Making a Case for Feminist Statistics
How commonly used quantitative approaches in adult education research marginalise and oversimplify diverse and intersectional populations
Keywords:
Gender equality, Quantitative research, IntersectionalityAbstract
In contrast to qualitative and theoretical approaches, the mainstream of quantitative research often still finds it difficult to incorporate modern concepts of diversity and intersectionality into its work. This article aims to highlight various aspects in which large studies and their evaluations marginalise or ignore certain parts of the population. In surveying data, large-scale surveys like the Programme for the International Assessment of Adult Competencies (PIAAC) often not only operate on a binary gender concept but also do not differentiate between a person gender identity and their social gender. In addition, commonly used methods keep unequal distributions invisible. Non-binary people are virtually invisible, unequal benefits for women remain hidden and the intersectional diversity inside the broad gender categories poses challenges to the mainstream of quantitative research in adult education. Therefore, there is a need for a feminist approach to statistics and quantitative research.
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References
Aassve, A., Fuochi, G., & Mencarini, L. (2014). Desperate Housework. Journal of Family Issues, 35(8), 1000-1022. https://doi.org/10.1177/0192513X14522248
Addey, C. (2018). Assembling literacy as global: The danger of a single story. In M. Milana, J. Holford, S. Webb, P. Jarvis, R. Waller, & S. Webb (Eds.), Palgrave handbooks. The Palgrave International Handbook of Adult and Lifelong Education and Learning (pp. 315-335). London: Palgrave Macmillan.
Allatt, G., & Tett, L. (2019). The Employability Skills Discourse and Literacy Practitioners. In L. Tett & M. Hamilton (Eds.), Resisting Neoliberalism in Education: Local, national and transnational Perspectives (pp. 41-54). Bristol UK, Chicago USA: Policy Press.
Auspurg, K., Hinz, T., & Sauer, C. (2017). Why Should Women Get Less? Evidence on the Gender Pay Gap from Multifactorial Survey Experiments. American Sociological Review, 82(1), 179-210. https://doi.org/10.1177/0003122416683393
Bowleg, L. (2008). When Black + Lesbian + Woman ≠ Black Lesbian Woman: The Methodological Challenges of Qualitative and Quantitative Intersectionality Research. Sex Roles, 59(5-6), 312-325. https://doi.org/10.1007/s11199-008-9400-z
Buckles, K. (2019). Fixing the Leaky Pipeline: Strategies for Making Economics Work for Women at Every Stage. Journal of Economic Perspectives, 33(1), 43-60. https://doi.org/10.1257/jep.33.1.43
Bührmann, A. D. (2010). Intersectionality – ein Forschungsfeld auf dem Weg zum Paradigma? Tendenzen, Herausforderungen und Perspektiven der Forschung über Intersektionalität. GENDER – Zeitschrift für Geschlecht, Kultur und Gesellschaft, 1(2). https://www.budrich-journals.de/index.php/gender/article/view/18052/15725
Buvinic, M., Furst-Nichols, R., & Koolwal, G. (2014). Mapping Gender Data Gaps: Data 2X.
Buvinic, M., & Levine, R. (2016). Closing the Gender Data Gap. Significance, 13(2), 34-37. https://doi.org/10.1111/j.1740-9713.2016.00899.x
Cascella, C. (2020). Intersectional Effects of socioeconomic Status, Phase and Gender on Mathematics Achievement. Educational Studies, 46(4), 476-496. https://doi.org/10.1080/03055698.2019.1614432
Crenshaw, K. (2017). On intersectionality: Essential writings. New York: The New Press.
Criado-Perez, C. (2019). Invisible Women: Exposing Data Bias in a World Designed for Men (1st ed.). London: Penguin Random House UK.
Degele, N., & Winker, G. (2007). Intersektionalität als Mehrebenenanalyse. https://www.tuhh.de/t3resources/agentec/sites/winker/pdf/Intersektionalitaet_Mehrebenen.pdf
Duckworth, V., & Smith, R. (2019). Research, Adult Literacy and Criticality: Catalising Hope and Dialogic Caring. In L. Tett & M. Hamilton (Eds.), Resisting Neoliberalism in Education: Local, national and transnational Perspectives (pp. 27-40). Bristol UK, Chicago USA: Policy Press.
Else-Quest, N. M., & Hyde, J. S. (2016). Intersectionality in Quantitative Psychological Research. Psychology of Women Quarterly, 40(3), 319-336. https://doi.org/10.1177/0361684316647953
European Commission (2013). Draft AES manual: Version 9. Brussels: European Commission.
Eurostat (2012). Das deutsche AES-Fragenprogramm 2012 [The German AES-Questionnaire]. Retrieved from: https://circabc.europa.eu/sd/a/505ef052-2507-4a39-863b-31ea3eebf04e/AES%202011_%20Questionnaire%20DE_German%20version.pdf
Ferrant, G., Pesando, L. M., & Nowacka, K. (2014). Unpaid Care Work: The missing Link in the Analysis of Gender Gaps in Labour Outcomes. Paris: OECD Development Centre.
Fine, C. (2011). Delusions of Gender: The real Science behind Sex Differences (Repr). London: Icon Books.
Foucault, M. (2019). Suhrkamp Taschenbuch Wissenschaft: Vol. 1809. Die Geburt der Biopolitik: Geschichte der Gouvernementatlität II. Vorlesung am Collège de France, 1978-1979 ((J. Schröder, Trans.)) (M. Sennelart, Ed.). Suhrkamp.
Fraser, G. (2018). Evaluating inclusive Gender Identity Measures for use in quantitative psychological Research. Psychology & Sexuality, 9(4), 343-357. https://doi.org/10.1080/19419899.2018.1497693
French, B. F. (2014). Test Bias. In A. C. Michalos (Ed.), Encyclopedia of Quality of Life and Well-Being Research (pp. 6619-6622). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-0753-5_2998
GESIS (2020). PIAAC 2022. Retrieved from https://www.gesis.org/piaac/piaac-2022
Grotlüschen, A., Buddeberg, K., Dutz, G., Heilmann, L., & Stammer, C. (2019). LEO 2018 - Leben mit geringer Literalität: Fragebogen [LEO 2018 – living with low Literacy: Questionnaire]. Hamburg: University of Hamburg.
Grotlüschen, A., Buddeberg, K., Dutz, G., Heilmann, L., & Stammer, C. (2020). Low literacy in Germany: Results from the second German Literacy Survey. European Journal for Research on the Education and Learning of Adults, 11(1), 127-143. https://doi.org/10.3384/rela.2000-7426.rela9147
Grotlüschen, A., & Heilmann, L. (Eds.). (2021). Between PIAAC and the New Literacy Studies: What adult education can learn from large-scale assessments without adopting the neo-liberal paradigm (1st ed., Vol. 14). Münster: Waxmann.
Harts, M., Lacy, S., & Rodsky, E. (2020). Women are drowning in unpaid Labor at Home. Stop making them do it at Work. Fast Company. Retrieved from https://www.fastcompany.com/90541130/women-are-drowning-in-unpaid-labor-at-home-stop-making-them-do-it-at-work
Heilmann, L. (2021). Subjekt, Macht und Literalität: Literalitätsdiskurse im Kontext von Gesundheit, Geschlecht und quantitativer Erhebung [Subject, Power and Literacy: Literacy discourses in the Context of Health, Gender and quantitative Research]. Publikationsbasierte Dissertationsschrift (1st ed.). Wiesbaden: Springer Fachmedien Wiesbaden GmbH; Springer VS.
Heilmann, L., Gal, I., & Grotlüschen, A. (2020). Do higher Skill Levels lead to better Outcomes? : The Disproportionality of Skills and Outcomes for Women. Gender, 12(3-2020), 87-106. https://doi.org/10.3224/gender.v12i3.07
Hester, N., Payne, K., Brown-Iannuzzi, J., & Gray, K. (2020). On Intersectionality: How complex Patterns of Discrimination can emerge from simple Stereotypes. Psychological Science, 31(8), 1013-1024. https://doi.org/10.1177/0956797620929979
Hoogland, K., Heinsmann, K., & Drijvers, P. (2019). Numeracy and mathematics education in vocational education: a literature review, preliminary results. In U. T. Jankvist, M. van den Heuvel-Panhuizen, & M. Veldhuis (Eds.), Proceedings of the Eleventh Congress of the European Society for Research in Mathematics Education (CERME11, February 6 – 10, 2019) (pp. 1341–1348). Freudenthal Group & Freudenthal Institute; Utrecht University; ERME.
hooks, b. (1982). Ain't I a Woman: Black Women and Feminism (1st ed. in Great Britain). London: Pluto Press.
ISO (2004). ISO/IEC 5218:2004 Information Technology - Codes for the Representation of human Sexes. Geneva, Switzerland: International Organization for Standardization
Knobloch-Westerwick, S., Glynn, C. J., & Huge, M. (2013). The Matilda Effect in Science Communication. Science Communication, 35(5), 603-625. https://doi.org/10.1177/1075547012472684
Kwak, S. K., & Kim, J. H. (2017). Statistical Data Preparation: Management of missing Values and Outliers. Korean Journal of Anesthesiology, 70(4), 407-411. https://doi.org/10.4097/kjae.2017.70.4.407
Magliozzi, D., Saperstein, A., & Westbrook, L. (2016). Scaling Up: Representing Gender Diversity in Survey Research. Socius: Sociological Research for a Dynamic World, 2(1), 237802311666435. https://doi.org/10.1177/2378023116664352
Merrill, B. (2005). Dialogical Feminism: Other Women and the Challenge of Adult Education. International Journal of Lifelong Education, 24(1), 41-52. https://doi.org/10.1080/026037042000317338
OECD (2010). PIAAC Background Questionnaire MS version 2.1 d.d. 15-12-2010.
OECD (2013). OECD Skills Outlook 2013. Paris, France: OECD Publishing. https://doi.org/10.1787/9789264204256-en
OECD (2017). The Gender Wage Gap. In OECD (Ed.), The Pursuit of Gender Equality - An Uphill Battle (Chapter 12, pp. 153-166). Paris: OECD Publishing. https://doi.org/10.1787/9789264281318-15-en
Pell, A. N. (1996). Fixing the leaky Pipeline: Women Scientists in Academia. Journal of Animal Science, 74(11), 2843-2848. https://doi.org/10.2527/1996.74112843x
Pettit, B. (2012). Invisible men: Mass Incarceration and the Myth of black Progress. New York: Russell Sage Foundation. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1069804
Polachek, S. W. (2004). How the Human Capital Model explains why the Gender Wage Gap narrowed. IZA Discussion Paper. Retrieved from https://ssrn.com/abstract=527142
Ranga, M., Gupta, N., & Etzkowitz, H. (2012). Gender Effects in Research Funding: A Review of the scientific Discussion on the gender-specific Aspects of the Evaluation of Funding Proposals and the Awarding of Funding. Bonn: Deutsche Forschungsgemeinschaft.
Robinson-Cimpian, J. P. (2014). Inaccurate Estimation of Disparities due to mischievous Responders. Educational Researcher, 43(4), 171-185. https://doi.org/10.3102/0013189X14534297
Rossi, A. S. (1965). Women in Science: Why so few? Social and psychological Influences restrict Women's Choice and Pursuit of Careers in Science. Science (New York, N.Y.), 148(3674), 1196-1202. https://doi.org/10.1126/science.148.3674.1196
Rossiter, M. W. (1993). The Matthew Matilda Effect in Science. Social Studies of Science, 23, 325-341.
Sarseke, G. (2018). Under-Representation of Women in Science: From Educational, Feminist and Scientific Views. NASPA Journal About Women in Higher Education, 11(1), 89-101. https://doi.org/10.1080/19407882.2017.1380049
Schreiber-Barsch, S., Curdt, W., & Gundlach, H. (2020). Whose Voices matter? Adults with learning Difficulties and the emancipatory Potential of numeracy Practices. ZDM, 52(3), 581-592. https://doi.org/10.1007/s11858-020-01133-1
Scott, N. A., & Siltanen, J. (2017). Intersectionality and quantitative Methods: Assessing Regression from a feminist Perspective. International Journal of Social Research Methodology, 20(4), 373-385. https://doi.org/10.1080/13645579.2016.1201328
Sennelart, M. (2019). Zusammenfassung der Vorlesung [Summary of the lecture]. In M. Sennelart (Ed.), Suhrkamp Taschenbuch Wissenschaft: Vol. 1808. Sicherheit, Territorium, Bevölkerung: Geschichte der Gouvernementalität I. Vorlesung am Collège de France, 1977-1978 [Security, Territory, Population: History of Gouvernementality I. Lecture Series at the Collège de France, 1977-1978] (5th ed., pp. 520-588). Frankfurt am Main: Suhrkamp.
Sinclair, S., Hardin, C. D., & Lowery, B. S. (2006). Self-stereotyping in the Context of multiple social Identities. Journal of Personality and Social Psychology, 90(4), 529-542. https://doi.org/10.1037/0022-3514.90.4.529
Stern, B. B., Barak, B., & Gould, S. J. (1987). Sexual Identity Scale: A new Self-Assessment Measure. Sex Roles, 17(9-10), 503-519. https://doi.org/10.1007/BF00287732
Takács, J. (2006). Social Exclusion of young lesbian, gay, bisexual and transgender (LGBT) People in Europe. Brussels, Belgium.
Tranmer, M., Murphy, J., Elliot, M., & Pampaka, M. (2020). Multiple Linear Regression: Cathie Marsh Institute Working Paper 2020-01. Retrieved from http://hummedia.manchester.ac.uk/institutes/cmist/archive-publications/working-papers/2020/multiple-linear-regression.pdf
Tsjeng, Z. (2018). Forgotten women: The Scientists. London, New York, NY: Cassell Illustrated; Distributed in the US by Hachette Book Group.
Westbrook, L., & Saperstein, A. (2015). New Categories are not Enough. Gender & Society : Official Publication of Sociologists for Women in Society, 29(4), 534-560. https://doi.org/10.1177/0891243215584758
Westmarland, N. (2001). The Quantative/Qualitative Dabate and Feminist Research: A subjective View of Objectivity. Forum Qualitative Sozialforschung, 2.
Wu, A. D., & Zumbo, B. D. (2008). Understanding and Using Mediators and Moderators. Social Indicators Research, 87(3), 367-392. https://doi.org/10.1007/s11205-007-9143-1
Yang, K., Tu, J., & Chen, T. (2019). Homoscedasticity: An overlooked critical Assumption for linear Regression. General Psychiatry, 32(5), e100148. https://doi.org/10.1136/gpsych-2019-100148
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