Procedure of Data Collection

Semi-structured face-to-face interviews were utilised for each participant at a scheduled time they had booked previously. Interviews are the most common source of data collection (Yin, 2009 ; Stake, 1995).

At the beginning of each interview, I welcomed and thanked the interviewee for their involvement and introduced myself to them, after which I explained the purpose of the research study to orientate the interviewees with the research topic.

Each interview was digitally recorded to enable it to be saved on to a computer, with the consent of the participant, to aid accurate transcription. This allowed me to concentrate on the conversation of each interviewee. This generated a data trail to which I could refer, as recommended by Polit and Beck (2013).

I used five combination types of questions as guidelines (Appendix 5.9) in the interviews as suggested by Krueger and Casey (2015). This combination of questions allowed the participants to focus on the important points of the research questions as suggested by Polit and Beck (2013).

The questions moved from general to more specific and from relative to important issues in the research literature (Krueger & Casey, 2015). During the interviews I took notes to help me concentrate on the participants’ response, develop probing questions and to explain the issues in depth or to clarify certain words. I continued to interview and probe until it was felt no more useful data could be gained (Merriam, 2009).

114 At the end of the interview, I debriefed participants by allowing sufficient time for each participant to raise concerns and to make sure they felt they could contact me if necessary. All interviews took place within the interviewees’ organisation or workplace and lasted between 30 to 60 minutes. Each interviewee was assured of anonymity and confidentiality regarding the information given.

Finally, each of the interviewees was thanked for their contribution and informed that they would receive a copy of their transcript by email. Following each interview, I immediately started reflecting on my notes and added any ideas or interpretations for any words related to the gathered data. Further, I reflected on the process of each interview to note anything that might have had an effect on the trustworthiness of the collected data or the rigour of the study to add in the final report. Each interview was conducted separately on different days.

5.3.3

Data Analysis

I started my transcription of the data by first listening to the recorded interview that was uploaded to the computer to ensure the accuracy of the recorded sound. The second time I listened to the whole interview without interruption whilst reading my review notes as annotated during the interview. This enabled me to remember the details and other nuances of the participants. For the second step of analysis, I opened a new Microsoft Word page in the database for the macro level data that included the electronic records of interviews. I developed one template page for each interview, including date, time, level and given code, within a table that included questions, answers, and researcher’s comments (Appendix 5.3). I believed that organising the work from the beginning would help me to work systematically and smoothly. I was very careful when I did the transcription, and for this reason I developed certain rules that I would follow, including: selecting a quiet place that contained an office or table and chair, and turning off my phone and annotating a hardcopy of the interview with a marker pen.

In addition, I used headphones to listen actively to the recorded interview to capture the conversation accurately, listening to full sentences before stopping the recorder to write. Sometimes I listened to sentences many times to capture the exact words by using the forward and back buttons to repeat the conversation, noting down the exact words and

115 including repetition, silence and pauses. All names were removed from the transcript and indicated by their given code as in Table 5-2.

Table 5-2: Macro-level (Decision-makers)

Position No Nationality Given code General Director 1 1 Saudi GD1 General Director 2 1 Saudi GD2 General Director 3 1 Saudi GD3 General Director 4 1 Saudi GD4

All the audio-recorded data obtained from the four face-to-face interviews was saved on the macro level database as an audio file. Each transcript was organised and given space to add any further notes during analysis. The transcription method was very time consuming; in order to fully engage in the data collection process, I personally transcribed all the data. The transcripts had many grammar mistakes because most of the participants didn’t speak English fluently and English is the second language in SA. However, despite the mistakes contained, the transcripts were not corrected to avoid changing the meaning of the interviewees responses. In order to back up the outline themes, quotations from interview transcripts are provided in original format (Section 7.2, 7.3, 7.4). Furthermore, to avoid changing the meaning, irrelevant parts of the transcripts have been removed as indicated by the ellipsis points [….]. An example of one-to-one interview transcript was attached in Appendix 5.4.

I reviewed the transcriptions with the original records to check the words and spellings and to correct some work to ensure the accuracy of data as suggested by Zhang and Wildemuth (2009) and explained in Section 4.7. A one-hour interview could take up to six hours to transcribe as the data was reviewed several times both by myself and a peer reviewer. In addition, the transcription report for each interview was sent to the interviewee to validate the information given. The majority of the participants (3 of 4) replied and agreed that the transcript reflected what was said in the final interview and one participant did not respond. A final check was conducted before all data was saved securely on my personal computer, flash memory, and email drop box.

I started coding any data related to the thematic framework (Section 4.7) by selecting the statement/words and copying it into another document, under an initial constructed

116 heading. For example, a title heading was made for this comment that provides an overall description of the sentiments described within the illustrated Figure 5-3:

NVivo software was used in a similar process to categorise the data and “drag and drop” the highlighted statement into a heading within the programme rather than in a separate document (Hilal & Alabri, 2013). According to Zhang and Wildemuth (2009), the basic functions are supported by the NVivo programme include text editing, note and memo taking, coding, text retrieval, and node/category manipulation. It has been suggested that using the software in data analysis adds rigour to qualitative studies (Greenhalgh, 2014). For example, I would select the entire section of a quote/statement and paste this comment under a heading that summarised the idea of that statement (Appendix 5.5). In some cases, the quote could be put under more than one heading, depending on whether more than one idea had been noted in the statement. If the quote did not fit into an existing heading, a new heading was created for it.

A log of the headings and the four participants was kept in either the NVivo programme, if used, or in an Excel file where qualitative software was not used. This was undertaken during the coding so that I could easily see the categories and trends, and the frequencies at the end of the analysis. Each statement was coded using this process, throughout the

I think for us culturally. They respect more the man than the woman. But over time we are improving the image of nurses for the Saudi people. Before they did not respect even man or woman. The males or females working in this career are not respected by others. Some patient look at the nurse as a housemaid or chamber maid. some of the people they see that female and males are working together, they thinking that of another way!!. Still there is some people have this bad perception”

“The nurse who has good knowledge and very good skills will gain more respect. Images of Nursing Images of Nursing culturally respect not respect housemaid chamber maid doing dirty work together bad perception. good knowledge good skills

117 transcription. More and more headings were built, and termed “categories,” and for example, coded sections were added to existing headings as illustrated in Figure 5-4.

Once the coding had been completed, headings were revisited to determine whether they were repetitive and could be combined. After this, headings and their similarity were reviewed to ascertain which could be grouped or “clustered” together by topic. This allowed organisation of the headings under several different “themes” of sorts, which generated the “thematic categories” presented in the write up.

A midmap for each thematic category title was generated, which included the headings – called constituents – that were grouped under this thematic category, and the number of participants that mentioned that particular element or constituent. To write up the section in the analysis report, all the comments made under that thematic category were reviewed along with the frequency of mentions, how the category was formed and the most frequent responses related to this category.

Addition of verbatim examples allows the reader to gain a “picture” of the participants’ experiences or thoughts on the topic and more specifically, the thematic category being presented. Once all the thematic categories had been described and presented, all the

Images of Nursing

Low status of profession, cultural influences, lack of family support, gender mixing, negative social image, low prestige, Arab media portrayals.

Negative Images Gaining respect, Increased job opportunities, financial income, further education, good knowledge and Skills

Positive Images

118 thematic category results were re-analysed, and high frequency data and extremely relevant data were noted, taking into consideration the narratives and individual textual descriptions of each participant. In places, the narratives gave a strong sense of certain elements that needed to be included in the final analysis. These high frequency and extremely pertinent data are grouped into overarching themes or composite structural descriptions that describe the findings representative of the sentiments of the group as a whole. These results are then analysed in relation to the research question in a discussion considering the implementation of the new policy requiring nurses to have a minimum of a Bachelor’s degree education.