Abstract
Though distinct, the practice of statistics and the scientific worldview in which it is almost always practiced are so entwined that they are often taken to be the same. Feminist and decolonization scholars, among others, have noted that the scientific worldview is control-centered, irresponsible, and socially exclusionary. This chapter, written as an autoethnography, documents the author’s efforts as a statistician in creating a new alliance of statistical practice with an alternative, socially inclusive worldview. Aided by the concept of community elicitation, the author argues that Bayesian data-analysis methods hold promise for interfacing with socially inclusive research approaches that include a quantitative component, such as mixed methods research.
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References
Allman, D. (2013). The sociology of social inclusion. SAGE Open, 1–16. https://doi.org/10.1177/2158244012471957
Berger, J. O. (2006). The case for objective Bayesian analysis. Bayesian Analysis, 1(3), 385–402. https://doi.org/10.1214/06-BA115
Berry, D. A. (1996). Statistics: A Bayesian perspective. Belmont: Duxbury Press.
Bloor, D. (1983). Wittgenstein: A social theory of knowledge. New York: Columbia University Press.
Cappo, D., & Verity, F. (2014). Social inclusion and integrative practices. Social Inclusion, 2(1), 24–33.
Conquergood, D. (1985). Performing as a moral act: Ethical dimensions of the ethnography of performance. Literature in Performance, 5(2), 1–13. https://doi.org/10.1080/10462938509391578
Daponte, B. O., Kadane, J. B., & Wolfson, L. J. (1997). Bayesian demography: Projecting the Iraqi Kurdish population, 1977–1990. Journal of the American Statistical Association, 92(440), 1256–1267.
de Sousa Santos, B. (2018). The end of the cognitive empire: The coming of age of epistemologies of the south (Kindle ed.). Durham: Duke University Press. Retrieved from Amazon.com
Diaconis, P., & Freedman, D. (1986). On the consistency of Bayes estimates. The Annals of Statistics, 14(1), 1–26. https://doi.org/10.1214/aos/1176349830
Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70(3), 193–242. https://doi.org/10.1037/h0044139
Ellingson, L. L., & Ellis, C. (2008). Autoethnography as constructionist project. In J. A. Holstein & J. F. Gubrium (Eds.), Handbook of constructionist research (pp. 445–466). New York: Guilford Press.
Ellis, C. (2004). The ethnographic I: A methodological novel about autoethnography. Walnut Creek: AltaMira Press.
Epstein, R. A. (2009). The theory of gambling and statistical logic (2nd ed.). Burlington: Academic Press. https://doi.org/10.1016/C2009-0-20160-7
Garthwaite, P. H., Kadane, J. B., & O’Hagan, A. (2005). Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100(470), 680–700.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). New York: Chapman and Hall/CRC. https://doi.org/10.1201/b16018
Goldman, A. I. (1999). Knowledge in a social world. Oxford: Clarendon Press.
Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis, 1(3), 403–420. https://doi.org/10.1214/06-BA116
Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
Harding, S. (2008). Sciences from below: Feminisms, postcolonialities, and modernities (Kindle ed.). Durham: Duke University Press. Retrieved from Amazon.com
Hathcoat, J. D., & Meixner, C. (2017). Pragmatism, factor analysis, and the conditional incompatibility thesis in mixed methods research. Journal of Mixed Methods Research, 11(4), 433–449.
Howson, C., & Urbach, P. (1989). Scientific reasoning: The Bayesian approach. La Salle: Open Court.
Kadane, J. B. (2011). Principles of uncertainty. New York: Chapman and Hall/CRC.
Kadane, J. B. (2016). Pragmatics of uncertainty. New York: Chapman and Hall/CRC.
Kimmerer, R. W. (2013). Braiding sweetgrass: Indigenous wisdom, scientific knowledge and the teachings of plants. Minneapolis: Milkweed Editions.
Koikkalainen, P. (2011). Social inclusion. In M. Bevir (Ed.), The SAGE handbook of governance. London: Sage.
Lenard, P. T. (2008). Trust your compatriots, but count your change: The roles of trust, mistrust and distrust in democracy. Political Studies, 56, 312–332. https://doi.org/10.1111/j.1467-9248.2007.00693.x
Levin, S. (2017, December 22). As Google AI researcher accused of harassment, female data scientists speak of ‘broken system’. The Guardian. Retrieved from https://www.theguardian.com/technology/2017/dec/22/google-ai-researcher-sexual-harassment-female-data-scientists
Lindley, D. V. (1958). Professor Hogben’s ‘crisis’ – A survey of the foundations of statistics. Journal of the Royal Statistical Society, Series C, 7(3), 186–198.
Lum, K. (2017, December 13). Statistics, we have a problem. [blog post]. Retrieved from https://medium.com/@kristianlum/statistics-we-have-a-problem-304638dc5de5
Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13, 14–19.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.
Mayo, D. G. (2005). Evidence as passing severe tests: Highly probable versus highly probed hypotheses. In P. Achinstein (Ed.), Scientific evidence: Philosophical theories & applications (pp. 95–128). Baltimore: The Johns Hopkins University Press.
Mayo, D. G. (2018). Statistical inference as severe testing: How to get beyond the statistics wars. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781107286184
Meixner, C., & Hathcoat, J. D. (2018). The nature of mixed methods research. In P. Liamputtong (Ed.), Handbook of research methods in health social sciences. Singapore: Springer.
Meixner, C., & Spitzner, D. J. (2021). Social inclusion and mixed methods research. In P. Liamputtong (Ed.), Handbook of social inclusion. Singapore: Springer.
Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences, 34(2), 57–74.
Moore, S., & Mitchell, R. (2011). Theorising rights-based restorative justice: The Canadian context. The International Journal of Children’s Rights, 19, 81–105. https://doi.org/10.1163/157181810X508483
Moss, J., & Howlin, P. (2009). Autism spectrum disorders in genetic syndromes: Implications for diagnosis, intervention and understanding the wider autism spectrum disorder population. Journal of Intellectual Disability Research, 53(10), 852–873. https://doi.org/10.1111/j.1365-2788.2009.01197.x
Nærland, T., Bakke, K. A., Storvik, S., Warner, G., & Howlin, P. (2017). Age and gender-related differences in emotional and behavioural problems and autistic features in children and adolescents with down syndrome: A survey-based study of 674 individuals. Journal of Intellectual Disability Research, 61(6), 594–603. https://doi.org/10.1111/jir.12342
Nind, M. (2014). What is inclusive research? London: Bloomsbury.
O’Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., … Rakow, T. (2006). Uncertain judgements: Eliciting experts’ probabilities. Wiley. https://doi.org/10.1002/0470033312.
Polite, A. (2018). To the roots and wounds: The case for restorative justice in the juvenile correctional system. 2018 Mellon Mays Undergraduate Fellowship Journal, 103–107. Retrieved from https://uraf.harvard.edu/mmuf-journal
Popper, K. R. (1962). Conjectures and refutations: The growth of scientific knowledge. New York: Basic Books.
Robert, C. P. (2001). The Bayesian choice: From decision-theoretic foundations to computational implementation (2nd ed.). New York: Springer.
Savage, L. J. (1954). The foundations of statistics. London: Wiley.
Simplican, S. C., Leader, G., Kosciulek, J., & Leahy, M. (2015). Defining social inclusion of people with intellectual and developmental disabilities: An ecological model of social networks and community participation. Research in Developmental Disabilities, 38, 18–29. https://doi.org/10.1016/j.ridd.2014.10.008
Startin, C. M., D’Souza, H., Ball, G., Hamburg, S., Hithersay, R., Hughes, K. M. O., … Strydom, A. (2020). Health comorbidities and cognitive abilities across the lifespan in down syndrome. Journal of Neurodevelopmental Disorders, 12(4). https://doi.org/10.1186/s11689-019-9306-9.
Tua, A., & Banerjee, S. (2019). Social inclusion model: An adolescent parents intervention case study for measuring social inclusion outcomes. Child Welfare, 97(3), 109–131.
Warren, K. (2015). Feminist environmental philosophy. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Retrieved from https://plato.stanford.edu/entries/feminism-environmental
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Spitzner, D.J. (2021). Socially Inclusive Foundations of Statistics. In: Liamputtong, P. (eds) Handbook of Social Inclusion. Springer, Cham. https://doi.org/10.1007/978-3-030-48277-0_17-1
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DOI: https://doi.org/10.1007/978-3-030-48277-0_17-1
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