Data analysis tools and methods

4.5.1 Overview of analysis procedures

One of main difficulties with qualitative research is that the approach generates a large unstructured data set because it relies on field notes, interview transcripts and documents. In qualitative research the gathering and analysis of data often occurs concurrently. There is a constant interplay of data and analysis, data informing analysis and analysis informing new data gathering. This section discusses the analysis procedures for all the different kinds of data gathered in Phase Two.

The interview transcripts provided a large amount of qualitative data. Thus, choosing strategies and tools for analysis was a crucial task. I adapted the interactive model suggested by Miles and Huberman (1994) to analyse the data. The process of analysing data is presented in Figure 10.

88 Documentary evidence Interview data Transcribing D at a g at h er in g Coding Concepts Comparing Categories

Text, tables, graphs

D at a re d u ct io n D at a d is p la y C on cl u si

on Factors affecting DLE DLE needs Revised DLE model

Suggestions

Figure 10: Data analysis procedures of the research

adapted from the interactive model (Miles & Huberman, 1994)

Figure 10 presents four sequential steps of the data analysis procedure: data gathering; data reduction; data display; and, conclusion. The data gathering (interview data and documentary evidence) was discussed in Sections 4.3.2 and 4.3.3. In the following sections I discuss four main aspects of the data analysis: transcribing the interview data; reducing data (coding and comparing); displaying data; and, drawing conclusions.

4.5.2 Transcribing the interview data

After conducting interviews, all interviews were transcribed for analysing. The outcome of the process was to provide the data in a form that was easier for analysing. There were some challenges that I faced in transcribing: (1) the interviewees did not always speak in nice finite sentences. I needed to reconstruct the sentences for the reader’s easy understanding; (2) the conversation sometimes was not easy to hear, especially when several people were talking at the same time. I had taken notes which I could then

89 compare with the audio versions; (3) the results are presented in English, while the interviews were conducted in Vietnamese (the language translation issue is discussed in Section 4.9). In some instances, I had to compare the transcripts with the notes taken while conducting the interviews in order to verify the meaning of the words spoken by the interviewees. After this step, the transcripts and documents were used for the next step: data reduction.

4.5.3 Data reduction

Data reduction is one of the most important tasks in the data analysis. The outcomes of this step were concepts and categories that were used to identify contextual factors affecting DLE development in Vietnam. The inputs for this step were the interview transcripts and documents from research organisations.

In this study, I used the technique of data reduction based on the tools and strategies suggested by Miles and Huberman (1994): contact summary sheets, pattern coding and writing memos. Gibbs (2002) defined coding as “the process of identifying and recording one or more discrete passages of text or other data items (e.g., parts of a picture) that, in some sense, exemplify the same theoretical or descriptive idea” (p. 57). It is a process to break the rough data into units for analysis, and to categorise the units (Denscombe, 2010). I used pattern coding for analysing data because (1) it helped to reduce the large amount of data, and (2) the analysis of data as it was gathered helped me to sharpen my focus in the later fieldwork.

I employed the same method to code the interview data and the documentary evidence. I used descriptive keywords and pattern coding based on the factors suggested by the initial model as a starting point to identify concepts and categories. Concepts and categories were changed and revised as data analysis progressed. During data analysis I worked intensively with my supervisors who helped me to revise my codes and gave suggestions for exploring more codes or combing concepts. This process is illustrated in Figure 11.

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Figure 11: Coding process

The process can be explained in three steps:

Step 1: Identifying initial codes: In this step I carefully read the full-text transcripts at least two times. I then picked key ideas from the text and labelled them with key words. I created a simple table using Excel (explained below) in which each key word was recorded along with the relevant text from the transcript.

Step 2: Developing concepts: After coding five transcripts, I reviewed the key words and revised and grouped them into suitable concepts, using the suggested factors in the initial model to develop the concepts. I also re-read the full-text to ensure the meaning of ideas/statements before grouping them together under the concepts. I then compared the concepts with those in the initial model, and where necessary I merged new concepts with existing ones. I also verified that no initial factors were inadvertently missed and I identified new concepts that were not present in the initial model.

Step 3: Establishing categories: In this step I compared the concepts, grouping some together in one category while moving others to another more suitable category. I also developed some subcategories for each category. As in the previous step, I compared the categories with those in the initial model to consolidate similar ones, check for missing categories, and identify new ones.

Table 12 gives a sample of coded interview data at the end of the three steps. Column one contains the categories into which the various codes/concepts were grouped. Column two provides three examples of the codes I applied to the raw data: understanding about DLs,

DL competence, and defining DL frameworks. Column three provides the relevant raw

Code 1 Code 2 Code 3 Concept 1 Code 4 Code 5 Concept 2 Category 1 Subcategory1 Subcategory2 Initial model

91 data extracted from the transcripts, and column 4 records the identification (ID) of the interviewees. Column five shows the relevant notes. As can be seen in this table, after comparing the concepts I decided that the code for the raw data from the HM-S2 transcript needed to be moved from the DL conceptualisation category to the DLE content category.

Table 12: Samples of coded interview data

Category Codes/ Concepts

Raw data from interviewees ID Note

DL con ce p tual isation Understanding about DLs

In my opinion, digital libraries do not separate to traditional libraries. DL are a group of digital collections, it archives digitised materials. The digital materials are published online and accessed from out of the libraries.

TX -L1

DL

competence

When teaching DL, we need to indicate the issues to help practitioners understand the importance of cooperation in developing DLs in terms of technologies, equipment, and information sharing and so on.

HM -S2 Move to Category: DLE content Defining DL frameworks

The most important thing here is that we have to define the concept of DL. The concept has to be mentioned in the law of libraries. If we do not have an idea or a concept of DLs, and you do not understand what DLs are, we cannot do anything.

DD -S4

I used MS Excel with the initial codes I developed from the raw data. This programme helped me be flexible in creating, filtering and grouping ideas, concepts, categories and themes. The ideas, views, comments and statements from all participants and documents were recorded verbatim. The quotations were extracted by keywords. The keywords were filtered and grouped in concepts. The concepts then were filtered and grouped in categories. Finally, the categories were used to establish themes which highlight key factors affecting DLE development. The revised themes and categories were used to revise the initial model.

4.5.4 Data display

At this stage, data were organised, compressed, assembled and presented by text, charts, diagrams and graphics. Data display techniques employed in this research included text,

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tables and diagrams. The techniques were used to discuss and interpret the factors which affect the development of DLE in Vietnam.

4.5.5 Drawing conclusions and verifications

This is the interpretive stage in which I attempted to draw meaning from the displayed data. I compared the findings of the study with the information in the literature review and the theoretical framework.

Toward the end of this stage, the contextual model of the study was assessed and revised. The final result of this stage was the development of the final model of factors affecting the development of DLE in Vietnam.