Berkeley CSUA MOTD:Entry 50361
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12/8    

2008/6/24-27 [Politics/Foreign/Asia/Japan, Politics/Foreign/Asia/China, Politics/Foreign/Asia/Others] UID:50361 Activity:nil 66%like:50358
6/24    Correlation between temperature, skin color, and IQ:
        http://preview.tinyurl.com/yvaru4 [majority rights]
        \_ What, you couldn't find a stormfront link?
2021/12/08 [General] UID:1000 Activity:popular
12/8    

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Cache (8192 bytes)
preview.tinyurl.com/yvaru4 -> majorityrights.com/index.php/weblog/comments/intelligence_and_skin_color/
Vrij Historisch Onderzoek Ecological correlations between IQ, skin color, temperature and GDP per capita Here is some cool research that is about to be published in the journal Intelligence, though this research is unlikely to get a lot of press in the MSM The researchers (Donald Templer and Hiroko Arikawa) computed the correlations between the predominant skin color in 129 nations, GDP per capita, high and low mean temperatures in the summer and winter, and skin color. The correlation between predominant skin color and population IQ was - 092! Intelligence (IQ) and skin color For those not familiar with correlation coefficients, these range in magnitude from 0 (no correlation) to 1 (perfect correlation). The sign of the correlation coefficient indicates the directionality of the correlation. Thus, given that predominant skin color was rated from 1 (white) to 8 (black), since lower values of skin color correspond to higher IQs, the correlation is very strongly negative. In the table above, "r" and "rho" represent two different correlation coefficients, Pearson's product moment correlation coefficient and Spearman's rho, respectively. Note that the correlations are at the level of populations, not individuals. Nations comprising of indigenous populations were selected (a total of 129); the excluded nations are either largely comprised of diaspora populations or did not have skin color data. Controlling for population size of different nations did not significantly change the correlations. The correlation between skin color and IQ remained the same even if India (IQ = 81) was excluded (in India, whereas the predominant skin color was rated 633 on the 1-8 scale mentioned above, skin color ranges from near-white to black). Actual IQ data were available from 55 selected nations and IQ data for 74 nations were estimated based on their racial composition and the known IQ of their neighbors. For the 55 nations whose IQ data were available, 50 national averages were based on the Raven's Progressive Matrices Test and/or the Cattell Culture Fair Test, "both tests being devoid of educational and specific cultural content." Before someone tries to dismiss the study because the authors have used the Lynn and Vanhanen summary, here is the authors' defense of their use of the summary of Lynn and Vanhanen: It could be argued that there are methodological limitations with respect to the mean IQs provided by Lynn and Vanhanen (2002). One possible limitation is that for 74 of the countries used in the present study the IQs were estimated on the basis of IQs in neighboring countries. The present authors maintain that the assumption of neighboring countries tending to have similar IQs is not entirely arbitrary and unjustifiable. We use the analogy of height because height and IQ have similar determinations such as genetics, nutrition, and health care. It would seem more reasonable to predict height of Norwegians from the height of Swedes than from the height of Italians. Furthermore, the fact that the correlations for the calculated IQ countries and estimated IQ countries are similar lends credulence to the legitimacy of the Lynn and Vanhanen procedure for estimating mean IQs. Another possible limitation is that the Lynn and Vanhanen means are based on different tests administered in different eras and in countries that differ in average educational attainment. However, Lynn and Vanhanen made adjustments for the "Flynn effect," an increase in intelligence test performance in recent decades (Flynn, 1987). And, for 50 of the 55 countries in which IQ was calculated and used in the present study, the Raven Progressive Matrices and/or the Cattell Culture Fair Test were used. Although both of these instruments appear to be void of educational and specific culture content, it cannot be assumed that they are equally effective in measuring intelligence around the world. The fact that skin color is not uniform within countries (as displayed in Biasutti, 1967), could also be seen as a methodological limitation. However, the very high inter-rater reliability, combined with the high correlations with skin color, indicate the effects of this limitation are rather small. Furthermore, the positive correlation between skin color and temperature provide evidence for the validity of the skin color map employed. The fact that the N for some of the mean IQ's of the countries provided by Lynn and Vanhanen are below optimal constitutes an additional limitation. Measurement instrument limitations ordinarily attenuate rather than inflate correlations. The present authors agree with Jensen and agree with Hunt and Sternberg that the methodology of the Lynn and Vanhanen international aggregation of IQs is far from perfect. The standardizations of the Wechsler tests of intelligence are also not perfect. There is, however, no other international aggregation of IQs. It is most unlikely that the authors of the individual IQ studies consistently tested below national average participants in the warmer counties and consistently tested above national average participants in the colder countries. In general, imperfections in measurement instruments are more likely to attentuate than inflate correlations. A correlation of 092 between two worthless instruments is not possible. On the other hand, we urge that this correlation not be viewed as immutable. Hunt and Sternberg failed to provide a balanced perspective in discussing the methodology limitations of Lynn and Vanhanen. They criticized these authors for averaging the means of IQs that have Ns of different sizes. This criticism would possibly be appropriate if Site A in a given country had a population of 1000 and 100 were tested and Site B had a population of 500 and 50 were tested. If, however, the selection of participants approached being haphazard as Hunt and Sternberg implied, it would not be mathematically justifiable to apply different weights to different sites. Furthermore, Lynn and Vanhanen reported high reliability for their national IQs using the 45 countries that have two measures of IQ and the 15 that have three or more (in which case the two extreme mean IQs were employed). The correlation between the two IQs in the 60 countries was 094. In regard to the use of IQ tests in non-Western populations, Lynn and Vanhanen pointed out that people from a variety of cultures, including Ugandans and Black South Africans, have the same pattern of intercorrelations and an identifiable g factor on ability tests. Lynn and Vanhanen provided evidence that reaction time, which correlates positively and substantially with IQ, has the same rank order as IQ in five countries of the world. Hong Kong, Japan, Britain, Ireland and South Africa rank in descending order in both IQ and six different reaction time measures. Lynn and Vanhanen reminded their readers that East Asians tend to score higher on Western developed IQ tests than do Europeans and Americans. The authors also cite a study of 20,000 skulls from 122 ethnic populations where the correlation between cranial capacity and distance from the equator was 062, ie, given that IQ is more strongly related to winter temperature than summer temperature, a plausible reason for the very strong correlation between skin color and IQ is that human populations that migrated north of the equator were selected for both lighter skin and higher IQs (the correlation between brain size and IQ is about 04). In this study, temperature was not an independent predictor of IQ in multiple regression analysis with IQ as the dependent variable and the other measures as independent variables. In his commentary, Arthur Jensen made an important point. A correlation between two variables, "A" and "B," can have three possible explanations: a causal relationship with "A" as the cause, a causal relationship with "B" as the cause or a third factor that is causally related to both "A" and "B." It would appear absurd to suggest that higher IQs lighten skin or lighter skin increases IQ, and the most likely explanation for the very strong correlation between these two variables is that a third factor selected for both lighter s...