Data analysis

Mixed analysis involves the analysis of quantitative and qualitative data which occur either concurrently or sequentially in various phases of the study (Holloway & Wheeler 2010:274; Onwuegbuzie & Combs 2011:2). Sequential mixed analysis was employed in this study with the quantitative analysis stages preceding the qualitative

Figure 4.3 Sequential mixed analysis

The use of sequential mixed analysis in this study addressed the research objectives and questions as well as the rationale for mixing the phases (Johnson & Christensen 2008; Tashakkori & Creswell 2007). In the paragraphs that follow, the analyses of both strands of this mixed research shown in figure 4.3 are discussed.

4.6.4.1 Quantitative data analysis

The questionnaire was administered by the researcher to student nurses that met the initial eligibility criteria for participation in the research and collected it as soon as the respondents had completed them. The questionnaires were numbered as soon as they were received and they were kept safe. Data were entered into SPSS version 20.0. Prior to analysis, all variables were checked for accuracy of data entry. Missing values analysis (MVA) programme in SPSS was used to check if there were any data missing. Questionnaires that had no answer to one or more questions on those that had more than one response for one item were excluded from the analysis (Rowe et al 2012:197). For example, the questionnaires in which there were two responses for one item such as ‘strongly disagree’ and ‘agree’. The data that had been analyzed were then summarized using frequency distributions in table and graphic presentations.

Survey data analysis: Pre—test SPSS V/20.0 Appreciative inquiry interviews; Thematic analysis Survey data analysis Post-test SPSS V/20.0 Individual interviews data analysis; Thematic analysis

4.6.4.2 Qualitative data analysis

Many researchers agree that the analysis of data in qualitative research is a process that begins when data collection is initiated; implying that it is done simultaneously with data collection (Andrew & Halcomb 2009:188; Polit & Beck 2012:504; Terre

Blanche, Durrheim & Painter 2006:322).Appreciative interviews data were collected,

analyzed and the findings were documented during the phases of the 4-D cycle of AI by the participants and the researcher. At the end of the interviews, all the documents that were created by the participants were collected and analyzed by the researcher. The individual interviews were audio-recorded and detailed notes were hand written.

Data from AI and individual interviews were analyzed using thematic analysis which is a search across a data set (interviews, focus groups or a range of data) to find repeated patterns of meaning. It involves identifying, analyzing and reporting patterns or themes within data (Creswell & Plano Clark 2007:129; Polit & Beck 2012:745). Thematic analysis was chosen because it is recommended as a useful method for working within a participatory research paradigm with participants as collaborators (Braun & Clarke 2006:79) and it provides a flexible and useful research tool which can potentially provide a rich and detailed account of data. Holloway and Todres (2003) regard thematic analysis as a foundational method for qualitative analysis which can be used within different theoretical frameworks. An inductive approach was used whereby specific information was taken to reveal broader patterns.

For individual interviews, thematic analysis was done according to Van Manen (1990 cited in Polit & Beck 2012:567) using holistic and selective approaches. According to Van Manen (1990), thematic aspects of experience can be uncovered or isolated from the participant’s description of the experience. In the holistic approach, the researcher reviews the text as a whole and tries to capture the meaning (Polit & Beck 2012:567). The process of data analysis went through the following series of phases which, according to Braun and Clarke (2006:77), researchers must follow in order to produce a thematic analysis:

Transcription of interviews: The audio recordings of individual interviews were transcribed verbatim into text as Microsoft file by the researcher. The transcript format made it easy to understand, manage and retrieve the data (Andrew & Halcomb 2009:188).

Immersion in the data; The transcribed data, participants’ created documents and the notes made during the interview were then read and re-read several times to familiarize the researcher with data and to get a sense of the whole in search for meaningful segments or units. The audio recordings of interviews were listened to in order to verify and to ensure the accuracy of the transcription.

Literature on qualitative analysis suggests that being able to draw on an understanding of the interview context brings depth to data immersion and enables subsequent interpretation to fully account for the research context beyond interview transcripts (Holloway & Wheeler 2010:281; Stephens 2009:101; Terre Blanche et al 2006:322). Green et al (2007:4) state that data immersion brings about clarity of the part played by both the interviewer and the research participants and lays the foundation for connecting disjointed elements into a clearer picture of the issue being investigated. These authors recognize the added benefit of early immersion in the data; that it makes analysis more manageable rather than waiting to wade through large amounts of data at one time.

Coding: The next step was to generate codes. Coding refers to the process of examining and organizing the information contained in each interview and the whole dataset into meaningful groups. It is described by Terre Blanche et al (2006:324) as the breaking up of data into analytically relevant units. Coding was done manually. The researcher selectively highlighted and pulled out statements or phrases that seemed essential to the experience under study (Polit & Beck 2012:568). While reading the transcripts, notes were made of any thoughts, observations and reflections that occurred. Different sections of data were identified by means of codes based on the meanings that were attributed to them. The codes identified features of the data that the researcher considered pertinent to the research question and they were added to phrases, lines, sentences and paragraphs. The coding process required the researcher to move forward and back through the transcripts, drawing

on in-depth knowledge connected with the study. This resulted in refinement of some of the meanings of the codes and re-coding previously coded transcripts.

Creating categories and building themes: Categories were created by the linking of codes. The categories created in this study were illustrated by means of relevant quotes from the interviews. The themes were identified from within each section of the transcript. A theme captures something about the data in relation to the research question and it represents some level of patterned response or meaning within the data set (Braun & Clarke 2006:80). Themes are identified by linking the categories with social theory, until eventually an overriding explanation is arrived at which makes sense of the various patterns that have emerged at the descriptive level. Once the themes had been identified, they became the object of reflection and interpretation through follow-up interviews with participants (Polit & Beck, 2012:569). The main features of the themes from the experience and confirmed by the research participants were produced as tables with evidence from the interview and quotations which, the researcher felt, best captured the essence of the person's thoughts, and their emotions about the experience of the AI. The final step involved weaving the thematic pieces together into an integrated whole to provide an overall structure to the data. The researcher suspended her presuppositions and judgments in order to focus on what was actually presented in the transcript data by means of bracketing. In summary, researcher coded and analyzed the data by grouping similar ideas into categories and themes, and elaborated on the data by breaking it down into smaller areas under the heading of sub-themes (Polit & Beck 2012:579). Lastly, the researcher interpreted and checked the data.

Details regarding data analysis and the findings of qualitative data (appreciative and individual interviews) are discussed in chapters 6 and the second part of chapter 7 respectively.