How do we like to learn qualitative data analysis software?

Abstract

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.