Data analysis

For analysing the data of this study, the software programme MAXQDA was used. It is an analysis software based on GT that allows one to organise, analyse and visualise all forms of data that can be collected electronically.

Looking at the immensity of the collected data, it is crucial to select relevant segments out of the data pool in order to analyse them thoroughly and come to a conclusion linked to the initial research question (Breuer 2009:79). The selection is done through coding,

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a method where recorded phenomena are assigned to (superordinate) concepts (:69). The process of coding does not unfold linearly, but through a constant seesaw of data acquisition, concept formation, model testing and a reflection on how the knowledge was obtained (:69). In this way, a data based theory ideally evolves at the end of the process. Coding is the heart and centrepiece of GT and is divided into three basic types that will be explained in the following subsections.

4.6.1 Open Coding

In open coding the researcher breaks up, analyses, compares and categorises the collected data for the very first time (Faix 2007:92). While sifting through the data step- by-step, codes are being set which describe, name or classify the phenomena under consideration. The pool of data is segmented into meaningful expressions to find main ideas, develop preliminary concepts and compare them for relations, similarities and dissimilarities (Strauss & Corbin 1996:145). The coding process is done in three steps: deduction, induction, and abduction. In deduction, a theoretical category from the questionnaire is extrapolated on the empirical material.

Induction, on the other hand, is about examining the interviews without specification of theory-based categories. Codes are analysed for similarities and then grouped into categories based on their common features (Khandkar 2015:1). This process is repeated several times. As the researcher goes back and forth and more and more data is coded, categories are compared, fused into new concepts and eventually renamed and adapted (Holton 2010:265). Induction thus makes it possible to find new concepts that emerge from the raw data and were not provided before.

In abduction, the researcher attaches importance to single statements that might be suitable for forming a new category. In contrast to the inductive procedure, there are no other comparable propositions. Though they cannot be assigned to already existing categories, the statements still seem to be of importance for the whole research study, the research question and the results so far. The statements are thus taken and hypotheses formed which are examined several times until a separate category or subcategory is built (Faix 2007:162).

In doing so, a multitude of codes will evolve eventually, making it possible to identify certain phenomena and bundle them theoretically in categories (Breuer 2009:81). Its

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features need be analysed by the researcher in more detail later. The goal of open coding is the building of a multidimensional, descriptive, preliminary framework for later analysis. Since it is built directly from the raw data, the process itself ensures the validity of the study (Khandkar 2015:2).

4.6.2 Axial Coding

Generally, axial coding follows the process of open coding. However, Strauss & Corbin (1996) point out the possibility to begin the process of axial coding parallel to open coding (:77). Axial coding involves the systematisation of the already generated codes through determining connections between a category and its sub-category (:76). In that way, the elaborated categories are correlated and then newly compiled.

Strauss & Corbin propose to use the following categories for axial coding: (1) Phenomenon: depicts the central incident/phenomenon under study (:79). (2) Causal conditions: refer to the incidents that lead to the occurrence or to the

development of the phenomenon (:79).

(3) Context: depicts those characteristics that belong to the phenomenon (e.g. the arrangement of incidents and events), but also those conditions under which the incidents happen (:81).

(4) Intervening conditions: are the broad and general conditions that influence the action and interactional strategies. These conditions contain: time, space, culture, socioeconomic status, technical status, career, history and the individual biography (:82).

(5) Action and interactional strategies: are those strategies that deal with or execute a phenomenon (:76). In axial coding, these actions and interactions need to have the following characteristics: 1) they need to be processual, 2) they are purpose- and goal-oriented with regard to the phenomenon and 3) the question concerning a lack of action/interaction and its cause need to be asked (Faix 2007:93).

(6) Consequences: each of the just mentioned actions provoke consequences, which are rarely intended and can mostly not be predicted. According to Strauss and Corbin (1996), they therefore need to be sought in the theoretical sampling. ‘Consequences’ in axial coding are either people, places or things (:85).

63 4.6.3 Selective Coding

When going through the results of axial coding, central phenomena should be captured. However, due to the amplitude of results, the research question is often lost sight of. To centre the chief phenomenon again, core categories are built and defined in selective coding (Faix 2007:95). In doing so, the decision for a certain core category ultimately reflects the central theme of the data content (e.g. in an interview). The remaining categories are not redundant, however. Rather, they are arranged around the core category and linked up with it.

Selective coding takes place at a higher level of theorisation than axial coding and helps to sum up the results concisely.