Is Quantitative Research Ethical? Tools for Ethically Practicing, Evaluating, and Using Quantitative Research
- 2.3k Downloads
This editorial offers new ways to ethically practice, evaluate, and use quantitative research (QR). Our central claim is that ready-made formulas for QR, including ‘best practices’ and common notions of ‘validity’ or ‘objectivity,’ are often divorced from the ethical and practical implications of doing, evaluating, and using QR for specific purposes. To focus on these implications, we critique common theoretical foundations for QR and then recommend approaches to QR that are ‘built for purpose,’ by which we mean designed to ethically address specific problems or situations on terms that are contextually relevant. For this, we propose a new tool for evaluating the quality of QR, which we call ‘relational validity.’ Studies, including their methods and results, are relationally valid when they ethically connect researchers’ purposes with the way that QR is oriented and the ways that it is done—including the concepts and units of analysis invoked, as well as what its ‘methods’ imply more generally. This new way of doing QR can provide the liberty required to address serious worldly problems on terms that are both practical and ethically informed in relation to the problems themselves rather than the confines of existing QR logics and practices.
KeywordsQuantitative research Quantitative methods Statistics Probability Regression Research design Data analysis Inductive inference
This research was supported by Australian Research Council’s Future Fellowship scheme (project FT140100629).
- American Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.Google Scholar
- Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research on teaching. In N. L. Gage (Ed.), Handbook of research on teaching (pp. 171–246). Chicago: Rand McNally.Google Scholar
- Daston, L. (2005). Scientific error and the ethos of belief. Social Research, 72, 1–28.Google Scholar
- Davies, W. (2017, January 19). How statistics lost their power—And why we should fear what comes next. The Guardian. Retrieved from https://www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy.
- Dewey, J. (1929). The quest for certainty. New York: Minton, Balch, & Co.Google Scholar
- Dunn, W. N. (1982). Reforms as arguments. Knowledge, 3(3), 293–326.Google Scholar
- Feldman, M. S., & Orlikowski, W. J. (2011). Theorizing practice and practicing theory. Organization science.Google Scholar
- Hacking, I. (1992a). Statistical language, statistical truth and statistical reason: The self-authentification of a style of scientific reasoning. In E. McMullin (Ed.), The social dimensions of science (Vol. 3, pp. 130–157). Notre Dame: University of Notre Dame Press.Google Scholar
- Hacking, I. (1992b). The self-vindication of the laboratory sciences. In A. Pickering (Ed.), Science as practice and culture (pp. 29–64). Chicago: Chicago Unviersity Press.Google Scholar
- Hacking, I. (1999). The social construction of what?. Cambridge: Harvard University Press.Google Scholar
- Jeanes, E. (2016). Are we ethical? Approaches to ethics in management and organisation research. Organization. doi: 10.1177/1350508416656930.
- Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 3–90). San Francisco: Jossey-Bass.Google Scholar
- Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts. Beverly Hills: Sage.Google Scholar
- MacKenzie, D. A., Muniesa, F., & Siu, L. (2007). Do economists make markets? On the performativity of economics. Princeton: Princeton University Press.Google Scholar
- Morgan, G. (2006). Images of organization. Thousand Oaks: Sage.Google Scholar
- OED Online. Oxford University Press, (June 2016). Retrieved June 10, 2016, from http://www.oxforddictionaries.com/definition/english/orient.
- *Orlitzky, M., Louche, C., Gond, J. P., & Chapple, W. (2015). Unpacking the drivers of corporate social performance: A multilevel, multistakeholder, and multimethod analysis. Journal of Business Ethics. doi: 10.1007/s10551-015-2822-y.
- Panter, A. T., & Sterba, S. K. (Eds.). (2011). Handbook of ethics in quantitative methodology. New York: Routledge.Google Scholar
- Pedhazur, E. J., & Schmelkin, L. P. (2013). Measurement, design, and analysis: An integrated approach. Washington, DC: Psychology Press.Google Scholar
- *Ralston, D. A., Egri, C. P., Furrer, O., Kuo, M. H., Li, Y., Wangenheim, F., et al. (2014). Societal-level versus individual-level predictions of ethical behavior: A 48-society study of collectivism and individualism. Journal of Business Ethics, 122(2), 283–306.Google Scholar
- Rorty, R. (2009). Philosophy and the mirror of nature. Princeton: Princeton University Press.Google Scholar
- Rose, N. (1985). The psychological complex. London: Routledge Kegan.Google Scholar
- Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed. New Haven: Yale University Press.Google Scholar
- Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. New York: Wadsworth Cengage learning.Google Scholar
- Shapin, S., & Schaffer, S. (1985). Leviathan and the air pump: Hobbes, Boyle and the experimental life. Princeton: Princeton University Press.Google Scholar
- Wasserman, L. (2013). All of statistics: A concise course in statistical inference. New York: Springer.Google Scholar
- Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT press.Google Scholar
- Young, I. M. (2011). Justice and the politics of difference. Princeton: Princeton University Press.Google Scholar
- Zyphur, M. J., Pierides, D. C., & Roffe, J. (2016a). Measurement and statistics in ‘organization science’: Philosophical, sociological, and historical perspectives. In R. Mir, H. Willmott, & M. Greenwood (Eds.), The Routledge companion to philosophy in organization studies (pp. 474–482). Abingdon: Routledge.Google Scholar