Approaches to Data Analysis

The data analysis followed different styles, according to sources from which it was collected. The most important distinction to make was that the research generated qualitative and quantitative data, and different a data analysis method was needed for each approach. Findings from interviews, observations, and documents followed a qualitative system of analysis. Data was coded following the three stages discussed by Burns (2000, 435). The first was to develop a list of coding categories (themes); the second was coding data and thirdly, data coded to each category was collected together. In the pilot study, data were analysed manually without the aid of any computer software. Recordings were listened to several times and as a result, a summary of each interview was written, and common issues were identified, which were later grouped into categories of themes and sub-themes. One important issue relating to the analysis was the way in which the findings from each source would be related and linked to each other in order to present each case study and make the final report.

The approach to analyse the given data was by going through questions togetherto identify potential problems. The questionnaire was amended and developed to make sure that the questions were unambiguous, appropriate and acceptable to respondents. When analysing the responses to open-endedquestions, all responses to aquestion were examined, categories for answers were devised, and then, data were coded in the same way as a closed response question. Alternatively,interesting responses can be quoted directly in the final report. The choice depends on the nature of the question and the range of the given answers. The following sections present analysis approaches used in both quantitative and qualitative data.

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3.7.1 Quantitative Data Analysis

Statistical Package for the Social Sciences has been used (SPSS version 17) for data entry and data analysis, which was obtained from the University of Leeds. In the first instance, two databases were constructed: one for the University of Leeds and the other for the King Saud University, but were later combined for the purpose of data analysis. The data analysis was divided into two stages: descriptive analysis and inferential analysis:

Descriptive Data Analysis

Descriptive analysis included describing samples of the study from the University of Leeds and King Saud University in terms of background characteristics, such as age, gender, years of experience, level of educational attainment, and years of experience in current role, administration position and formal job title. These variables were presented using different types of graphs, such as Pie, Histogram and Bar Columns.

In terms of decision-making, items for teaching, administration, financial, and research, frequency analysis was used, which summarised the percentage of areas of participation in decision-making, such as no participation, low participation, high participation and always participation. The mean and standard deviation was added to each item, which helped in making comparisons between the actual and desired participation, as well as the difference between King Saud University and the University of Leeds.

Inferential Data Analysis

Inferential data analysis was used to examine the statistical difference between actual and desired decision-making at both King Saud University and the University of Leeds. To achieve this purpose, analysis of variance (ANOVA) was used. ANOVA entails the examination of background assumptions which should be met, including normality of the distribution, continuity of the dependent variable and homogeneity of variance. Results of ANOVA showed that all these assumptions were met, and on this basis the analysis was performed. The multiple regression method was also used to explore the relationships between different types of decisions (teaching, research, financial and administrative) and participants’ background characteristics (age, position and title). It was also necessary to use multiple regression analysis to examine the relationship between the dependent variables (represented in the composite variables of teaching actual and desired, research actual and desired, financial actual and desired, as well as administrative actual and desired) and independent variables represented in the university (University of Leeds and King Saud

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University), such as years of experience, position in the School/Faculty of Education, and age.

3.7.2 Qualitative Data Analysis

Qualitative data analysis was conducted in three stages: transcription of data, categorising and coding (themes and sub-themes), and comparative approach.

Transcription of Data

All in-depth interviews were transcribed and prepared for data analysis. Reduction of Data: Themes and sub-themes

In order to classify themes and sub-themes, the transcriptions of data were read several times. Furthermore, the purpose of thematic analysis was to reduce the bulk of data and extract only relevant data to the study objectives. The focus was on the main questions that probe in the interview schedule.

Constant Comparative Approach

As this study aims to compare academic staff engagement in the decision-making process between the School of Education at the University of Leeds and King Saud University, a constant comparative approach was used. The essence of constant comparative approach focuses on comparing a piece of qualitative data to other pieces of data. For instance, it compares the interviews with each other, one main theme with other themes in the data, and one participant to another (Dye et al., 2000). In order to point out differences between the two universities, the comparison was made on bases of teaching, administrative, research and finance related decisions on an individual and collective level, which provided the opportunity to pick up a number of pieces from both groups.