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Research data alone does not answer research questions. Following collection, data must be analysed to make sense of it and the findings presented to the audience in a logical, understandable way (Parahoo 2014). It is imperative that data analysis is considered at the planning stage of any research project and the approach used will not only be influenced by the research method but also by type of data obtained (Wagstaff 2006). Different epistemologies advocate different techniques in interpreting data.

The analysis of data in quantitative studies from a positivist perspective usually takes place after data collection has ended and involves descriptive, correlational or experimental techniques. This is influenced by the research questions, the research objectives and the type of data that has been collected (Parahoo 2014). Computerised statistical packages are widely used in the analysis of quantitative data, although the researcher must prepare or code the data prior to using a computerised package, in order to give it a meaning (Wagstaff 2006).

The analysis of qualitative data obtained from an interpretivist approach demands a different approach in order to generate meaning and knowledge production. Contrary to the analysis of quantitative data, the process of qualitative data analysis commences during data collection, with themes becoming apparent during the collection, which may then be used to influence future data collection (Parahoo 2014). This allows the researcher to refine questions or pursue emerging areas of inquiry in greater depth as the data collection continues (Pope et al 2006). Carter (2004) suggests that the analysis of qualitative data is not something you ‘do’ to the data but a reflective process that the researcher does ‘with’ the data. It is an iterative process, meaning it is not just performed once, but the researcher returns to it and reconsiders and reflects upon it.

In contrast to quantitative analysis, qualitative analysis seldom involves numerical analysis and focuses on preserving the data in textual form, interpreting it to generate categories and theoretical explanations of phenomena (Pope et al 2006). Qualitative data analysis can be conducted with computerised analysis packages. However, they do not analyse data for the researcher and provide outcomes, but act as a way of organising and managing the vast amount of data that is generated from qualitative enquiry. Pope et al (2006) suggest there are three broad approaches to qualitative data analysis: thematic analysis, grounded theory and the framework approach.

The MRC framework does not prescribe the research methods that should be used to generate the theory on which the intervention is based. As described earlier, pragmatism underpins this thesis and the overarching MRC framework. Data was collected using a combination of techniques, including both qualitative and quantitative methods. Following this, data analysis was also undertaken using a combination of techniques.

3.9.1 Framework analysis

The framework approach was developed by the National Centre for Social Research in the United Kingdom and is a more deductive form of analysis (Spencer et al 2014). The framework approach (Ritchie & Spencer 1994) is especially suited to applied research which has clear objectives at the outset. This method of analysis is particularly appropriate for this research, as it was developed as an expedited pragmatic approach for an applied research study. Framework analysis is based on the original accounts obtained from the data collection; it does, however, start deductively from the original aims set at the commencement of the study (Pope et al 2006). It is a systematic, transparent process which can be assessed and followed by people outside of the research process and could therefore, arguably be replicated (Pope et al 2006). Prior to commencing data analysis using

a framework approach, it is important to identify any ‘a priori’ or pre-conceived themes, as these may influence the analysis and findings.

Framework analysis involves a five stage process of familiarisation, identification of a thematic framework, indexing, charting and finally, mapping and interpretation (Spencer et al 2014). Familiarisation involves the researcher immersing themselves in the raw data and reading interview or focus groups transcripts several times. This enables recurrent themes to be identified. A thematic framework is then created, where the main themes and sub-themes are sorted into a detailed framework. This is performed using any a priori themes that had been previously identified and acknowledged, and the original aims and objectives of the research. The framework produced provides a detailed index from which the data can be labelled. The thematic framework is then applied to the data and the data is indexed according to codes used in the framework. Once the data has been indexed, or labelled with the codes from the framework, it is then charted. Charting involves rearranging the data according to the part of the framework that they relate to. This creates a series of charts, each one linked to an original theme that was identified from the data during the familiarisation process. Data is extracted from the original text and synthesised during the process of charting, preventing large amounts of verbatim text being inserted into the charts. The final stage is mapping and interpretation: the charts are used to define concepts, create typologies and identify associations between the themes which are then used to provide explanations for the findings.

The data obtained during the telephone survey in part one of this work was analysed using descriptive statistics. The data from the completed questionnaires was coded numerically and inputted into SPSS, a widely used statistical package in the social sciences. Coding is the process of labelling data in order to classify it to allow it to be analysed (Cluett & Bluff 2006b). The data was then analysed using descriptive statistics, to analyse frequencies within the data. Document analysis was carried out on the guidelines that were received as part of the survey. Document analysis is a systematic procedure for reviewing and evaluating documents, in order for it to be interpreted and meaning generated (Bowen 2009). Similar to other qualitative analysis methods, document analysis involves the examination of the data and the interpretation of it, in order to draw meaning and understanding (Rapley 2007). Bowen (2009) suggests that documents may be used in research for a number reasons. They potentially have a number of uses including a method of tracking change and development, verification of findings by analysing several drafts of the same document, as a means of triangulation, and to provide data on the context within which the participants of the research work or live. He also suggests that documents may be

used in order to develop questions that need to be addressed as part of the research and they can also provide supplementary data and add to the knowledge base. It was for these latter two reasons that the clinical guidelines were collected and document analysis performed. This process is described in greater detail in Chapter 4. In this research, the documents provided supplementary data about current practice in relation to the management of obese pregnant women and were used, along with the data obtained during the telephone survey, to inform the next stage of the work and generate some of the focus group and interview questions for the health professionals.