Data Analysis (Quantitative and Qualitative)

A hundred and sixty-eight (168) responses were received from both the English and Arabic versions, 99 and 69 respectively, forming a moderate yet reasonable rate. However, due to suspicion that two respondents may have sent their reply twice, as one entry from each version was identical in every aspect to the succeeding response, only one of their answers was kept while the other discarded to eliminate any repetition, which could misinform the results. Furthermore, one single entry, which came from a non- Omani participant, was removed to create a homogenous sample of Omanis only. Eventually, the examination and analysis of the questionnaire was carried out with the remaining 165 responses and was undertaken in two stages: quantitative (demographics and closed-ended items) and qualitative (open-ended item).

Quantitative Analysis Methods and Tools

Initially, SPSS was employed to prepare and analyse the quantitative part. However, this was later abandoned because it proved superfluous. Considering that the data was only nominal and ordinal, this allowed for non-parametric instead of parametric approaches. So, rather than running inferential statistical analyses, descriptive statistical analyses were found to be more suitable for this type of the data, as the main objective of the questionnaire was to look at general perceptions and trends instead of making statistical inferences about the population. Even though the researcher was aware that factor analysis could have been an option, it was not essentially meaningful. Consequently, neither t-test nor ANOVA (analysis of variance) were conducted, for they may only be done after a factor analysis has been completed. In addition, there was no need to apply a normality test, as the questionnaire did not have any interval or scale data. Having said that, Excel was quite apt as an analytic tool at this stage to generate perfect descriptive results needed for the study and was thus used instead.

Several steps (See Table 5.2) were followed in processing the quantitative part of the responses, namely data preparation and coding, manual data check, running data analysis tests, and finally data presentation. The most painstaking task was integrating results from both Arabic and English versions of the questionnaire to create one Excel sheet because it was not possible to combine the two Google Forms into one form to produce one database. So, Arabic data were manually inputted into the Excel sheet that had data of the English questionnaire. Then, a meticulous process of checking each cell with the original data was carried out to correct any accidental slips. Fortunately, no issue with missing data was encountered since the online questionnaire was designed in such a way that a response was required for each item before proceeding to the next question.

Demographic characteristics based on subject descriptors were created first and data was reproduced and displayed in summary form. Regarding closed-ended items, the number of responses collected from the different subgroups was not well balanced. So, overall percentages were calculated for each question, but where possible a cross tabulation was performed to see how the results of the question were influenced by certain subgroups. Alternatively, a filter was also used for some cases to analyse the data by narrowing the focus to one specific subgroup to see how the people in that subgroup responded to the question. Data was then grouped to be reported, highlighting salient results and patterns.

Those findings will be presented and discussed in the first part of Chapter Six: Findings I (The Questionnaire).

Table 5. 2 Quantitative Analysis of the Questionnaire

Process Description

1. Data Preparation and Coding

English version questionnaire:

Responses were downloaded from Google forms into excel format; open-ended question responses were extracted to be separately analysed using qualitative techniques. Then responses were coded by assigning a numeric value to answers returned from questionnaire participants to facilitate data processing and analysis using Excel. The assigned values ranged from 0 (negative response) to 4 (positive response), 9 for ‘other’ and 99 for ‘I don’t know’.

Arabic version questionnaire:

Arabic responses were simultaneously translated and manually inputted cell by cell into the same excel sheet with the English- version questionnaire responses. Then, responses were also coded following the same method as above.

2. Data Check Arabic-version responses were printed out and cross-checked cell by cell with the data manually entered into the English- version sheet to make sure no accidental slips took place while inputting data. Any discovered mistake was corrected.

3. Data Analysis Tests

An exploratory data analysis was applied to look at what is in there in the data. First, the process started with descriptive statistical analyses for the personal details, followed by analysing each of the closed-ended questions.

4. Data Presentation Looking at the most salient results and general trends, a report of

the findings was produced in the form of paragraphs, tables and graphs. The results were later examined in relation to findings from the open-ended question and interview data findings to come up with meaningful inferences and answers to research questions.

Qualitative Analysis of the Open-Ended Item

Naturally, text data from the open-ended question was approached using qualitative methods. Out of the 165 respondents, 109 preferred not to give any comments. No particular pattern was found to explain why they chose not to express their views. The logical reason might be that they did not have anything to say or the common factor of time constraint in survey taking. Additionally, seven comments were discarded because they did not make any sense (See Appendix D for all comments of respondents). Thus, the remaining 49 comments were approached using a basic content analysis, with the aim of finding emerging patterns or themes. The analysis process is delineated in Table 5.3.

Content Analysis Approach and Tools

A conventional approach to qualitative content analysis where “coding categories are derived directly from the text data” was adopted to allow “categories and names for categories to flow” from the text comments of the respondents instead of the use of preconceived categories based on a “pre-existing theory” (Hsieh & Shannon, 2005, p. 1277 & 1279) or framework. In other words, emergent rather than a priori coding (Stemler, 2001) strategy was adopted.

The analysis was first attempted using NVivo to generate the codes and categories, but this seemed like “doing violence to the data” (Gillham, 2007, p. 64). As the text began to be chopped too finely, the emerging codes did not make sense nor did they seem to satisfy the aims of the study. The value of this information lay in identifying how these people negotiated and appropriated the use of Western methods in their HE system. Therefore, not small codes, but bigger categories in the form of patterns of views and general orientations of respondents were more relevant and hence more conducive to the research aims. That said, the unit of analysis was the entire comment given by the respondent. Subsequently, NVivo analysis was discarded and a simple content analysis was carried out manually supported by graphs and tables generated using Microsoft Excel and Microsoft Word. The findings revealed that the respondents were divided into one of five categories: identity defensiveness, scepticism, intermediate position, pro- Western and topic avoiders. These will be presented and discussed in the second part of Chapter Six: Findings I (The Questionnaire).

Table 5. 3 Content Analysis of the Questionnaire Open-Ended Item

Process Description

1. Data Preparation All comments were extracted from both versions of the questionnaire results and assembled in one place. ‘No comment’ responses were deleted. At this stage, all comments with text were considered for analysis.

2. Preliminary

Examination of Data

Overall and thorough reading and re-reading of all comments was done to get a sense of the whole and notice any general observations. Initial analysis and observations were noted down.

3. Grouping, Coding and Categorisation of data

Comments were cut and spread out on a big table. Then, each comment was read and re-read to get a sense of the view provided by the respondent. Similar comments were grouped together. For each group, codes and categories (themes) were developed. At this stage, invalid comments were removed. The justification of excluding them was provided.

4. Organisation of Data Categories/ themes were organised and presented in graphs

and tables. Translation of Arabic comments took place concurrently.

5. Reliability and Validity Check

An Omani PhD student, who is familiar with the context but has no knowledge of the content of study, was approached to peer-check and verify the codes, themes and translation of the data.

6. Data Presentation Results were presented and discussed.

Later, a comparison and cross-referencing with interview results were conducted.