Data Analysis of Case Study

Although data collection is crucial, the collected data makes no sense before being analysed. According to Yin (2009), “data analysis consists of examining, categorising, tabulating, testing, or otherwise recombining evidence, to draw empirically based conclusions. Analysing case study evidence is especially difficult because the techniques still have not been well defined. To overcome this circumstance, every case study analysis should follow a general analytic strategy, defining the priorities for what to analyse and why. The four strategies are relying on theoretical propositions, developing case descriptions, using both quantitative and qualitative data, and examining rival explanations. Using various computer aids to manipulate your data will not substitute for the absence of a general analytic strategy”.

4.11.1. Justification of Selecting Content Analysis Technique

This research uses Content Analysis, which is considered as a MAINLY Qualitative technique for analysing 'Qualitative' data. It helps, in part, to quantify the qualitative data (Saunders et al., 2009). For qualitative data that is collected through semi-structured interviews as the research instrument, the ‘Content Analysis’ technique is the most relevant and commonly used (Bryman and Bell, 2007). According to Cooper and Schindler (2008, p. 449), it is defined as “a research technique for the objective, systematic and quantitative description of the manifest content of a communication”. That definition includes the reason why this technique was chosen for the present study. After the interviews it would be necessary to obtain systematic and quantitative conclusions on the information collected from the interviewees.

To understand the justification behind the selection of Content Analysis for this research, it is necessary to remember that this study employs the ‘Case Study’ as its main strategy. While the Case Study strategy has many advantages that are discussed in the research strategy section, it has two main disadvantages, which hinder those studies that utilise this strategy. Content Analysis is believed to be a reliable analysis tool to deal with these weaknesses at a reasonable level (Cooper and Schindler, 2008). According to Yin (2009), “as a research endeavour, case studies have been viewed as a less desirable form of inquiry than either experiments or surveys. Perhaps

the greatest concern has been over the lack of rigour of case study research and its validity”. The next common concern about case studies is that “they provide little basis for scientific generalisation” (Yin, 2009).

This technique would have to use some 'Quantitative' results too in order to partly overcome the problem of lack of 'rigour' of the case study and ‘validity’ of findings that is one of the main weaknesses of the Case Study strategy. Content Analysis can partly address poor 'rigour' due to systemic and well-defined steps in conducting analysis. Similarly, use of Content Analysis techniques contributes some 'Quantitative' results too that can increase possibility of 'Generalisability'. As Yin (2009) emphasises, use of analytical techniques (e.g. Content Analysis) that quantify in part the collected qualitative data can lead to ‘statistical generalisation’ by enumerating frequencies. That is to say, use of Stratified sampling which is a probability method is an intentional attempt by the researcher to increase both Generalisability and Validity of the findings.

Regarding the steps in the process of Content Analysis, Krippendorff (2004) states that the analysis process should include the following steps:

1. Identify the main themes.

2. Assign codes to the main themes.

3. Classify responses under the main themes.

4. Integrate the themes and the responses in the report.

The collected data from Saudi and British academics and education managers would be analysed by using the Content analysis technique assisted by a Likert scale. In the process of Content analysis of the interviews that would be conducted with both Saudi as well as British education managers and academics, the content of each interview would be coded, then similar codes would be classified into a separate themes, and the repetition and degree of each code and each theme in each interview and all interviews would be quantified. As a result, it was expected to have some interesting quantitative findings emerging from the qualitative analysis of the interviews.

4.11.2. Likert Scale as Quantifier in Content Analysis

Although a Likert scale is widely used for collecting quantitative data, its function and potential for analysing qualitative data has not been explored enough by some researchers (Cooper and Schindler, 2008). While a Likert scale can be used independently as the only technique, generally, a Likert scale shows its full potential if it is used as a quantifier tool that is part of a more rigorous analysis system such as Content Analysis (Husrn, 2009). For this reason, it was decided to use a Likert scale as a quantifier element of Content Analysis techniques in this research. To avoid unnecessary complexity, a five-point Likert scale (totally agree, agree, neutral, disagree, and totally disagree) was used.

In short, this research employs Semi-structured Interviews, not Structured ones, so there is no Likert scale in the interview questions. The Likert scale was used for analysing data, not for collecting data. Then the researcher has chosen this tool, not the interviewees.

A Likert scale was used as a quantifier of Content Analysis of the conducted interviews with Saudi and British higher education authorities and academics. This analysis was done by coding the content of each interview, classifying similar codes into a separate theme, and quantifying the repetition and degree of each code and each theme in each interview and all interviews, which resulted in some interesting quantitative findings emerging from the qualitative interviews.

At the post-interview stage (process of data analysis), five Likert scale options (totally agree, agree, neutral, disagree, and totally disagree) were hypothetically considered as possible answers to each question/proposition in order to quantify the results of the interviews. By considering the words or statements used by each interviewee to explain their opinions regarding each question/proposition, the closest option among the five options (totally agree, agree, neutral, disagree, and totally disagree) was selected to represent each answer of each respondent. For example if an interviewee said “I do believe suitable leadership and strategic management has a positive impact on the quality of education”, because of using “do believe” that is strong support, “totally agree” was selected as equivalent to it.

One of the outputs of Content Analysis is a table of findings. The table summarises briefly the contents of interviews with Saudi and British lecturers and education managers in a quantitative format. Numbers inside each cell of this table

show the number of people (academics) that are in favour of each option. For instance, among six Saudi academics who participated in interviews, five of them

‘totally agree’ with the assumption of the first proposition and one academic ‘agrees’

with this proposition.

That is to say, the use of a Likert scale in Content Analysis may be particularly difficult for inexperienced researchers because the techniques have not yet been well defined. To overcome this problem, every case study analysis should follow a general analytic strategy, defining priorities for what needs to be analysed and why. Four strategies are: relying on theoretical propositions, developing case descriptions, using both quantitative and qualitative data, and examining alternative explanations. Using various computer aids to manipulate data is no substitute for the absence of a general analytic strategy

4.11.3. Statistical Analysis of Case Study Data

As was mentioned, “with appropriately fine-grained data, the analyses can incorporate statistical models, such as regression or structural equation models.

Throughout, a persistent challenge is to produce high quality analysis, which requires attending to all the evidence collected, displaying and presenting the evidence separate from any interpretation, and considering alternative interpretations” (Yin, 2009).

Time-series analysis can follow many intricate patterns, which have been the subject of several major discussions with single subjects (Kratochwill, 1978).

Compared to the more general pattern-matching analysis, a time-series design can be much simpler in one sense: in time series, there may only be a single dependent or independent variable. In these circumstances, when a large number of data points are relevant and available, statistical tests can even be used to analyse the data (Kratochwill, 1978).

Given that all the collected data for this thesis are qualitative (no numbers or figures have been collected), none of the highly quantitative statistical analyses (e.g.

regressions, correlations, and time-series) are suitable to analyse the data. Therefore, this study employs a ‘comparison of frequencies’ in the given answers to each question by different participants as the only statistical analysis technique completely suitable for this research.