Raven's test performance of sub-Saharan Africans: Average performance, psychometric properties, and the Flynn Effect

Wicherts, Jelte M.; Dolan, Conor V.; Carlson, Jerry S.; Maas, Han L. J. van der

doi:10.1016/j.lindif.2009.12.001

Cited by 92 publications

(54 citation statements)

References 136 publications

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“…There are strong differences of opinion on the causes of group differences in IQ: Rushton andJensen (2005, 2010) argue that there is a strong genetic component to group differences, whereas Nisbett (2009) argues that group differences are wholly due to Blacks and non-Western immigrants growing up in a lower-quality environment (see also Dawkins, 1982;Meisenberg, 2010;Nyborg, 2012, Wicherts, Dolan, Carlson, & Maas, 2010. Rindermann, Woodley, and Stratford (2012) show, based on evolutionary theory, that there are substantial correlations between mean IQ scores of countries and genetic markers.…”

Section: Discussionmentioning

confidence: 99%

Is the Flynn effect on g?: A meta-analysis

Nijenhuis

Flier

2013

Intelligence

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Section: Discussionmentioning

confidence: 99%

Is the Flynn effect on g?: A meta-analysis

Nijenhuis

Flier

2013

Intelligence

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“…We then re-evaluate a set of hypotheses that have been put forward to explain variation in national IQ as a case study to provide guidance for future studies. Note that while the global variation in mean national IQ has received considerable recent attention, it remains debateable whether variation in national IQ is a strict reflection of variation in underlying cognitive abilities that they are proposed to measure, since their psychometric properties may also vary across space (Wicherts, Dolan, Carlson, & van der Maas, 2010) and time (Wicherts, et al, 2004). For example, recent work has indicated that IQ score may vary with individual motivation, and that this simple phenomenon may confound relationships between individual IQ and late-life outcomes (Duckworth, Quinn, Lynam, Loeber, & Stouthamer-Loeber, in press).…”

Section: Introductionmentioning

confidence: 99%

Statistical inference and spatial patterns in correlates of IQ

Hassall

Sherratt

2011

Intelligence

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Cross-national comparisons of IQ have become common since the release of a large dataset of international IQ scores. However, these studies have consistently failed to consider the potential lack of independence of these scores based on spatial proximity. To demonstrate the importance of this omission, we present a re-evaluation of several hypotheses put forward to explain variation in mean IQ among nations namely: (i) distance from central Africa, (ii) temperature, (iii) parasites, (iv) nutrition, (v) education, and (vi) GDP. We quantify the strength of spatial autocorrelation (SAC) in the predictors, response variables and the residuals of multiple regression models explaining national mean IQ. We outline a procedure for the control of SAC in such analyses and highlight the differences in the results before and after control for SAC. We find that incorporating additional terms to control for spatial interdependence increases the fit of models with no loss of parsimony. Support is provided for the finding that a national index of parasite burden and national IQ are strongly linked and temperature also features strongly in the models. However, we tentatively recommend a physiological via impacts on host-parasite interactions rather than evolutionary explanation for the effect of temperature. We present this study primarily to highlight the danger of ignoring autocorrelation in spatially extended data, and outline an appropriate approach should a spatially explicit analysis be considered necessary.

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“…Notably, Wicherts, Dolan, Carlson, and van der Maas (2010) found that sub-Saharan African test takers often obtain lower average scores than Westerners on the various version of the raven's test. And although reliabilities of the scales were often fairly high, the convergent validity appeared to be lower in sub-Saharan African samples as opposed to studies in the Western world.…”

Section: What Causes Failures Of Measurement Invariance?mentioning

confidence: 99%