Data Analysis

Qualitative data analysis emphasises understanding in depth the meaning of the collected data, focusing on the context (Schutt, 2012) The process of analysing the collected data is significant as it draws the answers from the textual data collected into something meaningful (Saunders et al., 2015). In qualitative research, data analysis needs to be executed in a non- routine, original, iterative, non-linear and complete manner (Suter, 2011; Petty et al., 2012b). Without the analysis, qualitative data would be just a mass of texts collected from the data collection process.

Although there are numerous methods of data analysis, there is no specific method for case study (Petty et al., 2012b; Yin, 2012). Case study allows the flexibility to use any method. For this research, content analysis is employed to uncover patterns, identify themes and categories in order to understand the research subject.

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4.8.6.1 Content Analysis

Content analysis is widely used in qualitative research. It is a tool or method to extract significant desired raw information (implicit or explicit) from texts or images and organise it into systematic concepts before making valid inferences and interpretation (Krippendorff, 1989, 2004; Smith, 2000; Kulatunga et al., 2007; Colorado State University, 2014). Content analysis is also capable of quantifying qualitative data (Kulatunga et al., 2007; Vaismoradi et al., 2013). The advantage in using content analysis of data is its systematic ability to deal with raw and overwhelming amounts of data (Joffe & Yardley, 2004).

Kulatunga et al. (2007) discussed four approaches to content analysis. The first is word count, in which the frequency of identified words is counted, with the assumption the most frequent words used indicates the importance of these words. The second approach is conceptual content analysis, in which text or sets of text are examined for the presence and occurrence of identified concept and/or themes (Colorado State University, 2014). The concepts or themes can be predetermined from the literature review or may emerge from the data itself. The third approach is relational analysis, which analyses the relationship between the concepts inside the text (ibid). The fourth approach is referential content analysis, in which the text is examined for its underlying meaning and interpreted based on the judgement of the researcher.

Content analysis was selected for this research as it offers the possibility to examine the respondents’ responses through multiple approaches in order to find statements which are significant to the research. As this study aims to explore UKAS, the published documents related to its practice and the experience of the respondents were investigated. Considering the irrelevant and limited functionality of word count and relational analysis here, this research utilises conceptual content analysis to provide insight into UKAS practice. Using conceptual content analysis allows the researcher to interpret the text and identify the presence of explicit information that is relevant and essential to build up the case. Dealing with a large amount of text, conceptual content analysis is appropriate since it is a systematic approach to limit the subjectivity in the interview transcriptions.

4.8.6.2 Coding of Data

During the analysis of the pilot study, the researcher experimented with analysing the transcript manually and by using the Nvivo 10 software, deciding on the latter since it proved

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to be an appropriate tool in managing the interview transcription and aiding the execution of the content analysis. The amount of categories/nodes and coding created during the analysis process is enormous, hence the use of specialised software was appropriate. Nvivo 10 has no doubt assisted the coding process to be systematic and more precise. Despite limitations in the graphic presentation produced by the software, the researcher accepted this, with the intention of integrating it with other available software.

Coding is the process whereby the data collected is interpreted and defined by the researcher, an important step (Robson & McCartan, 2016). The analysis process began with familiarisation with the raw data. The audio recordings of the interviews were listened to repeatedly, until the researcher was accustomed with them. Documents were read and re-read to understand the context. The next step was to categorise the data through the process of coding, that is organising and sorting the raw data (Kohlbacher, 2006)

The categories used in the analysis are a combination of pre-determined and open categories. Pre-determined categories were identified during the familiarisation stage through the documents referring to UKAS, while open categories emerged from analysing the data itself. Both were created as nodes in Nvivo 10. These nodes were important in classifying the data into meaningful categories. Through the process of coding the text into relevant categories, sub-categories emerged as more refined categories were identified; these are sub-nodes in Nvivo 10.

The process of coding the raw data into nodes and sub-nodes depends on the interpretation of the researcher. The codes were identified for the potential and relevance of the raw data to existing or new categories. In Nvivo 10, the process involves the researcher examining each interview transcript for relevant text and patterns which fit any category, then assigning it under respective nodes and sub-nodes.

These processes were done repeatedly; similar relevant texts from other respondents was examined, coded and collated under existing or new categories until the subject crystallised to address the research objectives. The analysed data was then presented and arranged in a consistent order, as illustrated in Figure 4.9, for ease of understanding.

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Figure 4.9 – Example of Data Presentation Using Nvivo 10

The result of the analysis corresponds well to the purpose of examining PPP implementation in the Malaysian context through the experience of the actors involved. The results were then used to develop the frameworks aimed to enhance the procurement process for infrastructure delivery in Malaysian PPP by incorporating competition.