Research Instruments Data Analysis Techniques

D. Research Instruments

According to Merriam 2009: 15 the characteristic of qualitative research includes the researcher as the primary instrument for data collection and analysis. In addition, as stated by Moleong 2010: 168, in qualitative research, a researcher is a planner, a data collector, an analyst, a data interpreter and a reporter of research result. Therefore, as this research employed qualitative method, the primary instrument of the research was the researcher herself, who had the role of planning, collecting, analyzing and reporting the research findings. The researcher used the help of secondary instrument in the form of data sheet. The data sheet was in the form of a table and was used to note the impoliteness strategies performed through the utterances by the characters in Sherlock.

E. Data Analysis Techniques

Qualitative methods use inductive approach in analysing the data. An inductive approach is a process of reasoning where observation precedes proposition of a theory, the generation of hypothesis, and interpretation of data Vanderstoep and Johnson, 2009: 168. Correspondingly, Bogdan and Biklen 1982: 145 defines that qualitative data analysis is a process of collaborating data, arranging it, dividing it into feasible components, integrating it, looking for the designs, finding what is important and what is to be learned, and finally making decision what the researcher will tell to others. Patton in Moelong, 2010: 280 claims data analysis as a process of organizing and classifying data into certain pattern, category and basic units of analysis. As a result, the data can be used to discover the theme. The procedures of data analysis in this research were listed as follows: 1. categorizing the data based on three different classification, 2. applying the trustworthiness of the data by asking friends and lecturers, 3. analyzing the data, 4. describing and interpreting the data, and 5. deriving the conclusions based on the result of the research.

F. Data Trustworthiness