Virtual - 1pm to 5pm Eastern Time Each Day
January 24, 2021
January 24, 2021
January 28, 2021
Diversity and CoNECD Paper Submissions
Key Words: Engineering Education, Equity, Demography, Intersectionality, Critical Quantitative Methods
Studies have noted that engineering pathways are inequitable for marginalized groups, including but not limited to students of self-identifying gender, race/ethnicity, age, socioeconomic, sexual- orientation, and disability. This observation is unsurprising given knowledge of the many inequalities existent in engineering, and the resulting toxic culture. Amongst patterns of academic success, enrollment, persistence and other realms of study; some of which highlight these inequities; significant differences for many aforementioned groups have been noted, especially in quantitative work. While these quantitative studies studies seek to identify inequities in the engineering education space, there remains a question of whether the methodologies do a disservice to students at the intersections of various forms of inequality because their particular differences in intersectionality are not accounted for or represented, but rather essentialized into particular single-axis sex or race/ethnicity issues. This issue has been raised by Bahnson et al. (2018) as well. Particularly, because the norm of quantitative diversity research in engineering education is to identify students as belonging to a single or multiple dimensions of differences, and to identify those group differences, we through Intersectionality Theory situate here that students at intersections who experience both additive and multiplicative inequality go without adequate representation in quantitative results.
Feminist theorists Sigle-Rushton (2014) and Martinez Dy and colleagues (2015) suggest accurate representation through statistics is incredibly difficult and remains a philosophical tension, especially between the post-modern underpinnings of Intersectionality Theory and the expectation of post-/positivism in quantitative work. Particularly, the authors argue that quantitative research often depends on using “what is available,” not what is “real” and lived, to make some assumption about what is difficult to quantify, with the positive mindset that what knowledge can be gained will mean something. Additionally, there remains the issue of significance to necessitate rigorous research in engineering education, requiring larger N that is not always available, even for interaction effects (Pawley, 2017). Thus, the balance between representation and what is available remains a difficulty. In light of the social construction of categories used to represent the diversity of different peoples based in hegemonic practice, McCall (2002; 2005), Sigle-Rushton (2014), and Bowleg (2008) have discussed that the solution is to move to an anti-categorical stance, that is, they suggest research observe inequality in different forms of data without the use of categorical controls themselves. However, these authors have also argued that though anti-categorical variables are particularly enlightening, their existence is nearly impossible to determine. As a result, there is continuously a bounce back to what is available. It is thus important that “what is available” be reframed to maximize the disciplines potential to create an equitable engineering education.
In this proposed session, we ask what can be done about these complex and challenging issues that face researchers conducting diversity and inclusion work with quantitative methods. With the near impossibility to move beyond categorization, McCall (2002; 2005) and Sigle-Rushton (2014) suggest the only option is to use what is available until the anti-categorical makes itself known. For the time being, researchers can use what is available, but should interrogate how and why intra-categorical (between categories) results might exist in relation to the social construction of categories by observing the differences within categories (inter-categorical). Thus, what is available does not remain ignorant to systemic inequality, but rather scrutinizes itself for the betterment of research. However, this change requires a background of systemic issues in engineering, which not all researchers have. Thus a practical way of approaching this issue needs to be established so that the pursuit of equity in engineering continues on at the forefront of engineering education research. We argue that a practical solution is to study students intersectionally in whatever ways are possible. Additionally, we argue that doing something is better than nothing to begin to address current inequities in demography and other studies in engineering education. This call avoids a simplistic algorithm for what will and will not work, but moves towards an ethic of equity in research as suggested by Pawley (2017).
We posit that one possibility is to redefine how study demographics might be presented in engineering education at default. Currently, the norm is to present demographics as a list of single axis identities (i.e. by race/ethnicity, gender, socioeconomic designation, and other forms of identity, separately). However, with a switch to a table format which is friendly to the joint dissemination of single-axis demographics as well as intersectional demographics, authors can provide the single demographics that were necessitated for quantitative power (unless the required N to run intersectional controls was available), and also provide demographics in which the field can make basic interpretations about how results are or are not representative of the intersectionality present in the population of study.
While it would be more useful to provide a visual example to the reader, the format of submission unfortunately does not allow for it. However, if the reader can visualize it, we imagine a form in which, at the top and left of a table the single-axis demographics which are important to describing a study (gender and race as an example) will be situated. Additionally, within where intersections of those most outside cells meet, we imagine that the cells within the table can display that additional data which represents the intersectional demographics. The presentation of these figures thus allows members of the engineering education field to infer their own claims about intersectional issues that do not normally come to light in engineering education research. This change in the standards of demographic presentation allow presentation of demographics to continue as they have, but also default that intersectionality matters in engineering, even if statistically significant intersectional differences cannot necessarily be explored at this time. We also imagine there are more creative ways to address this issue.
Inherently, there are ethical issues to consider here related to small N research and the need to protect participants. These issues can be absolved with more generative terms such as entering an example “n < 10” into a table rather than “n = 1”, thus still allowing for intersectionality to be considered and presented while also abiding by the protection of marginalized participants.
An expanded paper will explore these issues further and provide visual examples of how interpretations can be expanded in the context of enrollment and persistence statistics. Additionally, ethical considerations will be expanded upon. Further, larger conference discussion could open up ways to expand upon this. Until the tensions of quantitative research and intersectionality research can be managed, this work allows for intersectionality to remain the default in engineering education while quantitative research continues to work with what is available.
Major, J. C., & Godwin, A., & Kirn, A. (2021, January), Working to Achieve Equitable Access to Engineering by Redefining Disciplinary Standards for the Use and Dissemination of Demographics in Quantitative Studies Paper presented at 2021 CoNECD, Virtual - 1pm to 5pm Eastern Time Each Day . https://peer.asee.org/36147
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