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County-level USA:No Robust Relationship between Geoclimatic Variables and Cognitive Ability

机译:美国县级:在地理脑域变量与认知能力之间没有强大的关系

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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.
机译:使用样本为〜3,100 uscorike,我们测试了对何地理位置能力因地理位置而异的地理束缚解释。这些模型的问题是,即使在回归方程中出现各种常见的控制,这种模型也能够强烈预测地理学​​的认知能力。通常不支持基于紫外线辐射(UVR)或其他地理基础模型。尽管UVR单独预测USCounty-Level(β= - 。33)的认知能力,但在气候和人口调节的存在下,其有效性明显减少(β= - 。16),并且进一步减小了空间滞后(β= - 。10)。对于气候模型,平均温度仍然是包含空间滞后(β= .35)的回归方程中的显着预测因子。然而,当我们通过种族/种族单独运行分析时,效果相对于典型的寒冷天气假设,效果是错误的方向.Ooreove,当含有GA的空间LAG.ANALYSES的模型中没有出现一致的模式。跨越县的P尺寸通常与来自UVR模型的预测不一致。在UVR模型中,结果似乎为组成模型提供支持。

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