Blog Archives

Stay on target! We’re too close! Stay on target!*

by Rachel Ridley

     After all the weeks of coding, re-coding, exporting, and now pulling it all together in a final analysis, we are finally at the end. We went through elephants, fences, and machambas [agricultural fields] all the way to standing water, hospitals, and weather (for Health in the SES Model, at least). It seems like we covered so much ground in so little time. I find myself still thinking back to coding interviews about elephant damage and realizing just how extensive this research was and is. I’m continually amazed by it as I sit down and try to bring it all to a close.
     One of the biggest struggles was having to organize all of the exported codes together and find themes in them. The problem is, not all of us (in my group) coded the same things in the same way. At first, this seemed immensely problematic. I kept thinking, “How is this going to add up to make any sense together at all?” But in the end, I think it was important that we had experience working on a project such as this in a group. I know I gained a lot from looking at the way the same thing could be interpreted in multiple ways.
     But now that we’re working on our final analyses, I realize that that wasn’t the hard part. Somehow we’ve got to pull all of what we’ve done through the whole semester into one analysis paper. I keep going back and forth, worrying that I won’t have enough to say and then realizing I probably have too MUCH to say to be concise! How to pull together these complex, interrelated themes without minimizing them or making them larger than life?
     Not to mention it’s the end of the semester (finally!) and there’s all kinds of absurdly long papers and cruel, unusual final presentations to do, and it’s hard to keep focus. I want to make sure that the material I create to finish up this project does the full experience and research justice. I keep stalling by making more and more maps or finding some new way to tie them together (I’m using online mapping software) and waiting around, as if I expect that if I do enough maps, a beautifully crafted full-length analysis will pop into my head, fully formed.
Finals1-e1326497361395
     In all, I think it’s bittersweet. Of course, here in this moment, I want everything I have to do for the semester to just be over and done with. I want to move on to holiday celebration, sleeping until 2pm, and not having any deadlines to even consider. Yet there’s a whole other part of me that wants it to continue. I want more time and more space to write about it, because in many ways, the interviews – the issues themselves – that we studied became important to me personally. I find myself regularly thinking about the problems and subject matter that I spent so much time organizing and analyzing. In some ways, it has become larger than life for me.
     We’ve all got until Friday to pull our masterpieces together, and I’m hoping we can make them just that! It has been more than fulfilling to spend so much time with this research, so here’s hoping we can all produce some worthwhile thematic analysis from it.
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     The KRAC Lab research assistants this term have struggled mightily to work though a ton of crazy data.  They’ve ridden a rollercoaster from learning how to code all the way through to their final analysis.  Their learning process is very much the same that socio-cultural anthropologists of all levels go through – down to the part of having so much to say and so little space to say it.  Their analyses has contributed to one submitted NSF proposal and two more in the works.  Their work will also go into a report for the Mozambican government and local communities, as well as multiple anticipated articles (and depending on how much work they’ve put in may include their names as co-authors).  I’m really proud of all of them.  ~ JS
* Gold Leader to Gold Five, Star Wars: A New Hope (Ep. IV)

The Home Stretch

by Hayatt Mohamed

So we’re in the home stretch! After weeks of coding we have finally reached the analysis phase, which feels like even more coding. While coding had its own share of frustrations between deciding whether or not a segment fits the criteria of the description of the code, the tedious re-reading, or the constant insecurity that you’re doing absolutely everything wrong (something that I personally always am worried about and anyone who is the lab with me probably gets tired of me asking and re-asking to make sure) it has been interesting. That’s the funny thing about coding though, what you may interpret the definition of a code may be different than how someone else may think the code to be. And you won’t even know that they are looking at it differently than you until you ask them a simple question, or until you are organizing and analyzing a code. Coding for the Health in the Social-Ecological model proved to be hard because I couldn’t help but think SES stood for socio-economic model, and I couldn’t help but focus on that. Even when coding I would look at the blockers that affect the community’s health like lack of infrastructure or transportation. I had to constantly re-remind myself to also look at climate and ecological factors. Though often ecological factors tied in with infrastructure at times (for instance in regards to quality of water and sanitation both factors in the spreading of malaria and cholera). There were times where I didn’t even use certain codes and times where I felt like something should have been created into its own code rather than lumped in a broad code but was overruled. The thing about coding is that it’s subjectively objective; the facts are there but are very open to interpretation.

rock-climbing-desktop-hd-wallpaper-free-mountain-pictures

The analysis has proven to be just as daunting as coding-if not more. Here is where we see if our coding was even remotely similar to our group members and also comparing how we coded and what our interpretations of the codes were. In my personal group when I have analyzed codes I noticed slight differences in what each member has found to be appropriate to code. Also some people tend to be more generous with their coding-something I tend not to do but the great thing about working in a group is that they might have caught something you intended to code and didn’t but it also becomes frustrating when you feel like maybe that should not have been coded and it is (once again these are times where it is great to have someone else in the room working beside you so you can ask them a few questions). Color coding the analyses by comment has definitely made these excel spreadsheets easier to look at and more fun and I just feel like color makes everything better. I’m still not quite done with my analysis but it’s oddly comforting. It feels good to turn disorder to order so although it’s tedious, I’m enjoying the process.

Coding has changed my life…

by Catherine Soriano Luna

Coding itself is not a hard task at all. That is, if the sentence under scrutiny falls neatly into a category, which is almost never the case. I have had a hard time determining whether or not a certain remark should be coded. I have spent, what has felt like hours, staring at the screen and deciding whether to code something this or that. The process can be frustrating, but it can also be rewarding. At the end, once the coding has been finished, I begin to see patterns and I catch a glimpse of the lives behind the codes. I remind myself these words belong to a group of people far from here, who are experiencing life in a different way than I am. Can I imagine myself going through their day-to-day lives? I cannot, but I beginning to understand where they come from and to appreciate their lifestyle. It is not the type of insight I experience in the classroom where we spend most of our time talking about theory (which is not my strongest suit). If classes dealt more with how things are (and with real, living people), then I’d be much more engaged. As it is, I have only a screen to turn to for a deeper understanding of human life.
As much as I would like to spend my time just reading the interviews, I have to deal with the technical side of things. The program we are using (MAXQDA) has done a good job of providing us the tools for coding, however, I wish I could organize things just a bit more than is currently allowed. I spent an hour ‘playing’ with the program and pressing random buttons in the hope that I would get the hang of it. Needless to say, an hour passed and I was no more informed than when I had started (though to be fair, I did learn how to alphabetize codes and use memos…really useful stuff). Spending more time on video tutorials will be necessary in the foreseeable future. I just hope it’ll be a lot easier to navigate the website than the program itself. If there is one great pleasure I partake in, it is in choosing the colors for the codes. I am easily amused. “Should I choose light blue or sky blue?” Very important decisions to be made for sure. And as the margins fill with color, the patterns begin to rise. And that has made all the difference.
real life safari

Navigating the Process of Team Work

by Jordan Tompkins

As Bryan Gerard mentioned in a previous blog post for our lab, we’ve now split our rather large research team (7 undergraduate students, 2 graduate students, and 1 professor/researcher) into three separate teams for data analysis. In this blog post, I want to talk about how we’ve navigated the process of data analysis thus far, and where we’re headed.

At the outset, each of the undergraduate students spent three weeks learning about text analysis, helping to create a codebook for the wildlife conflict project, and coding interviews and field notes. Then Jen made them go back to the original documents and recode everything they’d already coded. Why would she do this? Is she some sort of sadist who enjoys making others complete the same task multiple times?* While I can’t speak to the second question (don’t fire me, Jen!), there are several reasons to wipe the slate clean and start again: (1) anthropologists review our field notes multiple times to get a feel for different patterns that emerge from our data. Being familiar with the data is part of the job. (2) Codebooks often change during the course of data analysis. Patterns emerge, codes need to be collapsed together or separated from one another, etc. Our codebook went through multiple changes during the first phase of coding. We could have gone back and just added the new codes, but we needed to look at the data from the slightly modified direction of research. (3) Although I’m very familiar with MAXQDA, the qualitative data analysis software we use, I’ve only used it on projects where I’m the only person coding. Working as a team may seem like it’s simply an extension of a one-person project, but there are intricacies in the software that I never realized were there until we encountered problems. For instance, when the students began importing their coded documents, many of the codes were duplicated in the codebook on the program even though they had the same names. We had to figure out how to merge those codes and prevent this from being a problem in the future. Additionally, everyone had to learn about some of the more technical aspects of MAXQDA. Although I wouldn’t classify this as the fun part of data analysis, it’s absolutely essential to understand how/when/why to do things when working on a team. A small mistake made while importing a document can create a lot of unnecessary work.

Now the undergraduates have finished the second round of wildlife coding and have been assigned to analysis projects based on their interests. We’re still part of the larger team, of course, but I’m excited to work in a project with only three other people (Shout out to Bryan, Hayatt, and Rachel!). We meet on Monday to discuss aspects of socio-ecological systems theory, and how to incorporate those into our coding and analysis. Wish us luck!

The Sorcerer’s Apprentice – Goethe 1797/Dukas 1896/7

* I suppose having students recode something they’ve already done might seem sadistic.  Jordan pointed out 3 very good reasons.  I would like to add to this.  Learning new software and new analysis skills requires making mistakes, failing, and just general mucking about to see what happens when you push a button.  This is a process all of us face when we learn new skills; our first product out of the box is meh but the next one is better because we learn from experience.  While all of the students working in our research group are bright, this was their first time using both the software and coding text.  The wildlife conflict interviews were the most concise group of interviews to work and had a set purpose.  I wanted students to learn new skills and new software simultaneously, which meant that I was expecting mistakes, miscoding, etc.  However, this data is important.  It will be used to write a report and make recommendations about resolving human-wildlife conflict, as well as explore connectivity and elements in a complex social-ecological system.  Therefore the data prepped for analysis has to be in it’s best possible form.  Ergo, requesting students to recode the interviews with a set of codes once they’ve learned the skill and the software.  I have faith they’ll do a good job of it and I won’t come back to a lab full of brooms sweeping up an ocean of water.

Welcome Back

by Bryan Gerard

As the summer ended and the fall semester began, it was time to begin our research with Dr. Shaffer. The research team began to meet to discuss the direction of the study and learn what would be expected in this lab. During the first two weeks we were provided the opportunity to practice both inductive and deductive coding. However, this past Friday, 9/26, we decided to break up the research into several projects (Wildlife Conflict, Health, Indicators of Change, and Agency — also Local Mapping of the Social-Ecological System and GIS). Dr. Shaffer differentiated these projects to enable the research assistants to gain experience in fields that interest us. More importantly, the various projects, while seemingly diverse, also exemplify the interconnectedness of the challenges in an area experiencing a plethora of social and environmental changes.

However, before we focus on the varied projects, we are all coding interviews about wildlife conflict. This has become a major issue for the local population and Mozambican government as elephants from the neighboring reserve have wreaked havoc on the local community, destroying crops, homes, and in several cases killing humans. This work will then be used to write a report for the Mozambican government – which will ideally highlight where and how to make changes.

IMG_7120Anthro Student Researchers at the 26 Sept 14 KRAC lab meeting.