Evaluating the Method

Jannie Phienboupha

Keeping it in “the family”: How Gender Norms Shape U.S. Marriage Migration Politics

1. Summarize briefly each study: what are the researchers trying to accomplish?  Where do they get their data? What are their findings? What are they arguing?

Keeping it in “the family”: How Gender Norms Shape U.S. Marriage Migration Politics explored gendered norms of the family and sexuality to legitimize relationships that will go through U.S. immigration. These gendered standards were used to evaluate relationship genuineness and red flags for potential marriage fraud. The data collected for this study was from the Middle East/North African (MENA) forum and the Belarus/Russia/Ukraine (BRU) forum, free self-help immigration forums taken through Immigration Pathways.

Through the analytic process called constructive grounded theory, it would found that “genuine” relationships versus “red flags” in relationships were grounded in gender and sexuality norms. For instance, older women’s sexuality and physical appearance were highly associated with member posts calling them “desperate” and more vulnerable to scams. This is further perpetuated with the attachment of women’s worth with their fertility. Having children is a sign of a genuine relationship because it aligns with family expectations. As a result, aging women are seen as likely victims to fraudulent relationships.

On the other hand, men are seen as free agents seeking love. Men looking for love abroad are seen as just that: looking for love. The men on the forums describe women as passive bodies, waiting to be loved. Sexuality norms are further perpetuated with expectations of men’s virility. Men’s sexuality does not become a red flag for their relationships and because of this, their age does not become a factor in their relationships becoming a red flag. Their sexuality is not tied to their reproductive ability, thus they can reproduce the hegemonic family.

2. How are they using content analysis?  What are they coding for? What is their methodological approach to coding? (inductive? Deductive?)

As previously mentioned, the analytic process called constructivist grounded theory was used. This type of content analysis takes an inductive approach, incorporating members’ stories stories into the analysis to determine “what, why, and how”. To analyze red flags, posts were downloaded and sorted. Python was used to collect, clean, and conduct a key-word search for the term “red flag” on 48,017 of the threats from 2008-2014 across 13 regional forums.

3. What are the strengths and weaknesses of the methods that they are using?  What do they capture well? What do you think they are missing out on? If you were to conduct this study, would you do anything differently?  What and why or why not?

By conducting content analysis, the researcher was able to easily obtain data without the actual interaction with the users. As a result, collection was effective and inexpensive. Also, using python to script the data was effective in scouring all of those threads for the term “red flag”. The researcher also connects theory to the data exceptionally. Content analysis on its bare own is just the exploration of content and how it is presented through its median. To make it worthwhile is to connect the analysis to theory and in this case, gender and sexuality exceptions, norms, and policing. One failing of content analysis, however, is that results cannot be replicated without the explanation on the researcher’s part. As mentioned by the researcher, the data collected was primary from heterosexual relationships, thus excluding lesbian, gay, transgender, bisexual, and queer people. It would be interesting to see if the same gender and sexuality policing occurs in relationships among this population and how the perpetuation of the hegemonic family applies to international relationships among this population.

Speaking ‘unspeakable things’: documentingdigital feminist responses to rape culture

1. Summarize briefly each study: what are the researchers trying to accomplish?  Where do they get their data? What are their findings? What are they arguing?

In this study, researchers were interested in discovering what, how, and why girls and women use digital media to document experiences of sexual violence, harassment, and sexism. The data is pulled from three international cases: an anti-street harassment site called Hollaback!, the hashtag #BeenRapedNeverReported on Twitter, and interviews with teenage Twitter activists. Researchers found that participants created a sense of community, solidarity, and support from the usage of the hashtag. The anonymous posts on Hollaback! documenting everyday experiences of public street harassment was identified as a critical dimension of resisting rape culture by making silenced experiences visible. In sum, the researchers argued that digital spaces like Twitter provide platforms for visibility and provide opportunities for girls and women to connect, share, and find solidarity through sharing their experiences of rape culture.

2. How are they using content analysis?  What are they coding for? What is their methodological approach to coding? (inductive? Deductive?)

The researchers used content analysis to locate and measure the types and locations of harassment, victims, and perpetrators of harassment, and victims’ responses to it through simple frequency analysis. They did this through analyzing:

1) Anonymous posts on Hollaback!
2) Posts on Twitter using the hashtag #BeenRapedNeverReported
3) Interviews with Twitter users who use the platform to resist rape culture

From these analyses, the researchers were able to create their own theory to answer their research questions (inductive).

3. What are the strengths and weaknesses of the methods that they are using?  What do they capture well? What do you think they are missing out on? If you were to conduct this study, would you do anything differently?  What and why or why not?

As mentioned by the researchers, content analysis is a descriptive tool and is incapable of analyzing or interpreting meaning. As such, the researcher has to take the extra step to create or connect existing theories to their findings. However, using content analysis in this case seems to be most effective in terms of expenses. I also liked how the researchers in this study went the extra step to interview people. This adds another layer of rich qualitative data. If I were to do this study, I would include the #MeToo movement on Twitter and incorporate how people reacted to the movements, including negative reactions. I would also change the wording from victims to survivors of rape. Lastly, I would also see how racial representation of activists are represented on these platforms. The Me Too movement did not gain public attention until 2017 when a famous white actress tweeted, even though it was first started in 2006 by sexual harassment survivor and activist Tarana Burke, a black woman. Analyzing racial representation through Twitter activism can show exactly whose stories are heard and whose are silenced.

4 thoughts on “Evaluating the Method

  1. Hi Jannie!

    Great summary of both studies! I agree with you that that data should be collected on different types of relationships including but not limited to lesbian, gay, transgender, bisexual, and queer people. This would allow for a more comprehensive, diverse, and inclusive analysis. I also agree that the #metoo movement should be analyzed in the second study.

    Best,
    Alice

    • Hi Alice,
      Thank you for your comment! Creating a more comprehensive, diverse, and inclusive analysis is of course very important in research especially when it comes to generalizability.

  2. Thanks for your post, great summary of both research articles. I have mostly been commenting on posters’ strengths and weaknesses section so here goes:
    I agree that without the steps necessary to interact with or obtain consent that accessing the data is much faster and easier- one of the main advantages of big data. As you mentioned, using computer programming it is much easier to identify and define what data you want and create summaries of that data. Your mention of the limited scope of representativeness to other populations is one of the main disadvantages to big data, so you included several of the key components in discussing the pros and cons of data analysis with big data. I also agree that including other categories of marriages would be effective in learning more about the dynamics occurring in the processes of how these marriages are reviewed. In the other research study, I agree, adding the interview was an important step in expanding beyond the scope of coding analysis. Including interviews allows researchers to get a deeper understanding of the what is motivating users to post what they do on Hollaback or any platform. Also, I agree, including data from #MeToo would be valuable in expressing opinions across multiple digital spaces, not just a single page/site. You also made a great point regarding both research studies a characteristic of big data analysis- that content analysis is a descriptive tool and so requires additional efforts to explain meaning. So there is some resource savings but efforts are necessary in other areas- so how efficient has content analysis made research?

    • Thank you for your comment! Content analysis most definitely has its cons, but so does every other type of research methods so I think it plays out well. Like I mentioned, the researcher should try to aim and make their research as reliable and valid as possible. This involves multiple steps that have to be taken, like the researchers for these two articles. After collecting their data using content analysis, they had to translate that data and in one case, include interviews to further support their research. As such, its up to the researcher to show how useful content analysis is.

Leave a Reply