Data analysis of questionnaires

The quantitative data of teacher questionnaire, pupil questionnaire I and II were uploaded into SPSS for analysis. I collected the responses as an excel spreadsheet with a new row for each respondent, one line per participant on a case basis to make it easier and clear. SPSS allows me to use simple descriptions such as percentages, means and cross tabulations to examine the data. This allows me to look at strength of feeling and also inconsistencies in responses between sections. This makes analysis processes relatively simple but it is very important to recognize that, whilst this data can be analysed as a survey across all the classes in each country to compare the findings with the literature, the data can also be analysed case by case.

The subjects’ responses were measured according to a four-point Likert-scale: strongly agree, agree, disagree, and strongly disagree. Missing answers were given values so as to exclude them from subsequent analysis (Coleman, Galaczi et al. 2007). Since my study is to address the teachers and pupils perceptions about the teaching and learning of speaking and listening of MFL the analysis of my data were mostly descriptive. Therefore percentages were used to get an overall view of both the teachers and pupils opinions because of the difficulties of dealing with Likert scale data. My research does not seek to illustrate numerical estimates of the variability in the distribution (Punch 2009) because whether individual Likert items can be considered as interval-level data, or whether they should be treated as ordered-categorical data is the subject of considerable disagreement in the literature (Armstrong 1987, Jamieson 2004). The key issue is whether Likert items are interpreted as being ordinal data. The

points on my Likert scales are arbitrary and have no objective numerical basis. Furthermore, it is not possible to be sure that the ‘distance’ between each

successive Likert point (strongly agree and agree, for instance) in my questions is equivalent, although, as discussed in the method, above, I have taken care to address issues of bias. However, because of the dispute about the ordinalness of the data, use of means and standard deviation are a disputed issue, and remain problematic (Jamieson 2004). Therefore I have not used them in my study because my study is about the participants’ perception of the teaching and learning of speaking and listening in MFL classes. My study seeks, not numeric description of the data, but exploratory qualitative answers.

In my study, given that all questions use the same Likert scale and that the scale is an approximation to an interval scale, the responses may be treated as interval data and responses to several Likert questions may be summed (Jamieson 2004). I use SPSS to calculate the students’ number and percentage of strongly

agreement, agreement, disagreement and strongly disagreement to each item of the closed questions in pupil questionnaire I and questionnaire II and these were collected manually into a table according to each case and school. This allows me to compare the pupils’ perceptions about the teaching and learning of

speaking and listening between schools and cases. For teacher questionnaire each of the four point scale were given a number, i.e. 1= strongly agree, 2= agree, 3= disagree and 4= strongly disagree. The teachers’ answers were gathered

manually into one table so as to compare easily the teachers’ perceptions about each item of teacher questionnaire.

first typed out and then uploaded into NVIVO with all the answers to the same question of each case put together for easy analysis. Chinese children’s answers were in Chinese for avoiding misunderstanding. The answers were translated into English by the researcher. I did this in person for the reason that I was most familiar with my research and my personal experience and intuition could help handle the translation more accurately than any other person (Ungerson 1996). When I was translating the Chinese children’s answers to the open-ended questions I read again and again the equivalent answers of the English children and tried to make the translation as accurate and reliable as I could. Then a native Chinese speaker who has good mastery of English language and understand both English and Chinese education checked all the translation. In case of controversy a third person was involved to come to an agreement of the translation. After this I consulted a native English speaker who understands very well the English and Chinese education and has good knowledge of Chinese culture to further confirm the fidelity of the translation. The English translations of the Chinese students’ answers were uploaded into NVIVO with all the answers to the same question of each case put together for easy analysis. The answers were read again and again in order to find similar themes across cases. The answers were coded according to the recurring themes in the answers to each question. I considered the use of NVIVO to facilitate analysis of the data. However, following attendance at the training courses, I decided that whilst this software has particular advantages in identifying themes in multi source data, the hand investigation I had already undertaken to shape the data for loading was so complete that I was able to analyse the themes based on this method. I collected the recurring themes together manually and made comparisons between cases.