Tag Archives: data analysis

Jumping In

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I seem to have gone from walking to speed racing when it comes to projects. Not only do I have the Folklife paper I’m co-authoring for ASEE, but now I’m working on 3 more projects. Just last week I was tasked with doing new analysis on already collected data for a paper draft that’s due at the end of the month. So I’ve been slogging through file after file of the data, trying to make sense of it all so that I can get the analysis done by the end of the week. This is the first time I’ve been asked to do data analysis on data that I was not directly connected with collecting. I’ve always been very familiar with the data I was working with, as well as with the project it’s connected to. I have neither of those safety nets on this project, and it is really testing my abilities. Which is both exciting and terrifying. There is no backup plan if I am unable to get this done, so the pressure is really on. Personally I’m not a fan of pressure, I like to have things well laid out in advance with mini-milestones to keep me on track and keep the task from feeling overwhelming.

I just hope I’m able to rise to the challenge without completely freaking out.

Analyze this

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And now it comes to this: thesis data analysis. I am doing both qualitative analysis of the interviews and quantitative analysis for the eye-tracking, mostly. However, I will also quantify some of the interview coding and “qualify” the eye-tracking data, mainly while I analyze the paths and orders in which people view the images.

So now the questions become, what exactly am I looking for, and how do I find evidence of it? I have some hypotheses, but they are pretty general at this point. I know that I’m looking for differences between the experts and the non-experts, and among the levels of scaffolding for the non-experts in particular. For the interviews, that means I expect experts will 1) have more correct answers than the non-experts, 2) have different answers from the non-experts about how they know the answers they give, 3) be able to answer all my questions about the images, and 4) have basically similar meaning-making across all levels of scaffolding. This means I have a general idea of where to start coding, but I imagine my code book will change significantly as I go.

With the eye-tracking data, I’ll also be trying to build the model as I go, especially as this analysis is new to our lab. With the help of a former graduate student in the Statistics department, I’ll be starting at the most general differences, again whether the number of fixations (as defined by a minimum dwell time in a maximum diameter area) differ significantly:  1) between experts and non-experts overall with all topics included and all images, 2) between supposedly-maximally-different unscaffolded vs. fully-scaffolded images but with both populations included, and 3) experts looking at unscaffolded vs. non-experts looking at fully-scaffolded images. At this point, I think that there should be significant differences in cases 1 and 2, but hope that, if significant, at least the value of the difference should be smaller in 3, indicating that the non-experts are indeed moving closer to the patterns of experts when given scaffolding. However, this may not reveal itself in the eye-tracking as the populations could make similar meaning as reflected in the interviews but not have the same patterns of eye-movements; that is, it’s possible that the non-experts might be less efficient than experts but still eventually arrive at a better answer with scaffolding than without.

As for the parameters of the eye-tracking, the standard minimum dwell time for a fixation included in our software is 80 ms, and the maximum diameter is 100 pixels, but again, we have no standard for this in the lab so we’ll play around with this and see if results hold up over smaller dwell times or at least smaller diameters, or if they appear. My images are only 800×600 pixels, so a minimal diameter of 1/6th to 1/8th of the image seems rather large. Some of this will be mitigated by the use of areas of interest drawn in the image, where the distance between areas could dictate a smaller minimum diameter, but at this point, all of this remains to be seen and to some extent, the analysis will be very exploratory.

That’s the plan at the moment; what are your thoughts, questions, and/or suggestions?

An Interesting Result

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I’ve wrapped up my work with the NEES REU program, and as my final assignment I wrote a report on the Folklife Festival evaluation. I didn’t have time to do an in depth analysis, but I did enough to report that the activity was overwhelmingly fun, and that people felt like it was worth their time (despite the incredible heat). Based on anecdotal evidence from previous activities with the mini-flume, we weren’t exactly surprised by these results, but confirmation is always nice.

What was surprising showed up in the demographic information. We had the expected breakdown of men and women, race/ethnicity, and even age. But when I tallied highest education level, half of the participants reported having at least a master’s degree. Now I have questions about how and why we got this interesting demographic breakdown. Is the activity more appealing to this demographic? Was the Festival what was more appealing and we just caught the demographic?

Or was there something in my recruitment method that would have resulted in this odd sampling?

Folklife only counted visitors so I don’t have access to the demographics of the larger population, so for now I have no way of answering these questions, but I will keep it in mind as I do more in depth analysis on the Folklife data.