Process of Data Analysis

4.6.1 Quantitative Data

Before statistical analysis was performed on the quantitative data, normality test was conducted. Kolmogorov-Smirnov and the Shapiro-Wilk tests were used to verify the assumption of whether the sample data was drawn from a normally distributed population. It was found that both tests had a p-value that was less than 0.05 for all sample data. This result indicated that all the distributions were not normally distributed or failed to satisfy the normality test. Thus, non-parametric tests such as

Mann-Whitney test and Spearman's rho test would be used. The Mann-Whitney test

was used to compare two independent groups. The test is a reliable substitute of t-

test if the assumptions of the t-test (e.g. normal distribution) are violated because the

test is only slightly less powerful than the t-test (Morgan et al., 2004). The non- parametric test, Spearman's rho test would be used to measure the statistical dependence between two variables.

In the data analysis of quantitative data, statistical significance (p) and effect size (r) would be reported. Many research studies did not report the effect size while obviously showing the p-value that was not sufficient for the reader to fully comprehend the result and impact of quantitative research (Sullivan and Feinn, 2012). Statistical significance (p) is the probability that the observed difference between two

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groups is likely due to chance and effect size (r) is the magnitude of differences found (Sullivan and Feinn, 2012). In non-statistical terms, significance (p) is used to confirm whether or not there is a difference between two groups. However, effect size (r) is used to measure the extent or amount of differences. Cohen’s (1992) effect size was used as a reference: 0.1 for small, 0.3 for medium, 0.5 for large. In the analysis, an effect size of 0.3 and above will be the focus of discussion.

4.6.2 Qualitative Data

Analysis of qualitative data is a process of,

Making sense of data in terms of the participants’ definitions of the situation, noting patterns, themes, categories and regularities (Cohen et al., 2007,

p.461).

The main objective of qualitative data analysis is to reduce a large amount of data into manageable and understandable segments (Cohen et al., 2007). In a phenomenology study, data collected through in-depth interviews are reduced to a common core or essence of the experience as described by the research participants (Johnson and Christensen, 2008). Nevertheless, there is no single correct way to analyse qualitative data because multiple interpretations could be made (Cohen et al., 2007). Most importantly, researchers have to know how to reach the objective of a data analysis and abide by the principle of fitness for a purpose (Cohen et al., 2007). This means it is important to pre-determine the purpose of the study, as this will determine what kind of analysis to be undertaken on the data.

This study has employed content analysis procedure to achieve qualitative data reduction. Content analysis is defined as follows.

A process of summarising and reporting written data – the main contents of data and their messages… Content analysis can be undertaken with any written material, from documents to interview transcripts, from media products to personal interviews (Cohen et al., 2007, p.475).

The content analysis procedure was performed in nine stages guided by the literature (Cohen et al., 2007; Johnson and Christensen, 2008). In stage 1, the research questions in Chapter 1 were examined. In stage 2, the population was defined, which were the teachers, students, and parents.

Stage 3: Define the sample to be included - Purposive sampling was used to collect the

qualitative data. The sampling rules for people are also applicable to documents and texts such as the representation, access, size and generalizable of the result (Cohen et

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Stage 4: Define the context of the generation of the document - The qualitative data

was generated through a set of standardised interview questions. The interviews were conducted on a face-to-face basis.

Stage 5: Define the units of analysis - Categorical sampling was used and the sampling

units were students, teachers and parents. Members in each category had a unique role and they had something in common. The sampling unit should be as discrete as possible, and does not distort the representation of the context and other data (Cohen

et al., 2007).

Stage 6: Decide the codes to be used - Code is defined as follows.

A word or abbreviation sufficiently close to that which it is describing for the researcher to see at a glance what it means (Cohen et al., 2007, p.478).

Before starting the process of segmenting and coding, a coding decision had to be made. It is essential to determine whether a coding is for the existence or the incidence of the concept (Cohen et al., 2007). There are two types of coding systems - inductive codes and a priori codes.

A priori codes are codes that were developed before examining the current data. Inductive codes are codes that are generated by a researcher by directly examining the data (Johnson and Christensen, 2008, p.539).

In this study, both a priori and inductive codes were employed. It is a common practice by many researchers to adopt both a priori codes and inductive codes (Johnson and Christensen, 2008). Before I started the process of segmenting and coding, some a

priori codes had been determined. A priori codes may be established before the data

analysis based on their relevance to the research questions (Johnson and Christensen, 2008). The research questions were examined, and a priori codes were established to ensure that the research questions were answered. One of the examples is shown in the following table.

Table 4.1: A Priori Codes for Analysis of Qualitative Interview

Research Questions A Priori Codes

What are students’, parents’ and teachers’ gaming experiences?

Favourite types of games played, frequency of gameplay, pros and cons of gaming

Stage 7: Construct the categories for analysis - Categories are constructed to show the

links between units of analysis. An organising category is also known as a node. A node can be a process, group of people, concept, idea, place, or any grouping that the researcher defines (Cohen et al., 2007).

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Code is a label for a piece of text; a node is a category into which different codes fall or are collected (Cohen et al., 2007, p.479).

After categories had been formed, the relationships among categories were identified and presented as hierarchies. For example, the a priori codes (i.e. favourite types of games played, the frequency of gameplay, pros and cons of gaming) were categorised as a node called gaming experience.

Stage 8: Conduct the coding and categorising of the data - In this stage, segmenting

and coding were conducted simultaneously because both processes were interrelated. Segmenting involves locating significant statements, and coding involves marking or labelling those segments (normally text data) with symbols, codes, descriptive words or category names (Johnson and Christensen, 2008). In a phenomenology study, the researcher should search for significant statements that could be a few words, a phrase, a sentence, or a few sentences that are relevant to the phenomenon being studied (Johnson and Christensen, 2008).

Before the process of coding and segmenting, I went through the interview data several times. According to Hammersley and Atkinson (as cited in Cohen et al., 2007), a researcher should read and reread the data to note down any interesting pattern, any astonishing, puzzling or unanticipated feature, any obvious irregularity or disagreement. During the process of segmenting, the interview data was divided into meaningful analytical unit to ensure that all segments would be meaningful and significant to the research questions. During the coding process, I applied a priori

codes if they clearly fit the segments of data. If the data could not be fitted into any of

the a priori codes, an inductive code would be generated directly from the data. For instance, inductive codes derived from the students’ gaming interview data were

gaming attractions and metacognitive skills. After that, these a priori codes and

inductive codes were categorised into a more meaningful and manageable form known as a node called gaming experience.

Stage 9 – Conduct the data analysis: This would be discussed in Chapter 5 to Chapter

7.

4.7

Conclusion

In this study, the process of data collection was one of the hardest tasks because I had to work tirelessly under great time pressure. During the process, I had to wrestle with many difficult situations that were beyond my control. It tested not only my research skills but also my problem-solving and interpersonal skills to interact positively and work effectively with different groups of people such as the school principals, teachers, students, parents, translators (i.e. teachers), a proof reader (i.e. BM

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teacher), pilot respondents (i.e. friends and colleagues), Malaysian government agencies, and UNMC ethics committee. The greatest worry of all was that disapproval or rejection from any of these parties would fail the whole process of data collection.

In addition to that, there were a few incidents that had constituted ethical concerns. For instance, during my courtesy visits to one of the schools, the school principal requested payments to be made to the research participants in return for their participations. It raised my ethical concern about the possibility that offering monetary incentives for research participation could constitute coercion or undue influence. Since all research subjects should be recruited on a voluntary basis, no payments should be made as was stated in my ethics application. Voluntariness was understood as free from coercion and undue influence. Being a researcher, I had to hold on to my ethical conduct despite being keen to achieve my research objective.

After all the data required for this research had been collected, transcribed, translated and made ready for data analysis, the next three chapters would be to discuss the major themes or categories derived from the interview data. As mentioned earlier, the sampling unit used was categorical sampling – students, teachers and parents. Therefore, the following chapters will be presented according to each of this sampling unit.

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