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?:about
?:abstract
  • Cross-national analyses test hypotheses about the drivers of variation in national outcomes. However, since nations are connected in various ways, such as via spatial proximity and shared cultural ancestry, cross-national analyses often violate assumptions of non-independence, inflating false positive rates. Here, we show that, despite being recognised as an important statistical pitfall for over 200 years, cross-national research in economics and psychology still does not sufficiently account for non-independence. In a review of the 100 highest-cited cross-national studies of economic development and values, we find that controls for non-independence are rare. When studies do control for non-independence, our simulations suggest that most commonly used methods are insufficient for reducing false positives in non-independent data. In reanalyses of twelve previous cross-national correlations, half of the estimates are compatible with no association after controlling for non-independence using global proximity matrices. We urge social scientists to sufficiently control for non-independence in cross-national research. (xsd:string)
?:citation
?:contributor
?:dateModified
  • 2023 (xsd:gyear)
?:datePublished
  • 2023 (xsd:gyear)
?:doi
  • 10.1038/s41467-023-41486-1 ()
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  • true (xsd:boolean)
is ?:hasPart of
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  • en (xsd:string)
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?:issn
  • 2041-1723 ()
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is ?:mainEntity of
?:name
  • Cross-national analyses require additional controls to account for the non-independence of nations (xsd:string)
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?:publicationType
  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
?:reference
?:sourceInfo
  • GESIS-SSOAR (xsd:string)
  • In: Nature Communications, 2023, 1-13 (xsd:string)
rdf:type
?:url
?:urn
  • urn:nbn:de:0168-ssoar-100783-8 ()