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
DiscoverText is rolling-out an addition to its analytical toolkit: random sampling. The Web-service already offers an array of tools for text analytics and rigorous, team-based qualitative data analysis. These functions include the ability to code and annotate text, measure inter-rater reliability, adjudicate coder validity, attach memos to text, cluster duplicate and near-duplicate documents, share documents, and to classify text using an active-learning Naive-Bayesian classifier. While still in beta, random sampling is a key new addition. After DiscoverText users amass extraordinary amounts of social media data (for example via the Public Twitter API, the GNIP Powertrack, or the Facebook Social Graph), they can now more easily extract a random sample for analysis. The size of the sample is decided by the user in order to accommodate to iteration, experimentation and other scientific methods. The option is streamlined into the dataset creation process. On the new dataset creation page, you see a sample size prompt. This additional method for data prep and analysis augments current information retrieval techniques, such as search with advanced filtering. It also builds up our framework for expanding available NLP methods from straightforward Bayesian classification, which aims to analyze substantial quantities of data in their original bulk-form, to a menu of computationally intensive methods that can iterate more quickly and effectively against random data samples. For example, the LDA topic model tool we are releasing will be faster and more effective against smaller random samples. This new feature accommodates both an additional analytical approach as well as the opportunity to easily compare results between competing (or complimentary) analytic methods. We look forward to experimenting with this new tool and hearing about how random sampling will enhance the research of our users and users to come. Special Note to DT Users: We need to turn this feature on one account at a time while we are testing it. Drop us a line if you want to try the tool. We’ll keep you posted on the launch as more dataset modifications are pushed live. As always, if you have any questions, feel free to email us anytime at firstname.lastname@example.org. Your feedback is crucial. Sign up and try it out for yourself at discovertext.com.