2012 | 13 | 2 | 365-386
Article title

Statistics and Sociology: The Mutually-Supportive Development from the Perspective of Interdisciplinarization of Social Research

Title variants
Languages of publication
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.
Physical description
  • AGRESTI A., 1990. Categorical Data Analysis. Wiley. N. Y.
  • ALLISON P., 1982. Discrete-Time Methods for the Analysis of Event Histories. Sociological Methodology, 13, pp. 61-98.
  • ALONSO, W., STARR, P., 1987. The Politics of Numbers, Russell Sage Foundation, NY.
  • BARTHOLOMEW, D. J., STEELE F., MOUSTAKI I., and GALBRAITH J. I., 2002. The Analysis and Interpretation of Multivariate Data for Social Scientists, Chapman and Hall/CRC, Boca Raton, Fl.
  • BARTHOLOMEW, D. J., KNOTT M., MOUSTAKI I., 2011 Latent Variable Models and Factor Analysis: A Unified Approach. 3rd edition; Wiley Series in Probability and Statistics. John Wiley and Sons, Ltd.
  • BERNERT, CH., 1983. The Career of Causal Analysis in American Sociology, The British Journal of Sociology, Volume 34 Number 2 (June).
  • BLALOCK, H. M., 1961. Causal Inferences in Nonexperimental Research. New York: W.W. Norton.
  • BLAU, P. M., DUNCAN O. D., 1967. American Occupational Structure. Free Press, N.Y.
  • BUNGE, M. A., 1979. Causality and Modern Science. (3rd ed.), New York: Dover.
  • Camargo Alexandre de Paiva Rio, Sociology of Statistics: possibilities of a new field of investigation, Hist. cienc. saude-Manguinhos, vol.16 no.4 Rio de Janeiro Oct./Dec. 2009. (
  • CAMIC, CH., XIE, Y., 1994. The Statistical Turn in American Social Science: Columbia University, 1890 to 1915. American Sociological Review, Volume: 59, Issue: 5.
  • COX, D. R., 1971. Regression Models and Life Tables (with discussion). Journal of the Royal Statistical Society, Ser. B, 34, pp. 187-220.
  • DESROSIERÈS, A., 2011. Words and Numbers. For a Sociology of the Statistical Argument, chapt. 2 [in] Saetnan A. R., Lomell H. M., Hammer S. (ed.): The Mutual Construction of Statistics and Society, Routlege, New-York, pp. 41-63.
  • DUNCAN, O. D., 1979. How Destination Depends on Origin in the Occupational Mobility Table. American Journal of Sociology 84:793-803.
  • FIENBERG, S. E., A Brief History of Statistics in Three and One-Half Chapters: A Review Essay, Statistical Science, Vol. 7, No. 2 (1992), pp. 208-225.
  • FISCHER, M. M., GETIS, A., 2010, Handbook of Applied Spatial Analysis. Software Tools, Methods and Applications, Springer, Berlin Heidelberg.
  • FISHER, R. A., 1935, The Design of Experiments. Oliver and Boyd, Edinburgh.
  • FREEDMAN, D. A., 1987. As Others See Us: A Case Study in Path Analysis. Journal of Educational Statistics, 12: 101-223.
  • GOLDTHORPE, J., 2001. Causation, Statistics, and Sociology, European Sociological Review, Vol. 17 No. 1, 1-20.
  • GOODCHILD, M. F., ANSELIN, L., APPELBAUM, R. P., HARTHORN, B. H., 2000. Toward Spatially Integrated Social Science, International Regional Science Review 23, pp. 139-159, (April).
  • GOODMAN, L. A., 1979. Simple Models for the Analysis of Association in Cross-Classifications Having Ordered Categories, Journal of the American Statistical Association 74:537-52.
  • GOODMAN, L. A., 1985. The Analysis of Cross-Classified Data Having Ordered and/or Unordered Categories. Annals of Statistics 13:10-69.
  • GRACE, J. B., SCHOOLMASTER jr., D. R., GUNTENSPERGEN, G. R., LITTLE A. M., Mitchell B. R., Miller K. M., Schweiger E. W, 2012. Guidelines for a graph-theoretic implementation of structural equation modeling, Ecosphere 3(8):73.
  • HANSEN, M. H., MADOW, W. G., TEPPING, B. J.(ed.), 1983. An evaluation of model-dependent and probability-sampling inference in sample surveys, Journal of the American Statistical Association, 78: 776-793.
  • HECKMAN, J. J., 1979. Sample Selection Bias as a Specification Error, Econometrica, 47:153-61.
  • HEERINGA, S. G., WEST B. T., BERGLUND P. A., 2010. Applied Survey Data Analysis, Chapman and Hall/CRC, Boca Raton, FL.
  • HITCHCOCK, C., 2001. The Intransitivity of Causation Revealed in Equations and Graphs. Journal of Philosophy, 98: 237-299.
  • HOUSE, J. S., JUSTER, F. T., KAHN, R. L., SCHUMAN, H., and SINGER, E. (ed.), 2004. A Telescope on Society: Survey Research and Social Science at the University of Michigan and Beyond, University of Michigan Press.
  • JORESKÖG, K. G., 1973. A General Method for Estimating a Linear Structural Equa-tion System. Pp. 85-112 [in] A. S. Goldberger and O. D. Duncan (ed.) Structural Equation Models in the Social Sciences, Seminar. N. Y.
  • KISH, L., 1995. The Hundred Years’ Wars of Survey Sampling, chap. 3 [in] Graham Kalton and Steven Heeringa (ed.), Leslie Kish: Selected Papers, 2003. John Wiley & Sons, Hoboken, NJ.
  • KLUVE, J., 2001. On the Role of Counterfactuals in Inferring Causal Effects of Treatments. Alfred Weber Institute, University of Heidelberg and IZA Bonn Discussion Paper No. 354.
  • LAZARSFELD, P. F., 1961. Notes on the History of Quantification in Sociology--Trends, Sources and Problems. Isis, Vol. 52, No. 2. (Jun., 1961), pp. 277-333.
  • LAZARSFELD, P. F., 1950."The Logical and Mathematical Foundation of Latent Structure Analysis. p. 362-412 [in] Studies in Social Psychology in World War II. Vol. 4, Measurement and Prediction, edited by E. A. Schulman, P. F. Lazarsfeld, S. A. Starr, and J. A. Clausen. Princeton, NJ: Princeton University Press.
  • MANZO, G., 2010. Analytical Sociology and Its Critics, Archives of European Sociology. LI, 1 pp. 129–170.
  • MENZIES, P., 2008. Counterfactual Theories of Causation. The Stanford Encyclopedia of Philosophy, Vol: 2011, Issue: Dec. 16, Stanford University.
  • MORGAN, S. L., HARDING, D. J., 2006. Matching Estimators of Causal Effects: Prospects and Pitfalls in Theory and Practice. Sociological Methods and Research 35: 3-60.
  • MORGAN, S. L., WINSHIP, C., 2007. Counterfactuals and Causal Inference. Methods and Principles for Social Research. Cambridge University Press, N.Y.
  • MUTZ, D. C., 2011. Population-Based Survey Experiments, Princeton University Press, Princeton, NJ.
  • NEYMAN, J., 1933. An outline of the theory and practice of the representative methods: The method of stratified sampling and the method of purposive selection (in Polish – English version published in 1934. On the two different aspects of the representative method. Journal of the Royal Statistical Society, 97: 558-625).
  • OKRASA, W., 1999. The Dynamics of Poverty and the Effectiveness of Poland’s Safety Net. Policy Research Working Paper No 2221. The World Bank, Washington D. C.
  • OKRASA, W., 1999. Who Avoids and Who Escapes from Poverty during the Transition. Evidence from Polish Panel 1993-96, Policy Research Working Paper No 2218. The World Bank, Washington D. C.
  • OKRASA, W., 2012. Spatially Integrated Social Research and Official Statistics: Methodological remarks and empirical results on local development, Paper presented at the International Conference of Spatial Econometrics and Regional Economic Analysis, Łódź, 4-5 June (2012).
  • PEARL, J., 2009. Causality: Models, Reasoning and Inference. (2nd edition), Cambridge University Press, N. Y.
  • PEARL J., 2012. The causal foundations of structural equation modeling. Pages 68–91 in R. H. Hoyle (ed.). Handbook of structural equation modeling. Guilford Press, New York, New York, USA.
  • RAFTERY, A. E., 2001. Statistics in Sociology, 1950-2000: A Selective Review. Sociological Methodology, Vol. 31, pp. 1-45 American Sociological Association.
  • SHIPLEY, B., 2009. Confirmatory path analysis in a generalized multilevel context, Ecology 90:363–368.
  • STIGLER, S. M. (1986), The History of Statistics: The Measurement of Uncertainty before 1900, Harvard Univ. Press, p. 410 [Reissued in paperback edition (1990)].
  • SOBEL, M. E., 1995. Causal inference in the social and behavioral sciences, [in] G. Arminger, C. C. Clogg, and M. E. Sobel ed. Handbook of Statistical Modeling for the Social and Behavioral Sciences,Plenum Press, N. Y.
  • SOBEL, M. E., 1998. Causal Inference in Statistical Models of the Process of Socio-economic Achievement: A Case Study. Sociological Methods and Research 27: 318-48.
  • STARR, P., 1987. The Sociology of Official Statistics [in] Alonso, W., Starr, P., (ed.). 1987. The Politics of Numbers, Russell Sage Foundation, N. Y., pp.7–58.
  • Statisticians in History.
  • URRY, J., 2004, The Sociology of Space and Place, [in] Judith R. Blau (ed.) The Blackwell Companion to Sociology. Blackwell Publishing, Malden, MA.
  • WILCOX, W. F., 1930. Census, Encyclopedia of the Social Sciences, New York: Macmillan.
  • WOODWARD, J., 2003. Making Things Happen. A Theory of Causal Explanation. Oxford University Press, Oxford.
  • ZNANIECKI, F., The Method of Sociology, Farrar & Rinehart, New York 1934.
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.