Category: Black-White IQ Gap (Page 1 of 6)

Fair and Square: A Conclusion on IQ Test Bias

This is a 2-part article. In this first part, the most important studies on internal test bias with respect to racial groups in the item-level, subtest-level and construct-level are reviewed. The proposed causes will be discussed. Generally, the most commonly used IQ tests aren’t biased or only minimally biased as to be of no practical value.

The best methodologies with an application using the Wordsum GSS for the Black-White group will be discussed in the second part of the article : (link to the forthcoming article will be provided soon).
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The Inconvenient Truth Behind the Black-White Income and Mobility Gap

Back in 2014 I wrote an extensive review of studies on the income mobility rate over time and across countries and discussed whether it truly fits the Great Gatsby Curve, a term based on the observation of the negative relationship between mobility and inequality, that is considered by many as unfair because it implies that higher inequality causes lower mobility. However I did not consider Black-White difference in mobility. Because mobility and inequality are interrelated, I will cover both topics here.

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Re-analysis of Willerman’s Study: Race of Mother’s Hypothesis

It’s been almost 50 years now that the famous study of Willerman et al. (1974) has been published. This study is regularly cited as one of the most convincing evidence against the hereditarian hypothesis, despite strong emphasis by hereditarians on the failure of experimental efforts to raise IQ (more specifically, g) and population differences magnifying during adolescence or adulthood due to increasing heritability with age (Jensen, 1998, pp. 333-344, 359, 474; See Malloy [2013] for a case of a stability model with respect to the Black-White gap). Caution about this study is now vindicated. The data used by Willerman also revealed a pattern: the IQ deficits related to having a Black mother seem to vanish over time (Hu, 2022). Continue reading

The Persistence of Cognitive Inequality: Reflections on Arthur Jensen’s “Not Unreasonable Hypothesis” after Fifty Years

In 1969, Harvard Educational Review published a long, 122-page article under the title “How Much Can We Boost IQ and Scholastic Achievement?” It was authored by Arthur R. Jensen (1923–2012), a professor of educational psychology at the University of California, Berkeley. The article offered an overview of the measurement and determinants of cognitive ability and its relation to academic achievement, as well as a largely negative assessment of attempts to ameliorate intellectual and educational deficiencies through preschool and compensatory education programs. Jensen also made some suggestions on how to change educational systems to better accommodate students with disparate levels of ability.

While most of the article did not deal with race, Jensen did argue that it was “a not unreasonable hypothesis” that genetic differences between whites and blacks were an important cause of IQ and achievement gaps between the two races. This set off a huge academic controversy—Google Scholar says that the article was cited more than 1,200 times in the decade after its publication and almost 5,400 times by December 2019. The dispute about the article centered on the question of racial differences, which is understandable as Jensen’s thesis came out on the heels of the civil rights movement and its attendant controversies, such as school integration, busing of students, and affirmative action. Jensen questioned whether it is in fact possible to eliminate racial differences in socially valued outcomes through conventional policy measures, striking at the foundational assumption of liberal and radical racial politics. His floating of the racial-genetic hypothesis was what set his argument apart from the general tenor of the era’s scholarly and policy debate.

In this post, I will take a look at Jensen’s arguments and their development over time. The focus will be on the race question, but many related, more general topics will be discussed as well. The post has four parts. The first is a synopsis of Jensen’s argument as it was presented in the 1969 article. The second part offers an updated restatement of Jensen’s model of race and intelligence, while in the third part I argue, using the Bradford Hill criteria, that the model has many virtues as a causal explanation. In the fourth and concluding part I will make some more general remarks about the status and significance of racialist thinking about race and IQ.[Note]
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Racial and Ethnic Differences in Cognitive Skills in Working-Age, Native-Born Americans

Given the central role that testing plays in the American educational system, most datasets that we have on racial and ethnic differences in cognitive ability include only children, adolescents, or young adults. Most of the economic and social effects of cognitive differences are, however, produced by the working age population, so it would be useful to have test scores from older adults as well. The PIAAC survey of adult skills conducted by the OECD provides excellent data for this purpose. Continue reading

Measurement Error, Regression to the Mean, and Group Differences

Regression to the mean, RTM for short, is a statistical phenomenon which occurs when a variable that is in some sense unreliable or unstable is measured on two different occasions. Another way to put it is that RTM is to be expected whenever there is a less than perfect correlation between two measurements of the same thing. The most conspicuous consequence of RTM is that individuals who are far from the mean value of the distribution on first measurement tend to be noticeably closer to the mean on second measurement. As most variables aren’t perfectly stable over time, RTM is a more or less universal phenomenon.

In this post, I will attempt to explain why regression to the mean happens. I will also try to clarify certain common misconceptions about it, such as why RTM does not make people more average over time. Much of the post is devoted to demonstrating how RTM complicates group comparisons, and what can be done about it. My approach is didactic and I will repeat myself a lot, but I think that’s warranted given how often people are misled by this phenomenon.
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New MQ paper

Kirkegaard, E. O. W. & Fuerst, J. (2016). Inequality in the United States: Ethnicity, Racial Admixture and Environmental Causes. Mankind Quarterly 56(4).

Previously, we looked at the association between overall state-level biogeographic ancestry (BGA) and overall state-level outcomes. It was found that European BGA relative to African and Amerindian BGA was associated with better outcomes. In this paper, the analysis is extended by looking at the state-level ancestry-outcome associations individually for black and Hispanic self-identified race-ethnicity (SIRE) groups. General socioeconomic factor (S) scores were calculated for US states by SIRE groups based on three indicators. The S factor loadings were generally stable across subgroup analyses and the factor scores were stable across factor analytic extraction methods (for the latter, almost all r’s ≈ 1). For Whites, Blacks and Hispanics, there were strong correlations between cognitive ability scores and S factor scores across states (r = .55 to .78; N = 28-50). This pattern also held when all data were analyzed together (r = .86, N = 115). Furthermore, the size of the Hispanic-White and Black-White S and cognitive ability gaps strongly correlated across states (r = .62 to .69; N = 36-37). Lastly, parasite prevalence did not plausibly explain SIRE gaps in cognitive ability because gaps were smaller in more parasite-rich states (combined analysis r = -.17, N = 91). We found that climatic and geospatial variables did not correlate strongly with cognitive ability and S scores when scores were decomposed by SIRE group, but did so at the total state level, even after statistically controlling for SIRE composition.

The Measured Proficiency of Somali Americans

The discussion of the performance of African immigrants led by Chanda Chisala has been of unusually poor quality. As such, I thought that I might write a brief tutorial post on how to locate data and estimate differences in hopes that this will inspire better research practices and more rigorous debate. I will also elaborate on the Jensenist position and its predictions, as Chanda, and apparently many others, do not seem to have a good grasp of it at least in its quantified form.

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Using Surnames to Assess Ethnic Aptitude

Attempts to assess population aptitude from elite achievement go back to at least Galton. In Hereditary Genius, Galton used an estimate of the number of eminent persons produced by various ethnic and racial groups to quantify the differences between the means of these groups. Since his time, variants and refinements of this genre of analysis have become frequent. In “The Racial Origin of Successful Americans (1914)” Frederick Woods attempted to estimate ethnic achievement by counting and classifying the number of ethnic surnames in Marquis’ “Who’s Who” list. Lauren Ashe (1915) improved on the strategy by determining the representation of ethnic names in “Who’s Who” relative to that found in various U.S. city populations. In the 1960s, Nathaniel Weyl developed a variant of the “Who’s Who” surname method, one which relied on rare surnames, and in the 1980s he applied the method to National Merit Scholarship (NMS) lists (1), which record those high school seniors who obtained the top scores on College Board’s Preliminary SAT/National Merit Scholarship Qualifying Test (PSAT/NMSQT).

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