Interviews

Interviews are considered the main tool in qualitative research (Wengraf, 2001). According to Janesick (1998), interviews are a meeting between two or more people, in which information and ideas may be exchanged, interspersed with questions and responses about a particular subject, and during which meanings are constructed. Janesick distinguishes three forms of interviews: structured, semi-structured and open interviews.

The interviews constituted the main tool of data collection in this study. The interviews were held after the questionnaire data had undergone an initial analysis, and made use of the categories arising from the questionnaire. In this research, the interviews were semi-structured, with specific questions, but in which the order was changed based upon the interviewer's perspectives of what seemed most appropriate. Some changes were made to the questions, such as modification of wording, deletions, additions and explanations, depending on the

interviewee. For more information about the interview questions, see Appendix (4.4).

The interviews were carried out with all categories of participants: science student teachers, university coordinators, university supervisors, headteachers and cooperating teachers. The interviews included both genders, with males and females answering all the research questions.

Interview procedures

The interview questions were translated from English into Arabic and then carried out as a pilot study on one of each category of the participants (male only); the time for the interviews was measured. Any feedback comments were addressed and the schedule was re-drafted to be appropriate for each category of the participants. The interviews took from one hour to two-and-a-half hours. The main interview sample consisted of eight science student teachers, four university supervisors, four university coordinators, four school headteachers, and six collaborator teachers. They were chosen randomly and dependent on who agreed to participate in the interview.

The composition of the main sample was composed as shown in Table 4.6.

Table 4.6: Meta-data of interview participants

NO. Category of Interviewee Code Gender Duration of the interview

Nature of the interview

1 Science student teacher ST1 Male 1.07 Hours Face-to-face

2 Science student teacher ST2 Male 1.23 Hours Face-to-face

3 Science student teacher ST3 Male 1.10 Hours Face-to-face

4 Science student teacher ST4 Male 1.17 Hours Face-to-face

5 Science student teacher FST1 Female 1.30 Hours Through a female

mediator

6 Science student teacher FST2 Female 1.15 Hours Through a female

7 Science student teacher FST3 Female 1.47 Hours Through a female mediator

8 Science student teacher FST4 Female 2.13 Hours Through a female

mediator

9 University supervisor US1 Male 1.24 Hours Face-to-face

10 University supervisor US2 Male 2.11 Hours Face-to-face

11 University supervisor FUS1 Female 1.22 Hours By telephone

12 University supervisor FUS2 Female 1.37 Hours By telephone

13 University coordinators UC1 Male 1.00 Hours Face-to-face

14 University coordinators UC2 Male 1.09 Hours Face-to-face

15 University coordinators FUC1 Female 1.18 Hours By telephone

16 University coordinators FUC2 Female 1.25 Hours By telephone

17 Head teacher HT1 Male 2.44 Hours Face-to-face

18 Head teacher HT2 Male 2.30 Hours Face-to-face

19 Head teacher FHT1 Female 2.37 Hours Through a female

mediator

20 Head teacher FHT2 Female 2.19 Hours Through a female

mediator

21 Cooperating teacher T1 Male 1.26 Hours Face-to-face

22 Cooperating teacher T2 Male 1.32 Hours Face-to-face

23 Cooperating teacher T3 Male 1.28 Hours Face-to-face

24 Cooperating teacher FT1 Female 1.27 Hours Through a female

25 Cooperating teacher FT2 Female 1.11 Hours Through a female mediator

26 Cooperating teacher FT3 Female 1.34 Hours Through a female

mediator

Data analysis procedures

The general approach to the analysis of qualitative data is presented in this section. This includes the principles which have been used to identify codes and for organizing the major themes. These themes help to describe and discuss the findings that have emerged through the analysis stage. An example is shown of a model for the analysis software used which helped to arrange and classify codes into key themes. The codes or themes can be identified from the data through two basic methods of thematic analysis, either inductive or deductive. In the inductive approach, the codes and themes identified are strongly linked with the data themselves, and not driven by the researcher. This is reversed in the deductive approach, where the analysis more closely linked to the theoretical framework (Braun & Clarke, 2006).

The stage of transcription and data reading

To start with, the audio-recorded interviews were transcribed and organized through a programme called Max Q-Data; this prepared pieces of text from which the main codes were extracted. After that, the texts were read multiple times and at different intervals, to extract the codes using the "bottom up" inductive method. This was used at the beginning to access the participants‘ existing ideas and obtain the codes in order to reflect the data freely obtained from the pieces of text. Next, the data were analyzed by the "top down" way of the deductive method. The Activity Theory adopted in this research is governed by a theoretical framework; therefore, it was necessary to organize the codes under the elements of Activity Theory to make the analysis compatible with the theory used. Braun and Clarke (2006) confirmed that "researchers cannot free themselves of their

theoretical and epistemological commitments, and data are not coded in an epistemological vacuum" (p.12).

The stage of generating and aggregating the codes

After reading the data multiple times, a preliminary list of codes was generated and some ideas written and discussed until a final pattern was reached. This final pattern refers to the most basic parts of the data which are relevant and indicate the underlying meaning of the data for each theme. Braun and Clarke (2006) urged that "it is vital that you immerse yourself in the data to the extent that you are familiar with the depth and breadth of the content. Immersion usually involves ‗repeated reading‘ of the data, and reading the data in an active way - searching for meanings, patterns and so on"(p16).

The aggregation of relevant codes under each theme was created through a preliminary list of themes that were commensurate with the research questions. After obtaining the final list of themes, they were classified to fit with the elements of Activity Theory. The data were re-read to check for internal and external compatibility, to ensure that the codes under each theme were compatible and harmonized with each other, and also that the themes were harmonious and compatible with the elements of Activity Theory, and amendments were made to the list of themes where required. Braun and Clarke (2006) advised coding the largest possible number of potential themes in the beginning, arguing that any of them could be of interest later.

The stage of renaming and identifying the themes

The renaming of themes to reflect the codes that fell under them, and which also complied with the elements of Activity Theory, required a great effort. Supervisors were consulted here, as well as other colleagues interested in research, to ensure the trustworthiness of the thematic analysis. Braun and Clarke (2006) asserted that: "The need for re-coding from the data set is to be expected as coding is an ongoing organic process"(p.21). The final writing of the

analysis included writing an accurate description of each code in terms of the compatible data, and in contrast to the contradictory data. The qualitative data collected through the interviews and open questions of the questionnaires were in Arabic, the language of the research sample, therefore the data were analysed in Arabic. This helped the researcher understand the participants‘ responses and helped keep their sense where it was difficult to translate literally into English. Subsequently, after ensuring that the codes and themes were appropriate, the responses were translated into English. Halai (2007) mentioned this problem in saying: "It is very common to find research participants whose first language is not English and the issue that the translation of the interview data will lose a part of the richness, meaning, and cultural flavour in translation"(p.353).

The qualitative analysis software

The following figure shows the way the interview texts were analysed and coded through the MAXQDA programme.

Figure 4.5: Example for coding by MAXQDA

There are many computer applications that are used as tools in the analysis of qualitative data, but Max Q-Data software (MAXQDA)was chosen for certain

considerations, including that the functions listed support multiple languages. Although it does not include the Arabic language, the software enables easy dealing with Arabic text. In addition to ease of use of this programme, it incorporates many of the functions that assist in the analysis of qualitative data. Gibson and Brown (2009) confirmed that MAXQDA had become an "increasingly popular package". They stated that: "This package has much of the functionality of other programs, in terms of the coding of data, the use of written analysis through memos, the production of quantitative descriptive statistics of coding work, and the facilitation of collaborative and group analysis"(p.177).