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International Journal of Innovation in English Language … ISSN: 2156-5716 Volume 3, Number 1 © Nova Science Publishers, Inc. METHODOLOGICAL CONSIDERATIONS IN INTERVIEW DATA TRANSCRIPTION Handoyo Puji Widodo The University of Adelaide, Australia Politeknik Negeri Jember, Indonesia ABSTRACT This arti...

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International Journal of Innovation in English Language … ISSN: 2156-5716 Volume 3, Number 1 © Nova Science Publishers, Inc.

METHODOLOGICAL CONSIDERATIONS IN INTERVIEW DATA TRANSCRIPTION Handoyo Puji Widodo The University of Adelaide, Australia Politeknik Negeri Jember, Indonesia

ABSTRACT This article briefly presents key methodological issues in interview data transcription. These include (a) listening to talking data, (b) shaping talking data, (c) communicating talking data with an interpretative intent, (d) reproducing or (re)constructing talking data, and (e) building data credibility. This methodological consideration shapes how interview data in particular should be transcribed based on a particular methodological choice or orientation. The contribution of this article is to provide a conceptual and practical guide for novice qualitative teacher researchers in the area of TESOL who engage in continued professional development through teacher or practitioner research so that they are methodologically well informed of managing, analyzing, and interpreting interview data, so called written research artifacts. In this article, I contend that transcription serves as a useful tool for representing, analyzing, and interpreting talking data. This interview research can be one of the ways to explore teacher‟s articulated beliefs and practices in the TESOL landscape.

Keywords: Interview data, research methodology, teacher research, TESOL, transcription

INTRODUCTION Transcription has played a central role in spoken language analysis and representation. It is part of the qualitative research activities designed to capture and unpack the complicatedness and meanings of naturally occurring phenomena (e.g., values, beliefs, feelings, thoughts, experiences) in social encounters. These phenomena can be best captured through talking or stories constructed or jointly constructed by participants and researcher. This talking or story telling is commonly mediated through interviews. There is no denying that transcribing interview data becomes the norm in most qualitative research studies. Literally, transcription is a useful means for turning digitally recorded interview data (findings) into transcripts, but methodologically speaking, transcription is the act of 

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representing original spoken text (recorded talking data) in written discourse as well as analyzing and interpreting instances of these data (Bird, 2005). These data take the form of transcripts, which are viewed as texts, jointly created by research participants and researchers through dialogic and negotiated conversations. In other words, transcription is seen as the act of data representation, analysis, and interpretation. Indeed, it is a social activity that involves a sound methodological orientation inasmuch as the centrality of transcripts methodologically forms the basis for what research questions are being addressed. To fill this burgeoning need, this article briefly discusses some key methodological issues particularly in interview data transcription in order to help emerging or beginning researchers in the area of TESOL to prepare their transcripts drawn from interview data on the right track. In addition, interviewing is a tool for professional development in which language teachers engage in research that examines classroom realities drawn from teacher stories. These methodological considerations include (a) listening to talking data, (b) shaping talking data, (c) communicating talking data with an interpretative intent, (d) reproducing or (re)constructing talking data, and (e) building data credibility. These issues are drawn from a one semester long life story interview that I conducted with a Taiwanese EFL faculty member. The contribution of this article is to provide a conceptual and practical guide to managing, analyzing, and interpreting interview data; assuredly, it helps novice teacher researchers to have a better understanding of working with talking data methodologically. Some Five methodological issues discussed in this article become a springboard for exploring more issues in interview data transcription when doing qualitative research using a set of interviews.

LISTENING TO TAKING DATA The first step in organizing and analyzing talking or verbal data is doing the transcribing, which involves close observation of data through carefully repeated and attentive listening. Transcribing verbal data is a useful starting point for data organization and analysis for some reasons. Firstly, transcribing verbal data affords a teacher researcher the opportunity to carefully listen to, pay close attention to, and think deeply of digitally recorded data situated within a particular interview context. This socio-cognitive activity involves how researcher‟s mind interacts with spoken text. Secondly, as Anderson and Jack (as cited in Matheson, 2007) point out, transcribing own data “provides a unique opportunity for [researchers] to critique their own work and potentially improve upon their interviewing technique“ (p. 549). Thirdly, listening to verbal data allows a researcher to analyze what emerging finding themes should further be examined and enables her or him to reflect on what she or he has asked to the participants. To facilitate close observation (attentive listening) of transcripts or noticing of unanticipated phenomena, making recordings (e.g., digital audio recordings or digital video recordings) is needed to capture sufficiently accurate or detailed data for most qualitative research projects. Currently, digital recordings are widely deployed in that these can easily be connected to computer software for quick data storage and process, but digital files take up huge quantities of computer memory.

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Attentive listening can include the following steps, as listed below: 

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Playing back recorded data for recalling information (warm-up listening): A researcher needs to recall empirical information collected during the fieldwork. This first listening helps her or him remember and reflect on what she or he has recorded. This warm-up listening helps re-familiarize the researcher with the collected data, so she or he should play back the entire recorded data. Playing back the data for recognizing main points of the data (follow-up listening): At the second stage of listening, the researcher focuses on understanding main points and seeing some emerging findings that may answer or relate to research questions. This follow-up listening assists the researcher in looking at the global picture or gist of talking data. Listening to the talking data for detailed information (close listening): In this close listening, the researcher looks at more detailed information so that she or he can see a connection between the research questions and the emerging findings to help her or him analyze and interpret the data in depth. Listening while transcribing spoken data (repeated and selective listening): At this listening-while-transcribing stage, the researcher is listening to talking data intermittently and repeatedly while transcribing talking data. This process involves repeated listening, more focused writing, repeated writing, and reflecting on what has been transcribed. This activity is a way to transform talking data into written text and organize written the text accordingly in which the depth or selectivity of information depends on the foci of research. This repeated listening enables the researcher to scrutinize the data and interact with talking data in detail. It is also important to note that selective listening assists the researcher to focus on certain findings because some information may answer research questions or provide empirical insights into these questions. Listening for data analysis and interpretation (analytical listening): Once the researcher has transcribed all the data, she or he concentrates on analyzing and interpreting the transcribed data. This analytical listening allows for how the data are represented, examined, and conceptualized.

These types of listening enable a researcher to make an informed decision on what to transcribe and see emerging data. It is important to bear in mind that researchers can do five types of listening back and forth. Assuredly, such listening plays a pivotal role in enabling researchers to get closer and more familiar with the talking data gathered when working with these artifacts. This allows for accurate and in-depth analysis and interpretation.

SHAPING TAKING DATA How researchers shape or present verbal data in written form depends, to some extent, on transcript layout or format choices. I would like to suggest some layout features of transcripts. To begin with, researchers should provide data identity (e.g., data code and number, data collection date, involved participants, data collection methods). This information enables researchers to easily retrieve the data and allows for tidily organized data management. As

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exposure to participant‟s identity is much ethically concerned, a researcher is advised to assign a pseudonym to the participant‟s name. For this reason, data confidentiality needs to be clearly spelled out prior to the fieldwork. This ethical issue is usually communicated to prospective participants before interview sessions commence. Secondly, researchers had better leave space for transcription symbols to give the reader with “a set of [spoken] conventions for displaying actions and utterances in naturalistic situations” (Ochs, 1979, p. 61). These symbols or notations help the reader read the presented talking data. These symbols are viewed as mechanical features of talking or spoken text. Thirdly, a researcher should structure transcripts into line-by-line dialogs to show turn-by-turn dialogic interaction between participants and herself or himself the researcher. This organization allows for easy data coding and analysis. Further, the names of participants need to be coded by one or two letters possibly along with one or two numbers to allow more space for a dialog. What follows is that researchers had better number each line-by-line dialog to make data analysis easier. This is true when qualitative studies include narrative inquiries, diaries, case studies, and life story interviews in which a range of vignettes or recounts are presented. In addition, if participants and an appointed critical debriefer (peer) wish to verify information taken from a transcript, they will easily double check the information in the original transcript. Moreover, a researcher has to leave space for data feedback, verification, and accuracy when member checking is done. In member checking, the transcribed data are checked and verified by participants (This member checking will be chronicled in the next section). Here are other general guidelines for shaping talking data: 

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Prepare a template (transcript design/format): A researcher should prepare a template containing some features of a transcript as earlier mentioned. She or he can design a the template based on the nature of data organization as well as data storage and retrieval. Manage information (data management or organization): Once the researcher has designed the template, she or he proceeds to put and manage data in the transcript template. Numbering lines of dialog is a way to take a closer look at particular data in case there are a large number of data in the transcript. Humanize transcribed data (grammar of data): Interview data are naturally occurring information that should be presented in an interactionally adequate way. They should be assigned with spoken notations or symbols (e.g., intonations, hesitation markers, truncated talks). These symbols indicate the nature of conversational utterances and show the difference between spoken discourse and written discourse. Situating transcribed data ethically (ethical situatedness): Actors involved in the interviews should be kept confidential unless they would like their names to be identified as written in an informed consent form. The researcher should communicate this issue to her or his participants to avoid ethical conflicts. The degree of confidentiality and anonymity varies from a socio-institutional context to another.

Thus, transcribed data should be presented in such a reader‟s friendly manner to assist researchers in better analyzing particular information and re-examining this information for further emerging finding theme examination. The transcript layout varies, depending on the

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nature of qualitative research undertaken. More importantly, data should clearly be presented, readable (easy to read or identify), and easily analyzed and interpreted; indeed, clear transcribed data presentation enhances data readability.

COMMUNICATING TALKING DATA WITH AN INTERPRETATIVE INTENT Communicating talking data means detailing and interpreting them in a methodologically sound manner. This involves how much detail talking data should be transcribed. As a rule, this transcription practice involves two levels or approaches of transcription: naturalism and denaturalism. The former suggests that every instance of utterances should be transcribed in greater detail, but in the case of the latter, idiosyncratic elements of speech (e.g., stutters, pauses, fillers, non-verbal signals) need to be removed. These two methodological positions correspond to certain views about the representation of a language. In a naturalized approach, a language construes and represents real-life phenomena (Schegloff, 1997) in that both verbal and non-verbal languages and interactions shape communicative meanings, but what matters in denaturalized transcripts is that meanings and perceptions construct one‟s reality. The choice of a transcription approach, either naturalism or denaturalism, depends on a degree of reflexivity required, nature of research (e.g., a research design), data analysis focus, sensitivity to participants, and the nature of their involvement in the research. However, researchers who are willing to look closely at different aspects of talking data, a naturalized approach would be a good fit because this detailed transcription shows the complexity of the transcription process, maintain representation or authenticity of lived experiences, and modulate the interpretation of transcription data at a given delicacy level (Tilley, 2003). It is important to bear in mind that in talking data transcription, any grammatical mistakes are left uncorrected because spoken discourse may comprise grammatical mistakes, which sound natural as long as the intended meaning is successfully communicated or understood. In this respect, this transcription goes beyond the norm of standardization regarding language accuracy. Regardless of the transcription approaches, researchers should be responsible for making meaning of or unpacking verbal data with an interpretive intent. The interpretive intent has much to do with how talking data are qualitatively analyzed and interpreted. In this respect, a researcher should be able to make sense of the data, and she or he should recognize that the data should be differently interpreted through different theoretical and empirical lenses. It is also necessary to emphasize that this interpretative intent is affected by sensitivity to a socioinstitutional context or situatedness, commitment and rigor, transparency and coherence, and significance and impact because qualitative data are inherent in fluid socially built meanings, social standards and values, beliefs, locally situated knowledge and experience, and practices. Therefore, the trustworthiness of data interpretation depends entirely on close proximity to research context and inter-subjective exchange between participants and researcher in order for researchers to make sense and meaning of such social phenomena. This interpretative intent facilitates them in (re)constructing verbal data in relevant depth and breadth.

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COMMUNICATING TALKING DATA WITH AN INTERPRETATIVE INTENT Due to the advent of digital technology, audio and video recordings become a common practice of capturing or collecting verbal data in qualitative research. Digital recordings ease researcher‟s work regarding data transcription. Transcribing data does not simply mean (re)producing accurate transcripts, but communicating inner voices of research participants naturally and credibly (Hammersley, 2010). Transcribing verbal data is not sort of data (re)production because this implies that researchers exploit data from the exploited (research participants). Secondly, doing the transcribing as data (re)production suggests that researchers control how data are collected during interviewing. In an ethical sense, I argue that doing transcription is a way to (re)construct talking data because participants and a researcher jointly create data through socio-cognitive encounters (dialogic conversation or collaborative dialog). In this sense, participants are seen as co-actors who have a legitimate ownership of knowledge and help a researcher better understand realities or inner voices of the participants. Drawing on this notion, both participants and a researcher play a pivotal role as data coconstructors whose responsibility is to unpack the multiplicity of knowledge, voice, and experience from different perspectives. I argue that the ownership of such knowledge, idea, and experience belongs to research participants. For this reason, before fieldwork commences, it is useful for a researcher to spell out levels of involvement or roles both participants and she or he have to play to establish trust and show mutual respect. In short, both the participants and the researcher need to build and maintain a good rapport until the fieldwork is completed.

BUILDING DATA CREDIBILITY Credibility is a unique feature of empirical qualitative research. One of the main ways to achieve credibility is by doing member checking. Member checking allows research participants to provide feedback on the accuracy of how talking data are presented and interpreted. Buchbinder (2011) suggests another term „validation interview‟ as a means of facilitating “a dialogue between [research participants] and [researcher] intended to confirm, substantiate, verify or correct researcher[‟s] findings” (p. 107). I contend that whether a researcher uses the term, either „a member check‟ or „validation interview,‟ relies upon a particular methodological stance. The most salient thing is that a researcher should be willing to communicate transcripts to research participants in order to achieve data credibility because research participants serve as the source of knowledge; for this reason, the data co-constructed with the participants should be communicated to them at during while- and post-fieldwork briefing meetings. Moreover, a researcher can appoint a critical peer who is well versed in research methodology and nature of research to look at whether the data are appropriately presented, analyzed, and interpreted. This etic or outsider‟s input also helps enhance data credibility or trustworthiness. Thus, both member checking and critical audit trail (peer review) make data analysis and i...