In general, data analysis is about interpreting data. The definition of the research‟s analytic strategy determines the limits of data collection and dissemination of results (Amaratunga et al. 2002). While the general procedure in data analysis for qualitative and quantitative research methods is the same, procedures for preparing the data for analysis, exploring the data, analysing the data, representing the data analysis and validating the data, differ for qualitative and quantitative research (Creswell & Plano Clark 2006). According to Yin (2003), data analysis consists of examining, categorizing, tabulating, testing or otherwise recombining both qualitative and quantitative evidence to address the initial prepositions of the study.
Miles and Huberman (1994) define qualitative data analysis as consisting of three concurrent flows of activity: data reduction, data display, and conclusion drawing and verification. Data reduction refers to the process of selecting, focusing, simplifying, abstracting, and transforming the raw data that appear in written up field notes or transcriptions and continues throughout the life of any qualitatively oriented project. The second activity, data display, is an organised assembly of information that permits conclusion drawing. Data displays, including many types of matrices, graphs, networks, and charts are all designed to assemble organised information in an immediately accessible, compact form, so that the analyst can see what is happening and either draw justified conclusions or move on to the next step the display suggests may be useful. The last component, conclusion drawing/ verification, is about deciding what things mean, noting regularities, patterns, possible configurations ,causal flows and propositions (Miles & Huberman 1984). In summary, data analysis is about a search for pattern in data (Neuman 2007) or how to make sense out of data and passing on that sense to a specified audience through various forms of presentation.
Although final conclusions from the research study may not appear until data collection is over, qualitative data analysis is a continuous iterative process of data reduction, of display, and of conclusion drawing (Miles & Huberman 1984). Data analysis which begins at the start of data collection lets the field worker cycle back and forth between thinking about existing data and generating strategies for collecting new, often better quality data (Miles & Huberman 1994, Easterby-Smith et al. 2002).
However, before the beginning of data collection, the researcher has to have a general analytic strategy in place(Yin 1984). According to Yin (1984), there are three dominant modes of analysis. Pattern matching which compares an empirically based pattern with one or more alternative predictions, explanation-building whose goal is to analyse case study data by building an explanation of how or why something happened, and time series analysis whose objective is to examine some relevant how and why questions about the relationship of events over time. Since the study was exploratory, the author begun with a loose research framework indicating that the maintenance re-sourcing decision management is affected by factors that fall under three categories. The general analytic strategy employed for this study was the explanation building analytic strategy (Yin 2003). This strategy involves making an initial proposition about a phenomenon. This proposition is revised based on the data collected during the case study. New data is compared with the revised proposition and further revision undertaken based on the results of the comparison. According to Yin (2003), the main technique in explanation building is to ensure that rival explanations are entertained and to show that these rival explanations cannot be supported given the evidence from the case study events.
Data from a case study employing mainly qualitative techniques can take on a number of forms, field notes, interview transcripts, documents and other graphic representations (Coffey & Atkinson 1996). In this research the majority of the data was in form of texts developed from interviews and documents. Interpretation of texts may pursue two different goals. One is to reveal and uncover statements or to put them in their context in the text that normally leads to an augmentation of the textual material. That is, for short passages in the original text, page-long interpretations are sometimes written. The other aims at reducing the original text by paraphrasing, summarising, or categorising. These two strategies are applied either alternatively or successively (Flick 1998). The researcher used both these strategies.
In the first phase of data analysis, field notes from each contact were converted into intelligible write ups. These were further processed to fill contact summary forms using the example of possible „contact summary form‟ contents given by Miles and Huberman (1984). Contact summary forms included, people involved, the events and situations, main themes or issues from and with the contact, the main question centred on by the contact, and new insights provided by the contact. Similarly, relevant documents such as contracts, request for proposal, and performance evaluation reports were examined and significant information from them presented on document summary forms. A copy of a contact and document summary form is presented in Appendix, F and G respectively.
A combination of data analysis techniques was used. Most of the early data analysis structure was based on grounded analysis (Esaterby-Smith et al. 2002) an approach that uses some techniques from the grounded theory approach (Corbin & Straus 1990). The stages proposed by Easterby- Smith et al. (2002) for the grounded analysis methods were used to guide the process.
Familiarisation and reflection
The first stage of this approach is familiarising oneself with the data. At each stage of the case study, the author familiarised herself with the data that was collected through interviews and selected a set of issues that seemed to be important for understanding what was going on during the maintenance re-sourcing process. Some of the points of concern during the familiarisation and reflection stage were.
· What are the central activities that take place during the re-sourcing process? · What was each activity aimed at achieving?
· What are the facts provided by the informants about maintenance outsourcing, the maintenance re-sourcing decision process, factors that lead to a successful re-sourcing decision outcome, factors that lead to a poor re-sourcing decision outcome ?
· Do all informants provide the same facts regarding these issues?
· If all informants do not agree on the facts, what is different and what is the source of that difference?
· What are the differences between the maintenance re-sourcing decisions taken in the time frame considered? If the outcomes are different, what brought about that difference?
· What are the main points of concern for each activity? What do the decision makers ensure happens and why? What do they try to avoid and why?
· Are these concerns similar to those raised in the existing body of knowledge in reference to the preceding stages of the outsourcing cycle.
· If not, what is different?
Each time the author went through a set of write up field notes from a given interviewee, similar questions were raised and answers to them sought.
Conceptualisation and cataloguing concepts
This is the stage at which the researcher begins to identify concepts which appear to be important in understanding what is going on in the data (Easterby-Smith et al. 2002). By focusing on the first objective which was concerned with determining the factors that influence the management of maintenance re-sourcing decisions, the author grouped several similar issues raised in the interviews under organising categories. For example when the interviewees talked about issues
like measurement, key performance indicators, service level agreements, performance reports, and satisfaction in response to an interview request such as
“Could you please describe how you manage the maintenance sourcing process when an outsourcing arrangement with a particular supplier is about to end”
the author thought of a code to represent a category encompassing all these issues. “Performance evaluation” appeared to be a suitable code for this category. Several codes were then developed to represent categories that would put together issues addressing a particular activity of the re- sourcing decision process, the resources required for the process, an experience or concern. In this step, descriptive codes (Miles & Huberman, 1994) were used to name and classify data in the field notes. For example, some of the codes for categories addressing activities of the re-sourcing decision included maintenance re-sourcing process, performance evaluation, and capability assessment. Codes representing categories addressing issues of concern when designing decision criteria were organisational characteristics, characteristics of outsourced activity, the external environment, and desired goals.
After grouping similar issues under different categories, the field notes were now analysed to identify themes under each category. Using thematic analysis (Altride-Stirling 2001), each category and issues they represented were used to identify themes within the field write ups. In an iterative process, the categories were also used to identify other issues within the field write ups that could also belong to these categories. According to Strauss and Corbin (1990), coding represents the operations by which data are broken down, conceptualised and put back together in new ways(Corbin & Straus 1990). Some codes come more or less directly from the interviewee‟s words while others are a summary of what the interviewee seems to be referring to or describing at a particular point in text or reflect more directly the researchers conceptual interests(Coffey & Atkinson 1996). While some of the interviewees‟ words were used to code the data, most of the codes were summaries of what the researcher perceived the interviewee to be referring to at a particular point during the discussion and the researcher‟s conceptual interests. This approach was used because there was only one recorded face to face interview and one recorded telephone interview. According to Kelliher (2005), in situations where the researcher is unable to record, taking note of the meaning is more important than the exact words. Therefore, although the interviewer tried as much as possible to take note of the interviewee‟s words, much of the effort was directed towards understanding and recording the meaning of what was said. Consequently, most of the field notes generated already had the researcher‟s interpretive aspect in them. In this case, coding based on interviewees exact words was not always possible. Appendix H is a summary of the initial phases of the data analysis process including: identification of a category, the issues it represents and the themes relating two or more issues within the category.
Re-coding and linking
This is the stage at which the concepts developed are again checked against the original data to establish whether what the researchers knows about the concept is what the respondents actually said and whether different respondents had the same interpretation for the same concept. Where differences exist, the coding is revised. Linking has to do with identifying the patterns that exists between concepts. The process involves linking data with more general models by moving between evidence and literature in an iterative manner. The themes that had been identified were then later reorganised into broader categories that represented. In view of the themes that had been identified, the author then addressed two questions. Can these categories and the themes they represent be meaningfully placed under the categories proposed before the data was collected? The progression of the contents of appendix H is discussed in introductory part of chapter five where the findings related to the first objective are presented.