The study’s data analysis was supported by a computer assisted, qualitative approach software programme, NVivoTM version 10 (QSR International, 2014). The information
was audio recorded at the interviews and focus group meetings, and was transcribed verbatim and uploaded on NVivo. These transcripts were read several times to help the researcher immerse himself into the data. Conventional content analysis of the data was initially conducted (see chapter 5 and 6) before applying the framework to the analysis, as this would reduce the limitation of omitting vital detail that might be in
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the data as a result of using the construct in the framework. Categories were
developed from the constructs of the theory of care seeking behaviour. Information in the transcripts was copied and pasted into the categories that best described them. The information under the categories was then read carefully to examine if they fit appropriately in the category. In the case, where they were not an apt fit, they were transferred to the suitable category. Furthermore, subcategories were also developed under the categories for better classification. Also, the categories that did not fit within the constructs of the theory of care seeking behaviour were identified and later analysed to determine if they fit as a subcategory of an existing category or if they represented a new category altogether.
Qualitative research data as opposed to quantitative data cannot be analysed using computer software and it is believed that the positivist use this approach to ensure that their result is reproducible (Rodik & Primorac, 2015). Most of the analysis software offer researchers the opportunity to use an analysis tool (known as auto-
analysis), to help put together all the participants’ responses to a particular question. However, the qualitative researchers usually aim to understand a phenomenon in the view of the participants reactions and vital information might be lost if a computer software is used to analyse the data. Therefore, Lewins (2014) argued that the
computer assisted qualitative approach should not be used as an analysis tool but as a software for handling qualitative data. The software will offer the researcher an increased access to data, as compared to the traditional paper-and-pencil format. Furthermore, data analysis carried out with this approach appears to be more organised, and can be easily explored using text search tools, text annotations and marking, and data organisation. However, the main limitation of this approach is that it
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requires training to use the software, as it might be difficult to utilise it without proper introduction (Cope, 2014). Therefore, because of the suitable characteristics of this approach, the researcher of this study attended several training sections on using the software for data handling.
According to Lewins (2014) the most relevant software for handling qualitative data are ATLAS.ti5, MAXqda2 and NVivo7, due to their significant development in software functionality. Moreover, Roberts et al. (2013) provided evidence that the NVivo7 was relatively easier to learn by the participants. Hence, it is this study’s choice of software
for data handling.
A data analysis method that allows in-depth description of women’s views, with
minimal subjective or interpretative influence of the researcher, is the most appropriate data analysis method for descriptive qualitative studies- figure 4.1. (Sandelowski, 2010). The data analysis method that best fits this description is the qualitative content analysis (Elo et al., 2014). This involves the researcher reading, and rereading the data several times to become familiar with the information in it, and identify categories from the data. There are various approaches for qualitative content analysis - conventional, directed and summative approach. The appropriateness of an approach to a study is determined by the research aim and the availability of evidence on the topic (Hsieh & Shannon, 2005). Hence, the researcher’s choice is the directed
content approach to guide the data analysis process, as it fits well with the study’s aim.
Unlike the other two approaches that derive their coding categories from the text data, the directed content analysis uses the constructs of a theory or the findings of relevant research on the same topic, to guide the initial codes (Hsieh & Shannon, 2005). Thus, the constructs of the theory of care seeking behaviour in figure 4.2. would
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be used as the primary categories and other categories generated from the data would be classed as subcategories. The participants’ views would then be discussed in greater
depth in subcategories, within each category (Forman & Damschroder, 2008).
An inherent limitation to the use of the directed content analysis method is that the researcher approaches the data with strong bias informed by the knowledge gained from the existing literature (Hsieh & Shannon, 2005). Therefore, information that supports the theory is more likely to be seen in the data. However, to achieve an unbiased result, a proper audit trail must be provided with the study (Lincoln, 1985). In addition, to reduce the effect of this limitation on this study’s analysis an initial
conventional content analysis was conducted before applying the constructs of the theory of care seeking behaviour to guide the analysis. The conventional content analysis allows categories and themes to emerge from the data; thus, avoid the use of preconceived categories (Hsieh & Shannon, 2005).
‘Qualitative descriptive studies may begin with a theory of the target phenomenon or a framework for collecting or analysing data, but that does not mean a commitment to stay with this theory or framework (Sandelowski, 2010)’.
Therefore, the use of the constructs in the theory of care seeking to guide the study’s
data analysis does not mean that it cannot be abandoned if the nature of the
information in the data collected, appears to be different. Furthermore, the construct in this theory can be reduced or additional constructs can be added to the theory
depending on the information provided by this study’s participants regarding their
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The methods to this study’s data analysis consists of five steps (Hsieh & Shannon, 2005). These steps are explained in the following section.
Step 1- Data collection was with open-ended questions, followed by targeted
questions developed from the predetermined categories of the theory of care seeking behaviour (Hsieh & Shannon, 2005). The interviews and focus groups transcripts were then uploaded on NVIVO for analysis (Roberts et al., 2013).
Step 2- Immerse self in the data by reading the transcripts several times. This involves reading the transcripts several times. As the data being analysed is large there is the tendency for the researcher to not capture the pertinent issues in the transcripts; thus, the use of the techniques reported by Strauss and Corbin (1990) might reduce the effect of this bias (explained further in step 3).
Step 3- Develop themes and code parts of the data that explains these themes (Seers, 2012). This study used the techniques suggested by Ryan and Bernard (2003) and Strauss and Corbin (1990), as shown in table 4.3. below, in other to identify relevant themes in the data. This may might make the process of developing themes systematic and thorough; therefore, improving the credibility of the study (Ryan & Bernard, 2003).
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Table 4. 3. Techniques for identifying themes.
S/N Techniques Description
1. Repetitions Concepts that appear frequently in the transcripts, are more likely to be a theme (Lincoln, 1985).
2. Indigenous typologies, metaphors and analogies
Local terms that might seem unfamiliar or are used in the text in unfamiliar ways (Patton, 2002).
3. Similarities and differences
Constant comparison of concepts in the data with the themes that had been coded in the data to examine similarities and differences.
4. Transitions The start of new paragraphs might indicate a change in topic. 5. Linguistic
connectors
Careful examination of words/phrases such as; ‘because’,
‘since’ and ‘as a result’ might provide explanations to concepts in the text.
Step 4- Group similar themes into categories. This process is referred to as hierarchical or tree coding, as the branches of the categories or themes might be based on types, examples, or context (Taylor & Gibbs, 2010). The themes and categories that emerged from the data in this study are explored in depth in chapters five and six.
Step 5- Examine the association between the categories and themes generated from the data and the predetermined categories from the theory (see table in appendix H). The table shows the association between the interview and focus group data and the construct of the theory of care seeking behaviour (Neergaard et al., 2009). However,
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themes and categories that could not be associated with the predetermined categories would be given a new category.