Evolutions

When I first started this course, I was a little apprehensive. While I am able to pick up technology skills fairly easily, I'm still in the very early stages of my doctoral program. I have an idea for my dissertation topic, and I know that technology will play an important role in that -- both in content and in analysis of the data. But I haven't really collected any data yet. I was skeptical of my ability to learn the tools without having my own research already complete, and I was jealous of some of my colleagues who are deeper into the process (and closer to writing the dissertation) because they had more data to work with. 

The more I learn about CAQDAS technologies, however, the happier I am that I'm getting exposure to all of these tools now -- in the early stages. I feel certain it will save me countless hours down the road because I'll be better organized and prepared to use the tools, and I won't feel overwhelmed to dive into them. I think the Bazeley & Jackson (2013) text said it best:

"Starting early, if you are still learning software, will give you a gentle introduction to it and a chance to gradually develop your skills as your project builds up. This is better than desperately trying to cope with learning technical skills in a rush as you become overwhelmed with data and the deadline for completion is looming." (p. 26).

It's nice to know that I'll be able to use the same tools throughout the process, and I can slowly learn more features as they're needed.

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The evolution and use of CAQDAS tools fascinates me. I had no idea coming into this semester how interested I would be in this area, but I suppose it makes sense given that it's a great intersection of my research and technology interests. I was talking about QDAS with my husband recently, and it occurred to me that he uses some similar technologies for his job. My husband is an attorney who works in complex business litigation, and one of the things he frequently has to do is review documents. For example, he might have to read 10,000+ emails downloaded from a client's inbox, code them for content, and look for segments that may support or refute a particular argument. We were talking about the software he uses and how that process compares to the work I may ultimately do in ATLAS.ti, and he mentioned that the new trend in the legal field is to move toward predictive coding software. He doesn't have it at his firm yet, but he said that it's supposed to learn some of your coding habits and conduct some of the document analysis for you based on parameters you set. 

I immediately started thinking about that in terms of qualitative research, and I wonder if something like that will ever be used or accepted in our research community. I would need to know more about how it works to really form an opinion on it, but I can see potential advantages and disadvantages with it. If it really is a learning software that learns how I code and applies that knowledge to my projects, then I think it could be a huge time-saver. But it could also distance me from my data, and I would really want to scrutinize the process that it uses. It's like outsourcing -- there are some things (like housekeeping!) that I'm happy to outsource to others, but there are other things that just aren't worth outsourcing. Coding might be one of those things. I guess we'll see as the software continues to evolve. 

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I'm excited to learn more about Dedoose. I like ATLAS.ti so far, but I'm a fan of cloud computing, and I'm curious about how it might handle my mixed methods research. Two questions that came up as I was looking through the website:

1) Compatibility: It says in the video that it can pull in data from the software programs like NVIVO and ATLAS.ti, but is that relationship bi-directional? Can you import and export data with Dedoose? 

2) Pricing: I know Dedoose charges a monthly fee. Do you only pay for the months that you use it (e.g., sign in)? If you have a project uploaded in Dedoose that you don't touch for a month or two, do you have to pay the monthly fee because they're still housed on the platform?

Netnography, Virtual Worlds, and Tool Adoptions

Since the last class, I've been thinking a lot about my love for digital tools and whether I need to put my attitude about tool adopters vs. non-adopters in check. The best I can say is...maybe.

I didn't grow up around computers. I'm not a digital native. The first computer I owned arrived when I was a senior in high school, and I didn't get Internet service at home (dial-up) until after my Freshman year of college. My first laptop came after I graduated college. That was followed by a cell phone after I was married and a smartphone after I started my second career as a teacher. So technology hasn't always been a part of my life, but as I've experienced ways that it could make my life better and less complicated, I've embraced it. I'm pretty open-minded about playing with new tools, and if something doesn't work for me, I'm okay with abandoning it. The beauty of the digital playground is that there are always more toys.

So here's the issue for me: if tools exist to make the research process more efficient, transparent, and accessible, then why shouldn't they be widely used?

I appreciate that some people have found strategies that work for them and that tools may be difficult to learn, but I don't know if those reasons are good enough to warrant resistance. I think it comes down to a fundamental question of what is the purpose of research? If research is intended to be a primarily researcher-focused act--which it very well may be given that the researcher decides every aspect of the study--then the researcher should just use whatever works, digital or not. But if the purpose of research is to contribute more broadly to society and our understandings of the world or our fields of study, then I think digital tools are a necessary part of that. They allow closer and more verifiable examination of research, and they provide better data trails to assist novice researchers understand research practices. In this worldview, it seems selfish to resist using digital tools out of convenience.

I'm not saying that researchers have to learn and use every digital tool available. There are some that will be a better fit for the research and the researcher than others (I'm looking at you, EndNote...). But I don't think general ignorance of the tools or resistance to them is acceptable among those who want to do research professionally (i.e., academics). Tools are becoming more accessible and intuitive all the time, and even if a particular tool is rejected for one reason or another, researchers should at least consider them with an open mind.

So yeah, I guess I'm still on Team CAQDAS. Pretty passionately so...

Speaking of my CAQDAS passions, I was disappointed to see that the new Netnography book (Netnography: Redefined) isn't coming out until June. I've been having regular Amazon deliveries of books introduced through this class every Friday since the beginning of the semester. It will be weird not to race home on Friday to hide another package of qualitative research books before my husband sees it... I need to get better at reading nonfiction books on the Kindle...

I'll be curious to see how much of the Netnography book is actually "redefined." There are so many fascinating issues in the chapters we read that I can see applying to my own research of teacher bloggers. My research is going to examine experiences of bloggers and lurkers and see if there is any difference in how the quantify (with survey data) or account for (with interview data) their self-efficacy beliefs as teachers. I can imagine worlds in which aspects of Kozinets's four A's (adaptation, anonymity, accessibility, and archiving) could be relevant. For example, maybe adaptation differentiates those who blog vs. those who lurk. Maybe the bloggers are better able to adapt to the different types of technology involved in blogging. Anonymity is definitely an issue; teachers are highly public figures, so they have to be careful about any digital footprints they lead. Some will only blog or comment under pseudonyms while others are identifiable but careful about the types of information they share. Accessibility seems to be decreasing as an issue (and maybe the new book will speak to that since there are more recent Pew Internet Reports reflecting these trends). Archiving also factors in since everything is preserved on the many blogging platforms, and once something is published, it's hard to undo it. I want to explore more of the Netnography methodology to see exactly how it will fit into my research.

Finally, I enjoyed reading chunks of Holt's World of Warcraft dissertation. I didn't have a chance to read all of it, but it was interesting to learn about his research methodology. I think it would be incredibly challenging to research a MMORPG while immersed as a player. How do you juggle the research experience with the player experience? It seems like it would be hard to set playing goals such as getting to the raider/end-of-game level without letting that consume you or overshadow the research. But at the same time, I can't imagine any other way to study that culture. Similarly, I wondered about the possible ethical issues that could arise from having multiple identities (alts) within the game. It's definitely a possibility that is unique to the online world, and I wonder what issues that might present and how those are handled in the research. As always, there's a lot to consider.

Decisions, Decisions...

When it comes to using technology for qualitative research, I'm sold. I'm an early adopter of so many tools as it is, and there are many areas of my life where technology has made my work more efficient. It seems obvious that I would integrate technology as much as possible in my research. But when it comes to making decisions about the tools to use to start my qualitative research and data analysis, I feel like I'm car shopping. Do I go with ATLAS.ti or NVivo? Should I test drive both? Will they both get me where I need to go? Should I just commit to one now and roll with it?

I'm planning to do a mixed methods study for dissertation, and I'd ultimately like to become proficient in both qualitative and quantitative methods. I want a tool that will give me that flexibility. I'm tempted to go with ATLAS.ti since that's what we're using in this course, but I'm nervous to do so after the warning in the Silver and Lewins text that "caution[s] against choosing a package simply because it is the one you have the 'easiest' (e.g. immediate or free) access to" (2014, p. 22). Am I choosing it just because it's available? At the same time, it seems silly to reject it without knowing the real differences between the two programs. By the accounts that I've heard so far, both of these CAQDAS packages do essentially the same things, so if I could customize either one to fit my needs, does it even really matter? I'd hate to make my life unnecessarily difficult by going in a different direction if it's not going to make much of a difference. Is it even possible to make the "wrong" decision here? And would I even know what I was missing if I did?

I was also very interested in the Jackson (2014) paper about how QDAS fits into our ideas of transparency. As a tech-y person, I suspect that I could go on and on in my dissertation about how I'll ultimately use my QDAS tools. At the same time, however, I wonder if my dissertation would be the right place to do that. In my experiences with blogging about technology for my classroom and coaching other teachers in using technology, I find that most people just want to know the most basic details about tech tools. There's a weird stigma around technology where people often make it seem scarier and more overwhelming than it should be, and they hole up in a way that they wouldn't necessarily do if they were learning about any other topic. For some people, technology is scary and uncomfortable, and I would worry about alienating my readers too much by going into the finer details of how I use the QDAS. At the same time, transparency is something I really value, so I'm wondering if the descriptions of how QDAS influences the researcher needs to be contained in the final product itself. Could it, for example, exist elsewhere such as a publicly available blog? I could easily imagine myself blogging my way through the decision-making process, exposing how I'm using the tools for those who are genuinely curious, without alienating the less QDAS-familiar readers of my research. Is that a reasonable middle-ground as a qualitative (or mixed-methods!) researcher?

These are the things I'm considering as I prepare for this week's class.

Thanks for reading!