Qualitative Data Analysis: the Grounded Theory Method

The next stage of the research is Qualitative Data Analysis (QDA). The goal is to make sense of the collected interview data. In QDA, the researcher, by reading and rereading empirical materials, tries to identify key themes and draw a picture of the meanings that constitute the reality (Perakyla, 2005). Douglas (2003, p. 53) summarised the process of analysis and expressed that ―concepts has been identified, developed, discounted, and merged in order to produce the component concepts of the emergent theory‖. Seidel (1998) expressed that analysing qualitative data consists of three parts: Noticing, Collecting, and Thinking (Figure 38). He asserted that the process of QDA is not linear but is rather iterative (repeating the cycle), recursive (one part can call you back to a previous part) and holographic (each step contains the entire process).

Figure 38: Qualitative data analysis (Seidel, 1998)

In the context of this research, noticing has two meanings: (a) producing records:

preparing interview transcriptions, notes taken during interview sessions, photos

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119 taken from construction sites and documents provided by participants; and (b) coding: focusing on each record and finding and highlighting interesting and important themes in them. It also includes developing a descriptive naming scheme for themes. Collecting means sorting and organising data. After identifying codes, relevant data were assembled together in a meaningful way. In the thinking stage, codes were examined to figure out relationships and patterns. The aim was discovering similarities, differences and general rules by comparing and contrasting codes. In this research, first, gathered data was broken down into categories, relationships and context. Then, segments were integrated with each other to provide an answer to the research questions. Figure 39 shows the process of data analysis in this research.

Figure 39: Qualitative data analysis steps

There are several strategies that can be utilised for analysing qualitative data. One strategy that has been widely used for QDA is grounded theory. A term developed by Glaser and Strauss (1967), the grounded theory method is ―a set of flexible analytic guidelines that enable researchers to focus their data collection and to build inductive middle-range theories through successive levels of data analysis and conceptual development‖ (Charmaz, 2005, p. 507). In this method, theory emerges from data by making comparisons, development of categories and forming an analysis (Charmaz, 2005). In fact, there is no theory to be tested in the beginning of the research and rather the theory is the result of the research. Hence, the distinction between grounded theory and other methods is that it involves theory development. This research adopted modified grounded theory which permits reviewing literature before starting data collection. Grounded theory is flexible and allows the researcher to take a large number of issues about construction logistics into account and not

120 focus only on one single theory. The issues range from basic logistical tasks to cultural matters and the economic situation of Iran.

There are different approaches to grounded theory which are Glaser and Strauss (1967), Strauss and Corbin (1990), Glaser (1978, 1992) and the constructivist approach (Charmaz, 2005). This research utilised the constructivist approach because it takes ―a reflective stance on modes of knowing and representing studied life‖

(Charmaz, 2005, p. 509). Therefore, this approach is more compatible with the ontological and epistemological positions of the research. It should be explicated that the term 'Grounded Theory' has been used in two ways: (1) grounded theory as a methodology which is a set of rigorous research procedures and (2) grounded theory as a method of qualitative data analysis which leads to the creation of conceptual categories. This research has adopted the second approach and uses grounded theory as a method of data analysis. In general, grounded theory has five steps (Figure 40).

Figure 40: The process of grounded theory used in this research

The rough definition of the topic (literature review), interviews, and data saturation point were discussed above. An explanation about coding and how the hypotheses will be generated is required. The main step in grounded theory analysis is coding.

Large volumes of data attained from interviews appear to be unrelated, discrete and confusing. Coding enables the researcher to organise and put the data in an order.

Goulding (2007) defined coding as ―the conceptualisation of data by the constant comparison of incident with incident, and incident with concept, in order to develop categories and their properties‖. Codes are devices to label, separate, compile, summarise and organise data (Charmaz, 2005). There are three types of coding, as explained by Strauss and Corbin (1990):

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121 1. Open coding: ―the process of breaking down, examining, comparing,

conceptualising and categorising data‖ (Strauss & Corbin, 1990, p. 61).

2. Axial coding: ―a set of procedures whereby data are put back together in new ways after open coding, by making connections between categories‖ (Strauss

& Corbin, 1990, p. 96). This involves linking categories to their subcategories.

3. Selective coding: ―the procedure of selecting the core category, systematically relating it to other categories, validating those relationships, and filling in categories that need further refinement and development‖

(Strauss & Corbin, 1990, p. 116).

The process of coding in this research started by detailed line-by-line reading of the interview transcripts during which every line is searched for keywords that give insight into the study (e.g. site layout). The result of this stage was generation of initial categories. In other words, in this step, responses were classified under relevant categories. The process of categorising data was tentative and, therefore, tended to be in a constant state of potential revision and fluidity. The categories emerged from the data by progressing through the transcript of interviews. Under these categories, new sub-categories were developed with the same logic and process explained before. In some cases, not all the data fitted neatly into one precise category. Thus, for interpretation, it was required to cut across different categories.

The next stage is establishing relationships between categories and grouping them based on different characteristics, such as conditions, context and outcome. Also, to develop a hierarchy of codes, some categories were integrated together and constituted a new category.

The use of Computer Assisted Qualitative Data Analysis Software (CAQDAS) has increased among CM researchers owing to their ability to store, organise and manage qualitative data more efficiently (King, 2008). This research used the NVivo package for analysing interview transcriptions. The use of NVivo helped the researcher to work with large volumes of data gathered in an interactive and systematic way and also increased the speed of QDA considerably. To start the analysis, all transcriptions were transferred to NVivo to be codified. Two most useful NVivo features were digital coding (nodes) and use of memos. Digital coding means tagging a segment of text to allow for later retrieval, while memos are reflective comments on some aspect

122 of the data to be used for future interpretation (King, 2008). In fact, coding makes important components visible and memos add the relationships which link the codes to each other. Another advantage of using NVivo is that it allows the researcher to have a better view of the whole. In traditional manual QDA, transcripts were cut up and filed according to the codes and this may diminish the whole. It should be explained that the software role is only limited to organising data and not analysing them. Getting back to the Seidel (1998) model (Figure 38), the software helped only in noticing and collecting stages, and the thinking stage was done by the researcher himself.

After analysis, data should be interpreted and integrated into a coherent report. The report structure is formed by categories, subcategories and their relationships.

Wherever it was suitable in the report, the participants' direct quotes are cited anonymously to make the interpretation more meaningful. For confidential reasons, the name and organisation of the interviewees are not mentioned in the final text and instead they are named with specific labels. Labels are constituted from a letter and a following number, e.g. C08. The letter shows the role of each interviewee in the industry: contractor (C) or consultant (N). The number after each letter explains the order of interviewees in each role.

Using diagrams and models is an effective way of displaying data (Hunter & Kelly, 2008). They can clearly illustrate categories, subcategories and their relations using boxes and arrows. Using models helps to have an image of the whole process of the analysis and prevents loss of information when the researcher is dealing with large quantities of data. In this study, the results are visualised in the form of a model which will be described in Chapter eleven. To have a general view of the primary result of QDA, Figure 41 is provided. It illustrates the categories and subcategories of the construction logistics system. This diagram will be used in the quantitative inquiry to design the questionnaire.

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Figure 41: Primary result of QDA (Construction logistics categories & subcategories)