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CAQDAS Comparison

CAQDAS Comparison / Methodological Affinities

The other pages in this section have hopefully given you an overview about the functionality of CAQDAS, which can assist you in the choice of CAQDAS, if you know what kind of tools are required for your methodology. There are a few areas, which I have not covered, but which nevertheless may be crucial for your decision, which software to use for your work. Most importantly, I have not (yet) covered the softwares' capabilities for teamwork and the merging of projects. Also, I have left aside issues of data archiving.

Elsewhere (Koenig 2004), I have examined the usability of CAQDAS for a specific methodology that attempts to quantify and systematize the identification of frames in textual data. Clearly, this cannot be the place to discuss the suitability of each program for all sorts of methdologies, though: Given the variety of methodological approaches, this would rather require an eight volume encyclopedia. Nevertheless, some general trends regarding the methodogical affinities of the different CAQDAS might provide you with an initial idea, as to what program might be suitable for your methodology.1

Mixed Methods

In a recent review of the potentiality of QSR software for mixed methods, Bazeley (2002: 241) praises "the advantages NVivo offers to the mixed methods researcher, [which] are, in the first instance, enhanced flexibility and convenience." Our evaluation paints a different story: Because of its limitations for larger projects, NVivo is decidely unsuitable for mixed-method approaches that use larger amounts of data. From the QSR range, we would therefore recommend N6, if any, for mixed method studies. Unlike NVivo, N6 has no problems with larger datasets and unlike most other CAQDAS, N6 offers a scripting language, which lets you automate processes. Unfortunately, though, N6's capabilities with respect to the size of a project are bought with a decrease of flexibility, as N6 is the only current CAQDAS, which requires preformatted plain text files as input.

ATLAS.ti, on the other hand offers both flexibility and capacity for larger projects, but its speed does not allow for efficient mixed-method strategies, once a certain number of data files has been exceeded. As in NVivo, it is less the sheer amount of data which matters in this respect, but the number of files influences the speed much stronger: While there is no problem to handle 5 files of 1,000KByte each, 5,000 files of 1KByte each bring NVivo to a standstill, while a simple autocoding with ATLAS.ti will give you time to have a short coffeebreak. During that time Kwalitan might have produced 20 error messages, as its instability is a serious issue under Windows 2000. Finally, Qualrus contains no in-built autocoding functions and therefore is only be suitable for semi-standardized material, such as open-ended interviews. On these, it performs well, not the least because it is the only CAQDAS that offers a scripting language.

Most mixed methods studies will thus fare best with either MAXqda or QDA Miner, as these run fairly swiftly and stable across larger data sets. Unfortunately, neither offers currently multimedia support. If you also want to analyze videos and pictures, you therefore will have to recur to ATLAS.ti or HyperRESEARCH, whose speed we unfortunately did not test yet.

Discourse Analysis

Like many other methodological approaches to the analysis of discourse2, Discourse Analysis is a proliferated paradigm. Nevertheless, there are some recurrent themes in Discourse Analysis, which emphasize the discursive context (e.g. Antaki et al. 2002: 6) and the importance to examine discursive resources that are not utilized (Hammersley 2002: 768). In its narrow sense, coding, which seperates quotations from their contexts, and which cannot investigate silences, is therefore usually antithetical to Discourse Analysis. Although it is certainly possible to use coding primarily as a way of organzing data (Gibbs 2002: 4), the severe restrictions on the types of data most CAQDAS allow for and the decontextualizing effects of coding outweigh in my view the advantages such organization strategies have. Instead, most Discourse Analysts will primarily need to search their data. Of the classical CAQDAS, NVivo offers the largest variety of search functions. Except for fuzzy searches, which are important for data which contain many typographical errors, ATLAS.ti's search facilities are also quite sophisticated, and unlike NVivo is able to effectively handle (read: code) audio and video data. Yet, search functions are equally well performed by InfoRapid Cardfile, which also is the most liberal in the acceptance of file formats. Since with respect to coding, NVivo's only advantage is a fuzzy search facility, can be emulated by the freeware zip archive SimFuz, Cardfile seems the obvious choice for most Discourse Analyses that do not require coding features.


Since in ethnography the analyst is confronted with a host of different materials, it is imperative that a wide variety of data can be handled. Currently, only ATLAS.ti, HyperRESEARCH, and Qualrus allow for the effective integration of multimedia data.


  1. I would very much encourage you to contact me, if you would like to suggest further affinities.
  2. For frame analysis, cf. Entman 1993. Content Analysis is somewhat more coherent, but Weber (1990: 13) still rightfully insists that "there is no single right way to do content anlysis" (boldface: mine).

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