Data Analysis

Figure 15 Data analysis procedure

The analysis of data is integral to every stage of the research process. Its importance becomes greater when using a mixed methods approach due to the different types of data collection methods used. There is a strong link between the data analysis procedure and the research questions. The issue of mixed methods data analysis is addressed by Onwuegbuzie and Teddlie (2003):

The use of quantitative and qualitative analytical techniques, either concurrently or sequentially, at some stage beginning with the data collection process, from which interpretations are made in either a parallel, an integrated, or an iterative manner

Integration of results and drawing of conclusions

9 major themes

Stage 1

10 questions related to the training programme

+

38 questionnaire items relevant to heads’ needs with purposive sample of

(48)

Inductive thematic data analysis

Semi-structured interviews with sample of both secondary

and intermediate school (18) Stage 2

The sequential explanatory strategy which was adopted in this study dictated the procedure of data collection and the order of analysis (see Figure 11 on p. 65). This means that the quantitative data collected through the questionnaire was analysed first, followed by an analysis of the qualitative data collected by semi-structured interview. Both data sets were treated separately, taking into account the substantial issue of transforming the data from numeric to textual data; this is in line with the mixed design stages mentioned earlier in this chapter.

The quantitative data collected through the questionnaires were analysed statistically using SPSS software and the type of statistical examination was governed by the quantitative research questions. These questions were used to find out the following: investigate head teachers’ opinions about the programme they have received within a university context and then identify the professional learning, development and support needs of head teachers of both secondary and intermediate public schools in the city of Jeddah. The data from the questionnaire was uploaded into the SPSS computer programme to enable statistical analysis to be carried out, including descriptive statistics such as mean, percentage and frequency.

The semi-structured interviews data were obtained from analysis of the transcribed audio-recordings. The qualitative data analysis procedure was guided by the research objectives which focused mainly on the identification of the extent to which the programme fulfilled school head teachers’ needs, what they their development and support needs to be, and what obstacles they felt prevented them from developing their skills.

Therefore, among the several approaches to data analysis, the most appropriate to this study are the thematic and inductive analysis approaches. The actual and practical data analysis procedure commenced during the field study by taking notes, conducting and recording interviews, followed by taking notes and transcribing the interviews. In addition, the data analysis procedure applied the techniques of the inductive qualitative data analysis approach.

The overall analytical approach adopted in this study for the most part followed the conventions of template analysis, where a list of codes (a template) was produced

representing themes identified in the textual data (King, 2004). The analysis, as Thomas (2006) described it, includes data preparation, a text-reading familiarisation, the creation of both major and sub-themes, the overlapping of the coding and un- coding of text, and regular revisions of the frequency themes. Moreover, the study also used the thematic analysis approach, described by Braun and Clarke (2006), as the procedure for identifying the emerging themes and sub-themes.

The qualitative data analysis package NVivo was used for the initial stages of coding. For readers who have not used NVivo, Figure 16 displays illustrative screen prints of the project which may provide an idea of NVivo 10 and the analytical process within the software.

Figure 16 Main screens of NVivo 10

When the user opens a project in NVivo 10, they have three options: New Project, Open Project, and Help NVivo 10, as in Figure 16 above.

Following this, when the user chooses the New Project option, the screen in Figure 17 below appears. Then, the user has to provide a name or title for the project.

Figure 17 The process of creating a new project using NVivo 10

In the Node Explorer shown in Figure 18 below, the analyst can create nodes (codes), code data at the nodes created, make changes in the nodes as concepts and themes develop, record ideas about the nodes in memos, and search for links between nodes. This helped me firstly to index segments of the text to particular themes, and secondly to carry out complex search and retrieval operations in a short time. Finally, the research notes are linked to a coding system. In addition, the use of large amounts of text and complex coding facilitates a sophisticated analysis of the data.

Figure 18 Sources and Nodes of NVivo 10

The generation of the themes and sub-themes was guided by the use of template analysis (King, 2004). In the process of coding, every response was considered,

whether it was positive or negative. To ensure the trustworthiness of the findings and the accuracy of the coding, both the major and sub-themes that emerged were linked to the relevant coded passages. The outcome of this step was referred back to volunteers from the participants to judge the relevance of the coded passages to the major and sub-themes and their labels. This led to the identification of nine themes: barriers, communication, leadership, management, resources, solutions, support, training and development and support needs of head teachers. In each theme, there are a number of codes (nodes).