Case Study Writing Strategies

This is a tale of the two approaches I took to writing up case study research based on fieldwork and qualitative coding.

When I started writing up my dissertation case studies, I really had no idea how to do it. I’d read plenty of case studies but never tried to emulate them. I did, however, have a handy-dandy theoretical framework that needed to be worked into the findings.

I had three cases to report and more than enough data. Multiple case studies are typically used for comparative purposes, meaning that not only does this research design require writing up the individual cases, but also a cross-case comparison. I ended up writing four chapters to cover all of that material, with about 184 pages for the three cases and around 50 pages for the cross-case comparison.

I started off by writing up the case that had the most data – might as well get the big one out of the way, right? I wish I’d taken the reverse approach so that I would have saved some work when I found that my first try at writing up a case fell flat!

Method 1: Theoretical Framework Laundry List

I was told to be thorough in my dissertation writing. That may have been a mistake on my advisor’s part, as the final document was over 400 pages long, but I was determined to be as methodical and thorough as I could.

I started off by structuring my case description by the theoretical framework that I had developed. I went through every code in my framework and pulled out illustrative quotes that I organized under each heading, and then wrote up what I found for each concept in the framework. Even with rich and interesting empirical data to draw upon, however, it was deadly dull. It turned into a horrific laundry list in which readers became lost, much like one of those freaky hedge mazes you see in horror movies. It was ponderous and really soporific.

Repeating that two more times for the cases? No way. It was extremely slow and laborious writing, jerky and discordant, and there was no way I could meet my writing deadlines with that strategy. Fortunately, my writing group set me straight and offered suggestions of alternative structures. I listened, as one should when others are kind enough to read through drafts of heavy academic material and give thoughtful comment thereupon. Then I started over.

Method 2: Semi-Structured Thematic Template

I started over by cutting the chapter into strips and then physically coding and rearranging them into themes. Suddenly, there was a story and a flow to the material!

The first draft of the case study, cut into shreds and reassembled into a new structure.

It was done in a day. I remembered (just in time) to mark each strip of paper with the page number from which the material originated so that I could find it in the digital document to cut and paste. The process of cutting, pasting, and smoothing over transitions took another couple of days. I had every theoretical concept covered, and the material took on a much more palatable and interesting shape.

As I wrote the next two cases up, I started again with quotes, retrieving them systematically and writing up notes on the insights gleaned from them. Next, I organized them thematically rather than by conceptual framework constructs. It was easy to write the material that connected the quotes into a (mostly) coherent story, and much more interesting as the writing process generated more insights. I actually had fun with a lot of that writing!

I structured each case study chapter to start with sections providing the history and organizational setting of the case, an overview of the technologies and participation processes, and then continued from there with the thematic sections. At the end of each chapter, I included a summary with the main themes from each case and linked the highlights back to the research questions and constructs therein.

The overly-structured approach to writing a case study was painful and frustrating, but going with my intuition (while remaining steadfastly systematic) produced better results much faster. It also reduced repetition from linking concepts together and made those relationships much clearer. I expect every researcher will have to figure out an individual writing strategy, but it’s valuable to remember that the first approach may not be the best, and taking a different tack does not mean throwing out all the work you’ve already done.

The strategy for constructing the case comparison chapter, however, was a different matter entirely and a story for another day.

Qualitative Research: Why Do Participant Observation?

Writing up the case studies for my dissertation research on citizen science has required taking some time for reflection on the experience of doing qualitative research. I used a comparative case study methodology approach with fieldwork methods that included data collection through interviews, documents, and participant observation.

Participant observation, in particular, is time consuming and challenging. Retrospectively, however, I couldn’t imagine doing this research without participant observation, particularly for my “intensive” case, eBird. Why?

Here are a couple snippets of the case study that explain what fieldwork contributed to the study:

“My participant observation in eBird involved birding, monitoring and participating in birding listservs, recording my own usage of eBird over time, and attending meetings at the Cornell Lab of Ornithology. This experience was an integral part of this research. While I am not an ‘average’ eBirder, I match its new user demographics in terms of gender, memberships with related institutions, birding equipment owned, and level of education (nearly a third of new eBird users have a postgraduate degree.) At the time that I began fieldwork, I was younger than most new users of eBird and had no birding experience whatsoever.

Genuine participation in eBird meant that I had to learn how to bird. While I put up a bird feeder in my backyard in February 2009 when I first became interested in citizen science, I could identify only a handful of the most visible species in my area prior to participating in eBird. Learning to bird required a substantial time investment in learning how to identify wild birds, and additional investment in binoculars, field guides, audio recordings of bird calls, and backyard bird feeding supplies. As I developed basic bird identification skills and came to enjoy birding as a pastime for its own merits, I added time (and expense) to my business travels so that I could go birding in new and exotic locations. Field notes related to these birding experiences were made periodically throughout this study.”

What this translate to: birding is hard! It was much more difficult than I initially expected, and a lot more expensive.

“All of these forms of participation and observation contributed to substantially strengthening the research. I experienced the common challenges and triumphs of developing bird identification skills, learned the vocabulary of birding, and developed the same fascination with both birds and keeping lists of them that is typical of birders. Perhaps most telling in this respect, others started to describe me as an “avid birder” and friends began to come to me with questions about birds. It was a transformative experience that provided a deep appreciation of birders’ interests and enthusiasm for eBird. As a fellow birder, I now understand why each new feature elicits such excitement and gratitude from the birding community.

Following multiple email listservs provided a more thorough understanding of the broader context of the birding community and contextualized the community practices that interviewees discussed. In addition, many aspects of the birding experience are universal, and these interactions demonstrated that my that my birding and eBirding experiences are not unique.

A final benefit of participation was developing a genuine appreciation of the pleasure of birding. My daily life has been enriched by a heightened awareness of birds in my surroundings, and the rewards of birding – and more specifically, eBirding – continue to motivate me to further explore the world around me.”

What this translates to: I understand the context of this case in a way that would have been simply impossible without participant observation. And I had fun with it as well – how could I ask for anything more?

Oh yeah, let’s not forget – I got a postdoc out of it too. Not half bad, plus I have a pretty respectable life list after only a year and a half: 281 species, and counting!

Qualitative Analysis Tools

In part three of my review of software that I use for my academic work, I’m covering that all-time favorite, qualitative analysis tools! I have never seen a topic that gets so many requests for help (mostly from doctoral students) with so few useful answers. So here are a handful of tools that I have found helpful for my dissertation work, which involves qualitative analysis of semi-structured interviews, field notes, and documents.

As always, my main caveat is that these are Mac OS X programs. In fact, almost exclusively. If you’re spending a lot of time with a piece of software, having it behave like an OS native application is not worth the compromise. And as usual, I tend to favor of open source, free, or low-cost options. For the work that I’ve done, the applicable categories include data capture, transcription, coding, and theorizing (which might also apply for some quantitative work, depending on the nature of the beast.)

Data Capture

Sometimes you need screen shots. For this, I just use the Mac OS X built-in tool, Grab (may be under “Utilities” in your Applications folder), which works with keyboard shortcuts – my favorite! However, it grabs tiffs, which aren’t the most friendly format, and no matter what tool you use, screen captures are almost always 72 dpi = not print quality. So I resize to 300 dpi with Photoshop, making sure not to exceed the original file size (interpolated bits look just as bad as low dpi).

Sometimes you need to record a whole session of computer-based interaction. For that, nothing rivals Silverback for functionality and cost. It’s pretty cheap, works like a dream, and is great for capturing your own experiences, or that of participants. It uses your Mac’s built-in camera and mics to pick up the image and sound of the person at the keyboard, while logging and displaying keyboarding and mouseclicks. And it doesn’t make you record your settings until the end, so that’s one less thing to screw up when you’re setting up your session. Brilliant! I have to thank the WattBot CHI 2009 student design competition finalists from Indiana State for this discovery, since I never would have though to look for something like this. I use Silverback to log my own use of online tools for participant observation. It’s really entertaining to watch myself a year ago as I was just starting to use eBird. OK, more like painful. But compared to now, it’s really valuable to have those records of what the experience used to be like.

Transcription

I record all my interviews with a little Olympus digital recorder. It’s probably no longer on the market, but it was about $80 in 2007 and well worth every penny, even though at that time I mistakenly thought that I’d never do qualitative research. It was the second-best product from Olympus at the time, and has a built-in USB to move the files to a computer. Great. Except that all the files are in WMA format. All2MP3 to the rescue – free software is hard to beat. For awhile, I used a different audio converter, but it stopped working with an OS update and then I found this one. It’s dead simple, and despite the warnings that it always gives me about suboptimal formats, it works like a charm, every time.

But once those interviews are translated into a playable format, I still have to transcribe them. It’s good data review, of course, besides being cheaper than hiring someone – depending on your calculations. MacSpeech Dictate (now called Dragon Dictate) is my tool of choice for this task; it’s the Mac equivalent of Dragon Naturally Speaking, for you Windows users out there. Both softwares are owned by the same company, and you basically shouldn’t waste your time with anything else, because they are the market leader for a reason.

I use the voice recognition software to listen to my audio recordings with earbuds, and use the included headset to dictate the interview. The separate audio and voice systems are truly necessary, because if I can hear myself talking, it distracts me from what I’m dictating. It’s not flawless, but once the software was trained and so was I, it has worked pretty well. The big drawback is that it costs about $200. The big plus is that I went from 4-5 hours of transcription time for each hour of recording to 2-3 hours, and that’s a nontrivial improvement! I have definitely saved enough hours to make it a good deal for the grant that paid for it.

If you’re using dictation software, you have to dictate into some other software. And something has to play your audio files, too. Surprisingly enough (or not?), I have found open source software from IBM that works pretty well: it’s called IBM Video Note Taking Utility. Although it was originally PC-native, I begged the developer to encode Mac keyboard shortcuts as well, which he did – awesome!

The software was created for video transcription, but I just use it for audio. It’s very simple: you load up an mp3, it makes a text file, and you can use keyboard shortcuts to skip forward, backward, pause, and speed up or slow down the recording (plus some other stuff I don’t use). There are a couple of quirks, but the price is right and it does exactly what I want without lots of extra confusing stuff going on. Most of my transcription happens at 0.6 times normal speed, so when you take into account some correction time, the fact that I’m transcribing an hour of transcript in 2-3 hours means it’s nearly real-time transcription and there’s very little additional overhead. It’s just not possible to do any transcription at normal speaking speed, because unless you’re a court reporter, you just can’t keep up with what people are saying!

Coding/Annotation

When I first started working on qualitative research, one of my initial tasks was finding coding software that I liked. If you’re not using software for this task, consider joining the digital age. There are better options out there than innumerable 3×5 cards or sticky notes, even if you have to pay for it and spend a little time learning how to use it; the time you save is worth much more than the software costs. After some fairly comprehensive web searching, I was kind of horrified at how bad the options were for Mac-native software. $200 for what? Not much, I’ll tell you that. And from what I’ve seen looking over others’ shoulders, I don’t think the PC stuff is a ton better.

But there was something better than the modernized HyperCard option that I found, and pretty much everything else. And it, too, is open source! TAMS Analyzer has got my back when it comes to qualitative data analysis. It’s super-flexible, has a lot of power for searching, matching, and even visualizing your code sets, and can produce all the same intercoder reliability stats as the pricey licensed software. There’s a bit of learning curve, but I expect that’s true of any fully-featured annotation software. Plus, there’s a server version that has check-in/check-out control, which is awesome if you have multiple coders working on the same texts, and it’s pretty easy to set up (all things considered, you do have to be able to set up a mySQL database.) I have barely scraped the surface in terms of using its full capabilities. I’m constantly finding yet another awesome thing it can do, and I learn the functionality as I need it – all the really powerful stuff it can do doesn’t interfere with using it out of the box, so to speak.

And after you’ve spent some quality time with your coding, the time will come to sort those codes. For this, I use OmniOutliner, another product from the awesome OmniGroup. Once you have a huge heap of codes, the drag-and-drop hierarchical outline functionality is a highly convenient, fairly scalable way to handle getting your codes in order. I’ve done this with note cards, and it’s a big mess, excessively time-consuming by comparison to using digital tools, and wastes a lot of paper that is then hard to store. I also like keeping an “audit trail” of my work, so having the digitally sorted codes (in versioned documents) is a great way to do it.

Theorizing

Ah, theory. That’s what we’re all doing this academic thing for, right? Well, that or fame and glory, and we all know which one of those is more likely.

Everyone has their own way of thinking about this. I draw diagrams. And when I draw diagrams, whether for a poster, paper, or to sort out my own thinking, I use OmniGraffle. I can’t begin to say how awesome this software is, and how much mileage I’ve gotten out of my license cost. Enough that I should pay for it again, that’s how good it is. My husband calls OmniGraffle my “theory software” because when I’m using it, he knows I’m probably working on theory. I find it really useful for diagramming relationships between concepts and thinking visually about abstractions. Depending on the way you approach theorizing, it might be worth a try (free trials!)

So that’s the end of my three-part series on software to support academic work. I hope someone out there finds it useful, and if you do, please give one of these posts a shout-out on your social network of choice. You’ll be doing your academic pals a favor, because we all know that’s how people find information these days. :)

Getting It Done: Tools for Organizing and Writing

Some people believe that I never sleep, but that’s really not true. I do sleep, at least sometimes, and I’m also fairly productive.

Achieving a relatively high level of productivity depends in part upon having good tools to support your work, and tools that work well for your working style. So this is the first of two posts on the subject of software that supports academic work. “What software should I use for X?” is a perennial question posed by PhD students everywhere, and software is now pretty essential to academic productivity. This post focuses on tools for organizing, writing, and presenting (I covered poster design previously); the follow-up post will describe my favorite research tools.

The big disclaimer: I use a Mac. If you don’t use a Mac, your mileage may vary, but some of these programs do have versions for Windows and other operating systems. I generally avoid Microsoft software in favor of Apple software (much cheaper and generally good design) and open source software (generally awesome, and free!)

Organizing

Everyone has to stay organized somehow. Some of us make a lot of lists. I definitely make an excessive number of lists. To the point where I’ve made lists of lists. It eventually becomes unsupportable; at some points in time, I spent more effort on keeping lists updated than doing the stuff on the lists. But there’s a reason to make lists – it gets all that stuff out of your head, leaving your brain free to think about more important stuff!

My main tool for keeping all my to-do items in order is OmniFocus, which is wonderful Mac-specific software from OmniGroup. I’m a big fan of OmniGroup software; they make very well designed and thoughtful tools. There are versions of OmniFocus for desktop, iPhone, and iPad – and I use them all. This is one of those tools that can be really useful for supporting GTD, if that’s your thing. If you have your own way of doing things, you can still adapt your use of OmniFocus to do things your own way. So now I get to have my to-do list readily synced across my digital devices at all times. And the OmniGroup ninjas (that’s actually what they call their tech support) are responsive and have a sense of humor. How much better can it get?

Another aspect of keeping your stuff together is keeping files synced, if you use multiple machines. Keeping files synced becomes a problem the second you start using more than one machine. At this point, I work on my (dying) 15″ MacBook Pro, a beautiful zippy still-new 27″ iMac, plus my iPad. And sometimes my iPhone 4, when I have no other options. I use MobileMe to sync the Apple-specific stuff, like Contacts and Mail, but I use Dropbox (platform agnostic) for everything else.

Recent security hullaballoos aside, it’s a very usable solution, and that’s why so many people have adopted it. Although I already pay for MobileMe, it doesn’t behave the way I want, with the exception of the Apple-specific syncing, so now I pay for Dropbox storage as well. Without any additional effort or change to thoroughly-ingrained file management behaviors on my part, I can manage all my files locally in the same fashion that I always have, with the only change being that I use my Dropbox folder as my primary storage space instead of my Documents folder. And everything is then magically synced across machines.

Dropbox also gives me double-plus file backup: my files are now backed up three ways from Sunday, because they’re synced on every machine I use, they’re backed up on my Time Capsule (a simply brilliant piece of personal computing infrastructure), and they’re backed up to Dropbox in the cloud. That adds up to serious peace of mind when it comes to irreplaceable research data. Even better, there’s version control, so if I really screw something up and don’t have a Time Machine backup for whatever reason, there’s a Dropbox backup. On top of everything else, there’s nice file sharing with Dropbox, so that’s also been very handy for research collaboration, particularly when concurrent editing is not a concern.

Writing

Once you’ve got your stuff in order, you have to write about it; this is how we produce new knowledge (the part where I talk about producing the stuff to write about will be in the next post…) Everyone has their preferences for organizing ideas to write, and for word processing. Some of us even eschew word processing altogether, and go for the gold with typesetting.

OmniOutliner is my favorite tool for organizing ideas. It’s also from the OmniGroup (obviously, I think?) and is a really simple but highly functional program for making, what else, outlines! I find it more useful than most other tools when it’s time to start organizing ideas for writing. The interface is simple enough as to be non-distracting, and I like the ease of the drag-and-drop interface. It doesn’t export to other formats as easily as I want, but cut-and-paste will always save the day.

When doing collaborative writing where concurrent editing may occur (e.g., last minute papers with crazy late jam sessions) then Google Docs is a winner. It’s browser-based, so it doesn’t matter what kind of operating system you’re using. The interface has really improved, since there’s now an embedded chat functionality, commenting, and you can see the other people’s cursor positions. Google seems to have taken the best features from EtherPad and integrated them with the existing Google Docs functionality for a hands-down winner. Sadly, one of the only things it doesn’t do to my satisfaction is support LaTeX, but that’s only an inconvenience and easy enough to work around.

Some of you don’t know what LaTeX is. That’s OK, you probably don’t need to know. But I’m going to tell you anyway. It’s a free/open source software document preparation system with structural markup, much like HTML, but for making beautifully typeset documents, and it too is platform agnostic. Note that document preparation is not the same as “word processing.” LaTeX is what I prefer to use to write my papers, largely because Word has a tendency to crash on me, is inordinately slow, and is badly behaved in innumerable other ways. And I hate that stupid ribbon. I only use Word when my collaborators are unable to use anything else. I won’t lie – there’s a definite learning curve with LaTeX. It takes a little work, but I’ve found it completely worthwhile.

LaTeX is also nice because having structural markup means you can use style sheets, so you can change the appearance of multiple documents, and link documents, with relative ease. You can use any text editor to write a .tex file, so you can have a completely minimalist interface or something with lots of distracting buttons all over, whatever you prefer. Another benefit is the easy availability of many packages to do just the thing you want, and it is the only system that I have yet encountered that does any justice to mathematical equations. Math rendered in LaTeX looks like math ought to look. One of the few things I think it does really poorly, however, is tables. You can make great looking, tightly controlled tables in LaTeX, but it requires some patience. Even if you don’t want to get all control-freaky over your tables, you’re probably going to have to do that anyway.

Working with LaTeX becomes a little easier with the use of macros and a nice editing environment. You can edit your .tex files in emacs or vim (as I’ve done in the past) but I really like TeXShop for the easy, non-intrusive GUI. It comes with the MacTeX distro, so if you just download that nice big package, you’ll have all the pieces in one place. Another essential tool, for when an editor tells you to submit a final copy in Word after you’ve prepared the original submission in LaTeX, is latex2rtf. This tool lets you use your command-line (e.g., Terminal) interface to produce a Word-readable .rtf file out of your nice pretty LaTeX file. It won’t look as good as it once did, but all the stuff will be there in the right places, more or less. It’s the fastest way that I’ve yet found to convert a LaTeX file into Word, even if it does require a little post-hoc cleanup.

Reference Management

There are really only a few robust reference management software options out there. I’m sure Zotero has improved substantially since I last used it, but it was just plain inadequate when I last tried it, and I don’t have the time or energy to wait around for software to evolve. I am not about to spend a lot of time with Mendeley either, because it actually does way too much for what I want out of reference management software, and I prefer my tools as simple and reliable as possible.

I started off my academic career using EndNote, before I became a LaTeX convert. EndNote is nice enough, and has a bunch of good features, but I haven’t spent the additional $100 per update since EndNote X, largely because BibDesk is free (open source), works with LaTeX, and pretty much all of the reference managers are able to translate among one anothers’ formats. With greater or lesser ease, of course. A nice detail when using Google Scholar in a logged-in state is that you can set your preferences to provide a link for references in .bib format, suited for pasting into the bibtex files that go along with LaTeX documents.

Presenting

Everyone has to make a slide deck at some point, even if it makes Edward Tufte kill a kitten. I’m not a big fan of slides, but maybe I’m just being old-fashioned because I grew up on chalkboard dust.

Regardless, there are only a couple of options for presentation software. Most people use Powerpoint. I don’t really like it. It’s not as horrible as some other Microsoft Office products (I’m looking at you, Entourage) but it’s not great. I suspect the Google Docs version of ppt is much nicer, if only because it’s probably a bit more stripped-down – but I don’t use that either. And no, Open Office is just not adequate. I’m sorry, I really want to go with the open source option, but its interface (last time I looked at it) was stuck in the mid-1990’s and hurt my eyes.

Instead, I use Keynote, another Apple product. It’s slick, adequately functional, and pretty smart about a lot of little details. I get tired of the canned themes, but it’s easy enough to make your own. One of my favorite details about Keynote is that there’s an iPad (and iPhone!) version, so I can actually edit slides on my iPad, and present from it. That’s just lovely. Of course, the iOS version of Keynote is limited (this should be obvious, iOS is not OS X, just like an iPad is not a MacBook) but it’s quite functional for editing and presenting. I’ve never built an iOS Keynote presentation from scratch, but it can be done, and that’s nice flexibility to have.

Finally, when making your presentation, the last thing you want to have happen is your screen saver kicking in, or the power saving settings overtaking your display while you debate some point. I’ve seen this happen way too often, and it just doesn’t look very professional. Rather than edit your system preferences every time you get ready to make a presentation, I now rely on Caffeine. It’s a free Mac app (via the App Store, or just download and install as software the normal way) that, when activated, prevents your power saving settings from invoking and doesn’t permit your screen saver to take over your display. You turn it on and off from the menu bar by clicking on the little coffee cup icon. Simply brilliant!

Research-Life Balance

There’s no such thing. Well, maybe there is, but a lot of researchers probably don’t realize it.

I’ve been told several times by my learned elders that the best way to have a happy life as a researcher is to blur the line between work and play. The problem with this wisdom is that I can always tell the difference between work and play: some things are fun, and some are not. Things that I don’t really consider play include: reviewing papers, revising papers, articulation work (the work you have to do in order to do your real work), coordinating, logistics, transcription, data cleaning, grading – need I go on? There’s plenty more where that came from.

I can sort of relate to this research-as-play concept, however, since I’ve always loved doing analysis and I enjoy qualitative data collection. I like hearing people’s stories when I interview them. I like writing papers, for the most part, and I really like presenting. I like designing courses and working with students. Perhaps I’m just a little too literal when I question the idea that one should not experience the work-play dichotomy as such, but not all parts of research, or of the academic enterprise, are all that much fun. I just can’t find much fun in hammering out workshop logistics, for example, which is what I’ve been doing for the last few days.

But from another angle, I’ve been incredibly successful in blurring the line between research and play. The leisure activities that I most enjoy include spending time outdoors, hiking, photography, and now that I’ve taken it up for my dissertation research, birding. The topic of my research – technologies supporting public participation in scientific research – therefore lends itself very well to mixing business and pleasure.

My research requires me to spend time birdwatching, gardening, and hiking: next month I’ll be wrapping up my fieldwork by going on a wildflower hike in the White Mountains of New Hampshire. This experiential  approach (participant observation) helps me better understand how citizen science projects work, and it’s fun! Not just a little bit fun, but a lot of fun!

For example, today I dallied on my walk home from campus – in the rain – because I stumbled into a patch of warblers. I spent an hour spotting 9 species of warblers and an Indigo bunting, instead of zipping home in the usual 13 minutes. It was really exciting since it means 5 additions to my life list of birds, and they were beautiful creatures. Then I had to spend another hour with my field guides verifying my identifications of the birds. All of that is part of my research. Of course, I do have to take extensive field notes – not exactly fun per se – and spend oodles of time analyzing the experiences, but at least it’s something I can enjoy in the moment, and that pleasure is relevant to the research as well.

Enjoying my research is not accidental. When I chose a dissertation focus, I selected a topic that I’m passionate about, that capitalizes on my skills, and that offers endless variety. This is not a research topic I’m going to find boring by the time I defend my dissertation, and I’ve been working in this area for a little over two years. I did not pick this topic solely because I want to hang out in National Parks. I chose it because I think it’s really important: technology allows more and more people to become directly involved in doing science, which both enriches their lives and makes a substantial impact on what science can achieve. Every time I stop doing it long enough to think about it, I’m excited about my research.

People often respond to my description of my dissertation work by saying things like, “I should change my research focus so I can go hiking for my dissertation,” but the specifics of my fieldwork are atypical for my field. And the fieldwork is not the whole story; like nearly all of my colleagues, I spend substantially more time at a computer than I do in the field. Systematically analyzing the mountains of data produced by 5 days of hiking in the mountains is going to take many, many hours, and not all of that will be much (any?) fun. That’s just how research works.

But my fieldwork photos are going to make some awesome dissertation defense slides!