Big data in autism research: Methodological challenges and solutions / Brian K. LEE in Autism Research, 16-10 (October 2023)  

Public ISBD [article]  inAutism Research > 16-10 (October 2023) . - p.1852-1858 Titre : Big data in autism research: Methodological challenges and solutions Type de document : Texte imprimé et/ou numérique Auteurs : Brian K. LEE, Auteur ; Diana E. SCHENDEL, Auteur ; Lindsay L. SHEA, Auteur Article en page(s) : p.1852-1858 Index. décimale : PER Périodiques Résumé : Abstract While the concept of big data has emerged over the past decade as a hot topic in nearly all areas of scientific inquiry, it has rarely been discussed in the context of autism research. In this commentary we describe aspects of big data that are relevant to autism research and methodological issues such as confounding and data error that can hamper scientific investigation. Although big data studies can have transformative impact, bigger is not always better, and big data require the same methodological considerations and interdisciplinary collaboration as "small data" to extract useful scientific insight. En ligne : https://doi.org/10.1002/aur.3007 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513 [article] Big data in autism research: Methodological challenges and solutions [Texte imprimé et/ou numérique] / Brian K. LEE, Auteur ; Diana E. SCHENDEL, Auteur ; Lindsay L. SHEA, Auteur . - p.1852-1858. in Autism Research > 16-10 (October 2023) . - p.1852-1858 Index. décimale : PER Périodiques Résumé : Abstract While the concept of big data has emerged over the past decade as a hot topic in nearly all areas of scientific inquiry, it has rarely been discussed in the context of autism research. In this commentary we describe aspects of big data that are relevant to autism research and methodological issues such as confounding and data error that can hamper scientific investigation. Although big data studies can have transformative impact, bigger is not always better, and big data require the same methodological considerations and interdisciplinary collaboration as "small data" to extract useful scientific insight. En ligne : https://doi.org/10.1002/aur.3007 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513

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