What package do you pick?

My research partner and I were having a conversation about what programs we should use to code our open-ended survey data and interviews. So we began listing out all our options from Google documents to complex CAQDAS packages and then some. We soon discovered an issue…we didn’t have a shared skill set, meaning that software my partner had experience with I did not and vice versa.  So instead of trying to find common ground we started looking into what packages offered trial versions long enough for us to complete data coding over the course of a semester.

That narrowed down our list some…only then we ran into another issue. I am a window/linux user and my partner is an apple user that works off their mobile device more often than not. In the end we couldn’t really make a decision because of our differences…we decided that we would have to compromise.  And then for the third time we rewrote our list and began a conversation anew. This time it was guided by functionality and a discussion by Taylor, Lewins and Gibbs (2005).

Since the size of our data set is fairly small this wasn’t too much of an issue, so that didn’t knock any off our list.  The next topic was collaboration: we want to be able to either send documents back and forth via email/cloud or collaborate directly without much hindrance. Surprisingly, we didn’t think about our data type until the third conversation.  Since we have text, audio, and pdf artifacts we needed a CAQDAS package that could support coding directly in the platform for audio elements.  Our fourth criteria was based on using frequency and comparative matrices across our data types.  Because we are doing a mixed methods study, we are very concerned about convergence (or even divergence) across themes.  Additionally, we are very interested in working with quantitative data (for minimal descriptive statistics) within a single package as well. Finally we settled on two possible software options.

What a hassle? There has to be a simpler, comparative chart that would have allowed us to check properties across various CAQDAS packages and cross off options that didn’t meet our criteria.  Turns out that these charts are already floating around the web, we just didn’t look hard enough. Here is an example from UNC that compares ATLAS.ti, MAXQDA, NVivo and Dedoose, and another Stanford site that compares Nvivo, HyperResearch, Studiocode, Atlas.ti, and Tams Analyzer.

 

References:

Taylor, C., Lewins, A., & Gibbs, G. (2005, December 12). Debates about the software. Retrieved from http://onlineqda.hud.ac.uk/Intro_CAQDAS/software_debates.php

CAQDAS as researcher’s prosthetic

Much of the Konopasek (2008) article resonated with my general approach to qualitative research, and the integration of technological tools into said processes.  Konopasek (2008) argues that grounded theory methodology can be synchronous to qualitative research, and qualitative data analysis at large, when used for non-deductive research projects.  In grounded theory, the investigators remain the “primary instrument of data collection and analyses assumes an inductive stance and strives to derive meaning from the data” (Merriam, 2009, p. 29). 

Coming from a hard analytic background that consistently uses algorithmic software to assist with data analysis; we are used to contextualizing technological tools as simply tools that do what they are told!  Any data analysis tools should be critically considered, and their capabilities, before fully integrating that tool into the research design.  Konopasek (2008) claims that “humans, not machines, do the crucial work of coding and retrieving” (p. 2) and that qualitative data analysis is more than a “careful reading of data” (p. 3).  Comparative educationalist can work with a specialized computer program where information is manipulated.  In this manner the computer program could be considered a direct extension of a researchers’ thinking. 

While a computer program can provide valuable insight into data trends, they are often limited in creative approaches, flexibility, and issues dealing with uncertainty.  Issues of ambiguity, flexibility, creativity, expanded vocabulary, and ethics all need to be considered when coding data (Saldana, 2009, p. 29).  It is the duty of the researchers to manipulate knowledge to add meaning through these manipulation researchers can “show differences and similarities, emerging patterns, [and] new contexts” (Konopasek, 2008, p. 5).

 

 References

Konopasek, Z. (2008). Making things visible with ATLAS.ti: Computer assisted qualitative analysis as textual practices. FORUM: Qualitative Social Research 9(2).

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. (pp. 29-31). San Francisco, CA: John Wiley & Sons.

Saldaña, J. (2009). An introduction to codes and coding. In The coding manual for qualitative researchers (pp. 1-31). London: Sage Publications.