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CHAPTER 3 RESEARCH METHODOLOGY
This chapter discusses the methodology of the research, and it is divided into three parts: research type, research data, and data analysis. The research type
explains the kind of study this research belongs to. The research data provides description of the nature and the origin of the data used in this study. This part
also includes the explanation of how the data were collected and processed. The last part, data analysis explains how the data were processed and interpreted.
3.1 Research Type
In general, the research belongs to the domain of a Corpus Linguistic Study with some reasons. The first reason is that the research used a collection of
data set which is claimed to be natural namely corpus. The research is empirical, analyzing the authentic patterns of use in natural texts Biber, 1998. The natural
data is also said to be a „real world‟ text as the instances in the corpus are simply written from the real usage of language. The corpus machine gives access to
naturalistic language information, to texts which are products of real life situation. This research in fact took the natural language information from the corpus to
make a probabilistic model to grammar of the real life language users. The second reason is that the research used the corpus as foundation of
analysis. The research utilized a large and principled collection of natural texts, known as a “corpus” as the basis of analysis Biber, 1998. The research was
applied in the domain of the modern form of corpus linguistics, where the
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collection of the data relies heavily on the use of computer. The research gathered the instances from millions words of corpus. The research analyzed the cases of
benefactive construction which appeared in COHA data set. COHA corpus possesses four hundred millions of words written in Corpus BYU web site. The
corpus consists of a large number of naturally occurring texts. Although, the data is sometimes claimed not to represent the entire language, the validity tests done
to the data helped to convince the readers that the corpus information represents most of the language use in real life situation.
The third reason is that this research employed both automatic and manual procedure. Corpus linguistics research extensively uses computer for analysis,
using both automatic and interactive technique Biber, 1998. In this research, the computer played important role in selecting the data from the corpus machine.
This electronically readable corpus had reduced the time needed to find particular construction in this case benefactive construction. The researcher only needed to
put query into the string, then the instances involving the query appeared on the screen of computer. The researcher, however still needed to manually select,
recheck, and annotate the occurrences with benefactive construction. Finally, this research used both qualitative and quantitative analytical
technique. The research included the process of annotating the linguistic features of the instances. The research also occupied the analysis of significance of the
features to the choice of benefactive construction. The direction and the size of the effect of the relevant features were described qualitatively. At the same time, the
research observed the frequency of the benefactive occurrences. Also, the research involved the binary-coded features and continuous scale feature of dependent and
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independent variables. The coefficient B, standard error S.E, odds ratio expB, and 95 CI of the linguistic features were basically obtained from the
analysis of the numbers in the SPSS. This kind of analysis belongs to the domain of quantitative research. This fact confirms one of the characteristics of Corpus
Linguistic Study suggest by Biber 1998. In addition, the research also belongs to the domain of probabilistic
grammar. Theoretically, the research adopt the idea of Bresnan 2007 to apply a dynamic probabilistic grammar Bybee Hooper 2001; Bod
et al.
2003; Gahl Garnsey 2006; Gahl Yu 2006 to the domain of syntactic variation. Besides
aiming at finding the significant features which are relevant to the choice of benefactive, this research also tried to predict the occurrences of certain instances
based on the features found. Given the coefficients of the significant features, the probabilistic model was able to predict what construction tends to be used by the
speakers.
3.2 Research Data