Data Analysis and Coding for Sub Research Question 2

The data of learner experience collected through the Likert-scale questionnaire was analysed quantitatively, while qualitative analysis of the interview data further explained the quantitative findings. There has been a 60-year ‘great debate’ on the analysis of the Likert-scale data, especially in relation to the nature of the response categories, ordinal or interval, which decides the way to analyse them, parametric statistics or non-parametric statistics correspondingly (Carifio & Perla, 2008). From a conservative view of point, Likert scales are ordinal data in nature, and must resort to non-parametric analysis (Jamieson, 2004). Whereas with a liberal view (Knapp, 1990), some research (Pell, 2005; Carifio & Perla, 2008) tends to treat the ordinal Likert scales as interval data and encourages the use of parametric statistics.

Among those who support parametric analysis of Likert scales, some (Sullivan & Artino, 2013) claim that parametric analysis is possible provided that the data are normally distributed. Otherwise, a description of the frequency distribution of responses is likely to be more helpful than simply describing the means. However, Norman (2010) challenges the requirements of normal distribution and big sample size for parametric analysis of Likert scales, and argues with empirical evidence that parametric statistics such as ANOVA, Pearson Correlations can be used with Likert-scale data which are more likely to be non-normally distributed.

At the same time, there is a call for the differentiation from Likert-type and Likert-scale data (Boone Jr. & Boone, 2012). Clason and Dormody (1994, cited in Boone, Jr. & Boone, 2012) identify that if the researcher has the attempt of combining the responses of a series of questions/statements, they are Likert scale data. On the contrary, if they are single non-composite questions/statements, they are viewed as Likert-type data. The Likert-scale questionnaire used in the current research is to investigate learner experience of the SLEND. All the statements work together to reflect their experience as a whole and several statements are combined to reflect their experience of a particular trait/characteristic of the SLEND. Thus, they are considered as Likert-scale data not Likert-type data.

In this regard, the Likert-scale questionnaire data are treated as interval data. The analysis of the Likert-scale data in the current study looks at the Central Tendency (Mean) and Variability (Standard Deviation) as Boone Jr. and Boone (2012) suggested. When it comes to statistical tests, both sample size and distribution of data are taken into account. The distribution of the data decides the statistical tests used. If it is normal distribution, parametric tests are used. Otherwise, non-parametric tests are employed. At the first stage of quantitative analysis of learner experience questionnaire, descriptive statistics such as means, median, and standard deviation were calculated for the responses to each statement. Those statements with lower means (lower than 4) formed the base of questions for the interview. Within each statement, means for each group was also displayed. Several statements for the same characteristic/theme were analysed and reported holistically. For the later stage of analysis, statistical correlation tests were used to detect the association between responses to each characteristic/theme and responses to the holistic statements, for example, the relationship between learners’ responses to the factor of “sign bilingualism” and their responses to the holistic experience; and the association between the characteristics/themes, for instance, the relationship between the experience of sign

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bilingualism and the experience of Web 2.0 technology-enhanced provision. This correlation analysis was expected to shed some light on the relationship between the key characteristics of the SLEND established in sub research question 1.

The interview data were analysed with the assistance of the software, QSR Nvivo 10. With the function of auto coding in Nvivo, each question was auto coded as a node14_.

The answers to the questions were coded under each node (see Figure 3.11). As the interview was to further explore the reasons behind positive or negative experience of the SLEND, it was explorative in nature. Meanwhile, the justification, explanation and suggestions for each question were probed.

Figure 3.11 Auto-Coding of the Interview Data with Questions as the Nodes in NVivo

The analysis of the interview data was a combination of two techniques: quote-search and “unitizing-categorizing”. Folkestad (2008, p. 4) considers “quote-search” as “using quotes from interview as illustrative or confirming examples”. For example, learners felt that real life English materials were useful for instant application and provided examples. Their narration of these examples was quoted to confirm their positive experience with the characteristics of real life English of the SLEND.

However, the sole analysis of data through quotes is problematic. Folkestad (2008) proposes a more sophisticated technique of “unitizing-categorizing”, which are the first two parts of the four elements by Erlandson et al. (1993). In the current study, “unitizing- categorizing” was the main analytic technique supplemented with quotes. By means of the auto-coding function in Nvivo, all the interview scripts were unitized according to questions. For example, all the scripts for question 1 were regarded as unit 1. Within each unit, the content was categorized as: number of positive comments, Justifications, number of negative comments, argument/suggestion. Table 3.4 displays an example of how the data were organized and analysed.

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Table 3.4 Qualitative Analysis for the Group Interview to the Learners

Questions Positive Comments Justification Negative Comments Argument/ Suggestion 1. Do you feel

real life English topics useful from P2P Deaf literacy course? Why? 33 references o Real-life instant application. o New learning experience. o Acquiring new knowledge and improving English literacy. o Avoiding real-life problems and removing communication barriers. 0 --

Seeing from the table, there were 33 references regarding the positive experience of real life English. The effectiveness of real life English was justified by the interviews with a summary of “real-life instant application, new learning experience, acquiring new knowledge and improving English literacy, avoiding real-life problems and removing communication barriers”. No negative comments were found as well as suggestions. The rest of the interviews followed the same procedures and formats.

Despite the separate analysis of learner experience questionnaire and the interview, the findings from these two methods were integrated and reported together in Chapter 5. As further justification and explanation to quantitative findings of learner experience, the findings of the interview were presented when corresponding findings from the questionnaire were discussed.