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Choosing the right Software

Evaluating which specific software to use (and, in extension, how to use it for what) is not a trivial process. While larger commercial programs offer a wide variety of tools, they are still profoundly different in terms of detail. That is to say: There is no ‘best’ software, only a software that works best for you and your analysis (cf. Carvajal 2002). The ‘big’ packages such as NVIVO, MAXQDA (and in extension ATLAS.TI & Dedoose)  are versatile tools, but some of their functions will be more ‘at your fingertips’ than others. For some things, you’ll always have to make some detours, to bend the software a bit – this is part of the process.

Choosing a software that works best for you is first of all a matter of your data: Can the software import & adequately display/export my data? Secondly, it is a matter of method: Can your methodological process or workflow benefit from being done in the software? How easy is it to implement the core methodological steps in the software? Of course, in order to answer these questions it is helpful to already have a good idea of one’s data, method, and work flow. On the other hand, thinking about software choice is a great opportunity to think about these issues.

But there’s more to this than method and data. You have to be happy with the software you work with. You might end up looking for hundreds of hours at this software – working with a software that makes you cringe will not help you in the long run. Choosing software has a lot to do with gut feeling. We don’t know much about the impact of QDA software on academic research, but from a study by Lee & Fielding (1998) we do know that one of the biggest risks for research projects in terms of time loss emerges when researchers quit using software half way through – for example out of frustration.

Of course, there are other issues as well: Is there a good network for learning or support for the software I want to work with? What are my team members using? Which packages are available to me?


Expanding on the argument that there is no ‘best’ software it is important to remember that there are also freeware packages. Freeware – and ‘smaller programs’ are often more simple in the array of functions (which can be a great thing), and/or are catered to a specific style of analysis, or specific analytic needs. Especially if you work with smaller amounts of data (e.g. 20 transcribed interviews), a more simple package can be sufficient for the work you intend to do.

Test a very simple software package

Try exploring a simple software package, such as f4analyse, OpenCode or WeftQDA. Install the program, prepare one or two pages of your data for import. Start learning how to use the program – for example in a small research group, with friends, or by reading the very short manual. You can also contact me to set up a small workshop or individual session.

Working with simple software can help you figuring out whether you even want to use software, what kind of user you might be, and how you might prefer to learn software use. Here are a few questions that are useful in this reflective process. This list is based on Lewins/Silvers (2009) excellent remarks on choosing a software package:  

Should I get into this?

  • Do I have enough time to choose a package?
  • Do I have enough time and motivation to get into using certain software?
  • Do I feel good working in front of the computer for many extra hours?
  • How do my team members feel about this?
  • Is my set of data big enough to justify buying software?


What kind of user am I?

  • How do I handle data loss, system failure and incompatibilities?
  • How good and comfortable am I ‘finding detours’ through the software?
  • How much money can I spend?
  • Does the software run on my computer? What about my operating system?
  • How simple is it to handle my main type of data in the way I want to handle it?
  • Can I even import the data I want?
  • What kind of work do I really want to do ‘manually’ or in software I already use?
  • How do I prefer reading text? How does my gaze upon the text differ when I read a printed transcript?


What kind of learner am I?

  • How do I want to learn using software? In Workshops, individual sessions, online sessions, peer training, just the manual?


Making a wish list

Simple software illustrates the strengths of QDA software. But maybe you expect more than what the simple program offers. Working with a rather limiting software can start a second process of reflection: By hitting the restrictions of the program, you might become more clear about what you expect of software, and how you concretely want to handle your data. This can actually be a great reflection of your methodology. The following questions might help during this process.


Your wish list

  • What do I want to do with my data? How do I want to transform, deconstruct, reconstruct, represent my data? Where do I reduce data, where do I expand it?
  • Where does the program limit me?
  • Which kinds of data do I need to work with?
  • Which additional tools or features might help me?
  • Where does the software force me to detour? Can I live with that? Are there parts of my analysis where I can’t live with that?
  • How do I want my data to be displayed in the software? How should exports look so I can work with them in a satisfying way?


You’re frustrated with the limitations of the simple program? Great! Jot down a list with things that frustrate you. This will be your wish list for QDA software. With this wish list, you can effectively test the next program, you can ask precise questions in user forums, or have a great conversation with me!

If you decided that you want to use QDA software, you should try out different packages, and you should play around with them. Do not get distracted by the “wow factor” (MacMillan/Koenig 2004) of the software’s flashy features, especially when you are looking into using a commercial program.

Play and have fun!

It sometimes helps to explore the software without methodological thoughts ‘sitting on your shoulder’. Playfully exploring makes the software a bit more fun and learning it less daunting. Here’s a few things you can do:

  • Some ways of playing with software
  • Make a photo album.
  • Write a diary; use the software as a scrap book.
  • Plan a vacation with it.
  • Write a cookbook.




Carvajal, D. (2002). The Artisan ’ s Tools . Critical Issues When Teaching and Learning CAQDAS. Forum Qualitative Social Research, 3(2). Retrieved from <>>.
Fielding, N.G./ Lee, R.M. (1998). Computer Analysis and Qualitative Research. Sage Publications: London.
Lewins, A./ Silver, C. (2009). Choosing a CAQDAS package. Retrieved from: <<>>.
MacMillan, K., & Koenig, T. (2004). The Wow Factor: Preconceptions and Expectations for Data Analysis Software in Qualitative Research. Social Science Computer Review, 22(2), 179–186.

Author: Christian Schmieder,
Thumbnail image by Obsidian Soul (Own work) [CC-BY-SA-3.0 (], via Wikimedia Commons