For my small-n research approach I ask: What explains why women are more likely to contract HIV than other key populations in Tanzania? More specifically, I am exploring social or economic determinants of health that may potentially contribute to an individual’s likelihood for HIV infection. I chose to focus my research in Tanzania because there are currently 1.5 million people living there with HIV.[1] Most importantly, heterosexual sex accounts for the majority (80%) of all HIV infections in Tanzania, and women are particularly more impacted than men.[2] Therefore, I am most interested in determining why women in Tanzania are most at risk for HIV infection compared to women in other regions of the world. Unlike the Atzili reading, my case will not feature the “most likely case” approach.[3] Instead, my cases will be actual young women, who have been interviewed regarding their social and behavioral habits that possibly contribute to their HIV risk.
My dependent variable is new HIV infections. It is operationalized as a dummy variable (yes, she has HIV, or no, she does not have HIV). For this approach, I would likely have to use a new source that would provide me with detailed interviews with young women. This strategy has been used in almost every source that I’ve read on this topic.[4] For example, in an article by Mantsios, she recruited participants and conducted “27 in-depth interviews”.[5] Researchers asked about the participants age, marital status, how many children they had, education, and HIV status. They found that the majority of young women interviewed (80%) had no formal education past primary school and the majority (73%) were also infected with HIV.[6] In other words, they found a strong correlation to lack of education and increased risk for HIV. I believe that this strategy would be very well suited for my research question.
I could possibly continue on this path. However, the large-n work seems to fit my research question quite well. Whether or not I pursue this path would be dependent on my access to data or my ability to conduct interviews either domestically or internationally.
[1] AVERT, “HIV and AIDS in Tanzania.”
[2] Ibid.
[3] Boaz Atzili, “When Good Fences Make Bad Neighbors: Fixed Borders, State Weakness, and International Conflict,” International Security 31, no. 3 (2006): 139–173.
[4] Andrea Mantsios et al., “‘That’s How We Help Each Other’: Community Savings Groups, Economic Empowerment and HIV Risk among Female Sex Workers in Iringa, Tanzania,” PLoS ONE 13, no. 7 (2018): 1–16; Sarah Palazzolo et al., “Documentation Status as a Contextual Determinant of HIV Risk among Transgender Immigrant Latinas. In Press at LGBT Health.,” LGBT Health Epub ahead, no. 15 December 2015 (2016); Thespina J. Yamanis et al., “Social Venues That Protect against and Promote HIV Risk for Young Men in Dar Es Salaam, Tanzania,” Social Science and Medicine 71, no. 9 (2010): 1601–1609; Suzanne Maman et al., “Leveraging Strong Social Ties among Young Men in Dar Es Salaam: A Pilot Intervention of Microfinance and Peer Leadership for HIV and Gender-Based Violence Prevention Suzanne,” HHS Public Access 13, no. 11 (2016): 1–2; Thespina Yamanis et al., “Legal Immigration Status Is Associated with Depressive Symptoms among Latina Transgender Women in Washington, DC,” International Journal of Environmental Research and Public Health 15, no. 6 (2018): 1246, http://www.mdpi.com/1660-4601/15/6/1246.
[5] Mantsios et al., “‘That’s How We Help Each Other’: Community Savings Groups, Economic Empowerment and HIV Risk among Female Sex Workers in Iringa, Tanzania.”
[6] Ibid.
Atzili, Boaz. “When Good Fences Make Bad Neighbors: Fixed Borders, State Weakness, and International Conflict.”International Security 31, no. 3 (2006): 139–173.
AVERT. “HIV and AIDS in Tanzania.”
Maman, Suzanne, Lusajo Kajula, Peter Balvanz, Mrema Noel kilpnzo, Marta Mulawa, and Thespina Yamanis. “Leveraging Strong Social Ties among Young Men in Dar Es Salaam: A Pilot Intervention of Microfinance and Peer Leadership for HIV and Gender-Based Violence Prevention Suzanne.” HHS Public Access 13, no. 11 (2016): 1–2.
Mantsios, Andrea, Catherine Shembilu, Jessie Mbwambo, Samuel Likindikoki, Susan Sherman, Caitlin Kennedy, and Deanna Kerrigan. “‘That’s How We Help Each Other’: Community Savings Groups, Economic Empowerment and HIV Risk among Female Sex Workers in Iringa, Tanzania.” PLoS ONE 13, no. 7 (2018): 1–16.
Palazzolo, Sarah, Thespina Yamanis, Maria De Jesus, Molly Maguire-Marshall, and Suyanna Barker. “Documentation Status as a Contextual Determinant of HIV Risk among Transgender Immigrant Latinas. In Press at LGBT Health.” LGBT Health Epub ahead, no. 15 December 2015 (2016).
Yamanis, Thespina J., Suzanne Maman, Jessie K. Mbwambo, Jo Anne E Earp, and Lusajo J. Kajula. “Social Venues That Protect against and Promote HIV Risk for Young Men in Dar Es Salaam, Tanzania.” Social Science and Medicine 71, no. 9 (2010): 1601–1609.
Yamanis, Thespina, Mannat Malik, Ana del Río-González, Andrea Wirtz, Erin Cooney, Maren Lujan, Ruby Corado, and ToniaPoteat. “Legal Immigration Status Is Associated with Depressive Symptoms among Latina Transgender Women in Washington, DC.” International Journal of Environmental Research and Public Health 15, no. 6 (2018): 1246. http://www.mdpi.com/1660-4601/15/6/1246.
Lauren, this is a fascinating topic of study. I think it may be worthwhile to explore what education, as a variable, can also indicate. Although I know little on the subject of Tanzanian education, female education in Tanzania might not be the causal variable here, but may instead be an indicator for wealth. My suspicion is that wealth matters more in predicting HIV infection rates since wealth can be used in fighting/preventing disease. Just an idea though!
Second, I think it may be interesting to do a study using Mill’s Method of Difference if a certain case exists (note that this would mean switching your focus from individuals to states). If there is a case where the IVs point toward there being high rates of HIV infection of women, but actually have a DV of low rates of HIV infection among women, that case could be compared with Tanzania’s. It would allow you to see any discrepancies between the states or state/community interventions that are effective in combatting HIV. This is simply just an idea for another approach to small-N research.
I still think your research is headed in the right direction but would encourage you to go deeper on what your variables indicate within the larger picture. Good luck!
Lauren — you have a good start here on some data sources, but some work needs to be done to more clearly define your case/cases and the DV that you are analyzing. The first part of your post suggests that the case would be Tanzania and that the DV is the type of risk factors present (in which case, operationalizing as “yes, she has HIV, or no, she does not have HIV” doesn’t really work). In the second part of your post you shift to discussing individuals as cases, but that itself would not work with the small-n methodology where the focus needs to be an in-depth examination of one or a few cases to explain particular events/outcomes. With those thoughts in mind, what would your DV be and what would your potential case(s) be? Jordan’s suggestion of a comparison using one of Mill’s Methods to set up a structured comparison is a very good idea that I would suggest you think about as you continue your research.