General analytical procedure

The central concern of a typical phenomenographic study is to identify the qualitatively different ways in which the participants experience, understand and conceptualise a phenomenon. In this sense, data analysis is a process from which such categories of description can be derived. Marton (1986, pp.42-43) explains the general process from the finishing of the transcription to the formation of categories of description;

The first phase of the analysis is a kind of selection procedure based on criteria of relevance. Utterances found to be of interest for the question being investigated [...] are selected and marked. [...] The phenomenon in question is narrowed down to and interpreted in terms of selected quotes from all the interviews. […] The selected quotes make up the data pool which forms the basis for the next and crucial step in the analysis. The researcher's attention has now shifted from the individual subjects […] to the meaning embedded the quotes themselves. The boundaries separating individuals are abandoned and interest is focused on the “pool

of meanings” discovered in the data. […] A step-by-step differentiation is made within the pool of meanings. As a result of the interpretive work, utterances are brought together into categories on the basis of their similarities. Categories are differentiated from one another in terms of their differences. […] quotes are sorted into piles, borderline cases are examined, and eventually the criterion attributes for each group are made explicit. In this way, the groups of quotes are arranged and rearranged, are narrowed into categories, and finally are defined in terms of core meanings, on the one hand, and borderline cases on the other.

Booth (1993, p.188) also depicts the analytical process of the collected data;

The interviews are transcribed and the researchers immerse themselves in them, reading them carefully, focussing on different themes of interest, being aware of all their data at the same time as they look at a single statement. The researchers look for similarities and differences in the subjects’ statements, and their understanding of the statements hovers in a state of uncertainty, looking for further implications of the original interview context and the context of the totality of interviews. One differentiates between the first-order perspective, from which the researcher takes a subject’s statement and measures it against some predetermined standard, and the second-order perspective, from which the researcher sees statements as reflecting the subject’s own understanding of the phenomenon in question. […] The analysis process is essentially dialectical - the statement, the individual interview, the totality of interviews, all lend meaning to one another. The interviews have to be seen simultaneously as a whole, as taking up individual themes in certain sections, and as being permeated with references to the totality of themes of interest.

Several researchers (Dahlgren & Fallsberg, 1991; Khan, 2014; Sjöström & Dahlgren, 2002) have proposed the following seven steps to analyse the data in an attempt to structure the process and facilitate manipulation:

Step 1. Familiarisation: the researcher is introduced to the empirical data by reading through the transcripts. It may also include correcting errors in the transcripts.

Step 2. Compilation: compile students’ answers to certain questions and identify the most important elements in answers.

Step 3. Condensation or reduction: select quotes which seem to be relevant and meaningful for the study and remove the most redundant, irrelevant data.

Step 4. Preliminary grouping: categorise similar answers into the same group.

Step 5. Preliminary comparison of categories: establish borders between the categories. The revision of the preliminary groups may also happen. Step 6. Naming the categories: give each category certain names to highlight their essence.

Step 7. Final outcome space: a description of the unique character of every category, and a description of resemblances between categories.

Marton (1986) claims that, on the one hand, while this is a process of discovering different ways of experiencing a phenomenon, there is no ‘algorithm’ to do it. This situation has not been changed for more than two decades, as Yates et al. (2012, p.103) in a more recent study contend that “[t]here is no single process or technique prescribed for the analysis of phenomenographic data”. On the other hand, it is clear that this process is often highly lengthy and repetitive. Marton et al. (1993, p.282) deem that it should be "of an iterative and genuinely interpretive nature, guided by what we may call 'the hermeneutics of phenomenography'", and Åkerlind (2005d) depicts a similar meaning, stating that the analytical process is highly repetitive and comparative.

Even the definitions of the categories should be examined and renamed iteratively. Due to the essence of this analysis, Reed (2006, p.9) claims that not many researchers are likely to “spend time making their process explicit as it is not simply a structured series of steps that can be easily described”.

Evidently there is no universal solution to analyse the data collected for phenomenographic studies, since the procedures adopted by some researchers may be different from those of others. Unfortunately, it seems that these researchers have seldom considered the role of certain analytical tools in analysing data. Given that the framework developed from the anatomy of awareness could improve the research of conceptions (Harris, 2011), the following section will detail the data analysis in relation to the referential/structural framework.