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            Evidential Pluralism in the Social Sciences

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            Author(s)
            Shan, Yafeng
            Williamson, Jon
            Language
            English
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            Abstract
            This volume contends that Evidential Pluralism—an account of the epistemology of causation, which maintains that in order to establish a causal claim one needs to establish the existence of a correlation and the existence of a mechanism—can be fruitfully applied to the social sciences. Through case studies in sociology, economics, political science and law, it advances new philosophical foundations for causal enquiry in the social sciences. The book provides an account of how to establish and evaluate causal claims and it offers a new way of thinking about evidence-based policy, basic social science research and mixed methods research. As such, it will appeal to scholars with interests in social science research and methodology, the philosophy of science and evidence-based policy.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/164019
            Keywords
            causality;correlation;economics;evidence;evidence-based policy;evidential pluralism;law;legal studies;mechanism;mixed methods research;psychology;research methods;science;social science;sociology; thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology::JHBC Social research and statistics; thema EDItEUR::Q Philosophy and Religion::QD Philosophy
            DOI
            10.4324/9781003143000
            ISBN
            9780367697228, 9781003143000, 9780367697266
            Publisher
            Taylor & Francis
            Publisher website
            http://www.taylorandfrancis.com/
            Publication date and place
            2023
            Grantor
            • University of Kent
            Imprint
            Routledge
            Series
            Philosophy and Method in the Social Sciences,
            Pages
            191
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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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