Composición de la microbiota en cerdos adultos

As illustrated in Figure 4.3, Saunders et al. (2007) state that, because of its nature, there is no standardised approach to the analysis of qualitative data. Bryman notes:

P a g e | 107 “Clear-cut rules about how qualitative data analysis should be carried out have not

been developed”

Bryman (2004, p398) Yin (2003) mentions that the overall goal in data analysis is to treat the evidence fairly, produce compelling, analytic conclusions and rule out alternative interpretations.

It has been identified above that reliance on qualitative data will be required for this particular research; as qualitative data is vast and differences are great, its analysis also differs.

Although there is a lack of a standardised approach, the approaches do possess some common features; Miles & Huberman (1994, as cited by Robson, 2007) list them as a ‘fairly classic set of analytical moves’:

 Giving codes to the initial set of materials obtained from observation, interviews, documentary analyses.

 Adding comments, reflections or keeping memos.

 Going through the materials to identify similar phrases, patterns, themes, relationships, sequences, differences between sub–groups, etc.

 Gradually elaborating a small set of generalisations that cover the consistencies one discerns in the data.

 Linking these generalisations to a formalised body of knowledge in the form of constructs or theories.

Figure 4.3: Dimensions of Qualitative Analyses (Saunders et al., 2007)

Formulate relationships Deduction

P a g e | 108 Another attempt to identify the main elements of qualitative data analysis was by Lindolf (1995, as cited by Collis & Hussey, 2003) who mentions four interrelated domains:

1. Process - where the analysis of the data takes place continuously throughout the study.

2. Reduction in data - sorting, categorising and interrelating data by means of coding or placing data in charts or matrices.

3. Explaining - understanding the coherence of meaning and action in the case under investigation.

4. Theory - is the context in which the analysis of qualitative data offers explanations.

Robson (2007) also proposes various typologies linked to the methods of analysis and these are:

 Quasi-statistical methods.  Template approach.  Editing approach.  Immersion approach.

The differences in these approaches can be seen in Table 4.8.

Different Approaches to Qualitative Analyses

Quasi–statistical approach

Use word or phrase frequencies and inter-correlations as key methods of determining the relative importance of terms and concepts.

Typified by content analysis.

Template approach Key codes are determined either on an a priori basis (i.e., derived from theory or research questions) or from initial read of the data. These codes then serve as a template or bins for data analysis, the template in this case could be changed as analysis continues.

Typified by matrix analysis, where descriptive summaries of the text segments are supplemented by matrices, network maps, flow charts and diagrams.

Editing Approach More interpretive and flexible than the above. No or very few apriori codes used.

P a g e | 109

Codes are based on the researcher’s interpretation of the meanings or patterns in the texts.

Typified by grounded theory approaches. Immersion

Approaches

Least structured and most interpretive, emphasising researcher insight, intuition and creativity.

Methods are fluid and not systemised.

Close to literary/artistic interpretation and connoisseurship (i.e., calling for expert knowledge and targeted at a similarly skilled audience).

Difficult to reconcile with the scientific approach.

Table 4.8: Different approaches to qualitative analyses (Robson, 2007)

The value of the analysis of qualitative data depends on the quality of the researcher’s interpretation (Collis & Hussey, 2003). Collis & Hussey (2003) mention various criteria that can be used to evaluate a phenomenological study, which could be used to assess the quality of an analysis. Lincoln & Guba (1985, as cited by Collis & Hussey, 2003) suggest that four criteria can be used:

 Credibility: this demonstrates that the research was conducted in such a manner that the subject of the enquiry was correctly identified and described. Credibility can be improved by the researcher’s immersion in the study for a prolonged period of time, by persistent observation of the subject to obtain deep understanding.

 Transferability: this is concerned whether the findings can be generalised to another situation.

 Dependability: illustrates that the research process is systematic, rigorous and well-documented.

 Conformability: if the study has described the research process fully, allowing assessment on whether the findings flow from the data.

According to Yin (2003), there are five analytic techniques used for case study analysis:

1. Pattern Matching: pattern matching logic is to compare an empirically based pattern with a predicated one. If the case matches the predicted patterns then the case supports the theory in the same way as successful experiments

P a g e | 110 support a theory; if the pattern coincides, the results can help a case study to strengthen its internal validity.

2. Explanation Building: the goal of this technique is to analyse the case study data by building explanations about the case. Yin (2003) suggests that, in explanation building processes, the findings are compared to any statement or proposition created.

3. Time-Series: Yin (2003) argues that if the events have been traced in detail and with precision over time, the time-series analysis technique may be possible.

4. Logic Model: The logic model deliberately stipulates a chain of events over an extended period of time. The events are phased in with a repeated cause- effect-cause-effect pattern, whereby a dependent variable (event) at an earlier phase becomes the independent variable for the next phase. This process can help define the sequence of programmatic actions will accomplish the goals (Yin, 2009).

5. Cross-Case Synthesis: cross-case synthesis is a technique especially relevant to a research consisting of at least two cases. This technique treats each individual case study as a separate case (Yin, 2009).