Check out Two New Tools Built for Quickly Analyzing Rich(er) Data
A downside of collecting rich data is sifting through and analyzing all of the inputs. Wouldn’t it be nice to have ways to prioritize what’s most useful, and filter what isn’t?
That’s why we’ve been developing updates and new features that provide researchers with tools to better analyze submissions and projects in dscout, making it quicker and easier than ever to run and synthesize remote research. Read on to learn about the powerful new tools we’re putting into your hands, live now in the dscout platform.
Contextual Data, In-Context
The Analysis view allows you to choose a single open-ended or video question from your mission parts to analyze in-depth. You'll see a bubble chart of the words used most frequently in responses to that question, which can be filtered by part of speech and exported as an image for sharing. Beneath the bubble chart, you'll see all scouts' responses to that question, making it easier to spot common themes across participants. The bubble chart and responses list respect the same filters you use across our Diary tool, including close-ended questions, tags, and demographics.
We've made the Analysis view more robust with two new actions: Stop Words and Filter Responses.
With Stop Words, you can now exclude words from your analysis, making your bubble charts better represent your question response data. There is no limit on the number of stop words. To create one, just click the bubble containing the word you want to exclude, and select "Make [word] a Stop Word". A tally of your stop words will appear just below the bubble chart for easy tracking.
The second action, Filter Responses, allows you to view scout question responses containing your filtered word (or words). Do this the same way you select a stop word: Click on a word bubble and select "Filter Responses." When you do, a list of the scouts who used that word in their answer will appear below the bubble chart. Each scout response is clickable, taking you to the full entry, where you can dig deeper into the context of that moment.
Combine Stop Words with FIlter Responses to tailor, tune, and tighten your analysis—the scout response list below the bubble chart gets you to the moments of interest faster. We built this to speed up your analysis and synthesis process, getting you from context to insight ahead of schedule.
Learn more about the Analysis view here.
Machine Learning Meets Qualitative Data
We know sorting through scout responses—especially open-ended and video—can be tedious. Often, you just want some help determining great from good. That’s where dscout’s new Expressiveness sort comes in.
Using historical data coded by dscout's expert in-house researchers, we've developed machine learning algorithms to predict expressiveness in scouts' Recruit screener applications and in their Diary mission responses. Our Expressiveness models take into account a number of aspects strongly related to application and response quality: total and unique word counts, spelling, readability, and basic grammar (just to name a few).
We've integrated the Expressiveness sort into our popular Recruit screening tool to make it easier to quickly find the best scouts for your mission. To use the Expressiveness sort as you're reviewing your screener applications, click the "Sort Results" modal on the left, then select the "Expressiveness" option.
Instantly, your applications will sort from most to least expressive, shaving time off your review process by directing you to the best scouts first. Use the Expressiveness sort in conjunction with the other filters you know and love—like demographics or closed-ended questions—for a powerful and precise application review process.
Learn more about Expressiveness in Recruit here.
But we didn't stop there. You'll also find our Expressiveness sort in the updated Analysis view —check it out beneath your bubble chart in the scout responses list. There, you can sort those individual responses by their expressiveness, once again showing you the best responses first.
What’s great about creating an in-house algorithm? We can continually train and improve it … and this means you can, too! Now when you're reviewing a scout's entries within Diary, you'll see thumbs up and down icons next to each open-ended and video response. When you click those, you're helping train our Expressiveness model for responses, making it work better for you the next time you use it.
Both the updated Analysis view and the new Expressiveness sort help serve as your research assistant within the platform, guiding you as you begin your analysis and synthesis processes. Just a few more ways we’re making contextual research more accessible and scalable. Check them out in the platform!