Our Vanderbilt University team uses DiscoverText (DT) to support qualitative text analysis of 8,531 high school students’ responses about their in-school experiences of bullying. DiscoverText has offered us powerful ways to perform key steps throughout our coding process. Fundamentally, DT supports parsing our large data set into archives, buckets, and datasets. Thus, we are able to focus on key portions of our large data set to hone our initial hierarchical coding structure while retaining the ability to return to an untouched dataset for final coding. We use the diverse annotation tools in DiscoverText to mark singular problematic items for discussion at meetings. Our team was able to develop a complex coding structure with 58 codes (at one point we had 128), and begin coding in a month and a half. Undoubtedly, DiscoverText’s robust organizational and annotation tools, within an easy-to-use user interface, supported expediency. Following the development of our coding structure we employed DiscoverText’s analytic tools to better understand and improve our team’s inter-coder reliability. DT’s real-time coding analytics supports decision-making in meetings. Through the use of these tools, we raised our coding reliability from a .2 Kappa value to a .82 Kappa value after five training rounds. Given that four coders are using 58 hierarchical codes to code over 8,000 free-response items, the numbers represent a phenomenal increase in reliability. Presently, we are half way through coding the 8,531 items using overlapping coding patterns to ensure reliability. Out team members share their experiences below: “I am currently working with a research team that must code students’ responses about their bullying experiences. I had never coded before and was introduced to DiscoverText only a few months ago. Fortunately, I have found DiscoverText to be very user-friendly and easy to navigate. Despite my lack of formal coding experience, I have found the program to run smoothly and have already learned a great deal in such a short period of time. My favorite feature thus far would have to be the code-by-code comparisons. This allows us to discuss any discrepancies among the research team and to increase our reliability. I have enjoyed exploring the features of this program and look forward to discovering what more it can do.” – Abbie, undergraduate, Human and Organizational Development, honors track. “My team is using DiscoverText to code thousands of brief responses to a survey question about bullying. As someone who is new to qualitative research and coding programs, I have found DiscoverText easy to use. The coding process was very easy for me to learn, and I quickly became efficient at coding responses. Our initial looks at code comparisons have been fairly straightforward for me to figure out as well. As we move forward with more analysis, I anticipate other functions and features of DiscoverText will be similarly straightforward, and I will see more of the power of the program.” – Brian, master’s student, Human Development Counseling. “I’m working with DiscoverText as part of an academic research team analyzing high school students’ qualitative responses to questions about bullying. As we have been coding responses, we have found the coding process fairly smooth, although not without a few features that we would have done differently. Still, the process of coding is similar to that of other qualitative coding software (I’ve used NVivo). We haven’t yet gotten into any sophisticated filtering or analysis, but I’m expecting that it will be really useful. The biggest impression I’m left with after my three months of using DiscoverText is that it’s a powerful tool, and we’ve only scratched the surface of what it can do.” – Ben, doctoral student, Community Research and Action. Overall, DiscoverText enabled our team’s timely progress through a complex research process. Following coding, we intend to make use of DT’s meta data “tagging” capabilities such that we can meaningfully export coded response summaries to their “tagged” respective schools. Finally, we intend to continue to explore the useful capabilities of DT in our research. We find DiscoverText easy-to-use and helpful – our questions have been kindly answered by the Textifier support team or solved through processing the helpful support material on DT’s support site! Thanks a lot DiscoverText! Joseph H. Gardella
Due to popular demand, the product development team at Texifter is proud to announce that “TopMeta” is now exportable! What does this mean, you might ask? What is TopMeta? When you import either your own data or live social media feeds into DiscoverText, that data often includes various “metadata,” providing a wealth of revealing information about the Tweet, Facebook post, public comment, or survey response you will be analyzing. “TopMeta Explorer” is the function in DiscoverText that allows you to view the number of most (or least) frequently occurring metadata items and filter your data according to that metadata. Considering the wealth of metadata that may be within your data, the ability to easily organize and filter such metadata may turn out to be the difference between substantive and inadequate research. Metadata is Power When might the organization of metadata come in handy, you may also ask? It’s easy to imagine the answer to this question when you consider the kinds of metadata you may collect from live feeds such as the public Twitter API or the GNIP PowerTrack. From those feeds alone, you may collect any of the following metadata (depending on your search method): 1) The time & date of a Tweet, 2) the account name of the tweet’s sender, 3) the real name of the tweet’s sender, 4) the “hashtags” in a tweet, 5) the account name(s) “mentioned” in a tweet, 6) the shortened URL in a tweet, 7) the expanded URL in a tweet, 8) a link to the tweet itself, 9) a direct link to the media in a tweet, 10) the geo-coordinates from which a tweet is sent, 11) the number of “followers” of a tweet’s sender, 12) the number of those “following” a tweet’s sender, 13) the date that a tweet sender’s account was created, 14) the city of the tweet sender, and 15) the “Klout” score of the tweet’s sender. Exporting TopMeta Until now, the “TopMeta Explorer” function has allowed users to easily sort this kind of metadata within DiscoverText. As of this week, this metadata can now be exported as a .CSV file, empowering Enterprise DiscoverText users to more seamlessly utilize the capabilities of DiscoverText, in tandem with their other research tools. We’ll continue to keep you posted about exciting new developments in DiscoverText as they are launched. If you are interested in trying DiscoverText for yourself, sign-up at discovertext.com and email me at firstname.lastname@example.org. I’ll be happy to get you started.