Data Analysis

Data analysis is defined as a ‘process of bringing order, structure and meaning to the mass of collected data’ (Marshall and Rossman, 1995: 111). In this research, the case study methods of data collection which are interviews, observations and documentary data were used together to answer the research questions of the study by providing qualitative data regarding the understanding and attitudes of school principals, teachers and parents about inclusive education of deaf students and the factors that influence inclusive education. In this research, data analysis has organised the information and broken it into manageable components to determine what was significant to learn. In a qualitative study, transforming data into meaningful and relevant findings is not easy due to the multiplicity of data sources and forms (Miles and Huberman, 1994). In this regard, data analysis was the procedure that worked to reduce data from interviews, observations and documents data to become condensed to essential and important information (Kelle, 1999). Furthermore, qualitative data analysis depends on the interpretations of the researcher, where the researcher collects data from what they see or hear from the participants and then interprets it (Bryman and Teevan, 2005). It has been argued that combining data analysis is required for developing conceptualisation of the possible relationships between different parts of the data, where contrast analysis

research, the documentary data, which included policies and processes that refer to implementation of inclusive education, were analysed compared with responses of participants and practical observations in the inclusive schools.

In addition, Miles and Huberman (1994) assumed that data collection is not something easily separated from data analysis in qualitative research. For that reason, in this research, the data analysis began from the first day of the data collection process at inclusive schools. The researcher carried out an initial analysis of the material after each interview, observation and documentary reviews by using different techniques such as: post-interview analysis notes, initial reading of transcripts, writing memos and also some notes were taken for subsequent questions (Maxwell, 1996). In view of the fact that interviews have a huge amount of data, the early analysis assisted in reducing the problem of data overload (Cohen et al., 2007). The interviews, observations and documentary data were identified and categorised together as themes, from which several sub-themes emerged which later established the analytical framework, aimed to answering the research questions which were in three different chapters (Robson, 2002).

Additionally, when the researcher returned to the UK further data analysis was carried out. The qualitative data was analysed using an interpretive analytic framework which was prepared and managed based on the general guidelines proposed by many researchers (Miles and Huberman, 1994; Merriam, 2001; Cohen et al., 2007). It is worth mentioning that there is no one way or right technique of data analysis in qualitative research (Robson, 2002; Cohen et al., 2007). For instance, Miles and Huberman (1994) recommended three key phases in analysing raw data in qualitative research. The first phase is data management, in which the researcher categorised data for organising data collection, storage and retrieval. This phase includes transcribing and typing notes, and also formatting that through cross-referring and indexing. The second phase relates to data reduction, in which the researcher began to read transcripts and take notes, codes and memos to assist further thinking. The third phase is data presented, which refers to the organised assembly of data to facilitate the drawing of conclusions. In this research, the researcher transcribed the audio-recorded interviews and filed them with the notes taken during the observations, leaving space for coding. In addition, the researcher created files containing basic information for each participant and all data gathered from interviews as well as field notes from observations. Moreover, these files assisted the

researcher to sort out and reduce the information to a manageable size, and this provided a vital preliminary point for analysing emerging patterns and relationships (Strauss and Corbin, 1990).

Through data analysis, the researcher read all the interview transcripts many times to get a broader sense of the nature of the data (for example see appendix G). Additionally, holding the visual written transcript allowed the researcher to get a better understanding of the whole interviews, which led to moving quickly between different themes and sub-themes in order to analyse the rising conversation. The researcher used paper and pen to tag the hard copies of the interview transcripts for aspects that appeared, at that stage, to be relevant and interesting, and to specify some of the major aspects to which the researcher was paying attention and to ensure that these aspects were noted across all the interviews. Additionally, it is worth mentioning here that, although there are some computer software packages available for qualitative data analysis (Tesch, 1990) the researcher opted for manual analysis. The researcher believes that computer-based analysis focuses more on linguistic patterning in reducing the data and that it becomes less meaningful compared to a manual analysis by the researcher through more interaction directly with the data. Furthermore, analysis of qualitative data contains direct quotations regarding the participants’ feelings, views and knowledge (Patton, 1990), which is more effective to organise and analyse manually. Additionally, since the Arabic language was the mother tongue of all the participants in the study, all analyses were systematically processed manually. Also, the researcher decided to analyse manually since he would feel more confident that no important data had been left out.

Additionally, after the completion of management of the qualitative data, the researcher started the coding process. Codes normally are attached to ‘chunks’ of differing size words, phrases, or sentences, which are connected or unconnected to a specific setting (Cohen et al., 2007; Rubin and Rubin, 2012). In this regard, coding involves how the researcher differentiates and combines the information through labels for assigning parts of importance, sense and meaning to the descriptive or inferential data gathered throughout the research (for example see appendix F). Miles and Huberman (1994) proposed three analytical levels of coding of qualitative data, which reflect different levels of analysis ranging from descriptive to inferential. In this research, some codes

and the third rounds. Through the first level of coding, the researcher read through the transcript sheets to divide the interviews into chunks through several codings and labellings to assign units of meaning to the data. Assigning codes is a procedure for summarising pieces of data. These are descriptive codes and they entail little interpretation. The codes are used to retrieve and organise the ‘chunks’.

The early organising stage involved some system for categorising the various chunks, thus the researcher could quickly find, draw and cluster the segments relating to a specific part of a research question, themes and sub-themes. Additionally, the main focus at this stage was placed not on the words but on the meanings of participants about such context. There was a list of broad themes under each set of findings. Since the generated themes were initially descriptive, the researcher continuously re-examined the data in an effort to make them more conceptual. The main aim of the analysis was to understand the research situation and make meaning through data (Merriam, 2001). Care was taken not to impose my expectations on the data but to let the categories or the themes emerge from the data. Thus, inductive data analysis through a re-coding technique was used where data were checked and cross-checked several times to enhance the possibility of new understandings.

Following the researcher’s transcription and coding of the data a number of participants, colleagues and educationalists checked the codes and transcripts through email exchange (due to time and distance constraints). The interviewees reviewed the researcher’s interpretations and constructions of the data by reading the narratives based on our interviews to verify that the researcher had adequately represented their views and experiences. This helped to keep the interviewees in touch with the research, which is an essential aspect of qualitative research (Mertens, 2005). This was done out of the belief that:

‘if the purpose of a piece of qualitative work is emit, that is, if the intent is to give an account of how the participants in a situation see it, then checking the account with the participants (or with a selected informant) is a vital step’ (Philips, 1987: 20).

Furthermore, it is worth mentioning that the social and interactional models have been used as a guideline in the data analysis to understand and interpret participants’ (school

principals, teachers and parents) views about inclusive education for deaf students, and the problems and issues that need to be addressed in improving inclusive education for these students in Saudi Arabia. The theoretical views of these models were taken into account because they provide more comprehensive insights about participants’ views on deaf education and the problems and issues that need to be addressed to improve inclusive education for deaf students in Saudi Arabia. Additionally, dealing with students with deafness in inclusive educational settings and giving them the opportunity to benefit from participation in general schools, this may support them to active participation in society (Knight and Swanwick, 1999).