P ARTE S EGUNDA
LA DEBILIDAD DEMOGRÁFICA: UNA CONSTANTE SECULAR
The nature and complexity of qualitative data determines substantially how data are collected and analysed. Miles and Huberman (1984; 1994, in Ghauri & Grǿnhaug, 2005) differentiate between three analytical elements:
1. Data reduction (selecting, focussing, simplifying, abstracting and transforming data from field notes and other forms of data collection);
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2. Data display (Compressing, reduction organising and assembling of information); 3. Analytical activities
(Categorisation, abstraction, comparison,
dimensionalisation, integration, iteration and refutation)
The data analysis uses a number of these analytical elements to compose
meaningful insights. Chapter Five (Analysis and discussion) starts the analysis and discussion for each objective with a data synthesising map, showing how data are synthesised.
3.8.2.1.Consultant interviews
Interviews focussed on elementary data in relation to consultants’ knowledge and experience with Lean in the pipfruit and horticultural industries. Although interviews were semi-
structured, the iterative nature of interviews meant that structured questions were followed by probing questions based on earlier answers, facilitating simultaneous data collection and analysis (DiCicco-Bloom and Crabtree, 2006). Coding of resulting data was not required to identify themes as the themes were determined by the questions. Most data could be quantified and as such represented in graphs or tables.
3.8.2.2.Reflective practitioner review
The reflective review became essential because existing literature would not complete the understanding of the industry while some of the review information would contribute to the second, third and fourth research objectives. The reflective practitioner review is analysed in combination with the literature review, consultant interviews and stakeholder data. It is not analysed as a stand-alone set of data.
Da ta Da ta col l ection Concl us i ons : dra wi ng/verifying Da ta display
FIGURE 3-7: COMPONENTS OF DATA ANALYSIS: INTERACTIVE MODEL (RE-CREATED FROM MILES AND HUBERMAN, 1984).
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3.8.2.3.Action research
Analysing action research is different but paradoxically similar to analysing case studies. The purpose of action research is not only to understand, learn and gain insight, but also to effect change. Analysis is therefore a perpetually present activity during the inquiry in order to continue achieving improvement (Cardno, 2003). At the end of the inquiry however, final reflection on the action research project can be captured in a report very similar to a case study report. With action research, the process is steered more by the participants than by the researcher, but the researcher does have bearing on what eventuates. Where action research has a focus on learning during the process (Kolb, 1984), more traditional views claim that learning takes place as a result of passing information or research findings. In the curren t inquiry, analysis is proposed to follow the iterative cycle but the analysis also places the action research in a case study jacket with case study analysis instruments tested.
3.8.2.4.Case studies
Gaining understanding and insight was the key purpose of qualitative data collection to assist with constructing explanations and/or theory development. The case study protocol
prescribes a predetermined focus and the protocol key words can be used for analysis, however word counts are not sufficient; the researcher needs to determine if meaningful patterns are emerging (Yin, 2009). Miles and Huberman (1994) offer a set of data
manipulation techniques that assist analysis, looking at data from different perspectives.
Yin (2009) relates pattern matching case information to a priori assumptions, systematic patterns that confirm suppositions. The protocol presented several suppositions that are compared with the empirical pattern, irrespective of the purpose of the study (expl oratory or descriptive). The inclusion of rival explanations is used to confirm or disconfirm patterns within outcomes. Similarly, explanation building can assist with the data analysis. Yin (2009) further discusses time-series analysis, logic models and cross-case synthesis as patterns for analysis. Of these, cross-case synthesis is used to analyse the collected data.
The analysis considers two case study packhouses but includes the case of the action research packhouse as there are many overlaps between case study and action research. The
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3.8.2.5.Coding and categorising data
All records from meetings and interviews during the action research and case studies are analysed using a coding and categorisation technique that includes several steps as follows:
a) Records are coded manually, using keywords that link to Lean, are repetitive or appear significant.
b) Keywords are then assessed on meaning and grouped in a second analysis of the data. c) The keyword/codes are then factored into three groups of categories.
o The first group is looking for all comments and observations that link to the applicability of Lean for the pipfruit industry which is part of the third objective.
o The second group is looking for all comments and observations that link to the implementability8 of Lean for the pipfruit industry which is part of the third
objective.
o The third group categorises all other comments and observations according to repetitiveness or significance.
2.1.4
1. Applicability group. This group uses the ten Tapping et al (2002) categories to help determine if Lean is applicable for pipfruit organisations (objective 3); data are clustered to- and counted within predetermined Lean themes to analyse applicability (Miles & Huberman, 1994). The ten categories were taken from Tapping et al (2002) to offer triangulation options.
2. Implementation group. This group uses ten categories to capture all those keywords that are linked to the implementability of Lean for pipfruit organisations (objective 3); here, data are clustered to, and counted within emerging implementation themes (Miles & Huberman, 1994). Categories were established by generalising all comments made in relation to implementation of Lean into ten categories.
3. Other elements group. Other patterns or themes emerge that inductively form categories (Miles & Huberman, 1994). This group captures key words/codes that
8 The word ‘implementability’ is not commonly found in dictionaries but has been observed in academic
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indicate typical industry issues, researcher typical codes or repetitive but uncategorised keywords.
Frequency of codes are graphed to display the counts of each in order to assess what emerges within pre-determined categories, to assess what emerges outside of those predetermined categories, and to protect against bias (Miles & Huberman, 1994).
3.8.2.6.Synthesising the data
Collected data are synthesised where such synthesis would lead to more purposeful findings. Case study, for example, is using cross-case synthesis and pattern analysis to analyse the collected data and add validity to findings. Consultant interviews and stakeholder survey are synthesised with literature review and reflective practitioner review. Data are synthesised to summarise (integrative synthesis) and to develop concepts and theories (interpretive
synthesis) (Dixon-Woods et al, 2005). The different data collected are assessed to determine where they are complementary or provide other forms of added value to findings.
After completing the literature review in phase one of the research design (figure 3-8), phase two of the research design concerns the data collection structure for qualitative data through field work. In phase three of the research design, the data collected in phase one and phase two are coded where required, analysed, synthesised and discussed to arrive at a meaningful conclusion.
3.8.2.7.Wider horticultural industry
Data collected from other horticultural industries was both quantitative and qualitative. Some numbers were obtained relating to the industries (e.g. FreshFacts, 2010) to place these in context with the subject industry of the inquiry. Several stakeholders within other
horticultural industries were interviewed and summary data analysed for broad similarities. Analysed data were then used for comparative purposes between the pipfruit industry and these other horticultural industries. The comparative analysis has too few cases to permit the proper use of techniques of statistical control (Smelser, 1976; in Ragin, 2007) and comparative
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analysis was based on identified themes. This was then used to assess if the conceptual pipfruit model would be suitable for these other horticultural industries.