3.3.1 Mixed methods research. Mixed methods research can be defined as approaches that combine quantitative and qualitative components (Bergman, 2008). Mixed methods have grown in popularity in recent years, increasingly challenging the binary distinction between qualitative and quantitative research methods (Bergman, 2008). Such methods seek to integrate different types of data to arrive at a more complete understanding, yet there is no agreed consensus on how to do so (Bryman, 2006).
Typically, mixed methods approaches are either concurrent or sequential (Cresswell, Plano Clark, & Garrett, 2008). Concurrent designs include triangulation (one phase in which quantitative and qualitative data are collected and analysed in parallel) and embedded designs (enhancing a study based on one method with the use of a second dataset from the other method). Sequential designs (in which the qualitative and quantitative data collection are implemented in different phases) are explanatory, exploratory or embedded (Cresswell et al., 2008). Some of the
advantages of using mixed methods include: obtaining multiple perspectives to enhance and enrich the meaning of a singular perspective; contextualising
information to develop a more complete understanding; to develop a complementary picture; to compare, validate, or triangulate results; and to provide illustrations of context (Plano Clark, 2010).
However, mixed methods approaches are not without challenges. These may be due to difficulties with the integration of findings from qualitative and quantitative components of a study (Bryman, 2007). Potentially, issues may arise during data analysis and interpretation, for example, findings may conflict or be contradictory when merging data during a concurrent design (Bryman, 2006). Thus, a strategy of resolving differences needs to be considered, such as gathering more data or revisiting the data (Cresswell et al., 2008).
The reasons for adopting a mixed methods approach in this thesis are the following: a) the relative lack of research in shopping and health requires, in part, an
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58 exploratory approach; b) shopping activity is not covered well in survey data; c) shopping activity is a multidimensional concept, which requires multiple perspectives in order to get a comprehensive view.
3.3.2 Cross phase concurrent design. The approach used in this thesis is best described as a cross phase concurrent design, as illustrated in Figure 3.1. By using this approach the aim is to obtain different but complementary data in order to address the research questions. Hence, using different types of data will allow some cross-validation (within and between findings) as well as to enhance findings from one data source through further exploration with another data source. The strategies used for overcoming the potential methodological issues from a mixed methods approach include reanalysis of original data, using different participants and using preliminary results for further inquiry. It is hoped that by using different data sources and different types of data, a more complete picture of the role of shopping in later life will be attainable.
Figure 3.1. Cross phase concurrent design of data collection and interpretation.
3.3.3 Combining qualitative and quantitative data. The process of combining qualitative and quantitative data sources will be an iterative process. This means that both types of data will be used to contribute to the overall interpretation, as shown in
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59 Figure 3.1. As the primary qualitative data were collected in two phases, the initial phase 1 interpretation informed the phase 2 data collection and analysis. In turn, the phase 2 analyses informed subsequent analysis as well as reinterpretation of the data from phase 1. Each of these data collection phases and data sources will be described below.
3.3.4 Original study design. This PhD was funded through CFAS-Wales. The original plan was to interrogate cross-sectional and longitudinal data from the main CFAS-Wales sample to explore information concerning shopping, nutrition and cognition. A sub-sample of CFAS participants was to be interviewed twice, 18 months apart, to determine their shopping activities and the changes over time. Qualitative and quantitative data were to be synthesized to identify associations and to relate these to health changes in the main CFAS sample.
The original study was designed to collect and analyse qualitative data from a sample in North Wales using the grounded theory approach of Charmaz (1995, 2009), which appealed as a systematic and rigorous method of qualitative data analysis. The classic grounded theory of Glaser and Strauss (1967) has its roots in both mid-century positivism and the pragmatist philosophy of Chicago School
sociology. Glaser and Strauss combined dispassionate empiricism and its emphasis on emerging discoveries with interrogation into the social and subjective meanings within their data (Charmaz, 2009). They proposed a systematic approach to
qualitative analysis in order to construct theoretical explanations of social processes; thus, theory is discovered as emerging from data separate from the researcher. Their rigorous and specific method for grounded theory proposes a process of coding that begins early in the research in order to think systematically about the data in accordance with basic analytic strategies (Glaser & Strauss, 1965). Through a process of constant comparison, analytic distinctions as well as comparisons are made both within and between interviews at each level of analysis. The researcher derives analytic categories directly from the data rather than from preconceived concepts or hypotheses (Charmaz, 1995). Continuous, inductive processes determine further data collection and analysis so that, ultimately, the analytic framework forms a systematic substantive theory (Glaser & Strauss, 1965).
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60 Later, Glaser and Strauss went their separate ways and took somewhat
different approaches to grounded theory. A significant difference in subsequent versions was that for Glaser the researcher was passive and for Strauss the researcher became more of an active participant (Howitt, 2010). Glaser remained consistent with the earlier version so that grounded theory was defined as a method of discovery that emerged from the data and relied on direct empiricism (Charmaz, 2009). Corbin and Strauss (2008), drawing from both Interactionism and
Pragmatism, maintained that objectivity was not possible and that, as researchers bring their own perspectives and knowledge to research, they should have
‘sensitivity’ to the data. A further departure is that they use the term grounded theory in a ‘generic sense’ to denote theoretical constructs derived from qualitative analysis (Corbin & Strauss, 2008).
Charmaz (1995) has argued that grounded theory bridges traditional positivistic methods with interpretative methods and has offered a simplified, constructivist version. She maintains that her version is just one approach and emphasises flexible guidelines over methodological rules so that grounded theory methods are seen as a set of principles and practices (Charmaz, 2009). Furthermore, she suggests that basic grounded theory guidelines (such as coding and sampling) and comparative methods are neutral and can be adopted and adapted by researchers (Charmaz, 2009). It has been argued that this form of constructivist grounded theory furthers itself from the original ideas of Glaser and Strauss while continuing to adhere to the original core principles (Thomas & James, 2006). This includes the notion that ‘theory’ is ‘grounded’ and its implicit assumptions about social reality and how that knowledge can be arrived at, i.e. that the truth is there to be discovered, which is in contrast to Charmaz’s approach that a truth is constructed through interaction (Thomas & James, 2006).
Grounded theory has made an important contribution to qualitative research but it is not without its criticisms and, as the above discussion suggests, each version of grounded theory is built on different epistemological and ontological assumptions. The realities of the current research necessitated a pragmatic approach to data collection and analysis for which a cross phase concurrent design was employed. This mixed methods design required a theoretically informed analytical approach for the quantitative data in order to feed back into further qualitative analysis. This is in
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61 contrast to a more typical mixed methods design in which qualitative analysis informs quantitative analysis (Brannen, 2005). It is not that grounded theory is incompatible with mixed methods per se but that it becomes problematic when different
approaches (methodological and theoretical) are needed, as with the current study’s temporal structuring of the methodological phasing. Thus, it was decided that
thematic analysis would be a better way to integrate the qualitative and quantitative data in the cross phase concurrent design. However, the qualitative analysis draws from some of the pragmatist underpinnings of grounded theory, specifically through building inductive analysis that is grounded in the data.
3.3.5 Rationale. The implementation of this research design was, in part, serendipitous. Delays in availability of the qualitative (phase 2) sample resulted in extended data collection with the phase 1 opportunity sample. However, the initial interpretation of these data shaped subsequent research questions, informed the interview schedule and allowed for purposive sampling at phase 2. The primary qualitative data collection is especially important as the secondary quantitative data sources were not specifically designed to answer the research questions.
Furthermore, the rationale for the approach used in this study is threefold. First, the use of large-scale, nationally representative datasets with measures of physical and cognitive health can provide a more comprehensive account of health status in later life. Second, the findings from qualitative analysis can enhance the quantitative findings and vice versa. Third, the qualitative data both contextualise and illustrate the quantitative findings.
As argued by Brannen (2005), a research strategy should be devised as best suited to a particular purpose rather than being tied to a philosophical position. Hence, a pragmatic approach (and rationale for mixed methods research) is to be less purist in terms of methods and preconceptions. Consequently, the strategy adopted in this research is to use the most appropriate data available to address the research questions and to juxtapose the results of different data sources to generate complementary insights that create a bigger picture (Brannen, 2005).