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The nature of participants’ involvement in this study was voluntary. Bordens and Abbott 2008 suggest that voluntary-based participants have two major disadvantages, these are: 1
volunteer bias, and 2 the ungeneralizable nature of the research findings. These disadvantages were not issues in the study because: 1 the object of the study was the texts written by the
participants, not the participants who wrote them, for their course assignments -- not for the study; and 2 as stated previously that case study, the type of qualitative study this study belongs
to, is not intended to make generalization but to investigate one particular case Hood, 2009. The limitation of nine research articles in the study was for the purpose of comprehensive
analysis since larger amount of data would not allow such comprehensiveness. In addition, the rationale behind the involvement of the written work of the three participants in this study was
the fact that they were products of adult writers whose exposure to the mature scientific written work through their education entails likelihood of grammatical metaphor incorporation in their
texts Christie, 2002; Christie and Derewianka, 2008; Halliday, 1993 which was the main interest of this study.
3.5 Data Collection
Even though data collection and data analysis in qualitative research are conducted simultaneously Hood, 2009; Merriam, 1991, the two processes will be described separately in
this chapter for purposes of clear description. The study incorporated document analysis as the technique for data collection. The main
data source for this purpose was nine research articles written by three postgraduate school students, from each of whom three writing assignments were collected. The assignments were
written by these students as assignments in their first three semesters studying at the university.
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Thus, the texts used in the study possess high degree of objectivity and stability since they were produced in the absence of the researcher’s intrusion Lazaraton, 2009; Merriam, 1991.
However, as suggested by Merriam ibid, there are two major problems of data collection in document analysis, namely of authenticity and objectivity. These problems may
arise due to the fact that the data in such process “are subject to purposeful and nonpurposeful deception”. Of these two constraints, the main issue encountered in this study was that regarding
authenticity in form of plagiarism. This is due to the closely-relatedness of academic writing with referencing and quoting sources Tweddle, 2009. Incorrect ways in doing these may lead to
the infringement of plagiarism ibid. Due to time and software constraints in conducting a thorough selection to guarantee plagiarized-free research articles inclusion into the study, the
articles were included without any such process. Despite enrolling in the same year at the postgraduate school, the Field of the texts
written by the participants in the study might widely differ. This was due to the voluntary nature of this research in which the participants were free to submit the assignment from each semester
to this study on their own accord. To illustrate, there were five courses taken by the participants each semester and they were free to submit any research article of any course from each semester
to be involved in this study. The texts used in this study, along with the course for which each was written are presented in Table 3.1 below, while a full sample text can be seen in Appendix
3.5.
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Table 3.1 Texts Used in the Present Study Students
Semester 1 Title, Course
Semester 2 Title, Course
Semester 3 Title, Course
Low achiever Title: The Effectiveness of Using
Pictures in Descriptive Writing: A Case Study at the Second Year
Students of SMA Islam al Musyawarah
Lembang in
Academic Year 20082009 Course: EFL Methodology
Title: Indonesian EFL Curriculum and Malaysian
ESL Curriculum A Comparative Study of
Primary School and Secondary School
English Curriculum Course: EFL Curriculum
Analysis Title: Identifying the Types
of Teacher’s Questions Asked in the Teaching and
Learning Course: Language Testing
and Evaluation
Mid-achiever Title:
English Learning
Motivation Score
and Its
Correlation with Integrativeness and
Attitudes Toward
the Learning Situation
Course: EFL Methodology Title: Flouting of
Conversational Maxims Found in the Movie Kung
Fu Panda Course: Language in Use
Title: The
Functions of
Teacher’s Questions
in Learning process: A case
Study at SMU 1 CIsarua Course: Language Testing
and Evaluation
High-achiever Title: Grouping by Learning
Style: a Comparison with Unpremediated Grouping
Schemes in EFL Classroom Course: EFL Methodology
Title: Comparing
Educational-Unit-Based Curriculum
KTSP for
English as Local Content in State
and Private
Elementary Schools Course: EFL Curriculum
Analysis Title:
Teacher-Student Cultural Congruence as Reflected in
the Usage of Teaching Media Course: Language Testing
and Evaluation
Coding is one important aspect in qualitative data analysis Hood, 2009; Merriam, 1991; Seidel, 1998 in which each piece of data important for the purpose of the study is assigned a
unique, either textual or alphanumeric, marker system Hood, ibid. The writing assignment collected was coded SA1.A, SA1.B, and SA1.C; SA2.A – SA3.C This labeling is configured as
follows: SA stands for Student’s Assignment; number following SA indicates the writer of the assignment, Student 1 – Student 3; and the letter following the number indicates the semester
from which the assignment was taken, e.g. A refers to the first semester, B refers to the second semester and C refers to the third semester. So, for example a text coded SA1.A is the
assignment written by Student 1 as hisher first semester assignment; SA1.B is the assignment written by Student 1 of hisher second semester assignment; SA1.C is the assignment of Student
1 of hisher third semester assignment, etc. The detail of this labeling is illustrated in the following table.
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Table 3.2 Writing Assignment Labeling Writer
Semester Coding
Student 1 1
SA1.A 2
SA1.B 3
SA1.C Student 2
1 SA2.A
2 SA2.B
3 SA2.C
Student 3 1
SA3.A 2
SA3.B 3
SA3.C
3.6 Data Analysis
