With all the methods mentioned above, rich data have been collected during this research (See table 4.3.5). SPSS 15 and Nvivo 7 were employed in supporting data analysis. Apart from analysing quantitative data using SPSS 15, the analysis of qualitative data did not rely totally on Nvivo 7, as I found that Nvivo could not do the entire job properly. When
carefully before importing it to Nvivo and to double-check manually to make sure the coding really represents the study and the ideas of the researcher.
Table 4.3.5 Data collected during research
Different data have been analysed with the support of software SPSS or Nvivo. The aim of using this software was to assist the procedure of data analysis. The researcher believes that software provides tools to support research work; it is the researcher’s job to decide how the data will be presented in order to answer research questions. The next section will introduce and discuss the concerns of data analysis.
4. 4 Data analysis
Both quantitative and qualitative data were gained from this mixed design through the field work (See table 4.3.5). In such a huge collection, coding and interpreting data is
Data Type Data name Quantity Analysis
Questionnaires
Circle Time questionnaires (English) 92 SPSS & Nvivo
Circle Time questionnaires (Chinese) 207 SPSS & Nvivo
Self-esteem test 1131 SPSS
Western teachers’ opinions 4 Nvivo
Interviews
Interview English teacher 1 Nvivo
Interview English students 15 Nvivo
Interview Chinese students 33 Nvivo
Interview Chinese teachers 18 Nvivo
Documents
Chinese students’ academic results 2 SPSS
Chinese students’ summaries of Circle Time 64 Nvivo
essential to the research. Robson (2002) suggests that for a fixed design, data should be analysed after they have been safely gathered; while for a flexible design, data analysis should take place in the middle of the process. As a mixed design, however, data analysis in this study started from an early stage and throughout the whole journey of this research. Under pragmatic guidance, the data should work for the research questions.
Data from English students’ questionnaires and interviews from the preliminary study firstly was used to test the design; and therefore, were taken as comparative factors to classify the different perspectives of using Circle Time between English and Chinese students. In the main study, pre and post-experimental tests and Chinese students’ academic results gave a basic idea of how Circle Time worked in a Chinese school (refer to §5.1), while students’ and teachers’ interview data provided triangulation in supporting detailed information to draw a holistic picture of Chinese affective education. Both quantitative data and qualitative data answer the relevant questions respectively. For example, students’ questionnaire mentioned their positive view of Circle Time: “You can express yourself and communicate with others on your own initiative; you can exchange your ideas with the others. It helps us to build Friendship Bridges because we can have better communications (QC2-Q5-228)”. Students’ interview data provided more detailed information about what they like about Circle Time and what they can gain from Circle Time: “…During this lesson, I felt relaxed and happy. It was delivered in an active way. We can play games and have no distance with the teacher. We talk whenever we want to talk, with no restriction. During the session, the teacher always played games with us. Every time I went through, I felt so proud of myself. We had some questions which helped heal the wounds in our mind. On the whole, I like this subject and can’t live
without it (SG 21).” Teachers’ interviews also provided evidence about students’ perceptions of Circle Time from a different angle: “Circle Time for students is a time to relax. It can develop their friendship. Students in class 5 have a very close relationship. I think because they have Circle Time while the other class I teach doesn’t (ITC1-6T).” The data provided strong evidence for the validity and reliability of findings.
Data from the experimental design was analysed with the SPSS system. The analysis and
interpretation of the qualitative data was based on carefully going through the data,
making decisions about coding and categorizing, making sense of the meaning of the
words and language, to make the data speak to the research questions. With the support
of Nvivo, raw data were firstly categorized according to topics and concepts, and then
linked to the research questions, reflecting from the concepts of research questions and
data, to develop catalogues for detailed interpretation. As I have mentioned in section
4.1.2, mixed design enabled triangulation at different stages. Table 4.4 shows the
Table 4.4 Abbreviations of data source
Type of Data Abbreviation Example
1 Circle Time questionnaires
(English)
QE-Q(number of question)-(number code)
QE-Q5-1003
2 Circle Time questionnaires
(Chinese)
QC(class’s code)-Q(number of
questions)-number code)
QC3-Q5-334
3 Western teachers’ opinions IT(country’s initial)E-(number code) ITCE-03
4 Interview English teacher IET(teachers’ initial)-(number code) IETF-01
5 Interview English students IEB (number code)
IEG (number code)
IEB 03
6 Interview Chinese students ICB (number code)
ICG (number code)
ICG09
7 Interview Chinese teachers ITC(number)-teachers’ initial ITC10-YE
8 Chinese students’ summaries
of Circle Time
SB (number code) SG (number code)
SB25
9 Chinese students’ diaries and
compositions and letters
CD(number) CD07
10 Research note RN(number code) RN12/10/05
The next section will build arguments to test the validity and reliability of data and findings.