IST 700: Content Analysis and Learning Methods

I first ran into coding last semester in the methods class for our CCR major, a somewhat intuitive and exploratory method from Foss and Waters’ Destination Dissertation. 

In this method, basically, one works through the sample, looks for examples that connect to the research project’s focus and label them. Gradually, one refines the codes looking for higher-order conceptual connections.

From there, I got more into discourse analysis and critical discourse analysis. I also read through parts of Saldaña’s coding manual and tried practicing some of it in my own research–though I am still not very good.

I found myself exhausted by the time it takes and the slipperiness of interpretation involved with coding and content analysis, particularly the qualitative variety. As Herring (2004) notes, interpretation is both “art” and “craft,” but I often found this art and craft pervade the work more generally.

For one, there is the data. As Einspänner et al note, it’s often hard to come up with a question that one can answer with CMDA in Twitter, as there is so much data and the access can be complicated. But even outside of Twitter, I found myself facing a similar question: Why this data for this question?

I think that this is where some of my own inquiries breakdown somewhat, as I’m often a bit too idea-focused and not as data-focused. I tend to be more deductive, bringing an idea into a situation. This is not necessarily problematic, but I do so in a somewhat messy way, not being careful enough about what I can find out or argue based on the fusion of data and method and how both are guided by or informing the question.

For example, thinking through my last project, I was looking at intertextuality in a population of fan texts and coded for how it was being used or invoked. I found some “findings” of common processes that I saw coming up. But at the same time, I wanted to look at these texts more ecologically, hence my attempt at bringing in a websphere approach, as I wanted to see the sorts of sites that the texts were circulating, linking to, building from, and referencing.

But these are different focuses. In the coding elements, I’m looking at textual features; in the websphere method, I’m looking at structure. But I was not clear how these were working together. True, some intertextuality as hypertexuality was both textual and structural, employing the hypertext to link texts, but I sort of had this all mixed together. Looking back, I can theorize more for further projects, but at the time I was still figuring things out.

I think part of my issue, being in a digital humanities position, is trying to work with different methods, vocabularies, and outlooks that don’t always gibe well. Text, for example, differs from sites in a websphere. I do see some similarities, but the flexibility of “interfaces,” structures, protocols, procedure, practices, actacts, etc., complicates this similarity and what “text” can or can’t do as a concept and unit of analysis.

I also think I tend to start out too big and should look at my questions more squarely, trying to break them down and focus them as a nexus of method, data, methodology, audience, situation, and (if relevant) idea.

So for my second paper, I know I want to do interviews, giving me my method. My data is whatever fanfiction writers I can access at this point. My audience is the class and RSA at this point; the situation is a busy semester.

As such, I think my question is squarely: How do writers negotiate the differences and between works and interpretations of texts within an ecology? Here, by negotiate, I mean to ask about their practice–i.e., what they are doing–and theory–i.e., how they understand or frame what they are doing. I also need to define, at least to myself, what I mean by “work,” “interpretation,” “text,” and “ecology.” Or, moving another direction, define the research question in a different way.

But I already feel better moving forward. Even in terms of content analysis. I suppose that is the messy thing about learning methods: beyond the initial exposure to the main ideas and techniques, one learns best by doing. And doing can be a process fraught with false-starts and failures. The key, I think, is to learn from these issues, especially before conducting more serious research. It is a process-focused learning, meaning the products are a bit . . . iffy.

 

 

 

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