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In terms of qualitative approaches to analysing the narrative data, categories were developed and applied to the interview texts. To develop the conceptual categories, I tried largely to derive them from the theoretical framework and from the interview transcripts. There is a commonly-accepted belief that ―a rich source of ideas for categories can be found in the questions in terms of which the research originated and developed‖ (Dey 1993, p.99). Thus, such categories as self-perceptions as writers,

perceptions of writing, previous writing experience and writing stages emerged from the research questions and objectives. However, data analysis was not bound by the research questions and theoretical framework, but was also directed by other means of generating the descriptive categories.

Miles and Huberman (1994) indicated several tools for coding data. One technique entails a quick read through the document and a broad-brush coding of wide topics, such as whole paragraphs or speaking turns or responses to questions. To start coding, I chose two interview transcripts: one transcript which was rich in details and the second which provided less detailed responses. Additionally, the transcripts offered a contrast in terms of student motivation, ways of writing and perceptions of oneself as an academic writer. The first reading was quick, providing an overview of the data and specific themes to be watched for in the other transcripts. This process of coding led to the formulation of an initial framework of six main categories and nineteen subcategories (see Table 4.5).

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Table 4.5 Initial categories (I)

Categories Sub-categories

Prior writing experience Amount

Types of writing Writing practices

Self-perceptions as academic writer Positive Negative Mixed

Perceptions of writing process Writing process Support Revision

Composing behaviour Avoidance, adoption, assimilation Writing stages

Writing strategies

Writing issues Managing the resources

Motivation

Time management Writing blocks

Development as an academic writer New writing strategies Awareness of writing process Self-esteem as anacademic writer

Next, Miles and Huberman (1994) noted that once the relevant concepts become clearer, researchers can work reflectively through transcripts reading line-by-line to develop more discrete coding categories. Accordingly, in a second reading the initial conceptual framework was applied to two other transcripts. I read line-by-line each participant‘s response, each word combination, sentence and/or paragraph to categorise them into an existing category or into a new assigned category. Excerpts that were deemed irrelevant were assigned to the node not applicable. The second reading yielded in total ten categories, thirty-six subcategories and three sub-, subcategories (see Table 4.6).

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Table 4.6 Major categories (II)

Categories Sub-categories

Composing behaviour Acceptance

Resistance Writing strategies

Motivation Extrinsic motivation

Intrinsic motivation

Perceptions of writing Efficient writing skills Native vs. English

Perceptions of peer feedback Perceptions of tutor feedback Purpose of writing

Perceptions of writing quality Negative Positive Mixed

Previous writing experience Educational background Peer feedback

Tutor feedback Writing classes

Self-perceptions as writers Average self-esteem High self-esteem

Positive affective spectrum Low self-esteem

Negative affective spectrum

Writing issues Language (grammar, selling, wrong

wording) Referencing Subject understanding Syntax Task understanding Time management Word limit Writing blocks Critical/uncritical

Writing process Learning outcomes

Module background Revision

Potential revision changes Support Perceptions of support Writing stages Writing task University/course choice Not applicable

Such concepts as extrinsic/intrinsicmotivation, feedback, subject understanding, Native vs. English writing, perception of support, etc. emerged during reading the transcripts carefully for a second time.

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The earlier coding of the first two transcripts was invalidated and they were coded again, as more detailed codes were generated. Furthermore, the new conceptual categories were tested on subsequent transcripts to check on the relevance and richness of the categories and to facilitate the generation of theoretical points about the researched subject.

According to Gibbs (2002, p.167), the new data ―provide the research with information

that can increase the ‗density‘ and ‗saturation‘ of the emerging categories and themes‖. Density refers to the idea of a richer, more detailed and theoretically sensitive concept; whereas, saturation refers to the case when no more data collection or analysis reveals any more information.

Next, I proceeded with coding of another eight transcripts. At the end of this stage, I analysed the number of recurrences of each category. I located categories that had similar labels and/or contents, addressing the same conceptual theme. Additionally, I realised that it was not practical to segment the data into tiny chunks to capture each detail and simultaneously to make sense of the overall picture. As my knowledge about the data and confidence in using NVivo advanced, I reconsidered and reconfigured what I was doing. I also submitted the list of categories and subcategories alongside a piece of interview transcript to my supervisors. After some discussion, another framework of eight categories and twenty-seven subcategories (see Table 4.7) was developed and applied subsequently to all research transcripts.

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Table 4.7 Major categories (III)

Categories Sub-categories

Previous writing experience Educational background Peer feedback

Tutor feedback Writing classes

Composing behaviour Writing stages

Support Motivation

Writing strategies Module background

Perceptions of writing Perceptions of the quality of writing Native vs. English writing

Perceptions of learning outcome

Perceptions of support University support Department support Module support Desirable support

Writing issues Form (i.e. grammar, spelling, language expressions, wrong wording)

Critique/uncritical Referencing Subject understanding Syntax Task understanding Misunderstanding tutor-student Time management Word limit Writing blocks Presentation Self-perceptions as writers

Affective dimension (distress, guilt, sadness, confidence)

Non-applicable

The research started with a list of categories that evolved as I made more decisions about which bits of data could or could not be assigned to the existing categories. Categories that emerged during data analysis seemed to be better grounded empirically and revealed that ―the researcher is open to what the site has to say, rather than determined to force-fit

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The same procedures and tools were applied to code the semi-structured interviews conducted with tutors. Thus, I read line-by-line each response to categorise it into an existing category or into a new assigned category. The careful reading yielded four categories and seventeen subcategories (see Table 4.8).

Table 4.8 Major categories of interviews with tutors

Categories Subcategories

Perceptions of support University support Departmental support Module support

Perceptions of students’ writing issues Language competence Critique Constructing an argument Logic flow Relevance of materials Presentation Grammatical accuracy

Marking preferences Critique

Constructing an argument Logic flow Relevance of materials Presentation Grammatical accuracy Other Non-applicable

The next important stage in data analysis was to link categories together in meaningful ways in which it would be possible to classify and compare the important themes and to analyse the correlation between the variables and make inferences (Merriam 1998). This analysis consisted of comparing the data between two or more categories and

subcategories to identify possible connections and relationships in the findings. For instance, I examined the correlation between students‘ self-efficacy beliefs as academic writers and tutor written feedback.

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