Data analysis

The data obtained from the interviews were audio-recorded and transcribed.

Data were managed with the use of the audio and transcribing functions of the NVivo software, and a number of manual coding procedures²⁸³. Lacking sufficient current knowledge of self-management behaviours in patients with advanced cancer, a content analysis was conducted to gain knowledge about what patients with advanced cancer do to manage fatigue²⁸⁴. The content analysis subsequently informed the modification of the SMS-F, which developed items suitable for initial testing in patients with advanced cancer.

This study took a positivist stance, with the aim of generating results that were generalisable and applicable to future supportive care interventions for this population. In the past, there have been arguments concerning whether content

Chapter 4: Methods 69

analysis is qualitative or quantitative²⁸⁵. According to Yin (1989) and Morgan (1993), the distinction between the use of a qualitative or quantitative approach to content analysis is based on what the data are to be used for^{285, 286}. That is, a quantitative approach to content analysis seeks to answer questions about what and how many, whereas a qualitative approach seeks to answer questions about why and how the patterns in question came to be²⁸⁵. In this study, the analysis of data in this stage pragmatically focussed on generalisability (i.e. identifying codes representing patients’ self-behaviours), rather than identifying patterns (i.e. why patients perform certain behaviours) ²⁸⁵. The procedures for content analysis in the study were guided by Johnson and La Montagne (1993), as outlined below²⁸³:

Preparing the data for analysis

After data collection was completed, all digitally-recorded interviews were transcribed into text using NVivo on the same day as the interview or very soon thereafter. All transcriptions were rechecked against the original audio data for word-for-word accuracy.

Becoming familiar with the data

At this stage, the researcher familiarised himself with the data through repeated reading of the transcripts²⁷⁹, as no insights could arise from the data without a complete familiarisation with it²⁷⁹. As the repeated reading of the transcripts took place, notes were made on ideas and potential categories of behaviours for later stages of the analysis.

Identifying units of analysis

The unit of analysis was subsequently determined by reading through all of the transcriptions and underlined sections of texts, words, phrases, sentences, or paragraphs275, 287, 288. The transcripts were then divided into various codes representing individual self-management behaviours275, 287, 288. Given that the aim of the semi-structured interviews was to generate items for the SMSFS-A, these codes had to be behaviours that patients recalled having performed over the previous seven

days. All statements (quotes) in the transcripts that related to behaviours performed by patients to manage fatigue were marked. Self-management behaviours not used for fatigue were not marked. Subsequently, the coded self-management actions were considered “level one codes”^{285, 288}. These codes were descriptive words about specific behaviours used by the participants themselves²⁸⁰.

Defining tentative categories for coding the responses

The researcher read through the level one codes and combined behaviours that seemed to describe the same type of behaviours. He then organised these related behaviours into a list that could be categorised as level two codes (broader categories of behaviours). During this process, these categories had to be as mutually exclusive as possible, so that each unit of analysis would fit into only one category. Although these behaviours could conceptually overlap with each other, decisions were made at the discretion of the researcher to allocate the behaviours to the category that best fit.

Thus coding emerged by repeatedly reading and fully comprehending the behaviours.

Significant words and phrases were categorised.

Refining categories

Once the level two coding was completed, descriptions of specific or broader behaviours as discussed by the participants became available. Next, coded behaviours (level one codes) were compared within and between transcripts, to verify whether they described the same or different behaviours. At times, responses would not fit into an existing category. The categories were then refined by discarding inappropriate ones and creating new categories to integrate the items that did not fit into previous categories. The revised categories were used to repeat the process until all the responses were appropriately coded. Finally, all level two codes were further categorised into broader concepts (level three codes).

Establishing category integrity

To enhance the validity of the analysis and the synthesis of categories of self-management behaviours, the Principal Supervisor was asked to verify the accuracy of the categories. After discussion with the Principal Supervisor, minor alterations to

Chapter 4: Methods 71

the codes and categories were undertaken. Some of the original categories were refined or deleted to yield the final version of the codes and categories.

Lastly, a categorisation matrix was used to present the data²⁸⁷. The matrix demonstrated how different behaviours were coded during the analysis. It was expected that such strategies would increase the trustworthiness of the study by linking the results and the data^{275, 288}. At this stage, the items of SMS-F were modified by adding relevant self-management behaviours and removing irrelevant self-management behaviours, as informed by the results of the semi-structured interviews. This modified version of SMS-F was entitled the “Self-efficacy in Managing Symptoms Scale - Fatigue Subscale for Patients with Advanced Cancer”

(SMSFS-A).

4.4 EXPERT PANEL FOR ASSESSING THE CONTENT VALIDITY OF