Wechsler Subtest Patterns of Mentally Retarded Groups: Relationship to g and to Estimates of Heritability

Wechsler Subtest Patterns of Mentally Retarded Groups: Relationship to g and to Estimates of Heritability

HERMAN H. SPITZ (1988)

E.R. Johnstone Training and Research Center

From a survey of published data on the Wechsler subtest performance of primarily mild and borderline mentally retarded persons, 4304 protocols from 4004 individuals were collated and their subtest patterns on the WAIS, WAIS-R, WISC, and WISC-R were compared. Rank order correlations of subtest scores on the different scales were statistically reliable for all but the WAIS/WAIS-R comparison. On all but the WAIS, reliable inverse relationships were found between subtest performance and the subtests’ g-loadings, indicating that mildly retarded groups tend to score relatively lower on subtests that are better measures of general intelligence. Likewise, reliable and marginally reliable inverse relationships were found between subtest patterns of retarded groups and the subtests’ estimated indexes of heritability, raising the possibility that inherited capacities differentially influence the pattern of performance of these groups.

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Measurement Invariance in Confirmatory Factor Analysis: An Illustration Using IQ Test Performance of Minorities

Measurement Invariance in Confirmatory Factor Analysis: An Illustration Using IQ Test Performance of Minorities

Jelte M. Wicherts and Conor V. Dolan, University of Amsterdam (2010)

Measurement invariance with respect to groups is an essential aspect of the fair use of scores of intelligence tests and other psychological measurements. It is widely believed that equal factor loadings are sufficient to establish measurement invariance in confirmatory factor analysis. Here, it is shown why establishing measurement invariance with confirmatory factor analysis requires a statistical test of the equality over groups of measurement intercepts. Without this essential test, measurement bias may be overlooked. A re-analysis of a study by Te Nijenhuis, Tolboom, Resing, and Bleichrodt (2004) on ethnic differences on the RAKIT IQ test illustrates that ignoring intercept differences may lead to the conclusion that bias of IQ tests with respect to minorities is small, while in reality bias is quite severe.

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Explanation behind the non-g gains in the Flynn Effect : Introducing the measurement invariance model

The phenomenon known as secular IQ gains, or Flynn Effect, is sometimes or perhaps even regularly, viewed as a reason to expect the disappearance of the black-white IQ gap. Not only the logic behind this line of reasoning is a pure non sequitur, it is also disconcerting that the question of g (loadings) and measurement invariance (equivalence) seems to have been underdiscussed.

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Des arguments fallacieux dans le débat Race-QI : Some misleading arguments in the Race-IQ debate

The english text is in bold.

Plus souvent que l’inverse, je remarque que les arguments ad hoc viennent des environnementalistes, qui posent l’idée que les différences raciales n’ont pas de composantes génétiques (substantielles). Nous allons voir à quoi ces arguments ressemblent et pourquoi ils ne fonctionnent pas.

More often than not, I notice that ad hoc arguments seem to come from the environmentalists, which posit than group differences have no (substantial) genetic components. We will see what those arguments look like and why they are bogus.

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The Causes of Group Differences in Intelligence Studied Using the Method of Correlated Vectors and Psychometric Meta-Analysis

The Causes of Group Differences in Intelligence Studied Using the Method of Correlated Vectors and Psychometric Meta-Analysis

Daniel Metzen, Master thesis, 2012.

Abstract

The huge IQ gap between non-Western immigrants and ethnic Dutch has emerged as one of the primary explanations for the large differences in school and work achievement between these groups. Is there a genetic component in the IQ gap between immigrants and ethnic Dutch? Meta-analyses have shown that the group differences on IQ subtests correlate almost perfectly with the cognitive complexity of these subtests; moreover, the cognitive complexity correlates perfectly with heritability and strongly with physical characteristics of the brain. If no other causes for IQ differences show a strong correlation with g loadings, this would point to a strong genetic component in IQ differences between immigrants and ethnic Dutch. In the present study, we first seek support for the hypothesis that only variables under genetic influence show a strong positive relationship with general intelligence. These are group differences, heritability, and physical characteristics of the brain. Second, we test whether differences in IQ due to variables not under genetic influence, namely biological-environmental factors, aging, and autism show a negligible to weak correlation with general intelligence. Support for both hypotheses would suggest that group differences are primarily driven by genetic factors and only to a minor extent by non-genetic factors. Therefore, group differences between non-Western immigrants and ethnic Dutch should be regarded as stable over time.

Concerning the first analysis, we first conducted a full-fledged meta-analysis on reaction time differences between Whites and higher-IQ groups, and Whites and lower-IQ groups, and we conducted several bare-bones meta-analyses and analyses of individual studies on differences in IQ profile between groups of different ethnicity. Second, we explored subgroups on school type, and religion. Third, we carried out a meta-analysis on the question whether g-loadedness of reaction time measures predicted the heritability of these measures. Fourth, we carried out a meta-analysis on the link between g loadings and brain volume. Concerning the second analysis, we first conducted several bare-bones meta-analyses and analyses of individual studies on biological-environmental variables. Second, we conducted bare-bones meta-analyses on the psychological phenomena autism and aging.

The hypothesis was strongly supported: heritabilities and most group differences showed moderate to strong positive correlations with g, but the correlation of brain volume with g was quite modest. All other phenomena showed no strong positive correlation with g.

It is concluded that these findings are strongly in line with a substantial genetic component in group differences in intelligence. This suggest that the large group differences in school achievement and work achievement are stable and that I/O psychologists should find ways to deal with them instead of ways of trying to change them.

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Four psychometric meta-analyses on the relation of lead level, breastfeeding, and prenatal cocaine and smoke exposure with general intelligence

Are differences in IQ scores also differences in general intelligence? Four psychometric meta-analyses on the relation of lead level, breastfeeding, and prenatal cocaine and smoke exposure with general intelligence

Jan Smit, Master thesis, 2011.

Abstract

The central question addressed in this study is whether some potential biological and environmental factors might cause true differences in general mental ability (g) between groups, or just “hollow” score differences between groups.

Four bare-bones psychometric meta-analyses (MAs) were performed to test these premises. We predicted strong positive correlations between vectors of lowered IQ scores following lead exposure, prenatal cocaine exposure, and prenatal smoke exposure on the one hand; and vectors of g loadings on the other hand. We also predicted a strong positive correlation between the vector of increased IQ scores following breastfeeding on the one hand and vectors of g loadings on the other hand.

In line with our hypotheses, the meta-analyses showed correlations of .79 on breastfeeding (total N=7847) and .91 on prenatal cocaine exposure (total N=391). However, the variance explained by artifactual errors is too low to draw strong conclusions about the link between cocaine exposure and g, and about the link between breastfeeding and g. Contrary to our hypotheses, the meta-analyses showed correlations of -.19 on lead exposure (total N = 702) and -.19 on prenatal smoke exposure (total N = 443). We were not able to come up with strong theoretical explanations for these values of the correlation between lead exposure and g.

In sum, two of the four meta-analyses showed mixed support for the theory: high correlations between g loadings and effects, but little variance explained in the data points in the meta-analysis. The other two meta-analyses showed no support at all for the theory: an absence of correlations between g loadings and effects and virtually no variance in the data points explained. The amount of support for the theory in these four meta-analyses is therefore modest.

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Haplogroups as evolutionary markers of cognitive ability

Haplogroups as evolutionary markers of cognitive ability

Heiner Rindermann, Michael A. Woodley, James Stratford (2012)

Studies investigating evolutionary theories on the origins of national differences in intelligence have been criticized on the basis that both national cognitive ability measures and supposedly evolutionarily informative proxies (such as latitude and climate) are confounded with general developmental status. In this study 14 Y chromosomal haplogroups (N=47 countries) are employed as evolutionary markers. These are (most probably) not intelligence coding genes, but proxies of evolutionary development with potential relevance to cognitive ability. Correlations and regression analyses with a general developmental indicator (HDI) revealed that seven haplogroups were empirically important predictors of national cognitive ability (I, R1a, R1b, N, J1, E, T[+L]). Based on their evolutionary meaning and correlation with cognitive ability these haplogroups were grouped into two sets. Combined, they accounted in a regression and path analyses for 32–51% of the variance in national intelligence relative to the developmental indicator (35–58%). This pattern was replicated internationally with further controls (e.g. latitude, spatial autocorrelation etc.) and at the regional level in two independent samples (within Italy and Spain). These findings, using a conservative estimate of evolutionary influences, provide support for a mixed influence on national cognitive ability stemming from both current environmental and past environmental (evolutionary) factors.

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