Data analysis

Data from focus groups transcription were analysed using content analysis. The following section is a brief summary on the content analysis, its approach and its limitations.

Content analysis has become a well-known research tool used to determine the presence of certain words or concepts within texts (Krippendorff 2004). Traditionally it answers research questions by analysing texts, which are understood quite generally to include image, sound, websites, symbolic events, and even numerical data, that mean something in the chosen context (Berelson 1971). The approach has principally focused on linguistic references, expressions of attitudes, and evaluation. It is fundamentally a quantitative approach to unstructured data though, as applied in this research, it can be undertaken qualitatively (Krippendorff 2004). Norman and Fraenkel (2001) explain that in analysing text by content analysis each time a unit in a relevant category is found, it is counted. Coding in content analysis is a process of summarising responses into groups with the category or concept being given to the groups (Krippendorff

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2004). This process must be numerical due to the fact that this process requires the use of numbers in counting the frequency of certain words, phrases, symbols, pictures etc. A word-frequency count is used in the content analysis as it counts words that are frequently used or mentioned by respondents (Krippendorff 2004). According to Krippendorff (2004) researchers have three starting points for content analysis.

 Text-driven content analyses are motivated by the availability of texts rich enough to stimulate the analysts‟ interests in them.

 Problem-driven content analyses are motivated by epistemic questions about currently inaccessible phenomena, events, or processes that the analysts believe texts are able to answer.

 Method-driven content analyses are motivated by the analysts‟ desire to apply known analytical procedures to areas previously explored by other means.

A “problem-driven” approach was used in this research, rather than a text-driven approach because the text-driven approach is more suitable with a case of researchers without a clear or explicit research question in mind (Krippendorff 2004). The data were analysed starting from the research questions and carried through to find analytical paths from the texts to their answers. The study aimed to clarify how the interaction between local highlanders and ETAs has affected the villagers‟ perceptions of the socio-cultural characteristics of the community.

Method-driven analyses are intended for when researchers are motivated by the “Law of the Instrument,” that is, when a child discovers how to use a hammer, everything seems to be in need of hammering (Kaplan 1964, p. 28). It means, when researchers become experts in the use of a certain technique, they may well end up applying that technique to everything in sight (Kaplan and Goldsen 1965; Krippendorff 2004).

Having discussed the uses of content analysis, let us now turn to the advantages of content analysis in this study. The advantages of using content analysis in this study correspond with some of the advantages mentioned by Berelson (1971) which are:

 It gives the researcher an opportunity to choose whether to use qualitative, quantitative or a combination of both operations

 Analysing the vocabulary used by the respondents provides the researcher with valuable cultural insight

 It provides insight into complex models of the individual‟s thinking and language use

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A disadvantage of content analysis found in this study was that it was very time consuming due to the number of respondents in this study.

4.10.1 Data analysis approach

The purpose of this section is not to repeat the well-documented differences and functions among other alternative analysis methods, but to put forward the rationale for selecting the content analysis approach. This research involves determining the perceptions of individual socio-cultural impacts and changes perceived in their community resulting from the arrival of ETAs. Frequently, content analysis has most often been thought of in terms of conceptual analysis (Palmquist 2005). Therefore, the goal is to apply this approach to produce a better understanding on the topic as well as highlight some new thoughts, both implicit and explicit, from this first stage data collection.

As in most research, the researcher started by deciding on the level of analysis. To code for sets of words, “skill improvement.” was chosen to constitute a category for the research, rather than to code for a single Thai word (which translate into multiple words in English) such as be taught, be trained, learnt basic English conversation, etc., to avoid the complication from having too many stand alone categories. However, these single words were grouped under the concept of skill improvement to clarify how their skills were being improved as well as indicating the type of skill. This allowed the researcher to provide a bigger picture of the category as well as keep the analysis simple and clear.

After concluding how to code the data, the research aim and objectives were reviewed to maintain a clear understanding of the purpose of the research and enabled the researcher to assign different categories to code for. In addition, the idea of pre-defined was adopted to use for the coding. Determining a certain number and set of categories allowed for very specific items to be found from a text. In addition, coding for only the relevant issues also allowed the researcher to focus on the research aim, while recognising the possibility of missing some new important data that might have been significant to the findings. Punch (2005) mentions options for assigning codes to data:

At one end of the continuum, we can have prespecified codes or more general coding frameworks. At the other end, we can start coding with no prespecified codes, and let the data suggest initial codes….Nor…does it need to be an either-or decision. Thus, even when guided by an initial coding scheme, we can be alert to other labels and categories suggested by the data

(Punch 2005, p. 200)

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The pre-defined codes in this research were derived from the findings of the focus groups and interviews as well the relevant literature.

At this stage, it was crucial to determine the level of importance for some issues that occurred in the text. Therefore, the technique of coding for the frequency of word use was applied, for example, whenever the word “crowded” appeared in the text it was counted to indicate the degree of importance on this issue. However, the researcher was only interested in quantifying the words to see how many times they appeared and by whom, (this approach is known as a conceptual analysis), not to examine how they were related, (a relational analysis) (Krippendorff 2004).

After listening to the recordings of the data collection with the selected hill tribe people, the key-terms were assigned into different categories such as degree of involvement, motivation, conflict, impact and type of changes. Then the results were presented numerically by counting the key-words, phases, issues and themes in each category and summarising the word frequency lists occurs during every focus group to represent the villagers perceptions toward certain issues discussed during the focus group. Weber (1990) also affirms that determining word frequency generates results that allow more precise comparisons among texts. Moreover, it allow the researcher to know how much more (or less) attention is given to some topics than to others.

In this study, the transcribing and the content analysis were done in Thai. The coding was done by hand with the use of papers, colour pens and highlighters. Data collected from focus groups during the first field visit were initially organised by focus group topics into different categories.

As a result, new categories and subcategories were derived from the data. Same colour was highlighted, on the word with similar meaning, to indicate the category they belong to. After the data had been grouped into individual categories, each category was assigned a different page such as “lost sense of belonging” was assigned (written) to the category of negative impacts in one page, and “better access to use better services and facilities” was assigned to another page under the impacts on welfare as a subcategory under positive impacts, while “better appreciation of my culture” fell into impacts on culture as a subcategory under positive impacts.

However, some texts needed to be assigned to more than one category as they encompassed more than one theme such as “learning new way of work” would fit learning and skill improvement as well as change in original career path. Organising data in this manner enabled the researcher to provide clearer findings to develop a more refined questionnaire design. See Appendix 2 and 5 for the themes emerging from the first stage used to develop the questionnaire.

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The primary objective of this stage was to explore issues and define important value concepts of the study population to help design a large scale questionnaire for the second stage of the research to investigate similarities and differences in their perceptions.

4.10.2 Limitations of the content analysis

After transcribing all the focus group discussions, all the key-words, from each group were listed and placed into different categories. It might appear to be a normal routine for valid content analysis, but in reality it was more complicated because some key words were ambiguous, making, it difficult to decide what certain words used by participants were the most appropriate to each category.

The procedure of transcribing the focus group discussions was done in Thai to avoid further subjectivity from using English. Attention was paid to the language used to translate each key term cautiously and the most suitable English word or phrase for each particular Thai word or phrase was carefully chosen. Nonetheless, not only were there difficulties in choosing the most appropriate English words to explain precise details; but the selected participants often stopped themselves from using ethnically-specific words that were likely to be too complicated to explain. As a result, most of the answers from the first stage of data collection were general rather than specific.