4.4. The analytic journey

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

As in any research method, analyzing collected data is a necessary step in order to draw conclusions. Analyzing qualitative data is not a simple nor a quick task. Done properly, it is systematic and rigorous, and therefore labor-intensive and time-consuming “[…] good qualitative analysis is able to document its claim to reflect some of the truth of a phenomenon by reference to systematically gathered data” (Fielding 1993), in contrast “poor qualitative analysis is anecdotal, unreflective, descriptive without being focused on a coherent line of inquiry.” (Fielding 1993) (Pope et al. 2000, p. 116). Qualitative analysis is a matter of deconstructing the data, in order to construct an analysis or theory (Mortelmans 2009).

The ways and techniques to analyze qualitative data are not easy to describe as it requires a lot of “fingerspitzengefühl” and it is unrealistic to expect a kind of recipe book which can be followed in order to produce a good analysis. Therefore what we present here is a number of hands-on guidelines, which have proven useful to others.

The difficulty of qualitative analysis lies in the lack of standardization and the absence of a universal set of clear-cut procedures which fit every type of data and could be almost automatically applied. Also there are several methods/approaches/traditions for taking the analysis forward (see table). These move from inductive to more deductive, but in practice the researcher often moves back- and forward between the data and the emerging interpretations. Hence induction and deduction are often used in the same analysis. Also elements from different approaches may be combined in one analysis (Pope and Mays 2006).

Different aims may also require different depths of analysis. Research can aim to describe the phenomena being studied, or go on to develop explanations for the patterns observed in the data, or use the data to construct a more general theory (Spencer et al. 2014). Initial coding of the data is usually descriptive, staying close to the data, whereas labels developed later in the analytic process are more abstract concepts (Spencer et al. 2014).

The analysis may seek simply to describe people’s views or behaviors, or move beyond this to provide explanation that can take the form of classifications, typologies, patterns, models and theories (Pope and Mays 2006, p. 67).

The two levels of analysis can be described as following:

  • The basic level is a descriptive account of what was said (by whom) related to particular topics and questions. Some texts refer to this as the “manifest level” or type of analysis.
  • The higher level of analysis is interpretative: this is the level of identifying the “meanings”. It is sometimes called the latent level of analysis. This second level of analysis can to a large degree be inspired by theories.

 

The selected approach is part of the research design, hence chosen at the beginning of the research process.

In what follows we describe a generic theoretic process for qualitative data analysis.

 

Figure: Conceptual representation of the analytic journey of qualitative data with an inductive approach

 

 

Each theoretical approach adds its own typical emphases. The most relevant approaches are described in next section. These steps could also be useful in the processing of qualitative data following a system thinking method [ADD crossrefs].