What is Qualitative Data Analysis Software?
The technical backbone of the Data Jam Initiative is Qualitative Data Analysis Software – often abbreviated as QDAS, or CAQDAS (Computer Assisted Qualitative Data Analysis Software). This type of research and evaluation software is designed to support the analysis of large amounts of textual information. It allows for efficient data management and the distributed analysis of large datasets in large teams. While Qualitative Data Analysis Software (such as MAXQDA, NVIVO, Atlas.TI or Dedoose) cannot do qualitative analysis by itself, modern packages typically do offer a wide array of options for coding, documentation, teamwork, qualitative data visualization and mapping.
Focusing on Analytic Collaboration, not on where to Click
In a Data Jam, groups of colleagues analyze data together while using the same analytic software tool, and similar analytic techniques. This creates a common experience of bringing a tool (the software) and a process (the analytic techniques) together. We’re not teaching how to click through menus; we’re not teaching theoretical workflows. We analyze, we make things – with a real tool, a real question, real data and concrete techniques. These concrete analytic techniques emphasize writing and documentation throughout the process, and they focus on utilizing analysis groups to drive the analysis. In a Data Jam, colleagues practice how to stay focused on their research question, and how to work as an analysis group to produce a write-up at the end of the day.
Qualitative Data Analysis Software empowers colleagues to quickly explore our large datasets, and to dive into the data in an engaging way – as such, this software is a powerful tool to illustrate and practice methodological workflows and techniques. We’re not only building individual capacity – we are building a community of practice around data analysis in our institution. I will focus on this aspect in the third blog post, but I will briefly describe outcomes on the individual level here.
Individual Capacity Building & Improved Perception of Institutional Data Collection
On the individual level, we are seeing two outcomes in our ongoing evaluation of the initiative: Firstly, we build analytic capacity and evaluation capacity. Colleagues learn how to analyze textual data using state-of-the-art analytic tools, and they learn how to integrate these tools into their evaluation and research work flows. View the 3-minute video below to view some impressions and learning outcomes from a 4-day Data Jam for Extension research teams.
Secondly, colleagues gain a better understanding regarding how (and that!) the data that they enter in the central data collection system are being used. Our evaluations show that colleagues leave our Data Jams with an increased understanding as to why we collect data as an institution, and as to why it is important to enter quality data. Experiencing the role of the analyst seems to have a positive effect on colleagues’ perceptions of our central data collection effort, and leaves them excited to communicate how the data are being used to their colleagues.
Not every colleague will use the software or engage in research in the future; our goal is not to make everyone an analyst. But we establish a basic level of data literacy across the institution – i.e. a common understanding of the procedures, products, pitfalls and potentials of qualitative data analysis. This type of data literacy is a crucial core skill as we are undergoing the Data Revolution.