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

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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"Killing Jensen": A preliminary comment on Chanda Chisala’s environmentalist argument

Chanda Chisala, a visiting Fellow at Stanford University, has developed what he considers to be a devastating argument against Jensenism (racial-IQ-hereditarianism). He develops this in his 2014 blog post, “Killing Jensen — part I“. Pithily put, the reasoning runs:

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Bifactor Model Investigation of Spearman's Hypothesis

I’ve been catching up on recent research on psychometrics, behavioral genetics, race differences, and so on. I’ll be posting some comments on papers I found particularly interesting. The first is Frisby and Beaujean’s study of Spearman’s hypothesis. Continue reading

Genetic and Environmental Determinants of IQ in Black, White, and Hispanic Americans: A Meta-analysis and New Analysis

The PDF and data file are available at Open Behavioral Genetics. You can also read the article below the cut.

Published:  September 15, 2014

John Fuerst [1]

Dalliard

Abstract:  The authors conducted a meta-analysis of interactions between behavioral genetic variance components (ACE) and race/ethnicity for cognitive ability. The differences between the variance components for Black and White Americans were small, despite the large average test score differences. More substantial differences were found between Hispanics and non-Hispanic Whites, though results were based on only two studies. A biometric re-analysis of the CNLSY survey was then conducted and new meta-analytic results were provided. Results were discussed in light of the bio-ecological model which proposes that when the scores of subgroups are environmentally depressed, heritabilities will be likewise.

Keywords:  Race, Ethnicity, Heritability, IQ, Environment, ACE model, bio-ecological model

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The Elusive X-Factor, or Why Jonathan Kaplan Is Wrong about Race and IQ

Philosopher Jonathan Kaplan recently published an article called Race, IQ, and the search for statistical signals associated with so-called “X”-factors: environments, racism, and the “hereditarian hypothesis,” which can be downloaded here. His thesis is that the black-white IQ gap could plausibly be due to racism and what he calls racialized environments. He presents simulations in support of this argument. He also argues that “given the actual state of the world there is no way to generate any reasonably strong evidence in favor of the hereditarian hypothesis.”

I have written a detailed critique of his claims. In short, he is wrong. Here’s the abstract of my article:

Jonathan Michael Kaplan recently published a challenge to the hereditarian account of the IQ gap between whites and blacks in the United States (Kaplan, 2014). He argues that racism and “racialized environments” constitute race-specific “X-factors” that could plausibly cause the gap, using simulations to support this contention. I show that Kaplan’s model suffers from vagueness and implausibilities that render it an unpromising approach to explaining the gap, while his simulations are misspecified and provide no support for his model. I describe the proper methodology for testing for X-factors, and conclude that Kaplan’s X-factors would almost certainly already have been discovered if they did in fact exist. I also argue that the hereditarian position is well-supported, and, importantly, is amenable to a definitive empirical test.

The PDF is available at Open Differential Psychology. You can also read the article below the cut. Continue reading

Racial differences in the long-term trend NAEP scores (1975/78-2012)

I analyze the LTT NAEP achievement scores, a public data set available at NCES. In general, minority-majority ethnic groups show a secular decline in d gap, for both math and reading tests, and this occurs at all ages of assessment (9, 13, 17), and at all percentile levels. Some exceptions are noteworthy. There is no secular gain at age 17 among whites, and no meaningful decline in black-white difference for the NAEP math at ages 13 and 17. Within each year of assessment, no evidence is provided for the hypothesis that the racial gaps (notably, the black-white gap) widen with age after entering schools. There was simply no trend at all.
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Ethnic/Race Differences in Aptitude by Generation in the United States: An Exploratory Meta-analysis

An early version of this paper was posted on June 25th. The paper has since been extensively edited and corrected and, subsequently, published at Open Differential Psychology on July 25/26th, 2014. The paper and data files can be found here at the Open Differential Psychology site.

PDF.

Abstract

Cognitive ability differences between racial/ethnic groups are of interest to social scientists and policy makers. In many discussions of group differences, racial/ethnic groups are treated as monolithic wholes. However, subpopulations within these broad categories need not perform as the racial/ethnic groups do on average. Such subpopulation differences potentially have theoretical import when it comes to causal explanations of racial/ethnic differentials. As no meta-analysis has previously been conducted on the topic, we investigated the magnitude of racial/ethnic differences by migrant generations (first, second, and third+). We conducted an exploratory meta-analysis using 18 samples for which we were able to decompose scores by sociologically defined race/ethnicity and immigrant generation. For Blacks and Whites of the same generation, the first, second, and third+ generation B/W d-values were 0.79, 0.79, and 1.00. For Hispanics and Whites of the same generation, the first, second, and third+ generation H/W d-values were 0.76, 0.67, and 0.57. For Asians and Whites of the same generation, the first, second, and third+ generation d-values were -0.08, -0.21, and 0.00. Relative to third+ generation Whites, the average d-values were 0.99, 0.84, and 1.00 for first, second, and third+ generation Black individuals, 1.04, 0.71, and 0.57 for first, second, and third+ generation Hispanic individuals, 0.16, -0.18, and -0.01 for first, second, and third+ generation Asian individuals, and 0.24 and 0.04 for first and second generation Whites.

Keywords: Immigrants, group differences, race, ethnicity, aptitude, National IQ

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