Article

Statistical inference and spatial patterns in correlates of IQ

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Abstract

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 mission, 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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... This is also true for research on cognitive and socioeconomic differences between administrative divisions such as countries or states, or between regionally delineated divisions such as geographic races. There are a few notable exceptions to this tendency (Gelade, 2008;Hassall & Sherratt, 2011;Piffer, 2015a). Gelade (2008) pointed out the problem and provided measures of SAC for IQ, temperature and precipitation and showed that SAC was higher for national IQs than for the climate variables. ...
... We sought to find methods to measure the amount of SAC in our variables and to analyze their relationships when SAC was controlled for. The method used by Hassall and Sherratt (2011) could not be used because it relied upon a third party program, and the method used by Piffer was untested. For these reasons, one of us developed another method for measuring SAC and two more for controlling for it. ...
... Figures 29-31 show, for each dataset, correlations between neighbor-based predictions of values and the actual values for k 1-20, as well as an unweighted mean across datasets. Overall, we see strong positive SAC in each dataset for each measure, as expected from prior studies (Gelade, 2008;Hassall & Sherratt, 2011). The values of k yielding the strongest SAC results were 3, 2, 2, respectively, for cognitive ability, S and European admixture. ...
Article
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We conducted novel analyses regarding the association between continental racial ancestry, cognitive ability and socioeconomic outcomes across 6 datasets: states of Mexico, states of the United States, states of Brazil, departments of Colombia, sovereign nations and all units together. We find that European ancestry is consistently and usually strongly positively correlated with cognitive ability and socioeconomic outcomes (mean r for cognitive ability = .708; for socioeconomic well-being = .643) (Sections 3-8). In most cases, including another ancestry component, in addition to European ancestry, did not increase predictive power (Section 9). At the national level, the association between European ancestry and outcomes was robust to controls for natural-environmental factors (Section 10). This was not always the case at the regional level (Section 18). It was found that genetic distance did not have predictive power independent of European ancestry (Section 10). Automatic modeling using best subset selection and lasso regression agreed in most cases that European ancestry was a non-redundant predictor (Section 11). Results were robust across 4 different ways of weighting the analyses (Section 12). It was found that the effect of European ancestry on socioeconomic outcomes was mostly mediated by cognitive ability (Section 13). We failed to find evidence of international colorism or culturalism (i.e., neither skin reflectance nor self-reported race/ethnicity showed incremental predictive ability once genomic ancestry had been taken into account) (Section 14). The association between European ancestry and cognitive outcomes was robust across a number of alternative measures of cognitive ability (Section 15). It was found that the general socioeconomic factor was not structurally different in the American sample as compared to the worldwide sample, thus justifying the use of that measure. Using Jensen's method of correlated vectors, it was found that the association between European ancestry and socioeconomic outcomes was stronger on more S factor loaded outcomes, r = .75 (Section 16). There was some evidence that tourist expenditure helped explain the relatively high socioeconomic performance of Caribbean states (Section 17).
... This could result in imprecise parameter estimates when the variables are strongly intercorrelated, as they usually are with highly aggregated data [10] . Third, spatial autocorrelation (SAC) issues are abundant in national and subnational geographic data ( [37][38][39] ) but León and colleagues have not taken these into account (excepting one case; [34]). Unmodeled SAC has the potential to bias results due to unmeasured spatially autocorrelated confounders. ...
... Hassall and Sherratt [38] raised concerns about confounding due to spatial autocorrelation (SAC). Thus, we calculated a spatial lag term for each county by averaging the cognitive ability scores for each of the county's three closest counties (termed k-nearest spatial neighbor regression with k = 3). ...
... SAC in residuals is regarded as a problem because it can result in spurious associations and it can lead to overestimated precision of model estimates because the data points are not fully independent. Thus, in line with previous studies ( [37][38] ), we recommend that researchers employ spatial statistics in their regressions when using aggregated data. The supplement includes spatial lag variables computed for this study (for counties and states) which can be used by others. ...
Conference Paper
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Using a sample of ~3,100 U.S. counties, we tested geoclimatic explanations for why cognitive ability varies across geography. These models posit that geoclimatic factors will strongly predict cognitive ability across geography, even when a variety of common controls appear in the regression equations. Our results generally do not support UV radiation (UVR) based or other geoclimatic models. Specifically, although UVR alone predicted cognitive ability at the U.S. county-level (β = -.33), its validity was markedly reduced in the presence of climatic and demographic covariates (β = -.16), and was reduced even further with a spatial lag (β = -.10). For climate models, average temperature remained a significant predictor in the regression equation containing a spatial lag (β = .35). However, the effect was in the wrong direction relative to typical cold weather hypotheses. Moreover, when we ran the analyses separately by race/ethnicity, no consistent pattern appeared in the models containing the spatial lag. Analyses of gap sizes across counties were also generally inconsistent with predictions from the UVR model. Instead, results seemed to provide support for compositional models.
... This is as expected given the way the data were generated. In dataset 6, the outcome variable is 2 steps away from SACV, while it is only 1 step away in dataset 5. Also worth noting is the SAC in the model residuals in dataset 5, which was discussed by (Hassall & Sherratt, 2011) as indicating a problem. This indicates that one or more causes not included in the model are SAC. ...
... It is worth comparing the two measures of SAC examined in this paper to the more standard methods in the field. Two methods are widely used to measure the amount of SAC in a dataset: Moran's I and Geary's C (Gelade, 2008;Hassall & Sherratt, 2011;Radil, 2011). These two measures are described as approximately inversely related and often only the first is used. ...
... Another method used by (Hassall & Sherratt, 2011), spatial eigenvector mapping, to correct for SAC could not be used because I could not find a suitable R implementation. ...
Article
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Two new approaches to spatial autocorrelation (SAC) were examined for their ability to measure SAC: correlation of distance scores and k nearest spatial neighbor regression. Furthermore, the methods were compared to a more traditional measure of SAC, Moran’s I. All methods were found to give similar results as measured by their intercorrelations. Three approaches, correlation of distance scores, k nearest spatial neighbor regression and spatial local regression, were used on simulated datasets to see if they could successfully distinguish between a true and a spurious cause. The correlation of distances approach was found to be lacking, while the other two methods clearly distinguished between the spurious and true causes and gave similar results.
... Finally, we controlled for spatial autocorrelation. This results from the non-independence of spatially distributed data-points (such as countries) owing to a) the tendency for variables to spatially cluster and b) the tendency for data points to be arbitrarily defined, such that having lots of countries within a given region with essentially arbitrary national boundaries can significantly inflate N, thus giving rise to inflated correlation magnitudes and significances (Hassall & Sherratt, 2011). To control for the effects of spatial autocorrelation a distance based spatial lag variable was incorporated into the regression and path analyses as a predictor to produce spatially autoregressive models (Anselin & Bera, 1998). ...
... To this end the Moran's I was calculated for the IQ values of all nations and found to be I = .556 this suggests that there is a high degree of spatial autocorrelation in national cognitive ability values which accords with the findings of Hassall and Sherratt (2011), and provides a strong justification for controlling spatial autocorrelation. ...
... One issue is the existence of cluster effects, or the observation that countries in close proximity tend to have similar characteristics and, therefore, are not independent observations. That IQ has a geographic distribution is well-known ( Lynn & Meisenberg, 2010a;Gelade, 2008;Hassell & Sherratt, 2011). Controlling for this by including regional dummies in the estimated regressions is one approach. ...
Article
Previous research has found that there is a statistically significant, positive link between country-level IQ and various measures of aggregate production, such as GDP. This study extends that analysis by estimating the relationship between IQ and a new measure of economic welfare. Developed by Jones and Klenow (2016), welfare is not a measure of spending on public assistance programs, but a theory-based empirical construct combining several metrics of economic well-being. Using this new economic welfare index for a large sample of countries (74), we find that IQ is a statistically significant (5% or better) and economically important predictor of welfare growth. A one-point increase in IQ is associated with a 4% increase in welfare growth for the average country. Our results support the view that national IQ is an important determinant of cross-country differences in economic activity and welfare.
... Unlike many other instances in which data are unavailable after publication (Wicherts et al., 2006 , 2011 ), Kanazawa’s (2008) data could be submitted to secondary analyses. These analyses cast some doubt on his hypotheses (Wicherts et al., 2010b ; Hassal and Sherrat, 2011 ). ...
Article
Full-text available
With the emergence of online publishing, opportunities to maximize transparency of scientific research have grown considerably. However, these possibilities are still only marginally used. We argue for the implementation of (1) peer-reviewed peer review, (2) transparent editorial hierarchies, and (3) online data publication. First, peer-reviewed peer review entails a community-wide review system in which reviews are published online and rated by peers. This ensures accountability of reviewers, thereby increasing academic quality of reviews. Second, reviewers who write many highly regarded reviews may move to higher editorial positions. Third, online publication of data ensures the possibility of independent verification of inferential claims in published papers. This counters statistical errors and overly positive reporting of statistical results. We illustrate the benefits of these strategies by discussing an example in which the classical publication system has gone awry, namely controversial IQ research. We argue that this case would have likely been avoided using more transparent publication practices. We argue that the proposed system leads to better reviews, meritocratic editorial hierarchies, and a higher degree of replicability of statistical analyses.
... With regard to the Milieu (EM), recent research has identified a hitherto unsuspected causal influence of individual differences in cognitive abilities: the burden imposed at a national level by parasitic and infectious diseases, called the DALY index. It explains to a significant degree cross-national differences in IQ (Hassall & Sherratt, 2011 ), as well as crossstate IQ differences in the USA (Eppig, Fincher, & Thornhill, 2011). It remains to be seen if a similar impact will appear at the level of individual differences. ...
Article
Full-text available
definitions of giftedness (and talent) abound in the literature / so numerous are the definitions that Stankowski [1978] found it necessary to extract a more synthetic view through a classification system in five categories: (a) after the fact definitions focusing on adult prominent accomplishments, (b) IQ definitions specifying a particular threshold score, (c) talent definitions emphasizing outstanding performance in specific artistic and/or academic fields, (d) percentage definitions varying from a generous 20% or more to a strict 3% or less and (e) creativity definitions stressing original and productive accomplishments in a particular field Stankowski's system emphasizes the two general components implicitly or explicitly present in most definitions and models: (a) a "what is" statement pertaining to the core nature of the construct, its central or prototypical elements and (b) a "to whom" or "how many" statement about the size of the population targeted by the label / these two statements correspond to the usual distinction, in logic, between the comprehension of a concept and its extension; these two components will be examined separately (PsycINFO Database Record (c) 2012 APA, all rights reserved)
... Furthermore, the study is based on correlation only, and so any imputation of causation must be tentative although path analysis indicates a reasonable fit to models in which assumptions about the direction of causation amongst the variables consistent with the theory are tested. A final issue is spatial auto-correlation, which results from the non-independence of data-points owing to their proximity in space (Hassall & Sherratt, 2011). It has been argued that controlling for spatial autocorrelation might actually obviate meaningful ecological relationships (Legendre, 1993), and on these grounds a case can be made for not incorporating it into ecological study designs. ...
Article
This article examines the hypothesis that although the level of democracy in a society is a complex phenomenon involving many antecedents, consanguinity (marriage and subsequent mating between second cousins or closer relatives) is an important though often overlooked predictor of it. Measures of the two variables correlate substantially in a sample of 70 nations (r = −0.632, p < 0.001), and consanguinity remains a significant predictor of democracy in multiple regression and path analyses involving several additional independent variables. The data suggest that where consanguineous kinship networks are numerically predominant and have been made to share a common statehood, democracy is unlikely to develop. Possible explanations for these findings include the idea that restricted gene flow arising from consanguineous marriage facilitates a rigid collectivism that is inimical to individualism and the recognition of individual rights, which are key elements of the democratic ethos. Furthermore, high levels of within-group genetic similarity may discourage cooperation between different large-scale kin groupings sharing the same nation, inhibiting democracy. Finally, genetic similarity stemming from consanguinity may encourage resource predation by members of socially elite kinship networks as an inclusive fitness enhancing behavior.
... Gelade (2008), showed how national IQs present spatial autocorrelation, given that neighbouring countries tend to have similar scores. Hassall and Sherratt (2011) analysed the parasite-IQ relationship, controlling for autocorrelation in the predictor variables used in the Eppig et al. (2010) study. They substantially confirmed previous results, suggesting that temperature probably influences IQ through its relationship with infections and parasite distribution, rather than its eventual evolutionary effects on intelligence. ...
... Infectious disease burden as measured by Disability Adjusted Life Years (DALY) has also been found to be a robust predictor of IQ at the cross-national level (Eppig, Fincher, & Thornhill, 2010). A recent study has found that infectious disease burden remained strongly linked with national IQ even after sophisticated controls for spatial autocorrelation and stringent model selection criteria were employed (Hassall & Sherratt, 2011). DALY data were obtained for infectious disease burden from the Global Burden of Disease: 2004 Updates (World Health Organization (WHO), 2004a). ...
Article
Despite the fact that the recently evolved Microcephalin and the related Abnormal Spindle-like Microcaphaly Associated (ASPM) alleles do not appear to be associated with IQ at the individual differences level, the frequencies of Microcephalin have been found to correlate strongly with IQ at the cross-country level. In this study, the association between these two alleles and intelligence is examined using a sample of 59 populations. A bivariate correlation between Microcephalin and population average IQ of r = .790 (p ≤ .01) was found, and a multiple regression analysis in which the Human Development Index, Disability Adjusted Life Years (DALY) lost due to Infectious diseases, DALY Nutritional deficiencies, and Würm glaciation temperature means were included revealed that Microcephalin remained a good predictor of IQ. Path analysis, with both direct and indirect paths from Microcephalin to intelligence, showed good model fit. These multivariate analyses revealed strong and robust associations between DALYs and Microcephalin, indicating that the former partially mediates the association between the latter and IQ. A second smaller correlational analysis involving ten country-level estimates of the frequencies of these two alleles collected from the 1000 genomes database replicated this pattern of results. To account for the findings of this study, we review evidence that these alleles are expressed in the immune system. Microcephalin is strongly associated with DNA repair, which indicates a special role for this allele in the intrinsic anti-viral immune response. Enhanced immune functioning may have advantaged both hunter–gatherer and agrarian societies coping with the heightened disease burden that resulted from population growth and exposure to zoonotic diseases, making it more likely that such growth and concomitant increases in intelligence could occur.
... Therefore, despite the significant degree of spatial autocorrelation for state cancer mortality rates and state heart disease mortality rates, the respective multiple regression state residuals did not show spatial autocorrelation. This is a critical distinction because it is the independence of residuals rather than spatial independence of the dependent variable that ultimately is crucial for the assumption of independence in multiple regression analysis (e.g., Diniz-Filho, Bini, & Hawkins, 2003;Hassall & Sherratt, 2011;McKitrick & Nierenberg, 2010). 4 When "the dependent variable is spatially autocorrelated but the residuals are not, this provides evidence that the explanatory model is well-specified and autocorrelation does not bias the inferences" (McKitrick & Nierenberg,p. ...
Article
Full-text available
Relations between state-aggregated responses of 619,397 residents to the neuroticism items of the Big Five Inventory and 2005-2007 age-adjusted state cancer, heart disease, total all-cause, other-disease, and non-disease mortality rates for the 50 states were examined. Partial correlations controlling for four state demographic variables and three risk variables showed neuroticism correlated significantly only with cancer mortality (.34) and heart disease mortality (.31). Hierarchical regression with demographic variables entered first, neuroticism second, and risk variables last showed neuroticism accounted for another significant 7.6% of cancer mortality variance and an additional significant 4.6% of heart disease mortality variance. Significant βs of .28 and .30, respectively, showed higher neuroticism was associated with higher cancer and heart disease mortality when all seven demographic and risk variables were controlled. Overall, the results show resident neuroticism is related to state cancer and heart disease mortality rates but not to total all-cause, other-disease, or non-disease mortality rates.
... With regard to the Milieu (EM), recent research has identified a hitherto unsuspected causal influence of individual differences in cognitive abilities: the burden imposed at a national level by parasitic and infectious diseases, called the DALY index. It explains to a significant degree cross-national differences in IQ (Hassall & Sherratt, 2011), as well as crossstate IQ differences in the USA (Eppig, Fincher, & Thornhill, 2011). It remains to be seen if a similar impact will appear at the level of individual differences. ...
Article
Full-text available
This article offers an overview of the author’s theory of talent development, called the Comprehensive Model of Talent Development (CMTD). It brings into a unified whole two earlier models, the well-known Differentiated Model of Giftedness and Talent (DMGT), and the more recently proposed Developmental Model for Natural Abilities (DMNA). The DMGT defines talent development as the progressive transformation of outstanding natural abilities (called gifts) into outstanding knowledge and skills (called talents). Two types of catalysts, intrapersonal and environmental, actively moderate the talent development process. These four causal components dynamically interact to foster, or sometimes hinder, the emergence of talents. Research has shown that the four causal components, but especially the natural abilities and intrapersonal catalysts, have significant biological underpinnings. These biological roots first appeared in the form of ‘basements’ to the DMGT; they were eventually dynamically integrated into the Developmental Model for Natural Abilities (DMNA), contributing to the growth of natural abilities through a developmental process based on maturation and informal learning, plus the necessary contribution of both sets of I and E catalysts. Their fusion into the CMTD creates a seamless developmental process that begins with the biological foundations and eventually culminates into high-level expertise.
... With regard to the Milieu (EM), recent research has identified a hitherto unsuspected causal influence of individual differences in cognitive abilities: the burden imposed at a national level by parasitic and infectious diseases, called the DALY index. It explains to a significant degree cross-national differences in IQ (Hassall & Sherratt, 2011 ), as well as crossstate IQ differences in the USA (Eppig, Fincher, & Thornhill, 2011). It remains to be seen if a similar impact will appear at the level of individual differences. ...
Article
Full-text available
The field of gifted education defines its special population around two key concepts: giftedness and talent. Using the entries for these two terms in the Subject Index of the first edition of this book (Sternberg & Davidson, 1986)-or, for that matter, the present edition or any handbook in the field (e.g., Colangelo & Davis, 2003; Heller, Mönks, Sternberg, & Subotnik, 2000)-the curious browser will soon discover the fascinating creativity of scholars in their attempts to circumscribe the nature of giftedness and talent. In some cases, the concept of talent does not appear or is not defined (e.g., Davidson, 1986; Renzulli, 1986; Sternberg, 1986); in other cases, which is the dominant position in the literature, both terms are used as synonyms, as in Marland’s (1972) well-known definition (“Gifted and talented children are….” p. 4). Csikszentmihalyi and Robinson explicitly announce that nondifferentiation, stating: talent, giftedness, and prodigious performance [italics in text] will be used interchangeably” (1986, p. 264). Occasionally, talent becomes a subcategory of giftedness: The second component of giftedness is talent,” affirms Feldhusen (1986, p. 113); or “giftedness encompasses a wide variety of abilities, talents, or propensities” (Haensly, Reynolds, & Nash, 1986, p. 131). For his part, Feldman (1986) associates talent with potential and giftedness with achievement. He affirms: Talent from a cognitive-developmental perspective is the potential for constructive interaction with various aspects of the world of experience…. If these processes of interaction lead to high level performance, then it is appropriate to speak of giftedness”.
... With regard to the Milieu (EM), recent research has identified a hitherto unsuspected causal influence of individual differences in cognitive abilities: the burden imposed at a national level by parasitic and infectious diseases (called the DALY index). It explains to a significant degree crossnational differences in IQ (Hassall & Sherratt, 2011), as well as cross-state IQ differences in the USA (Eppig, Fincher, & Thornhill, 2011). It remains to be seen if a similar impact will appear at the level of individual differences. ...
Article
Full-text available
This article begins with a brief survey of the recent update of the Differentiating Model of Giftedness and Talent (DMGT). The DMGT defines talent development as the transformation of outstanding natural abilities (called gifts-G) into outstanding knowledge and skills (called talents-T). Two types of catalysts, intrapersonal (I) and environmental (E), actively moderate the talent development process (D). These causal components of talent development have biological underpinnings; I propose here a way to integrate these biological roots to the DMGT in the form of 'basements' that exert their influence upwards to moderate the development of natural abilities, as well as many intrapersonal catalysts like temperament, needs, interests, and volition. This new tri-dimensional approach to the structure of talent development leads to two hitherto unpublished proposals. The first one is a Developmental Model for Natural Abilities (DMNA), in which biological building blocks create a diversity of natural abilities, through a developmental process based on maturation and informal learning, and with the necessary contribution of both sets of I and E catalysts. The second one integrates the new DMNA and the DMGT into an Expanded Model of Talent Development (EMTD) that begins with the biological foundations and ends with high level expertise. © 2013 International Research Association for Talent Development and Excellence.
... With regard to the Milieu (EM), recent research has identified a hitherto unsuspected causal influence of individual differences in cognitive abilities: the burden imposed at a national level by parasitic and infectious diseases, called the DALY index. It explains to a significant degree cross-national differences in IQ (Hassall & Sherratt, 2011), as well as crossstate IQ differences in the USA (Eppig, Fincher, & Thornhill, 2011). It remains to be seen if a similar impact will appear at the level of individual differences. ...
Article
Full-text available
The Differentiated Model of Giftedness and Talent (MDDT) is a broad theory of talent development in academics, arts, science, and sports. At its core is a clear distinction between outstanding aptitudes (the gifts or G) and outstanding achievements (the talents or T). Talent development (the D component) represents the progressive transformation of specific gifts into outstanding competencies (knowledge and skills) in a given field of human activity. Apart from this basic trio of dynamically linked components, the DMGT includes two additional components, called catalysts: intrapersonal (I) catalysts (e.g., temperament, needs, interests, will power) and environmental (E) catalysts (e.g., social and family environments, significant persons, enrichment provisions). The DMGT represents a psychosocial model of talent development. But, all the components have their roots in biological and genetic underpinnings that contribute significantly to their development. Complex interactions between the G, D, I, E, and T components, as well as the positive or negative influence of chance, will determine to what extent high aptitudes - the gifts - will eventually be realized as talents.
... Given the concerns about SAC, Hassall and Sherratt (2011) suggested the following protocol: check for SAC in the raw data; if this is significant, check for SAC in model residuals; if this is significant, control for SAC. Since SAC in model residuals cannot be attributed to SAC in the proposed causal variables, when found, this suggests that found associations between variables could be spurious. ...
Article
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The authors reply to 6 comments on their target article on admixture in the Americas. Theoretical and methodological issues are clarified.
... Together with IT-specialists and librarians at the University of Amsterdam, we are currently developing a repository that will eventually include much of the data from over 40 years of our freshmantesting program. Moreover, Elsevier's ScienceDirect offers the possibility to archive data as an online appendix (e.g.,Hassal & Sherrat, 2011;Johnson & Bouchard, 2011). To illustrate the possibilities offered by Elsevier, we attach a data set to the current editorial that is potentially interesting for future use. ...
Article
The authors argue that upon publication of a paper, the data should be made available through online archives or repositories. Reasons for not sharing data are discussed and contrasted with advantages of sharing, which include abiding by the scientific principle of openness, keeping the data for posterity, increasing one's impact, facilitation of secondary analyses and collaborations, prevention and correction of errors, and meeting funding agencies' increasingly stringent stipulations concerning the dissemination of data. Practicing what they preach, the authors include data as an online appendix to this editorial. These data are from a cohort of psychology freshmen who completed Raven's Advanced Progressive Matrices, tests of Numerical Ability, Number Series, Hidden Figures, Vocabulary, Verbal Analogies, and Logical Reasoning, two Big Five personality inventories, and scales for social desirability and impression management. Student's sex and grade point average (GPA) are also included. Data could be used to study predictive validity of cognitive ability tests, Extraversion, Neuroticism, Conscientiousness, Openness to Experience, Agreeableness, and the general factor of personality, as well as sex differences, differential prediction, and relations between personality and intelligence.
... A few additions were to the datasets for IQ and Muslim % based on interpolating values from nearby countries. This method has been validated by previous research (Lynn & Vanhanen, 2012, p. 10) and works due to the strong spatial autocorrelation for the variables (Fuerst & Kirkegaard, 2016;Gelade, 2008;Hassall & Sherratt, 2011). Figure 2 shows the scatterplot of the net fiscal contributions for the two countries. ...
Article
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The relationships between national IQs, Muslim% in origin countries and estimates of net fiscal contributions to public finances in Denmark (n=32) and Finland (n=11) were examined. The analyses showed that the fiscal estimates were near-perfectly correlated between countries (r = .89 [.56 to .98], n=9), and were well-predicted by national IQs (r’s .89 [.49 to .96] and .69 [.45 to .84]), and Muslim% (r’s -.75 [-.93 to -.27] and -.73 [-.86 to -.51]). Furthermore, general socioeconomic factor scores for Denmark were near-perfectly correlated with the fiscal estimates (r = .86 [.74 to .93]), especially when one outlier (Syria) was excluded (.90 [.80 to .95]). Finally, the monetary returns to higher country of origin IQs were estimated to be 917/470 Euros/person-year for a 1 IQ point increase, and -188/-86 for a 1% increase in Muslim%.
... The greater the distance, the greater are the differences. The implication for data analysis is that the data points are not independent of each other and that traditional significance testing, which assumes independence of data points, is not applicable (Eff, 2004;Hassall & Sherratt, 2011). This is especially the case when features such as average PISA score, income or life expectancy form a geographic gradient, or cline, over an extended territory. ...
Article
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In a number of countries, earlier studies have reported significant associations between regional differences in intelligence within countries and economic and social phenomena. Using scores on the Program of International Student Assessment (PISA) tests as indicator of intelligence, we find statistically significant correlations for the 27 states of Brazil between intelligence and nine indicators of socioeconomic development. Spatial analysis indicates that relationships are present both at the level of differences between adjacent states and over long-distance clines. Most of the relationships observed after initial analysis persisted after controlling for spatial autocorrelation. Among the socioeconomic variables, those that describe the standard of living of the less affluent sections of the population tend to correlate most with PISA scores.
... Countries, of course, have spatial positions, and this allows the use of spatial statistics (Gimond, 2019) . Results from such analyses show that there is a very high degree of (positive) spatial autocorrelation in the data, which in plain language means that country IQs are highly predictable from neighboring countries (Gelade, 2008;Hassall & Sherratt, 2011) . This, of course, also means that one can impute missing data with high accuracy, justifying Lynn and Becker's method. ...
... The greater the distance, the greater are the differences. The implication for data analysis is that the data points are not independent of each other and that traditional significance testing, which assumes independence of data points, is not applicable (Eff, 2004;Hassall & Sherratt, 2011). This is especially the case when features such as average PISA score, income or life expectancy form a geographic gradient, or cline, over an extended territory. ...
Article
Full-text available
In a number of countries, earlier studies have reported significant associations between regional differences in intelligence within countries and economic and social phenomena. Using scores on the Program of International Student Assessment (PISA) tests as indicator of intelligence, we find statistically significant correlations for the 27 states of Brazil between intelligence and nine indicators of socioeconomic development. Spatial analysis indicates that relationships are present both at the level of differences between adjacent states and over long-distance clines. Most of the relationships observed after initial analysis persisted after controlling for spatial autocorrelation. Among the socioeconomic variables, those that describe the standard of living of the less affluent sections of the population tend to correlate most with PISA scores.
... Standard statistical methods assume that error terms are not correlated in any meaningful way, but with spatial autocorrelation in the data, the error terms (residuals) of nearby cases can become correlated, which has the effect of biasing some model estimates (Gelade, 2008;Hassall & Sherratt, 2011). (2011) proposed the following strategy for handling the problem: ...
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A dataset of socioeconomic, demographic and geographic data for US counties (N≈3,100) was created by merging data from several sources. A suitable subset of 28 socioeconomic indicators was chosen for analysis. Factor analysis revealed a clear general socioeconomic factor (S factor) which was stable across extraction methods and different samples of indicators (absolute split-half sampling reliability = .85). Self-identified race/ethnicity (SIRE) population percentages were strongly, but non-linearly, related to cognitive ability and S. In general, the effect of White% and Asian% were positive, while those for Black%, Hispanic% and Amerindian% were negative. The effect was unclear for Other/mixed%. The best model consisted of White%, Black%, Asian% and Amerindian% and explained 41/43% of the variance in cognitive ability/S among counties. SIRE homogeneity had a non-linear relationship to S, both with and without taking into account the effects of SIRE variables. Overall, the effect was slightly negative due to low S, high White% areas. Geospatial (latitude, longitude, and elevation) and climatological (temperature, precipitation) predictors were tested in models. In linear regression, they had little incremental validity. However, there was evidence of non-linear relationships. When models were fitted that allowed for non-linear effects of the environmental predictors, they were able to add a moderate amount of incremental validity. LASSO regression, however, suggested that much of this predictive validity was due to overfitting. Furthermore, it was difficult to make causal sense of the results. Spatial patterns in the data were examined using multiple methods, all of which indicated strong spatial autocorrelation for cognitive ability, S and SIRE (k nearest spatial neighbor regression [KNSNR] correlations of .62 to .89). Model residuals were also spatially autocorrelated, and for this reason the models were re-fit controlling for spatial autocorrelation using KNSNR-based residuals and spatial local regression. The results indicated that the effects of SIREs were not due to spatially autocorrelated confounds except possibly for Black% which was about 50% weaker in the controlled analyses. Pseudo-multilevel analyses of both the factor structure of S and the SIRE predictive model showed results consistent with the main analyses. Specifically, the factor structure was similar across levels of analysis (states and counties) and within states. Furthermore, the SIRE predictors had similar betas when examined within each state compared to when analyzed across all states. It was tested whether the relationship between SIREs and S was mediated by cognitive ability. Several methods were used to examine this question and the results were mixed, but generally in line with a partial mediation model. Jensen's method (method of correlated vectors) was used to examine whether the observed relationship between cognitive ability and S scores was plausibly due to the latent S factor. This was strongly supported (r = .91, Nindicators=28). Similarly, it was examined whether the relationship between SIREs and S scores was plausibly due to the latent S factor. This did not appear to be the case.
... The existence of spatially autocorrelated residuals in regression equations can imply "biased estimates of the residual variance and inefficient estimates of the regression coefficients" (Cliff & Ord, 1981, p. 197). However, it is the independence of residuals that is crucial for the assumption of independence in regression analysis (e.g., Diniz-Filho, Bini, & Hawkins, 2003;Hassall & Sherratt, 2011;McKitrick & Nierenberg, 2010). ...
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The two present nomothetic studies focused on the period from 1996 to 2012 to determine relations between handedness and political orientation using the 48 contiguous American states as analytical units. The estimated percentage of left-handers in each state operationally defined handedness. A composite measure of Conservative-Republican preference was created from CBS/New York Times/Gallup polls of state resident conservatism and the percent in each state voting Republican in each presidential election from 1996 to 2012. Study 1 showed that state levels of left-handedness correlated to an extremely high degree with Conservative-Republican preference (r = −.80). As well, with common demographic differences between states reflected in socioeconomic status, White population percent, and urban population percent controlled through multiple regression, handedness still accounted for an additional 37.2% of the variance in Conservative-Republican preference. Study 2 found that each of the Big Five personality variables correlated significantly with handedness and with Conservative-Republican preference, but in the opposite direction. Furthermore, Study 2 demonstrated quite surprisingly that all Big Five personality relations to Conservative-Republican preference were eliminated when handedness was controlled in multiple regression equations. For all regression equations, the global Moran’s I test specifically developed for detecting residual spatial autocorrelation indicated no significant spatial autocorrelation.
... Given the concerns about SAC, Hassall and Sherratt (2011) suggested the following protocol: check for SAC in the raw data; if this is significant, check for SAC in model residuals; if this is significant, control for SAC. Since SAC in model residuals cannot be attributed to SAC in the proposed causal variables, when found, this suggests that found associations between variables could be spurious. ...
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Fuerst and Kirkegaard (this issue) showed in various American countries that European ancestry positively determines cognitive ability and socioeconomic outcomes regardless of the effects of infectious diseases and other variables. In this paper I show that this is not the case in the United States of America when saturated path analysis models which minimize multicollinearity are applied to state data. It is latitude which positively determines cognitive ability and this in turn positively determines income per capita regardless of race and infectious disease rate. U.S. Census self-classification as White has non-significant effects on cognitive ability and has negative effects on income per capita among U.S. states once relevant variables are controlled. Similar results are obtained when the Eugenomic variable of Fuerst and Kirkegaard is targeted in the path analyses. Thus, the evidence does not uphold their conclusion that European ancestry explains differences in cognitive ability among U.S. states.
... Although country-level "ecological" correlations do not always replicate individual-level correlations (Hammond, 1973;Meisenberg, 2012), in the case of morningness-eveningness and national IQ, as well as morningness and conscientiousness, they do. All aggregated datasets, such as country-level comparisons, may be a prone to spatial autocorrelation because of the non-independence of data points (see Hassall & Sherratt, 2011). Also, aspects of the biased datasets have to be considered, because most studies in psychology are done in developed, western countries on the northern hemisphere (Henrich et al., 2010). ...
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Chronotype or morningness–eveningness (M/E) is an individual trait with a biological basis. In this study, I analysed the relationship between M/E and nationwide available data, such as economic variables, school achievement, intelligence and conscientiousness, which is a personality trait. These variables have been chosen because, first, they are linked on the individual level with circadian preference, and, second these associations have been found based on meta-analyses, which gives these findings a high plausibility. In addition, economic status has also been proposed to be related to M/E. Higher developed countries showed a lower morningness, based on both, the ranking of countries as well as on the HDI value. Similarly, GNI was related to morningness, while higher intelligence and performance in PISA were related to eveningness. Conscientiousness was related to morningness, although the results failed the significance level marginally. When using IQ as a control variable in partial correlations, the relationship between GNI and morningness disappeared, as did the correlation between eveningness and PISA results.
... This has come to be called Tobler's first law of geography (for historical review, see Tobler, 2004; and for a powerful modern illustration, see Li et al., 2014), though it relates back to Francis Galton, and the problem is known also as Galton's problem (Eff, 2004). As many have noted (Gelade, 2008;Hassall & Sherratt, 2011), when SAC is present, datapoints are not fully independent and then residuals from models will usually also be SAC which violates the assumption of most regression methods. The regression results are shown in Table 5. ...
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We examined regional inequality in Belgium, both in the 19 communes of Brussels and in the country as a whole (n = 589 communes). We find very strong relationships between Muslim% of the population and a variety of social outcomes such as crime rate, educational attainment, and median income. For the 19 communes of Brussels, we find a correlation of-.94 between Muslim% and a general factor of socioeconomic variables (S factor) based on 22 diverse indicators. The slope for this relationship is-7.52, meaning that a change in S going from 0% to 100% Muslim corresponds to a worsening of overall social well-being by 7.52 (commune-level) standard deviations. For the entire country, we have data for 8 measures of social inequality. Analysis of the indicators shows an S factor which is very similar to the one from the Brussels data only based on the full set of indicators (r's = .98). In the full dataset, the correlation between S and Muslim% is-.52, with a slope of-8.05. Adding covariates for age, population density, and spatial autocorrelation changes this slope to-8.77. Thus, the expected change going from a 0% to 100% Muslim population is-8.77 standard deviations in general social well-being. We discuss our findings in relation to other research on immigration and social inequality, with a focus on the causal influence of intelligence on life outcomes in general.
... Seventh, regional data are known to be spatially autocorrelated, meaning that units close to each other are more similar than would be expected by chance. This results in correlated errors in models which possibly confound results (Gelade, 2008;Hassall & Sherratt, 2011). Several methods have been developed for examining spatial autocorrelation issues, but these have been inconsistently applied in the regional studies reviewed here (Fuerst & Kirkegaard, 2016a;Kirkegaard, 2016b;Lynn, Antonelli-Ponti, Mazzei, Silva, & Meisenberg, 2017). ...
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Differences in intelligence have previously been found to be related to a wide range of inter-individual and international social outcomes. There is evidence indicating that intelligence differences are also related to different regional outcomes within nations. A quantitative and narrative review is provided for twenty-two countries (number of regions in parentheses): Argentina (24 to 437), Brazil (27 to 31), British Isles (12 to 392), to 79), Spain (15 to 48), Switzerland (47), Turkey (12), the USA (30 to 3100), and Vietnam (61). Between regions, intelligence is significantly associated with a wide range of economic, social, and demographic phenomena, including income (r unweighted = .56), educational attainment (r unweighted = .59), health (r unweighted = .49), general socioeconomic status (r unweighted = .55), and negatively with fertility (r unweighted = −.51) and crime (r unweighted = −.20). Proposed causal models for these differences are noted. It is concluded that regional differences in intelligence within nations warrant further focus; methodological concerns that need to be addressed in future research are detailed.
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The idea that infectious disease during childhood affects the developing brain to impact intelligence has been around for decades. Recent evidence from more rigorous studies, which have controlled carefully for other factors such as nutrition and education, has strengthened the case. If these new epidemiological and molecular studies really do confirm a clear link between childhood infection and intelligence, the consequences for health policy and development assistance could be profound. The results could mandate an increased focus not only on eradicating or controlling infectious diseases, but also on reducing their impact on children in the absence of cures or vaccines. > If these … studies do show a clear link between childhood infection and intelligence, the consequences for health policy and development assistance could be profound Yet, even in the light of new evidence, it is hard to unravel causes from effects, and the debate continues over which diseases are most responsible, along with the precise physiological and molecular mechanisms involved. There is no shortage of theories to explain why infectious disease seems to have so profound an effect on intelligence, and, as a result, on the intellectual and economic performance of whole nations or regions. The stage is set for more studies to drill down into neurological and cognitive mechanisms: to explain why the prevalence of infectious disease is a reliable predictor of intelligence at the population level; to differentiate between the impact of various pathogens; and to identify the evolutionary rationale of these links. There is also mounting evidence that some parasites can alter their host's personality through mechanisms evolved to modify their host's behaviour to their own advantage, which could explain environmental risk factors for mental disorders, such as schizophrenia. After a few intermittent references earlier last century, the US economist Andrew Kamarck made the first attempt to …
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In 50 U.S. states, we found a positive manifold across 11 measures including IQ, skin color, birth rate, infant mortality, life expectancy, HIV/AIDS, violent crime, and state income with the first principal component accounting for 33% of the variance (median factor loading=.34). The correlation with a composite of total violent crime was higher with skin color (r=.55), a more biologically influenced variable than with GDP (r=−.17), a more culturally influenced variable. These results corroborate and extend those found at the international level using INTERPOL crime statistics and at the county, provincial, and state levels within countries using local statistics. We interpret the cross-cultural consistency from an evolutionary life history perspective in which hierarchically organized traits culminate in a single, heritable, super-factor. Traits need to be genetically organized to meet the trials of life—survival, growth, and reproduction. We discuss brain size and the g nexus as central to understand individual and group differences and we highlight melanin and skin color as a potentially important new life history variable.
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El triatlón es un deporte joven en el que, actualmente, se ha incrementado notablemente la participación en todos los grupos de edad, así como el nivel de rendimiento. Por eso, es necesario la identificación de factores relevantes para el óptimo desarrollo deportivo en jóvenes triatletas. Un total de 353 triatletas alevines, infantiles, cadetes y juniors participaron voluntariamente en el estudio y que se clasificaron en tres grupos según sendos programas de entrenamiento (Club, Tecnificación Valenciana y Desarrollo Europeo). Datos relacionados con la edad relativa, factores morfológicos, físicos y fisiológicos, técnicos y tácticos; y psico-sociales fueron analizados mediante diversos análisis estadísticos univariantes. Parece que un óptimo programa de desarrollo del talento deportivo específico de triatlón debería contar, por un lado, con factores relevantes como la edad relativa y la composición corporal, especialmente en chicas. Por otro, parece que los test de rendimiento en natación (100m y 400m) parecen correlacionar con el rendimiento global en triatlón a corto plazo, así como el test 3 minutos all-out en ciclismo, con el rendimiento mostrado en competición en el segundo segmento. Finalmente, la familia y el entrenador son figuras relevantes para el desarrollo del talento en jóvenes triatletas, por tratarse de un deporte de iniciación temprana pero especialización tardía, y destacar los contenidos de programas de desarrollo a largo plazo.
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This paper uses data from 130 IQ test administrations worldwide and employs regression analysis to try to quantify the impact of living conditions on average IQ scores in nationally-representative samples. The study emphasizes the possible role of conditions at or near the test-takers' time of birth. The paper finds that the impact of living conditions is of much smaller magnitude than is suggested by just looking at correlations between average IQ scores and socioeconomic indicators. After controlling for test-takers' region of ancestry, the impact of parasitic diseases on average IQ is found to be statistically insignificant when test results from the Caribbean are included in the analysis. As far as IQ and the wealth of nations are concerned, causality thus appears to run mostly from the former to the latter. The test-takers' region of ancestry dominates the regression results. While differences in average scores worldwide can thus be plausibly viewed as being influenced by genetic differences across world regions, it is also possible that score differences are influenced by regional differences in culture that are independent of genetic factors. Differences in average IQ across world regions may change in the years ahead insofar as the strength of Flynn effects may not be uniform, but some regional differences in average g levels seem likely to continue indefinitely.
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Spatial autocorrelation is a measure of the degree in which objects, situated in a close proximity, have a tendency for similar values of a given index. Since recently psychology started to study spatial autocorrelation of national IQ (mean intelligence in a country). The article presents the results of the calculation of spatial autocorrelation of educational attainment (calculated from a mean score of the Unified National Exam of young people, who made it into a college for a budget education in 2014), as well as crime, birthrate, infant mortality, urbanization, net migration and personal income for 75 regions (subjects) of the Russian Federation. These results showed that, though all the mentioned indices are characterized by the spatial autocorrelation, its value varies. Low spatial autocorrelation has net migration, which is probably due to the fact that even a slight difference in life conditions between neighbor regions may promote intensive migration from the region with the worse conditions to the regions with better conditions, including the neighboring one. Low spatial autocorrelation of personal income can be explained by the fact that in the Russian Federation personal income in the region to a great extent is determined by oil and gas production, while mineral deposits are hardly characterized by the spatial autocorrelation on the level of such territorial units as subjects of the Russian Federation. Spatial autocorrelation of educational attainment is probably lessened by the fact that the scores were received by averaging the scores of the Unified National Exam for all specialties of all universities of the region without the consideration of possible differences of regions in prestige value of specialties and number of budget places in universities. © 2018 National Research University Higher School of Economics. All rights reserved.
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Both generalized trust and intelligence are correlated with economic development. However, recent research has shown that trust and intelligence are themselves correlated, both across countries and among individuals. Theory suggests that causality runs from intelligence to trust at the individual level, which raises the possibility that the association between trust and development is explained by intelligence. Indeed, intelligence may cause both trust and development. Alternatively, development may lead to higher intelligence, which in turn gives rise to greater trust. Note that intelligence may cause trust not only because individuals with higher intelligence tend to report greater trust, but also because such individuals tend to be more trustworthy. This study analyzes data on trust, intelligence and economic development for 15 Spanish regions, 20 Italian regions, 50 US states, and 107 countries. In all four domains, there is a statistically significant positive relationship between trust and intelligence (r = .74, r = .74, r = .72 and r = .50, respectively). Moreover, partial correlations suggest that intelligence accounts for some or all of the association between trust and development in at least two out of the four domains.
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In the process of studying, students have to learn to identify independently the main points of the material studied, make up and apply the algorithms of searching problems solutions, find the rational answers to problem solutions, critically analyze the results obtained and be able to use them further on. All the above mentioned facts lie in the basis of forming and developing the logical thinking. The objective criteria of assessing the current functional state and the readiness of students are the physiological indices reflecting the state of the mechanisms of vegetative regulating the cardio-vascular activity. In this respect, the analysis of heart rhythm variability (HRV) is becoming increasingly common. It is a simple, non-invasive and informative method of researching the vegetative nervous system. The balanced regulation of the physiological functions allows students, having a proper motivational level, to use the opportunities for maintaining cognitive functions to the full, which significantly influences the process of studying. This article deals with studying the mechanisms of maintaining psychological processes and possibilities of applying the method of Kirlian photography for assessing the psycho-emotional state in the process of logical thinking. 175 female volunteers aged 18-22, students of the faculty of Biology, Ecology and Medicine of Oles Honchar Dnipropetrovsk National University took part in the experiment. At the moment of conducting the research they all were practically healthy, did not have any complaints of headaches or any other ache, physical fatigue or sleepiness. Testing was conducted in the morning hours which made it possible to exclude the influence of the vegetative heart rhythm fluctuations during different parts of the day on the results of the research. The experiments were carried out at two stages. During the first stage, the individual psycho-physiological characteristics of the students were studied, during the second stage, the saturation level was assessed and the heart rhythm variability was analyzed. According to the results of the psychological testing, the right-handed, ambivert students who did not have asthenia, had a medium level of stress resistance, a medium anxiety level and a strong type of the nervous system were selected. According to the thinking type, the groups with low and high level of logical thinking were formed. At the second stage, the oxygen saturation degree of the arterial blood hemoglobin and the pulse rate in the process of intellectual activity were researched. The neurovegetative state was assessed by the heart rhythm variability. While doing the tasks connected with logic, the students with a low level of logical thinking demonstrated the increase in the oxygen saturation degree of the arterial blood hemoglobin and the decrease in the heart systole rate (HSR). The change of the indices of the heart rhythm variability – the decrease in mode amplitude along with the simultaneous increase in the vegetative indices and significant increase in stress index – indicates the activation of the central mechanisms of mental processes regulation. The students with a low level of logical thinking were in tense state before doing the tasks, and doing the tests connected with the logical thinking experiences some difficulties in finding logical connections and gave the majority of answers randomly, without making any efforts. The students with the medium level of logical thinking, while doing the tasks connected with the logical thinking, demonstrated the reliable increase in the saturation level and heart systole rate. The tested reported that they were making efforts doing the tasks. The correct answers given by the students of this group made 50-70%. The mode and mode amplitude indices before and after the cognitive activity were within the norm. The reliable decrease in the variation span testifies to the decrease in parasympathic influence. The increase in the stress index and the decrease in heart rhythm variability testify to the central mechanisms of regulating the logical thinking process, which is more expressed in people who are more successful in doing tasks. The final stage of this investigation included studying the possibilities of applying Kirlian method for assessing the psycho-emotional state of a person in the process of logical thinking. The registered images of the third (middle) right hand finger, the sectors of which by P. Mandel corresponded to reactive cardiovascular and lymphatic systems, were to be computer analyzed. The students with a low level of the logical thinking who did not cope with the task and showed low results (up to 40% of right answers) demonstrated the decrease in the crown luminescence area of the third right hand finger. The students with a medium level of logical thinking demonstrated the increase in the crown luminescence area of the third right hand finger. We should remark, that the higher the level of logical thinking, the more the luminescence area. Studying the scientific analytical works on neuro-cognitology, unfortunately, did not give the information about applying Kirlian graphology in the modern research in the cognitive brain function. On the basis of our own data, we can conclude that the method applied allows studying neuropsychological processes of higher nervous activity in a new way, by the complex combination of psychometric and apparatus research, which makes it possible to visualize the changes in psychic, physical and bio-energyinformative level of human organization under the conditions of the experimental, in particular cognitive, activity. Taking into consideration the high sensitivity of Kirlian method, it can be recommended for identifying the individuality of students which was not detected by the results of psychological tests
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In this study, we hypothesize that the worldwide distribution of cognitive ability is determined in part by variation in the intensity of infectious diseases. From an energetics standpoint, a developing human will have difficulty building a brain and fighting off infectious diseases at the same time, as both are very metabolically costly tasks. Using three measures of average national intelligence quotient (IQ), we found that the zero-order correlation between average IQ and parasite stress ranges from r ¼ 20.76 to r ¼ 20.82 (p , 0.0001). These correlations are robust worldwide, as well as within five of six world regions. Infectious disease remains the most powerful predictor of average national IQ when temperature, distance from Africa, gross domestic product per capita and several measures of education are controlled for. These findings suggest that the Flynn effect may be caused in part by the decrease in the intensity of infectious diseases as nations develop.
Book
This book develops and tests an ecological and evolutionary theory of the causes of human values-the core beliefs that guide people's cognition and behavior-and their variation across time and space around the world. We call this theory the parasite-stress theory of values or the parasite-stress theory of sociality. The evidence we present in our book indicates that both a wide span of human affairs and major aspects of human cultural diversity can be understood in light of variable parasite (infectious disease) stress and the range of value systems evoked by variable parasite stress. The same evidence supports the hypothesis that people have psychological adaptations that function to adopt values dependent upon local infectious-disease adversity. The authors have identified key variables, variation in infectious disease adversity and in the core values it evokes, for understanding these topics and in novel and encompassing ways. Although the human species is the focus in the book, evidence presented in the book shows that the parasite-stress theory of sociality informs other topics in ecology and evolutionary biology such as variable family organization and speciation processes and biological diversity in general in non-human animals. © Springer International Publishing Switzerland 2014. All rights are reserved.
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Cambridge Core - Cognition - The Nature of Human Intelligence - edited by Robert J. Sternberg
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В монографии предлагается нетрадиционный подход к изучению образовательной политики на основе сопоставления альтернативных данных из достаточно различных областей научного знания: истории, политологии, антропологии, кросс-культурной психологии, а также эконометрических исследований. Вынесение на первый план наиболее острых проблем позволяет избежать излишней дробности представленного материала, а контекстный и сравнительно-исторический методы анализа – не сбрасывать со счетов африканскую в целом и региональную (в отношении конкретных стран) специфику образовательной политики. При изучении ее контекста акцент делается на образовательных экспериментах, исламо-христианских отношениях, влиянии этнических и внешнеполитических факторов, становлении и трудном преодолении расовой сегрегации, реальной ситуации в классной комнате, проекте развития независимого школьного образования и политической борьбе.
Chapter
The parasite‐stress hypothesis of economics proposes that variation in infectious disease across regions causes variation in economic productivity by three proximate causes. (1) Infectious diseases cause morbidity, reducing people’s capability to produce. (2) Parasite stress evokes people’s values, which, in turn, cause regional economic parameters. For example, as parasite stress increases, regions become increasingly collectivistic. Collectivism causes parochial economics, political corruption, autocratic governance, and reduced innovativeness and diffusion of innovations. These effects stifle economic productivity of a region. In contrast, individualism causes willingness to transact with a diversity of people, creating broad economies and interregional sharing of ideas and products, increased innovativeness, governmental transparency, and democracy. These effects promote economic prosperity and equality. (3) Infectious disease limits cognitive ability, which reduces innovativeness and thus economic well-being in a region. Evidence supporting this framework is both diverse and copious. We discuss the established negative relationships between two important economic indicators, GDP per capita and Gini, and parasite stress and collectivism across the countries of the world. Studies also have confirmed the negative relationship between the diffusion of various innovations and parasite stress and collectivism across countries and US states. Evidence shows that even the routine purchases of people at supermarkets are consistent with the parasite-stress theory of values. We also discuss research indicating that parasite-stress variation across the globe affected wealth of regions as far back as 1500 ad. Cognitive ability is correlated negatively with parasite stress and collectivism both across countries and US states.
Chapter
Researchers have studied extensively regional variation in religious commitment and participation (religiosity). Such research, whether based on economic theory or evolutionary theory, emphasizes the high costs to individuals of religiosity. We have offered a new hypothesis of religiosity based on the parasite-stress theory of values. It relies on the theory of honest signaling in biology. We propose that religiosity is one important way that people engage in and display their in-group allegiance and boundary in order to avoid and manage infectious disease threats. In support of this, we provide evidence that religiosity is an aspect of in-group assortative sociality—and therefore an aspect of the behavioral immune system—and that religiosity correlates positively with parasite adversity, both cross-nationally and across states of the USA. We suggest additional tests of the parasite-stress theory’s application to religiosity. Other hypotheses of religiosity in the literature are evaluated. The parasite-stress theory of values appears to best account for religiosity and its diversity across regions. Our findings on religiosity have implications for a multitude of other areas of research such as secularization, health, ontogeny of religious values, life history, and geographical expansions of religion.
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Temperature, geo-residential pattern of subpopulations, prevalence of infectious diseases, and UV radiation have been proposed to explain the declining cognitive ability observed with proximity to the equator in the USA. This study tested the cognitive effects of the four variables. The results reveal that the latitudinal decline of cognitive ability is strongly correlated with the UV Index rather than with the other variables among White children. The decline in measured cognitive ability from north to south is absent among African American and Hispanic children, plausibly because the high levels of skin melanin among these ethnic groups, by absorbing and dissipating light, prevent the occurrence of radiation’s cognitive effects among these populations at USA latitudes. The possible physiological mediators (oxidative stress, folate degradation, sexual hormones) suggest diet, family planning, and educational methods as mitigating strategies; however, specific studies measuring the mediating variables are needed to confirm their role and further strengthen UV radiation as an explanatory concept.
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The classic elitists were „anti-democratic” because they were skeptical regarding the concrete possibilities of implementing democracy. Pareto, Mosca and Michels have properly noticed the inadequacy of the democratic ideals to the political reality of the time when they wrote their works. The fact that the society cannot be governed directly by the people is a reality that cannot be contested not even today. The radical elitists (Th. Veblen, C. Wright-Mills, and W.G. Domhoff) will reaffirm the leading potential of elites based on empiric proofs provided by the way the American society was 0perating; a society dominated by corporatism and conservatism in the second half of the 20th century. Unlike the monism of the classical and radical elitist theories, the pluralist elitism represents a theoretical and methodological „jump”, meaning that the elites are analyzed as social groups in a fierce competition for power. The first successful attempt of bringing together the elite theory and the democracy theory will belong to the pluralists (R. Aron, R.A. Dahl, J.A. Schumpeter), who see the „democratic game” as a competition between a plurality of elites. If the pluralist elitism has tried and succeeded to give an answer to the question how is it possible for a „dominant minority” to govern when the electoral majority decides who will govern, the democratic elitism faces the problem of the „commitment” of the political elites towards the democratic values, norms and rules. According to John Higley and Heinrich Best, „the democratic elitism describes the elites as being guardians of democracy”.
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Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.
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The authors examine the hypothesis that the intelligence of the population is a major factor determining national differences in economic development. To test the hypothesis, national IQs were calculated for 81 nations and economic development measured by real Gross Domestic Product at Purchasing Power Parity for 1998. The correlation between the two is .733, indicating that 54 per cent of the variance in GDP is attributable to the IQs of the populations.
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There are signs that the debate over racial and gender differences in intelligence is about to begin again. In this article we will be concerned primarily with racial differences but will make remarks about gender differences where applicable. Previously there have been bitter arguments over whether or not races exist, over whether it is either important or proper to study racial and gender differences in intelligence, and over the conclusions that have been drawn about environmental and genetic causes as determinants of these differences. We argue that races do, indeed, exist and that studying differences in cognitive competence between groups is a reasonable thing to do. We also point out that past research on both racial and gender differences in intelligence has been marked by methodological errors and overgeneralizations by researchers on all sides of the issue. We propose ten principles of design, analysis, and reporting that ought to be considered carefully when doing or evaluating research in this area. © 2007 Association for Psychological Science.
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Evidence is reviewed pointing to a negative relationship between intelligence and religious belief in the United States and Europe. It is shown that intelligence measured as psychometric g is negatively related to religious belief. We also examine whether this negative relationship between intelligence and religious belief is present between nations. We find that in a sample of 137 countries the correlation between national IQ and disbelief in God is 0.60.
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We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950-2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing.
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Kanazawa (2008), Templer (2008), and Templer and Arikawa (2006) claimed to have found empirical support for evolutionary theories of race differences in intelligence by correlating estimates of national IQ with indicators of reproductive strategies, temperature, and geographic distance from Africa. In this paper we criticize these studies on methodological, climatic, and historical grounds. We show that these studies assume that the Flynn Effect is either nonexistent or invariant with respect to different regions of the world, that there have been no migrations and climatic changes over the course of evolution, and that there have been no trends over the last century in indicators of reproductive strategies (e.g., declines in fertility and infant mortality). In addition, we show that national IQs are strongly confounded with the current developmental status of countries. National IQs correlate with all the variables that have been suggested to have caused the Flynn Effect in the developed world.
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The gains of scores on standardized intelligence tests (i.e., Flynn effect) have been the subject of extensive debate concerning their nature, causes, and implications. The aim of the present study is to investigate whether five intelligence tests are measurement invariant with respect to cohort. Measurement invariance implies that gains over the years can be attributed to increases in the latent variables that the tests purport to measure. The studies reported contain original data of Dutch Wechsler Adult Intelligence Scale (WAIS) gains from 1967 to 1999, Dutch Differential Aptitude Test (DAT) gains from 1984 to 1995, gains on a Dutch children intelligence test (RAKIT) from 1982 to 1993, and reanalyses of results from Must, Must, and Raudik [Intelligence 167 (2003) 1–11] and Teasdale and Owen [Intelligence 28 (2000) 115–120]. The results of multigroup confirmatory factor analyses clearly indicate that measurement invariance with respect to cohorts is untenable. Uniform measurement bias is observed in some, but not all subtests. The implications of these findings are discussed.
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Clifford, Richardson, and Hemon (1989, Biometrics 45, 123-134) presented modified tests of association between two spatially autocorrelated processes, for lattice and non-lattice data. These tests are built on the sample covariance and on the sample correlation coefficient; they require the estimation of an effective sample size that takes into account the spatial structure of both processes. Clifford et al. developed their method on the basis of an approximation of the variance of the sample correlation coefficient and assessed it by Monte Carlo simulations for lattice and non-lattice networks of moderate to large size. In the present paper, the variance of the sample covariance is computed for a finite number of locations, under the multinormality assumption, and the mathematical derivation of the definition of effective sample size is given. The theoretically expected number of degrees of freedom for the modified t test with renewed modifications is compared with that computed on the basis of equation (2.9) of Clifford et al. (1989). The largest differences are observed for small numbers of locations and high autocorrelation, in particular when the latter is present with opposite sign in the two processes. Basic references that were missing in Clifford et al. (1989) are given and inherent ambiguities are discussed.
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In a number of economically developed nations the intelligence of the population has increased by approx. 1 standard deviation (SD) over the last half century. No satisfactory explanation for this increase has yet been forthcoming. In this paper it is argued that the major causal factor is improvements in nutrition. These have led to parallel increases in height, head circumference and brain size, and to improved neurological development and functioning of the brain. These are responsible for higher intelligence. Nutrition is still suboptimal for substantial proportions of the population and further increases in intelligence can be anticipated if standards of nutrition could be improved.
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In this ecological study, a robust negative correlation of r = - .62 (P < .01) is reported between national IQs and consanguinity as measured by the log10 transformed percentage of consanguineous marriages for 72 countries. This correlation is reduced in magnitude, when IQ is controlled for GDP per capita (r = - .41, P < .01); education index (r = - .40, P < .01); and democracy index (r = - .42, P < .01). Multiple regression analysis revealed that in the absence of the democracy index; percentage consanguineous marriages, education index and GDP per capita all exhibited stable final standardized β coefficients, however consanguinity had the least impact (β = 0, P > .05) whereas GDP per capita had the highest (β = .35, P > .01). This result is interpreted in light of cultural feedback theory, whereby it is suggested that consanguinity could subtly influence IQ at larger scales as a result of small IQ handicaps bought about through inbreeding being amplified into much larger differences through their effect on factors that maximize IQ such as access to education and adequate nutrition. Finally, consideration is given to future potential research directions.
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This book is simple in conception. By taking estimates of IQ for almost every country in the world, and running these against per capita gross domestic product (GDP) data at various times since 1820, Lynn and Vanhanen show significant positive correlations both of absolute GDP per capita levels and of long-run rates of national economic growth against IQ. IQ is shown to be a powerful predictor of both these dependent variables, although not, of course, a monocausal explanation. By employing regression analysis, the authors isolate deviant data points, and try to explain why the individual countries they represent at these points in time deviated significantly from the expected trend-line values.
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AN important question in studies of mental ability concerns the effect of parental socio-economic status (SES) on the IQ of their offspring. Only a full cross-fostering study, including children born to biological parents from the most highly contrasting SES and adopted by parents with equally constrasting SES, can answer this question. Previous adoption studies using incomplete cross-fostering designs1–3 have indicated an effect of postnatal environment on the IQ of children born to low-SES backgrounds and adopted by high-SES parents. They have not shown whether a low SES reduces the IQ of children born to high-SES parents or whether the SES of biological parents has an effect on IQ, or whether the effect of the SES of adoptive parents is independent of the SES of biological parents. We present a full cross-fostering study dealing with IQ, and find that children adopted by high-SES parents score higher than children adopted by low-SES parents; children born to high-SES parents score higher than children born to low-SES parents; and that there is no evidence for an interaction between these two factors on children's IQ.
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How did human intelligence evolve to be so high? Lynn [Lynn, R. (1991). The evolution of race differences in intelligence. Mankind Quarterly, 32, 99–173] and Rushton [Rushton, J.P. (1995). Race, evolution, and behavior: A life history perspective. New Brunswick: Transaction] suggest that the main forces behind the evolution of human intelligence were the cold climate and harsh winters, which selected out individuals of lower intelligence. In contrast, Kanazawa [Kanazawa, S. (2004). General intelligence as a domain-specific adaptation. Psychological Review, 111, 512–523] contends that it is the evolutionary novelty of the environment which increased general intelligence. Multiple regression analyses support both theories. Annual mean temperature and evolutionary novelty (measured by latitude, longitude, and distance from the ancestral environment) simultaneously have independent effects on average intelligence of populations. Temperature and evolutionary novelty together explain half to two-thirds of variance in national IQ.
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Spatial autocorrelation analysis tests whether the observed value of a nominal, ordinal, or interval variable at one locality is independent of values of the variable at neighbouring localities. The computation of autocorrelation coefficients for nominal, ordinal, and for interval data is illustrated, together with appropriate significance tests. The method is extended to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities being considered, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Autocorrelation analysis is applied to microgeographic variation of allozyme frequencies in the snail Helix aspersa. Differences in variational patterns in two city blocks are interpreted. The inferences that can be drawn from correlograms are discussed and illustrated with the aid of some artificially generated patterns. Computational formulae, expected values and standard errors are furnished in two appendices.
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Migration, both within and beyond borders, has become an increasingly prominent theme in domestic and international debates, and is the topic of the 2009 Human Development Report (HDR09). The starting point is that the global distribution of capabilities is extraordinarily unequal, and that this is a major driver for movement of people. Migration can expand their choices —in terms of incomes, accessing services and participation, for example— but the opportunities open to people vary from those who are best endowed to those with limited skills and assets. These underlying inequalities, which can be compounded by policy distortions, is a theme of the report. The report investigates migration in the context of demographic changes and trends in both growth and inequality. It also presents more detailed and nuanced individual, family and village experiences, and explores less visible movements typically pursued by disadvantaged groups such as short term and seasonal migration. There is a range of evidence about the positive impacts of migration on human development, through such avenues as increased household incomes and improved access to education and health services. There is further evidence that migration can empower traditionally disadvantaged groups, in particular women. At the same time, risks to human development are also present where migration is a reaction to threats and denial of choice, and where regular opportunities for movement are constrained.
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The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and two-dimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.
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The new paradigm of evolutionary social science suggests that humans adjust rapidly to changing economic conditions, including cognitive changes in response to the economic significance of education. This research tested the predictions that cross-national differences in IQ scores would be positively correlated with education and negatively correlated with an agricultural way of life. Regression analysis found that much of the variance in IQ scores of 81 countries (derived from [Lynn, R., & Vanhanen, T. (2002). IQ and the wealth of nations. Westport, CT: Praeger]) was explained by enrollment in secondary education, illiteracy rates, and by the proportion of agricultural workers. Cross-national IQ scores were also related to low birth weights. These effects remained with national wealth, infant mortality, and geographic continent controlled (exception secondary education) and were largely due to variation within continents. Cross-national differences in IQ scores thus suggest that increasing cognitive demands in developed countries promote an adaptive increase in cognitive ability.
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This paper examines the distribution of national IQ in geographical space. When the heritability of IQ and its dependence on eco-social factors are considered from a global perspective, they suggest that the IQs of neighboring countries should be similar. Using previously published IQ data for 113 nations (Lynn, R., & Vanhanen, T., (2006). IQ and global inequality. Athens, GA: Washington Summit Publishers.) the relationship between geographical location and national IQ is formally tested using spatial statistics. It is found that as predicted, nations that are geographical neighbors have more similar IQs than nations that are far apart. National IQ varies strikingly with position around the globe; the relationship between location and national IQ is even stronger than the relationship between location and national average temperature. The findings suggest that Lynn & Vanhanen's national IQ measures are reliable and adequately representative, and that their procedures for estimating missing national IQ scores from the scores of nearby nations are defensible.
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This paper presents a systematic review of published data on the performance of sub-Saharan Africans on Raven's Progressive Matrices. The specific goals were to estimate the average level of performance, to study the Flynn Effect in African samples, and to examine the psychometric meaning of Raven's test scores as measures of general intelligence. Convergent validity of the Raven's tests is found to be relatively poor, although reliability and predictive validity are comparable to western samples. Factor analyses indicate that the Raven's tests are relatively weak indicators of general intelligence among Africans, and often measure additional factors, besides general intelligence. The degree to which Raven's scores of Africans reflect levels of general intelligence is unknown. Average IQ of Africans is approximately 80 when compared to US norms. Raven's scores among African adults have shown secular increases over the years. It is concluded that the Flynn Effect has yet to take hold in sub-Saharan Africa.
Article
Plots of mean IQ and per capita real Gross Domestic Product for groups of 81 and 185 nations, as collected by Lynn and Vanhanen, are best fitted by an exponential function of the form: GDP = a * 10b*(IQ), where a and b are empirical constants. Exponential fitting yields markedly higher correlation coefficients than either linear or quadratic. The implication of exponential fitting is that a given increment in IQ, anywhere along the IQ scale, results in a given percentage in GDP, rather than a given dollar increase as linear fitting would predict. As a rough rule of thumb, an increase of 10 points in mean IQ results in a doubling of the per capita GDP.
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On the basis of several reviews of the literature, Lynn [Lynn, R., (2006). Race differences in intelligence: An evolutionary analysis. Augusta, GA: Washington Summit Publishers.] and Lynn and Vanhanen [Lynn, R., & Vanhanen, T., (2006). IQ and global inequality. Augusta, GA: Washington Summit Publishers.] concluded that the average IQ of the Black population of sub-Saharan Africa lies below 70. In this paper, the authors systematically review published empirical data on the performance of Africans on the following IQ tests: Draw-A-Man (DAM) test, Kaufman-Assessment Battery for Children (K-ABC), the Wechsler scales (WAIS & WISC), and several other IQ tests (but not the Raven's tests). Inclusion and exclusion criteria are explicitly discussed. Results show that average IQ of Africans on these tests is approximately 82 when compared to UK norms. We provide estimates of the average IQ per country and estimates on the basis of alternative inclusion criteria. Our estimate of average IQ converges with the finding that national IQs of sub-Saharan African countries as predicted from several international studies of student achievement are around 82. It is suggested that this estimate should be considered in light of the Flynn Effect. It is concluded that more psychometric studies are needed to address the issue of measurement bias of western IQ tests for Africans.
Article
Generational intelligence gains are one intriguing finding in science. Nutrition and cognitive stimulation are among the most remarkable causes of the upward trend in intelligence. The nutrition hypothesis predicts a primary impact on the most deprived, producing disproportionate gains at low intelligence levels. The cognitive stimulation hypothesis predicts gains along the intelligence distribution. However, data from the entire distribution are rarely available. The present study compares a sample of children tested in 1970 with an equivalent sample tested 30 years later. Data for the entire distributions were available. The results are consistent with the nutrition hypothesis, because the gains were mainly concentrated in the lower and medium halves of the distribution and were negligible in the very top half of the distribution. Moreover, an impressive gradual decrease in the gains was observed from the lower half to the top half of the distribution.
Article
The impetus for our study was the contention of both Lynn [Lynn, R. (1991) Race differences in intelligence: A global perspective. Mankind Quarterly, 31, 255–296] and Rushton (Rushton [Rushton, J. P. (1995). Race, evolution and behavior: A life history perspective. New Brunswick, NJ: Transaction; Rushton, J. P. (1997). Race, intelligence, and the brain: The errors and omissions of the revised edition of S.J. Gould's the mismeasurement of man. Personality and Individual Differences, 23, 169–180; Rushton, J. P. (2000). Race, evolution, and behavior. A life history perspective (3rd edition). Port Huron: Charles Darwin Research Institute] that persons in colder climates tend to have higher IQs than persons in warmer climates. We correlated mean IQ of 129 countries with per capita income, skin color, and winter and summer temperatures, conceptualizing skin color as a multigenerational reflection of climate. The highest correlations were − 0.92 (rho = − 0.91) for skin color, − 0.76 (rho = − 0.76) for mean high winter temperature, − 0.66 (rho = − 0.68) for mean low winter temperature, and 0.63 (rho = 0.74) for real gross domestic product per capita. The correlations with population of country controlled for are almost identical. Our findings provide strong support for the observation of Lynn and of Rushton that persons in colder climates tend to have higher IQs. These findings could also be viewed as congruent with, although not providing unequivocal evidence for, the contention that higher intelligence evolves in colder climates. The finding of higher IQ in Eurasians than Africans could also be viewed as congruent with the position of Diamond (1997) that knowledge and resources are transmitted more readily on the Eurasian west–east axis.
Article
Autocorrelation is a very general statistical property of ecological variables observed across geographic space; its most common forms are patches and gradients. Spatial autocorrelation, which comes either from the physical forcing of environmental variables or from community processes, presents a problem for statistical testing because autocorrelated data violate the assumption of independence of most standard statistical procedures. The paper discusses first how autocorrelation in ecological variables can be described and measured, with emphasis on mapping techniques. Then, proper statistical testing in the presence of autocorrelation is briefly discussed. Finally, ways are presented of explicitly introducing spatial structures into ecological models. Two approaches are proposed; in the raw-data approach, the spatial structure takes the form of a polynomial of the x and y geographic coordinates of the sampling stations; in the matrix approach, the spatial structure is introduced in the form of a geog
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The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r greater than or equal to 0.28 or r(2) greater than or equal to 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. Then, using real ecological data, I demonstrated the utility of various statistical techniques for enhancing the reliability and interpretation of ecolo
Article
Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro-scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least-squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non-detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de-emphasized predictors with strong autocorrelation and long-distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.
Article
The presence of positive spatial autocorrelation in ecological data causes parametric statistical tests to give more apparently significant results than the data justify, which is a serious problem for both statistical and ecological interpretation. In this paper, we review this problem and some of the statistical approaches that have been used to address it, concentrating on statistical methods rather than on sampling or experimental design. We then describe in more detail the technique of adjusting the "effective sample size" based on the autocorrelation structure of the data. Unfortunately, the effective sample size cannot be reliably estimated from the data, and therefore this approach may not be a general solution to the problem. An alternative approach is to determine a parametric model of the data and its spatial autocorrelation structure, and then to use a Monte Carlo approach to generate the distribution of the test statistic of interest using that model. We suggest that this latter approach should be used in situations in which no robust analytically derived solution is available.
Article
Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.
Article
Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present SAM (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). SAM also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of SAM's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/).