How do We Like to Learn Qualitative Data Analysis Software?

  • Fábio Freitas
  • Jaime Ribeiro
  • Catarina Brandão
  • António Pedro Costa
  • Carla Azevedo de Almeida
  • Francislê Neri de Souza
Keywords: Computer Assisted Qualitative Data Analysis Software; CAQDAS learning; Andragogy


The Computer Assisted Qualitative Data Analysis (CAQDAS) learning can represent a great challenge and obstacle to the adoption of these tools in support of research. This specific software packages, to support qualitative research, enable the organization and systematization of data collection and analysis, as well as enhancing the definition of dimensions, categories and subcategories of analysis, usually very laborious processes (Neri de Souza, Costa, & Neri de Souza, 2015). On the other hand, qualitative research often produces a large amount of data that requires "organization, structuring and reduction without prejudice the quality of the inferences that are sought to produce. The rigor should guide the moment of data processing and interpretation, and the qualitative researcher must rely on all available tools to ensure the quality of his work, such as the use of dedicated software, as do those who use inferential statistics for evidence of hypotheses." (Ribeiro, Brandão, & Costa, 2016, p. 158). Thus, it seems imperative that CAQDAS developers devise strategies and tools that will stimulate and support researchers in the learning process of their applications. We could explain the limitations and potentialities of using these tools, but the characteristics that currently constitute them give them the credibility necessary to be increasingly exploited, making them also more robust (Costa & Minayo, 2018). On the other hand, many users rely too much on these packages that often create unrealistic expectations. Bazeley (2007) refers that the relative ease of software-assisted coding can reduce critical and reflexive reading, mechanizing qualitative analysis and thus compromise the exploratory and interpretive character of most qualitative investigations.

To this end, this study focuses on the learning preferences of CAQDAS users. Many CAQDAS present training solutions that are intended for self-study and that are marketed as complete learning solutions; however, little is known regarding how well they work, under what conditions they can be used and if they adjust at all to the self-learning preferences of researchers (Freitas et al., 2017).

To collect data for this study a focus group was conducted with experienced CAQDAS users and an online questionnaire was administered to 232 users from 29 different countries and representing a diversity of 26 CAQDAS. The obtained data allow to deduce that the users privilege the learning in context of training, but, when it comes to self-learning, they tend to opt for interactive tools and to resort to tutorial videos. Results show yet that when learning a CAQDAS, the user resource to various strategies, which we believe reflects their own search for tools that best fit their learning style, and their specific questions or doubts at a given moment. This seems to indicate that users are looking for solutions that provide them with a learning experience that is more adapted to their style and in the shortest time possible.