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Statistics and Sociology: The mutually-supportive development from the perspective of interdisciplinarization of social research

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  • Włodzimierz Okrasa

Abstract

Statistics emerged as a scientific discipline and has been developed as such, especially extensively over the past century, not only due to an extraordinary service it provides to other disciplines but also thanks to ideas, questions and approaches originally formulated in different fields of empirical research, including sociology, which also contributed to statistics. The confluence of developments in these two disciplines (statistics and sociology) seems to be one of the most successful and beneficial for both of them. Yet, it has become a focus of systematic reflection only recently. The aim of this paper is to make a concise overview of the logical scheme of this interaction and to stress the importance of the counterfactual causal modeling being currently under constant refinement. A more explicit formula of interdisciplinarization that underlies such an interaction anyway would add to overcoming the methodological challenges it poses to either discipline. While contributing to the advancement of ‘cause-and-effect’ oriented quantitative sociology this would enhance methodology of social science research in general.

Suggested Citation

  • Włodzimierz Okrasa, 2012. "Statistics and Sociology: The mutually-supportive development from the perspective of interdisciplinarization of social research," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 365-386, June.
    • Handle: RePEc:csb:stintr:v:13:y:2012:i:2:p:365-386
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    References listed on IDEAS

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    1. Okrasa, Wlodzimierz, 1999. "Who avoids and who escapes from poverty during transition? - evidence from Polish panel data, 1993-96," Policy Research Working Paper Series 2218, The World Bank.
    2. Okrasa Włodzimierz, 2012. "Spatially Integrated Social Research and Official Statistics: Methodological Remarks and Empirical Results on Local Development," Comparative Economic Research, Sciendo, vol. 15(4), pages 191-206, December.
    3. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    4. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, October.
    5. Kluve, Jochen, 2001. "On the Role of Counterfactuals in Inferring Causal Effects of Treatments," IZA Discussion Papers 354, Institute of Labor Economics (IZA).
    6. Okrasa,Wlodzimierz, 1999. "The dynamics of poverty and the effectiveness of Poland's safety net (1993-96)," Policy Research Working Paper Series 2221, The World Bank.
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