A sequential explanatory mixed methods design was employed and involved collecting quantitative data followed by a qualitative data collection phase in order to explain and follow up on the quantitative data from the earlier phase of the research in more depth. Sequential designs in which quantitative data is collected first can use statistical methods in order to determine which findings augment in the following phase (Creswell and Plano Clark, 2007). Anchoring the research topic in mixed methods design can help critique the complexity and meaning of patient and health care professional perceptions of care and caring. Following the guidance of Creswell and Plano Clark (2007) mixed method design,
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this research used an explanatory sequential mixed methodology to study the emerging perceptions of caring. The use of a sequential paradigm provides a framework for comparing and analysing differences in caring perceptions. Hence to achieve an ethnographic perspective of the phenomenon, there is a need to reconstitute mixed methodology from the everyday experiences of the individual subjects. It was the desire to understand the everyday lived experiences of the subjects in relation to care and caring which guided the implementation of mixed methodology and as such the process and data collection were implicitly blended in the everyday reality (LeCompte and Schensul, 2010).
Figure 1: The research design
The research design is based on a sequential mixed method research design and made the qualitative method dominant and the quantitative method subsidiary, the design has three stages (Figure 1). Collecting both closed-ended quantitative data and open-ended qualitative data helped to gain in-sight into the research problem. The collection of numerical data allowed for the reporting of caring behaviours whereas the qualitative data elicited rich and meaningful accounts of caring.
In the first quantitative phase of the study and after receiving permission from the authors Wolf et al., (Appendix 4) data was collected using the Caring Behaviours Instrument (CBI) tool from 78 participants in an orthopaedic setting at a district general hospital location in order to test patient and health care perceptions of care and caring; and explain what factors influence perceptions of care and caring within the speciality of orthopaedics in both groups of research subjects. The second and third qualitative phases were conducted using observation and semi-structured interviews in order to gain a more in-depth understanding of the factors that influence perceptions of care and caring. In this
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sequential explanatory follow up the central phenomenon, caring, was tentatively explored with the participants to help build upon and explain initial quantitative results. 4.6. Rationale for selection of thematic narrative analysis
In ethnographic research, data analysis is a continuous process that transcends the pre- fieldwork stage through to the production of the final written report. Narrative analysis in the context of human sciences relates to a category of approaches to diverse types of text which share a common storied form (Reissman, 2004).These texts are said to be ‘narrative’ through sequence and consequence whereby events are selected, organised, connected and evaluated into meaning for a given audience; representing a storied way of knowing and communicating (Hinchman and Hinchman, 1997). Several typologies exist in narrative analysis (Cortazzi, 2001), thematic, structural, interactional and performative analysis. Thematic analysis focuses on the content of the text, what is said rather than how it is said, the ‘told’ rather than the ‘telling’ (Reissman, 2004). Researchers collect numerous stories and inductively from the data produce conceptual groupings. The typical representation is via a typology of narratives arranged by themes illustrated through case studies or vignettes (Reissman, 2004). Examination of the data sought to acquire essentialist information, which looks at the experiences, meanings and realities of participants, in conducting the analysis.
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Table 4.4: Phases of thematic analysis
Phase Description of Process
1. Acquaint yourself with the data:
Where necessary transcribe data, read and re-read data, make a note of initial ideas
2. Generating initial Codes: Systematically coding interesting features across the entire data set, collating relevant code data.
3. Searching for Themes: Collate codes into possible themes and gathering all data relevant to each possible theme. Using criteria of recurrence, repetition and forcefulness in disclosure [Owen, 1984].
4. Reviewing Themes: Check to see if the themes work in relation to the coded extracts [level 1] and the complete data set [level 2] thereby generating a thematic ‘map’ of the analysis 5. Defining and naming
Themes:
Ongoing analysis to refine the specifics of each theme and the overall story it tells, generating clear definition and names for each theme.
6. Producing the report: This stage presents the final opportunity for analysis. Vivid, compelling abstract examples selected, final analysis completed. Relate back analysis to research question and literature. Write report.
Adapted from Braun & Clarke (2006)
The aim was to go beyond merely describing thematic observation and narrative in an attempt to theorize the significance of the patterns and their broader meanings and implications (Braun and Clarke, 2006). Table 4.4 highlights the phases of thematic analysis as suggested by Braun and Clarke 2006.
The inductive approach of thematic analysis of the narrative was selected for Stage Two and Three as it allows research findings to emerge from, frequent, dominant or significant themes inherent in raw data, without these being restrained by structured methodologies (Bryman and Burgess, 1994). In so doing possible reasons and implications for the observed themes can be theorized and provide a more complex and perceptive analysis of the caring phenomenon (Boyatzis 1998; Roulston, 2001).
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Thematic analysis is used as a tool for processing qualitative information that requires a specific ‘code’. This code may be formulated from a list of themes: a complex model of themes, indicators, and qualifications which are all causally related (Boyatzis, 1998, p.4). Themes are primarily patterns that are located in the information, and at the very least serve to describe and arrange the possible observations, but can ultimately facilitate the interpretation of some aspects of the phenomenon being studied. Narrative thematic analysis is seen as a method used in the identification, analysis and reporting of patterns or themes within data, that facilitates the organization and describing of data in rich detail (Boyatzis, 1998).
Thematic analysis is widely used within the research field although there is little consensus about what and how it is applied (Attride-Stirling, 2001; Boyatzis, 1998; Tuckett, 2005). It does not seem to exist in a ‘named’ analysis in the same way as grounded theory or interpretative phenomenological analysis (IPA). Yet it is argued that analysis on the whole is essentially thematic (Meehan, Vermer & Windsor, 2000). Thematic analysis differs from other analytic methods such as IPA, grounded theory or ‘thematic’ discourse analysis. For example, both IPA and grounded theory search for patterns or themes within the data but are theoretically bounded. IPA is linked to a phenomenological epistemology (Smith, Jarman and Osbourne, 1999; Smith and Osbourn, 2008) with experience acknowledged as a key factor (Holloway and Todres, 2003) and is concerned about making sense of an individual’s experience of everyday reality, in such detail, in order to gain a deep understanding of the phenomenon (McLeod, 2011).
Adding to the complex nature of analysis, different versions exist within grounded theory (Charmaz, 2002). Thematic discourse analysis makes reference to a variety of pattern type data analysis ranging from thematic analysis, where socially produced patterns are identified but discursive (lengthy) analysis is not undertaken, to interpretive analysis seen in discourse analysis (Clarke, 2005).
A specific form of discourse analysis, known as thematic decomposition analysis, identifies themes within data but views language as not simply descriptive of real
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phenomena but as constitutive of (what we believe or have come to believe as) reality (Parker, 1990; Weedon, 1996).
All of these methods share a common search for given themes or patterns across data sets. This differs from the likes of narrative analysis where the search for themes is confirmed to a data item, such as an individual interview in a biographical format (Murray, 2007). The theoretical and technological knowledge approach as seen in discourse analysis and grounded theory are not required in thematic analysis. Therefore thematic analysis offers more accessibility especially to inexperienced qualitative researchers (Braun & Clarke, 2006).Thematic analysis can be employed with a variety of theoretical frameworks. Consequentially, its method can lie within (a) the realms of essentialism or realism, reporting on participant experiences, meanings and reality, (b) a constructionist method, examining how events, experiences and realities are the effects of a series of discourses functioning within society (Braun and Clarke, 2006), (c) a contextualist method, here lying between essentialism and constructionism, and consisting of theories of critical realism. This gives credence to the way in which individuals use experience to give meaning to their lives (Willig, 1999). In conclusion thematic analysis can be a method employed to both reflect and unravel ‘reality’, although it’s theoretical position must be made clear at all times (Braun and Clarke, 2006).
In thematic analysis it is important to determine the type of analysis to be undertaken. For this research thesis, which investigates an under researched area, it was important to provide a rich thematic narrative description of the entire data set so that important themes are related to the reader. The identified themes, codes and analysis are accurate reflections of the entire data set content. Although depth and complexity is lost, a rich description overall is maintained (Braun and Clarke, 2006). In summary thematic narrative analysis was selected as an appropriate method for the following reasons:
Thematic analysis is seen as a foundational method for qualitative analysis, acknowledged for its flexibility and ability to produce rich, detailed and complex accounts of data through theoretical freedom (Braun and Clarke, 2006).
It involves searching for themes that emerge as being important to the description of the phenomenon (Daly, Kellehear, Gliksman, 1997).
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Theme identification is achieved through “careful reading and re-reading of the data” which allows for new themes to arise as the data is explored (Rice and Ezzy, 2000, p.258).
It can be viewed as an essentialist or realist method for reporting patient experience (Braun and Clarke, 2006).
It can also be viewed as a constructionist method (i.e. Potter and Hepburn, 2005), examining ways in which events, realities and meanings are effected by a variety of communications within society (doctor/patient interactions, for example). It can be a contextualist method characterized by theories such as critical realism
(e.g.Willig, 1999). This approach focuses on the way in which individuals make meanings of their experiences as well as how the broader social context impinges on those meanings. Therefore thematic analysis can work to both reflect reality and unravel its surface.
It is useful for theorising across a number of cases in order to find common thematic elements with research participants and the stories they tell.
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