For the analysis of the transcriptions, I used a software program called NVivo. This program allows you to upload documents, such as transcriptions, and then offers multiple ways to analyze those documents. I primarily used NVivo for coding, and I found it tremendously helpful. NVivo allows the user to create “nodes,” which are essentially themes of coding. In the program, you can create a node for anything you think might be important: a theme, a phrase, an entire conversation, and so on. You can later combine the nodes into groups of similar things or hierarchies, in which one large node contains several smaller nodes. For instance, I created a “parent node” for all references to pornography, with “child nodes” under it for separate categories like feelings about pornography, violence in pornography, and learning from pornography. The nodes are also not exclusive, which means that I could code the same
exchange in a focus group under more than one category. If, for instance, the participants talked about watching pornography with their partners, I could code that under both “pornography” and “talking to partners.” This program is basically the electronic version of sticky notes and
highlighters, but with major technological upgrades.
I went through each focus group transcription and code as I went while also adding pieces of dialogue to already existing codes. This was the most “grounded theory” part of the process, because I created nodes as themes emerged. I went through the focus groups in order, uploading the transcription to the NVivo software and coding each line of the transcription, from beginning to end. When I had finished the last focus group, I went back through and did this process again, sometimes adding new nodes but mostly checking that I still agreed with my previous coding. As I was writing the analysis, I occasionally went back and checked the coding again, looking for specific things, but most of my original coding was retained.
I ended up with thirty-five nodes, although some of those are parent nodes and had additional children nodes underneath. NVivo also enables the user to see how many source groups are included in each node, so I was able to see which elements of my coding existed in each group and which were only mentioned once or twice. This allowed me to measure occurrence and saturation. For example, “who you talk to” was a node I created to address to whom the participants were comfortable talking about sex, and that node was present in every focus group. Other nodes, like “threesomes” and “friendzone” were only present in one or two focus groups, and because I was able to count the occurrences of each node I was able to see which nodes were the most prevalent and thus likely the most important.
Another helpful aspect of NVivo was that I was also able to keep track of which
could track how many times A3 spoke as opposed to A4, for instance. I could also add
identifying characteristics to each speaker, thus enabling me to keep track of the speaker’s race, virginity, age, and so forth. Using these identifiers I could measure things like how often white men spoke about condoms or whether there was a correlation between age and participation in hook up culture, for instance. Although such correlations could certainly be made without software, I found this element of the program very useful.
The final stage of analysis, of course, is finding out what the data are really telling you. I used a modified grounded theory method for this project, by which I mean I had some idea of what I hoped to find, based on my review of the literature, but I was also open to seeing what emerged. As my analysis progressed I was intrigued by the themes that emerged and also by the themes that failed to emerge, so to speak. I knew that I would find certain themes based on the questions that I prepared and which each focus group was asked, but over the course of analysis I found that some of my questions were never really answered and others, which I did not prepare, were answered in great detail. Such an emergence of unanticipated data is typical of grounded theory and of qualitative research in general (Birks and Mills 2015). Bearing that in mind, the following chapter I discuss what I found in the transcriptions, how some of the findings were unanticipated, and what those findings may mean.
4 WHO DO YOU TALK TO ABOUT SEX?
“Like with your boys you don’t talk about your feelings and everything, too.” (E4)
One of the organizing questions for this project was “who do you talk to about sex?” The focus group moderator started by asking if the men talked to anyone about sex, and they nearly universally said “yes.” The follow-up, naturally, was a question regarding to whom they talked.
In general, their answers broke down into three separate categories: their friends, their sexual partners, and their parents. The conversations they had with each of these disparate groups were obviously different, but each group provided unique opportunities to discuss their sexual
experiences. With their friends, the young men could be open and perhaps a bit crude. Most of the conversations revolved around whether or not they had had sex – it was essentially a binary. Occasionally, with friends they considered close, the young men would discuss with whom they had had sex or with whom they were trying to have sex, but very rarely would they discuss their feelings. Second, the young men talked about sex with their sexual partners. In some cases this was as simple as discussing the details of the sex they had just experienced together, but in other cases young men expressed that they felt there was value in having open and honest
conversations with their partners. They felt both an emotional closeness and an improvement in the quality of their sex lives after discussing the sex that they were having with their sex partners. Finally, with family, it was generally a matter of either learning about sex or being told to be careful when having sex. Whether participants felt more comfortable talking to their mothers or their fathers varied, but it was nearly always uncomfortable.