Quantitative data analysis

The data obtained at this stage were from questionnaires. Using questionnaires enabled the researcher to have an understanding of the characteristics of this particular set of data when the data was performed in certain ways (Johnson & Christensen, 2004). Statistical data in this study provided an opportunity for the researcher to access the numerical data in a form of scales. Consequently, she could understand the participants’ views and opinions in the questionnaire items. Also, it could address the specific research questions (Pallant, 2007). At this stage, the SPSS software package was performed to interpret the relationships between participants’ views and scaled numbers. As the SPSS software has various techniques for analyzing data, such as t-tests, frequency tables, and non- parametric tests, to show the relationships between the variables (Bryman, 2008; Huizingh, 2007), using this software package to analyze the quantitative data was considered as the most appropriate tool at this stage.

Within this study, the SPSS software was adopted to analyze the participants’ responses to the questionnaire items in order to find out the differences between English learning and teaching in high schools and in the university. Several analysis tests were utilized, including descriptive analysis, Non-parametric tests, Kruskal-Wallis test, Mann-Whitney U Test and Spearman’s Rank Order Correlation (rho). How these tests were performed will be discussed and presented in the next chapter

There are several stages in the process of setting up data and analyzing the data (Pallant, 2007). Firstly, the researcher needed to prepare the data file, which can be also imported from other spread sheet-typeprograms, such as Excels. And then the coded data was entered into the SPSS (Pallant, 2007). Meanwhile, the researcher should screen the data file for errors in case there were data errors as they would influence the validity and credibility of the research (Neuman, 2004). Furthermore, the data was explored by using descriptive statistics and graphs. Finally, the variables were modified for further analysis. A flowchart of this data

analysis process is shown in Figure 4.3. Details of the quantitative data analysis using SPSS will be further explained in Chapter 5.

Figure 4.3 Flow chart of data analysis process

4.5.2 Qualitative data analysis

Compared with the numerical data collected at the quantitative stage, qualitative data analysis is based mainly on analyzing textual formats, such as documents and texts. Many scholars believe that the qualitative data analysis process is full of art (Hesse-Biber & Leavy, 2011; Mills, 1959; Tesch, 1990). However, it also requires “a great amount of methodological knowledge and intellectual competence” (Tesch, 1990, p. 97). Thus, the researcher needed to break down the data analysis and interpretation process into a series of steps.

The first step that was followed was a data preparation phase. In this phase, the researcher needed to consider what kind of data she would use to provide an understanding of the research questions. Due to some data being collected from interviews, the researcher needed to transcribe the data. The data transcription process was more interactive rather than passive, as the research engaged herself in the process of deep listening, analysis and interpretation (Hesse-Biber & Leavy, 2011). During this process, the researcher gained an opportunity to engage in the initial data collected and connected with her data in “a grounded manner that provides for the possibility of enhancing the trustworthiness and validity of their data-gathering techniques” (Hesse-Biber & Leavy, 2011, p. 304). The next two steps were data exploration and data reduction, which was worked hand in hand. In these two phases, the researcher began to read the data collected in forms of text and audio and then to mark up and write down the important textual words. In order to keep those important lines in mind, memo and diagrams were used. The researcher’s opinions and ideas were essential in the interview after she gained more familiarity of the data collected by reading and writing up in a memo. When finishing this, the researcher was able to code the data. The coding process

Prepare the codebook

Set up data files _{Data entry}

Screen data for errors

Exploring data Modify variables for further analysis

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can be flexible, either simultaneous with the data collection process or after data collection.

In this research, the qualitative data collected from the semi-structured interviews were analyzed using NVivo software as a tool and the data coding process was adopted a constructivist grounded theory approach. The NVivo software is widely used by researchers to import and work with documents, PDFs, spread sheets, audio, video and pictures. Adopting this software to organize qualitative data is efficient and convenient. Moreover, the NVivo software ensures that the work is systematically categorized and makes it unlikely that anything is missing in the data. Therefore, this software was utilized to organize transcriptions, and to interpret participants’ answers recoded in the interviews at this stage.

Grounded theory is a research method developed by Barney Glaser and Anselm Strauss (1967). This method is a general methodology for developing theory that is ‘grounded’ in data systematically gathered and analyzed (Glaser & Strauss, 1967). Grounded theory uses an inductive method to code and analyze observational data to obtain research findings. In other words, grounded theory is a theory that could be tested comparatively in further data collection using systematically data collection so as to create refined conceptual categories (Strauss, 1987). Glaser and Strauss (1967) provide a clear description of the method of grounded theory generation and they believe that data collection should not be influenced by preconceived theories. Rather, “systematic data gathering and analysis can lead to a theory” (Ezzy, 2002, p. 7). Grounded theory was developed as a reaction to the deductive model of theory generation that dominated in the United States in the1960s.

Up until now, there have been at least three types of grounded theory in the methodology literature: the original version by Glaser and Strauss (1967); the systematic version by Strauss and Corbin (1990, 1994, 1998a); and the constructivist approach of Charmaz (2003, 2006). According to Creswell (2007), the systematic and the constructivist approaches are popular among the scholars. Compared with the original version by Glaser and Strauss and the systematic use of it by Strauss and Corbin, Charmaz provides a more flexible and scientific approach (Neuman, 2004).

Apart from its emerging prominence, there are several other reasons to use constructivist grounded theory approach as the most appropriate strategy at the

qualitative stage in the study. First, it consists of “systematic inductive guidelines for collecting and analyzing data to build middle-range theoretical frameworks that explain the collected data” (Charmaz, 2000, p. 509). Secondly, the researcher gathers and analyzes the data, which is ‘grounded’ systematically (Strauss & Corbin, 1998a). Thirdly, the theory is generated and emerged from the raw data, which avoids the researcher’s presumptions and perceptions. The theories was able to generated in the process of analyzing comparatively among patterns, themes, and categories from participants’ responses within this research (Babbie, 2011). Therefore, this constructivist grounded theory was appropriately utilized at the qualitative stage.

There steps of using constructivist grounded theory to analyze the data were open coding, axial coding, and selective coding. After studying carefully the initial data, the researcher grouped various codes into upper-level themes and finally synthesized them into core categories (Ryan & Bernard, 2000). The coding process assisted the researcher to generate the theory from the various categories (Sarantakos, 2005). Also, it provided the researcher with an opportunity to re- examine the data (Fan, 2011).

The open coding was the first step within the data analysis. At this stage, the first- order concepts and substantive codes were identified, developed as well as analyzed and compared constantly before being delimited into upper-level categories (Creswell, 2009; Fan, 2011; Fei, 2007). The researcher remained open to the raw data without any perceived codes (Glaser, 1992). Due to the nature of grounded theory, the researcher needed to keep these questions in her mind and asked continually at this stage: “what is the study from this data? What category does this data indicate? What is actually happening in this data? What does the incident mean in people’s social life? What accounts for this problem and process?” (Glaser, 1978, p. 57). With these questions in mind, the researcher named each segment of the raw data, and moved quickly through it to construct meanings of the questions in the interviews and questionnaire. Then the researcher generated the codes related to the participants’ English learning experiences and their teachers’ teaching in high schools and in the university. These codes from the textual data and labelled into 98 codes of the third and the fourth levels in this process. The participants’ responses were recorded and grouped according to the frequency of their occurrences emerged in the transcripts (Appendix 6).

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Compared with open coding, axial coding was putting an ‘axis’ through the data to connect between the concepts, which allowed the researcher to make interconnections with the substantive codes (Sarantakos, 2005). Thus, the researcher needed to make detailed comparisons between the concepts in this stage in order to construct higher-order concepts (Sarantakos, 2005). The constant comparisons between the concepts ensured the researcher to make visible links between open codes and to group them into higher-order themes (Fan, 2011). These constant comparisons and visible links helped the research to understand the codes and phenomenon better. 46 codes were generated in this step (as shown in Appendix 6).

The last step was the selective coding process. It means “the analyst delimits coding to only those variables that relate to the core variable in sufficiently significant ways to be used in a parsimonious theory. The core variable becomes a guide to further data collection and theoretical sampling” (Glaser, 1978, p. 61). At this stage, the researcher may write a “story line” by working through the axial codes and connect them together into higher levels of abstraction (Creswell, 2007, p. 67). Finishing this step, the six key categories that emerged in the perceptions of the participants on their English learning experiences and their teachers’ teaching in high schools and in the university. Table 4.1 below provides a schematic overview of the procedures of how the grounded theory approach was utilized in this research. Detailed elaboration of the qualitative data analysis will be in Chapter 7.

Table 4.1 An overview of using GT approach in the research

Process Main activity Explanation of these activities

1 Identify and develop substantive data

 Open to the data

 Name the codes of the raw data

 Construct meaning

2 Generate codes  The codes that were generated related closely to the research questions in the interviews

3 Connect the concepts  Compare codes and phenomenon constantly

4 Higher levels of abstraction

 Search for the key phenomenon, codes, elements and categories

 Need to further refine and develop those categories