by Greta Underhill
Are you interested in qualitative research? Are you currently working on a qualitative project? Some researchers find it helpful to use a computer-assisted qualitative data analysis software (CAQDAS) program to help them organize their data through the analysis process. Although some programs can perform basic categorization for researchers, most software programs simply help researchers to stay organized while they conduct the deep analysis needed to produce scientific work. You may find a good CAQDAS program especially helpful when multiple researchers work with the same data set at different times and in different ways. Choosing the right CAQDAS for your project or team can take some time and research but is well worth the investment. You may need to consider multiple factors before determining a software program such as cost, operating system requirements, data security, and more.
For the Ecampus Research Unit, issues with our existing CAQDAS prompted our team to search for another program that would fit our specific needs: Here’s what we were looking for:
|General qualitative analysis
|We needed a program for general analysis for multiple types of projects; Other programs are designed for specific forms of analysis such as Leximancer for content analysis
|Compatibility across computer operating systems (OS)
|Our team used both Macs and PCs
|Adherence to our institution’s IRB security requirements
|Like many others, our institution and our team adhere to strict data security and privacy requirements, necessitating a close look at how a program would manage our data
|Basic coding capabilities
|Although many programs offer robust coding capabilities, our team needed basic options such as coding one passage multiple times and visually representing coding through highlights
|Export of codes into tables or Excel books
|This function is helpful for advanced analysis and reporting themes in multiple file formats for various audiences
|A low learning-curve
|We regularly bring in temporary team members on various projects for mentorship and research experience, making this a helpful function
|A one-time purchase
|A one-time purchase was the best fit for managing multiple and temporary team members on various projects
Testing a CAQDAS
I began systematically researching different CAQDAS options for the team. I searched “computer-assisted qualitative data analysis software” and “qualitative data analysis” in Google and Google Scholar. I also consulted various qualitative research textbooks and articles, as well as blogs, personal websites, and social media handles of qualitative researchers to identify software programs. Over the course of several months, I generated a list of programs to examine and test. Several programs were immediately removed from consideration as they are designed for different types of analysis: DiscoverText, Leximancer, MAXQDA, QDA Miner. These programs are powerful, but best suited for specific analysis, such as text mining. With the remaining programs, I signed up for software trials, attended several product demonstrations, participated in training sessions, borrowed training manuals from the library, studied how-to videos online, and contacted other scholars to gather information about the programs. Additionally, I tested whether programs would work across different operating systems. I kept recorded details about each of the programs tested, including how they handled data, the learning curve for each, their data security, whether they worked across operating system, how they would manage the export of codes, and whether they required a one-time or subscription-based payment. I started with three of the most popular programs, NVivo, Dedoose, and ATLAS.ti. The table below summarizes which of these programs fit our criteria.
|General Qualitative Analysis
|Basic coding capabilities
|Low learning curve
I began by evaluating NVivo, a program I had used previously. NVivo is a powerful program that adeptly handled large projects and is relatively easy to learn. The individual license was available for one-time purchase and allowed the user to maintain their data on their own machine or institutional servers. However, it had no capabilities for cross-OS collaboration, even when clients purchased a cloud-based subscription. Our team members could download and begin using the program, but we would not be able to collaborate across operating systems.
I had no prior experience with Dedoose, so I signed up for a trial of the software. I was impressed with the product demonstration, which significantly helped in figuring out how to use the program. This program excelled at data visualization and allowed a research team to blind code the same files for interrater reliability if that suited the project. Additionally, I appreciated the options to view code density (how much of the text was coded) as well as what codes were present across transcripts. I was hopeful this cloud-based program would solve our cross-OS collaboration problem, but it did not pass the test for our institution’s IRB data security requirements because it housed our data on Dedoose servers.
ATLAS.ti was also a new program for me, so I signed up for a trial of this software. It is a well-established program with powerful analysis functions such as helpful hierarchical coding capabilities and institutive links among codes, quotations, and comments. But the cross-OS collaboration, while possible via the web, proved to be cumbersome and this too did not meet the data security threshold for our institution’s IRB. Furthermore, the price point meant we would need to rethink our potential collaborations with other organizational members.
Many programs are now cloud-based, which offer powerful analysis options, but unfortunately did not meet our IRB data security requirements. Ultimately, we had to cut Delve, MAXQDA, Taguette, Transana, and webQDA. All of these programs would have been low-learning curve options with basic coding functionality and cross-OS collaboration; however, for our team to collaborate, we would need to purchase a cloud-based subscription, which can quickly become prohibitively expensive, and house our data on company servers, which would not pass our institutional threshold for data security.
After testing multiple programs, I started looking beyond just qualitative software programs and into note-taking programs such as DevonThink, Obsidian, Roam Research, and Scrintal. I had hoped these might provide a work around by organizing data on collaborative teams in ways that would facilitate analysis. However, most of them did not have functionalities that could be used for coding or had high learning curves that precluded our team using them.
It seemed like I had exhausted all options and I still did not have a program to bring back to the Research Unit. I had no idea that a low-cost option was just a YouTube video away. Stay tuned for the follow-up post where we dive into the solution that worked best for our team.