Velocidad de los desplazamientos de CSAT a lo largo de la costa

The study data analysis process is divided into 2 main stages. In stage 1, all qualitative data that gathered from individual interviews and focus group interviews were analyzed and coded. This process involves the development of the codes, code-categories and inter-relationship of categories which is based on the GT process and coding strategy. The details process of this stage will be discussed in part 4.8.1. Meanwhile in stage 2, the qualitative and quantitative data from the received questionnaire have been tabulated and analysed. The results from this stage were used to validate the analysis result in stage 1. The detail explanations of this stage will be discussed in part 4.8.2. The overall processes of current research data analysis is illustrated in figure 4.6.

Figure 4.6: Research Triangulation Design Data Analysis

Stage 1 _{Stage 2}

Data Analysis (Qualitative and Quantitative) Data Analysis

(Qualitative)

Result (Qualitative)

Result

(Qualitative and Quantitative)

Compare and validate result

• Qualitative Content Analysis

The challenges of analyzing the interview and focus group interview data lies in making sense of the substantial amount of data, identifying significant patterns and construction of a framework to communicate the essence of what the data reveals (Denzin et al., 2000). Therefore in order to analyze these data researchers had applied quantitative content analysis approach (Elo and Kyngas, 2008). Elo and Kyngas (2008) claimed that content analysis is a method that suitable to analyze the written verbal or visual communication transcripts. Hsieh and Shanon, (2005) in their explanation regarding qualitative contents analysis have defined qualitative content analysis as “the subjective interpretation of the content of the text data

through the classification process of coding and identifying themes or pattern”. Therefore for the current research, the qualitative data from the interview and focus group were transformed into transcripts and organized according to the pattern emerges during the analysis. These data were used as an input to the coding procedure in order to refine the abstract constructs and define the concept and categories. In order to assist researchers in analyzing qualitative data, three coding techniques proposed by GT methodology: open coding, axial coding and selective coding (Straus and Corbin, 1998) have been applied. These data analysis methods also have been recommended in qualitative data research (Denzin et al 2000; Patton, 2002) in order to guide researchers in analyzed the qualitative method more systematically. The Atlas.Ti software also was used to help in coding the interview text and linking this code on the semantic network. The decision was making from these transcripts, vague at first and increasingly explicit and grounded (Strauss et al., 1990). Social scientists (Miles et al., 1994; Patton, 2002) acknowledge that data collect and analysis in qualitative inquiry are integrative, iterative, synergistic and interactive in nature. The applied coding processes for the data collection are as below:

• Open Coding - From each transcribed interview transcripts, researchers have analysed the text using line by line or incident by incident coding before allocating an open or initial codes to the text. For this activity researchers have followed Charmaz (2006) initial codes approached which was done by using gerunds as this process will help researcher to detect the process and stick to the data. She also recommended to consider the following questions in order to guide researchers to create an open code:

o What is the data a study of? o What does the data suggest?

o From whose point of view?

After open code have been assigned and created, lists of open code then are sorted into categories based on how different codes are related and links. These emergent categories are used to organize and group initial codes into a meaningful cluster. This process involved the breaking down interview data and focus group data into discreet parts, close examined and compared for similarities and differences. Open

codes that was found to be conceptual similar or related was group under more abstract categories based on their ability to explain the SPI, knowledge, team and standard issues which are the main unit of analysis (Elo and Kyngas, 2008) as in this research conceptual diagram as described in figure 3.6. Then all these open codes were then linked and grouped based on similar issues on the broad categories that represent the unit of analysis. Some of the open codes allocated in this way are known as an “in vivo” code. In-vivo codes are especially important in that they come directly from the interviewees, do not require interpretation by the researcher, and provide additional ontological clarification or context-description. Appendix E and J shows how the initial codes have been created and groups.

• Axial Coding – Axial coding is the process of relating codes (including

categories and properties) to each other into subcategories (Strauss and Corbin, 1998). In this process all the general categories in open coding process were grouped under higher ordering heading. The purposes of grouping data were to reduce the number of categories by merging those similar and dissimilar into broader higher categories. In addition the merging process provides a mean for describing the situation to increase researcher understanding and to generate more knowledge. The process was continued with the abstraction process (Kohlbacher, 2006). The purpose of abstraction process is to detail up the categories by identifying the subcategories and how it link to another’s. Subcategories with similar occurrence and incidents are grouped together as categories and categories are group as core categories. The abstraction process is an iterative process and continues as far as it is reasonable. Appendix F shows the example of the axial coding and abstraction process.

In general this activity’s termed axial because coding occurs around the axis of a category linking categories to subcategories at the level of properties and dimensions. This involves documenting category properties and dimensions from the initial coding phase; identifying the conditions, actions and interactions associated with a phenomenon and relating categories to subcategories.

• Selective coding – The third coding process in the analysis of qualitative data is the selective process. Selective coding is the process of selecting the core

category, systematically relating it to other categories, validating those relationships and filling in categories that need further refinement and development. In this process, the first step is to identify the main or ‘core’ category that related to the collected data. The core category acts as the hub for all other identified categories. In this part, the researcher using the Atlas.Ti tools in creating a network diagram based on the abstraction process result as in axial coding phase. The network diagrams were isolated in the beginning and merged at the end of the process. The merging network diagram helped researchers to produce an inter-related network diagrams that represent as a theoretical network diagram for the current research study. Appendix G and H shows the individual network diagram on selective issues and the theoretical diagram.

• Atlas. TI - Atlas. TI is a qualitative analysis tool which was designed

specifically for use with GT (Coleman, 2006). It also allows researchers to link, search and sort the data. The tool is also capable to manage the interview transcripts, create a code, and store the quotation and memo. Furthermore its capability to create a category, link the categories and produce a network diagram from the categories helps researchers to under more about the current research issue. This tool helped researchers to upload the interview scripts, identify the code, create categories and link the categories in order to represent the overall picture of the current research issue as explained in axial and selective coding process (Straus and Corbin, 1998).

• Quantitative Data Analysis

Data gathered from the survey are tabulated and this process is using statistically analysis software (SPSS). Due to the small number of research quantitative data gathered, researchers have determined to use 3 basic statistical methods in order to analyse the quantitative data; Descriptive statistics, Mean analysis and Frequent analysis. Appendix I show all the statistical analysis processes for the collected data.

• Descriptive statistics - Descriptive statistics are numbers that are used to summarize and describe data. The word "data" refers to the information that has

been collected from the research questionnaire survey. Descriptive statistics are just descriptive and not involve generalizing beyond the data at hand. According to Pallant (2005) the descriptive procedure is ideal for obtaining an overview of the distributional properties of numeric variables.

• Means Analysis - As for most statistical analyses, the mean is the most often used measure of central tendency. The mean is used most often, because of its relationship to the variance statistic. The mean is also important in the sampling distribution, which is formed from the distribution of all possible individual sample means, and has as its center, the mean of the population. The mean is affected by the presence of extreme scores (outliers) which may not be typical of the sample (or population) as a whole. The mean is preferred when a distribution is symmetric and interest is centred on a score that represents all scores (Pallant, 2005). Therefore this method been followed to understand the means population for the survey data.

• Frequency Analysis - The frequent analysis process provides additional information about the nature of each variable distribution (Pallant, 2005). All values are shown for each variable, as well as label, frequency, percent, valid percent and cumulative percent. This analysis involves constructing a frequency distribution. The frequency distribution is a record of the number of scores that fall within each response category. The frequency distribution, then, has two elements: (1) the categories of response, and (2) the frequency with which respondents are identified with each category.

4.8 Summary

This chapter presented a number of suitable methodologies that could be applied in a research study. It also has presented the differences between quantitative and qualitative research methodologies. This chapter have discussed in details the mixed-method research methodology, the methodology chosen for present research study. A detail explanation on the present research process, starting from the overall study research process design, data collection process and data analysis process are also elaborated and presented. The next chapter will discuss the findings and results from the data analysis process.