Using Environmental Features to Maximize Prediction of Regional Intergroup Bias

Using Environmental Features to Maximize Prediction of Regional Intergroup Bias
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利用环境特征最大限度地预测区域群间偏差

DOI:
10.1177/1948550620909775
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发表时间:
2020
影响因子:
5.7
通讯作者:
Jimmy Calanchini
Jimmy Calanchini
中科院分区:
心理学2区
文献类型:
--
作者:
Eric Hehman;Eugene K. Ofosu;Jimmy Calanchini

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本研究采用数据驱动的方法来确定环境特征与不同类型的区域内偏见之间的关系。在整合了大量的环境属性数据集(N=813)后,我们使用现代模型选择技术(即弹性网络正则化)来开发简约模型,用于区域内种族、宗教、性倾向、年龄和基于健康的群体偏见的隐含和显式测量。已开发的模型一般预测了区域偏差的大量差异,最高可达62%,并预测的区域偏差的差异比基本的区域人口统计数据要大得多。环境的人类特征和环境中的事件强烈而一致地预测偏见,但环境的非人类特征和人口特征不一致地预测偏见。结果揭示了不同地区群体间偏见的共同心理原因,揭示了不同地区间偏见之间的差异,并有助于发展地区偏见的理论模型。
The present research adopts a data-driven approach to identify how characteristics of the environment are related to different types of regional in-group biases. After consolidating a large data set of environmental attributes (N = 813), we used modern model selection techniques (i.e., elastic net regularization) to develop parsimonious models for regional implicit and explicit measures of race-, religious-, sexuality-, age-, and health-based in-group biases. Developed models generally predicted large amounts of variance in regional biases, up to 62%, and predicted significantly and substantially more variance in regional biases than basic regional demographics. Human features of the environment and events in the environment strongly and consistently predicted biases, but nonhuman features of the environment and population characteristics inconsistently predicted biases. Results implicate shared psychological causes of different regional intergroup biases, reveal distinctions between biases, and contribute to developing theoretical models of regional bias.
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