The Case for Including Disability in Social Science Demographics

The Case for Including Disability in Social Science Demographics

October marks Disability Awareness Month at UC Berkeley, providing an opportunity for us at the D-Lab to reflect on how we can enhance accessibility in computational social science. This applies not just at the institutional level, but also in our roles as individual computational social scientists. You might wonder, though: what can we do personally to make a difference?

As computational social scientists, we use computational methods and data science approaches to collect, process, and analyze large and complex data sets from new sources to better understand human behavior, interactions, and social phenomena (Wallach, 2018). Through these insights, we aim to promote social progress and address global challenges by advancing knowledge, fostering innovation, and shaping policy and decision-making.

When considering how we can each contribute to increasing accessibility, I suggest drawing on the wisdom of Confucius: "Do your duty, and you will be a good person" (Analects 5.16). With regard to promoting accessibility, our duty as computational social scientists is to apply our tools and methods to understand the barriers faced by individuals with physical and psychosocial disabilities. By doing so, we can provide meaningful insights and develop practical policy recommendations to help dismantle those barriers.

However, not all of us focus our research on disability-related topics, and that’s perfectly fine. But a simple yet important step we can take is to consider including disability as a demographic characteristic in our data collection and analysis. Demographic characteristics are specific traits that allow us to accurately describe and analyze the structure of a population, or in the case of a research study, sample participants. These characteristics often include age, sex, race, religion, occupation, education, and income, among others. They help us better understand the dynamics and trends within a population or sample. More importantly, they are essential for understanding the diverse effects of interventions or independent variables on social behaviors, economic conditions, and individuals' overall well-being.

In social science research, it is standard practice to analyze the impact of demographic factors such as age, gender, race, education, and income level when studying social phenomena. We recognize that historical and ongoing systemic marginalization and discrimination often lead to disparities in outcomes related to health, educational performance (Ghaleb et al., 2021), and nearly every other aspect of life for different groups within populations. Yet, we rarely, if ever, consider collecting data on disability, despite the fact that disabled individuals have historically and continue to face stigma, ostracization, discrimination, and marginalization. These challenges can have a pervasive and pernicious impact on the daily lives of those affected (Shakespeare, 2017).

Social science—much like society at large—has consistently failed to prioritize disability (Altman & Barnartt, 2000). In doing so, I argue that we have created a significant blind spot that prevents us from fully understanding the social phenomena we seek to explore. This blog post is an invitation for us to consider a simple yet highly impactful approach to fostering a more inclusive and accessible academic environment and society. As we say in Swahili, "kidogo kidogo, hujaza kibaba," meaning "little by little, a little becomes a lot." By making small but meaningful changes, we can contribute to broader progress.

It would be remiss not to address the ethical concerns that this proposal presents. It is essential to take an ethical and social justice approach when including disability as a demographic characteristic in research. This means ensuring informed consent and transparency, collecting only the necessary data, and safeguarding privacy and confidentiality throughout both the data collection and analysis processes (Herington, Connelly, & Illes, 2023).

I recognize that this seemingly small step may require significant effort, especially for researchers who are not accustomed to considering disability in their work. One particular challenge is defining and operationalizing disability (Grönvik, 2007). There are various criteria for measuring disability, and none is universally accepted as the standard. Therefore, I suggest that it should be up to the researcher to determine what is most appropriate and feasible, based on the nature of their study.

According to the ethical principle of Justice outlined in the Belmont Report, we have a responsibility to be inclusive, particularly when excluding certain groups would deny a significant portion of the population the potential benefits of research outcomes. However, this proposition is not just a directive, but an invitation to start a conversation. It encourages us to explore the potential benefits as well as the challenges of adopting such an inclusive approach in our research.

References

1. Altman, B.M. and Barnartt, S.N. (2000), "Introducing research in social science and disability: An invitation to social science to “get it”", Altman, B.M. and Barnartt, S.N. (Ed.) Expanding the Scope of Social Science Research on Disability (Research in Social Science and Disability, Vol. 1), Emerald Group Publishing Limited, Leeds, pp. 1-30. https://doi.org/10.1016/S1479-3547(00)80003-2
2. Ghaleb A. El Refae, G. ., Kaba, A., & Eletter , S. . (2021). The Impact of Demographic Characteristics on Academic Performance: Face-to-Face Learning Versus Distance Learning Implemented to Prevent the Spread of COVID-19. The International Review of Research in Open and Distributed Learning, 22(1), 91–110. https://doi.org/10.19173/irrodl.v22i1.5031

3. Grönvik, L. (2007). Definitions of Disability in Social Sciences : Methodological Perspectives (PhD dissertation, Acta Universitatis Upsaliensis). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7803

4. Hanna Wallach. 2018. Computational social science ≠ computer science + social data. Commun. ACM 61, 3 (March 2018), 42-44. https://doi.org/10.1145/3132698

5. Herington J, Connelly K, Illes J. Ethical Imperatives for Working With Diverse Populations in Digital Research. J Med Internet Res. 2023 Sep 18;25:e47884. doi: 10.2196/47884. PMID: 37721792; PMCID: PMC10546274.
6.  Shakespeare, T. (2017). Disability: The Basics (1st ed.). Routledge. https://doi.org/10.4324/9781315624839
7. The Analects of Confucious: https://www.sfu.ca/~etiffany/teaching/phil120/Analects.html