (1) is also interesting for theoretical reasons: If the gap, of approximately 1SD, at older ages is to be explained by the effects of home influences, given a within population shared envrionmentality of no more than 0.2 at this age, at least 2.2 SD of early home influences needs to be posited. But, were there were 2.2 SD of early home influences, then the gap in early childhood, when the shared environmentality is around .4, should be at least 1.4 SD below the White mean. Alternatively, if the gap is, at most, 0.7 SD in early childhood when the shared envrionmentality is 0.4, then to explain the gap 1.1 SD of effect needs to be proposed. But if there is only 1.1 SD of effect this could only explain 0.5 SD at older ages when the shared environmentality is 0.2. This theoretical prediction, of course, substantially over predicts the magnitude of the gap at young ages (or under predicts that magnitude of the gap at older ages). From this we can infer that either (a) shared environmental factors do not account for a large part of the IQ gap at older ages or (b) that the effect of these amplify with age between populations, despite diminishing within. Point (4) seems to support (a) rather than (b). We will return to this issue latter on.
Despite the considerations above, sociologists frequently make the case that the gap can be largely or fully explained through SES and Parental factors. For example, Duncan and Magnuson (2005) make the case that SES can statistically explain 50% of the “school readiness” gap. And Brook-Gunn and Markman (2005) make the case that parental factors (e.g., nurturance, discipline,teaching, language, monitoring, etc.) can explain statistically 25-50% of the gap. In terms of causal explanation, the author of the preface to “School Readiness: Closing Racial and Ethnic Gaps” concludes:
[T]he message of this issue is similar: taken together, family socioeconomic status, parenting, child health, maternal health and behaviors, and preschool attendance likely account for most of the racial and ethnic gaps in school readiness.
It’s notable that none of the authors are unaware of the sociologist fallacy, the fallacy of assuming that a correlation between environmental variables and IQ is necessarily causal. Duncan and Magnuson (2005) note that their estimate provides an upper limit as a full set of genetic controls are unavailable and Brook-Gunn and Markman (2005) evoke behavior genetics in defense of their position. So it would be a mistake to dismiss the implied argument – and one is surely being made — on those grounds. We have to take the argument seriously. While it is not made explicit, it seems to be: (x) If between races, environmental factors which are not conditioned by the children, explain the gap at young ages, then the gaps are likely largely not genetic at young ages. (y) If the gaps in childhood are not largely genetic, then the gaps in adulthood are likely largely not. And (z), since environmental factors causally explain the gaps at younger ages, they likely causally explain the gaps at older ages. It’s difficult to make sense of the “close the early age ethnic/racial gap” project without assuming that (y and z) are assumed. Otherwise, why would there be a specific focus on the ethnic/racial gaps (e.g., “If parenting interventions are to narrow ethnic and racial school readiness gaps, they … should be offered to proportionately more minority than nonminority families. This could be achieved if.”?). Discriminating for Blacks to close a Black-White childhood gap that is thought to arises from a genetic parental gap would be an exceptionally perverse exercise in PC. Ditto closing the gap at young ages, if the gap at older ages was though to be caused by a largely different set of environmental influences that in turn lead, via parenting, to the gap at young ages.
Now point (x) is not without basis. Statistical analysis is not without import when it comes to the nature/nurture debate, as a genetic hypothesis seems to predict that there should be some residual unexplained variance. Specifically, a genetic hypothesis would predict that for latent g, at young ages, when the within population heritability is, say 40%, a complete set of environmental influence, that does not control for parental or offspring geneotypic IQ, should not able to explain about 40% of the between population variance times the magnitude to the proposed genetic difference at adulthood (assuming an equivalence of within and between population heritability, to keep things simple); to the extent that these factors index parental IQ (e.g., SES), given the 0.5 genetic correlation between parent and offspring, the unexplained portion of the gap should around one half of that (See Cleveland et al.. (2000) for a discussion of the empirical results; genes explain approximately 50% of IQ variance explained by environmental factors.) To the extent that these factors index offspring IQ (e.g., the number of books parent’s read), it will be less. At older ages, when the within population heritability is say 70% (again assuming an equivalence of within and between population heritability), 70% of the variance should not be explainable, with the same caveats above.
To put it another way, a genetic hypothesis would seem to predict some unaccounted for variance. Showing that all of the variance can be accounted for would leave little room for one. There are a few points of caution here though: The prediction above would be for latent g and attenuated to the degree that measures were polluted by other factors; the exact relation between age, the within group heritability of g, and the between groups heritability of g is unknown. As such predictions are ballpark estimates; and the extent to which offspring geneotypes condition environmental conditions (e.g., parental behaviors) is not clear. It’s likely non-trivial as the heritability of such conditions (e.g., number of books read to) is modest (see: Plomin, 1994).
Now point (z) is related to our point (1) above and our discussion of it. We conjectured that either (1a) shared environmental factors do not account for a large part of the IQ gap at older ages or that (1b) the effect of these amplify with age between populations, despite diminishing within. Point z and 1b travel together. And we can test them by simply seeing if the factors that explain the gap at young ages do, in fact, explain the gap, to the same extent, at older ages. If so, our theoretical point (1) will be undermined. If not, then our point 1 stands and the gap at older ages needs to be explained by additional influences that extent outside the family. The importance of this will be elaborated latter one.
To evaluate the situation, we need to make some predictions concerning the residual amount of the gap that should be unexplained, given the logic above. Assuming an adult genotypic gap of 1 SD, plausible estimates would be (BGH and WGH stands for between group and within group heritability, respectively):
Adulthood (23+) WGH=0.75, BGH=1, unexplained, residual gap = 0.5
Young adulthood (17-22) WGH=0.6, BGH=0.8, residual gap = 0.4
Early and late adolescence (12-16) WGH=0.5, BGH=.07, residual gap = 0.35.
Middle childhood to later childhood (8-12) WGH=0.4, BGH=0.5, residual gap = 0.25
Early childhood to middle childhood (3-7) WGH=0.3, BGH=0.4, residual gap = 0.2
We can then compare this with the empirical findings. (Below). Two facts stand out. First, the residual gap, averages across studies, is not inconsistent with a genetic prediction for the age groups above. Second, the amount of variance explained by home environmental factors substantially negatively correlates with age. Home environmental influences –many of which are shared family influences – have a diminishing ability to explain the gap with age as was predicted by (1). Other data agrees with this. For example, Cordero-Guzman (2001) found that home environmental factors (i.e., receives magazines, has a library card, net family income, highest grade of mother +father), school factors (quality of school), and individual factors (highest grade achieved) accounted for only 50% of the one standard deviation B-W difference in AFQT scores in the NLSY97 (ages 12 to 17). This can be contrasted with Yeung and Pfeiffe’s age 5 to 12 data and other’s.
Now, why is this important? From a genetic perspective, environmental influences can explain no more of the differential than would be predicted by a strong genetic hypothesis. From an environmental perspective, which assumes no genetic differences and therefore that no genetic influences are being controlled for, only half at most of the adult gap can be explained by family influences. Extra family influences need to be sought out as explanatory factors (e.g., peer influences.). And yet, there is the issue of stability. In the Bell Curve, M & H, made the point that, by adulthood, IQ differences are stable. Within populations, this stability can be seen in the high test-retest correlation in adulthood as compared to the low correlation in early childhood (see Brody, 1992 p. 233; Intelligence, Chapter 8: Continuity and Change in Intelligence). The difference between individual Blacks and Whites is likewise stable by adulthood. This latter stability needs to be explained.
One way was to invoke childhood developmental factors and hypothesize an interaction between exposure to sensory-information and neurological growth. Since during the developmental years IQ stabilizes, within populations, it was hypothesized that it could stabilize, in a parallel manner, between populations. The analogy was language acquisition and the critical period in youth when this is most readily done. As Yeung and Pfeifer (2009) note, “According to these perspectives, there are periods of sensitive life stages, mainly in early childhood, when a child must be exposed to certain experiences, or lasting damage will be done to his or her cognitive development.”
This explanation is, of course, somewhat deceptive. Within populations, the longitudinal stability of IQ is conditioned by genes, which increasingly exert themselves, not by cultural influences. How could cultural “developmental factors” between populations explain the adult stability between populations, when these factors don’t contribute to stability within populations? However so, since early environmental influences fail to explain at least half of the gap at older ages, and extra family influences which come on line with age need to be posited, we can conclude that at least this portion of the difference is not developmentally fixed. What then explains the stability? It must be maintained that the environmental influences themselves exert a stable continuous effect. It’s not readily evident what cultural influences meet this criteria in addition to the others mentions (e.g., fairly uniform across the Black population, not primarily of the shared family types, capable of causing a Jensen and Spearman effect, etc.)
US Collaborative Perinatal Project in Fryer, 2010. Children born between 1959 and 1965. Measure: various. Controls: parental income , parental occupation, mother’s age, number of siblings, mother’s reaction to and interaction with the child, birth weight, prematurity.
Children of the National Longitudinal Survey of Youth 1979 in Duncan and Magnuson, 2005. Children born in the 1990s. Measure: PPVT. Controls: grandparents’ education; grandparents’ occupation; Southern roots; mother’s number of siblings; mother’s number of older siblings; no one in mother’s family subscribed to magazines, newspapers, or had a library card; percent of white students in mother’s high school; student-teacher ratio in mother’s high school; percent teacher turnover in mother’s high school; mother’s educational expectations; mother’s self esteem index; two indicators for mother’s sense of control or mastery; interviewer’s assessment of mother’s attitude toward interview; mother’s education; father’s education; child birth weight; child birth order; family structure; mother’s age at child’s birth; household size; set of dummy variables for average income; mother’s AFQT score; mother’s class rank in high school; and interviewer’s assessment of mother’s understanding of interview.)
The Children of the National Longitudinal Survey of Youth 1979 in Fryer, 2010. Children born mostly in the 80s to 90s. Measure: PIAT (math and reading). Controls: Free lunch status, special education status, whether the child attends a private school, family income, the HOME inventory, mother’s AFQT..
The Early Childhood Longitudinal Study, Birth (1-4) /Kindergarten (6+) Cohort in Fryer, 2010. Children born in the 90s. Measure: Various: Controls (ECLS-B) socioeconomic status, mother’s age, number of siblings, family structure (child lives with: \two biological parents,”\one biological parent,”and so on), Nursing Child Assessment Teaching Scale (NCATS), birthweight, the amount premature that the child was born). ECLS-K: parental education, parental occupational, status, household income, child’s age at the time of enrollment in kindergarten, WIC participation, mother’s age at first birth, birth weight, and the number of children’s books in the home.
Panel Study of Income Dynamics (PSID) in Yeung and Pfeiffer, 2008. Children born between 1980 and 1992. Measure: Woodcock-Johnson subtests. Controls: grandparents’ education, mother’s characteristics at child’s birth (whether received AFDC while pregnant, whether a teenage mother), and child’s characteristics (gender, low birthweight, birth order), parental SES, number of children at family, family structure, urbanicity index, and whether the child ever attended a private school, parenting behavior, mother’s test score
National Education Longitudinal Survey in Fryer 2010. Children born in late 1970s. Measure: Math and reading achievement tests. Controls: family income and parents’ levels of education.
 Of the plausible explanations for the Black-White intelligence difference in the US, psychometric bias is not one of them. The most detailed work on this issue was Jensen’s 700+ page “Bias in mental testing,” which prompted both the National Academy of Science and the National Research Council to set up special committees to determine the issue; after exhaustive review of the evidence, both substantially agreed with Jensen (1980) (e.g., Wlgdor and Garner, eds, 1982). Since, a plethora of studies have investigated this issue using diverse methods and confirmed the conclusion. The most compelling evidence of the absence of psychometric bias comes from studies of factorial invariance in standardization samples (e.g., Dolan, 2000; Dolan & Hamaker, 2001; Lubke, Dolan, Kelderman, & Mellenbergh, 2003; Edwards and Oakland, 2006). The finding of factorial invariance implies that the factorial difference between groups are of the same nature as differences within groups (Wu et al., 2007) [a]. In light of the accumulated evidence, psychometric bias explanation is simply untenable.
References: Edwards and Oakland, 2006. Factorial Invariance of Woodcock-Johnson III Scores for African Americans and Caucasian Americans; Dolan, 2000. Investigating Spearman’s hypothesis by means of multigroup confirmatory factor analysis; Dolan and Hamaker, 2001. Investigating black–white differences in psychometric IQ: Multi-group confirmatory factor analysis of the WISC-R and K-ABC and a critique of the method of correlated vectors; Lubke, et al., 2003. On the relationship between sources of within- and between group differences and measurement invariance in the common factor model; ; Wlgdor and Garner, eds., 1982. Ability testing: Uses, consequences, and controversies. Part I: Report of the Committee. Part II: Documentation section; Wu et al., 2007. Decoding the Meaning of Factorial Invariance and Updating the Practice of Multi-group Confirmatory Factor Analysis: A Demonstration With TIMSS Data
[a] Wu et al. explain: “Mellenburgh (1989), Meredith (1993), and Meredith and Millsap (1992) provided a statistical definition of MI. Namely, an observed score is said to be measurement invariant if a person’s probability of an observed score does not depend on his/her group membership, conditional on the true score. That is, respondents from different groups, but with the same true score, will have the same observed score”
Anum et al., 2009. Genetic contributions to disparities in preterm birth.
Brooks-Gunn and Markman, 2005 The Contribution of Parenting to Ethnic and Racial Gaps in School Readiness.
Cleveland et al. 2002. Genetic and shared environmental contributions to the relationship between the HOME environment and child and adolescent achievement.
Corruccini, 1983. The epidemiological transition and anthropology of minor chronic non‐infectious diseases.
Cordero-Guzman, 2001.Cognitive skills, test scores, and social stratification: The role of family and school-level resources on racial/ethnic differences in scores on standardized tests (AFQT).
Dickens and Flynn, 2006. Black Americans Reduce the Racial IQ Gap Evidence From Standardization Samples.
Duncan and Magnuson, 2005. Can Family Socioeconomic Resources Account for Racial and Ethnic Test Score Gaps?
Fryer and Levitt, 2004. Understanding the black-white test score gap in the first two years of school.
Gottfredson et al., 2009. Does a fitness factor contribute to the association between intelligence and health outcomes? Evidence from medical abnormality counts among 3,654 US veterans.
Haiback et al., 2011. Motor Learning and Development.
Jensen, 1998. The g factor: The science of mental ability.
Plomin, 1994. Nature and nurture: genetic contributions to measures of the family environment.
Rippeyoung, 2006. Is it too late baby? pinpointing the emergence of a black-white test score gap in infancy.
Wiessen, 2009. Possible Reasons for the Black-White Mean Score Differences Seen With Many Cognitive Ability Tests: Informal Notes to File