4.2. Definition

Author(s): 
Laurence.Kohn
Author(s): 
Wendy.Christiaens

“Qualitative data analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating”. (Lewins et al. 2010)

In general qualitative data analysis means moving from data to meanings or representations. Flick (Flick 2015) defines qualitative data analysis as follows:

The classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it” (p. 5).

The aims of qualitative data analysis are multiple, for example:

  • To describe a phenomenon in some or greater detail
  • To compare several cases (individuals or groups) with focus on what they have in common or on the differences between them
  • To explain a phenomenon or gain insight in a problematic situation
  • To develop a theory of a phenomenon

There are several ways to analyze textual data. “Unlike quantitative analysis, there are no clear rules or procedures for qualitative data analysis, but many different possible approaches” (Spencer et al. 2014), p. 270). “Qualitative analysis transforms data into findings. No formula exists for that transformation. Guidance, yet. But no recipe.”  (Patton 2002)

 

Alternative traditions vary in terms of basic epistemological assumptions about the nature of the inquiry and the status of the researcher, the main focus and aims of the analytic process (Spencer et al. 2014, p. 272). Generally speaking, the analysis process begins with the data management and end up with abstraction and interpretation, from organizing the data, describing them to explaining them (Spencer et al. 2014).

According to Spencer et al. (2014), the hallmarks of rigorous and well-founded substantive, cross-sectional qualitative data analysis are:

  • Remaining grounded in the data
  • Allowing systematic and comprehensive coverage of the data set
  • Permitting within- and between-case searches
  • Affording transparency to others