Building Collections, Transcribing and Data Analysis

This section presents how the interaction data were approached, starting from the same recordings being examined multiple times in order to get the overall view of the recorded interaction. Following this, the interaction involving assessment sequences was collected and compiled into collections and the data were transcribed and analysed, as described in the following section.

3.5.4.1 Building the Collection

Building collections of instances of a particular phenomenon of interest is an important stage for analysis as it allows the systematic examination of the phenomenon in different sequential environments and across participants. Sidnell (2010) emphasised that building the collections of instances across sequential contexts is of great importance in that ―different cases reveal different aspects or features of a phenomenon‖ (p. 31). Another advantage of building collections of the phenomenon of interest was raised by Liddicoat (2007), who

58

argued that analysis of collections of instances can permit the researcher to test the robustness of a particular action of interest by examining it in repeated instances.

Building the collections generally involves the process of identifying the phenomenon that is centrally examined in the study: assessment sequences, described in this study as talk and the embodiments which are produced by the participants and appear to display (and are treated to display) their negative stance or evaluation towards artefacts and practices in the co-participants‘ countries (see the detailed description of negative assessments in Section 2.4.4).

Once the phenomenon of interest has been described and located, the possible instances can be gathered into a collection.

The building of collections involves examining recordings for all candidate cases of the phenomenon (i.e. negative assessments). With a preliminary description of the negative assessments, the recordings were examined to build a collection of assessment sequences. It is suggested by CA guidebooks (e.g. Sidnell, 2010) that the instances of the phenomenon of interest should be gathered widely and generously; everything that seems to satisfy the description can be collected, so the range of variation in the phenomenon of interest will be included for analysis. Hoey and Kendrick (2018) echoed this generous collection of instances, claiming that this action can help resist the researcher‘s rigid labelling of the phenomenon before its nature becomes clear and possibly help refine the description or criteria of the phenomenon of interest once the collection is expanded, covering it in a range of different environments of interaction.

Following the above guidelines for the collection building, assessments that can be describable as negative assessments were collected. Both turns that clearly include assessment terms (e.g. ―not good‖, ―so small‖) displaying speakers‘ evaluation towards the referents and the turns which are potentially assessments (e.g. without the use of assessment terms but are treated by the recipients as such) were included into the collections. That is, the identifying process goes beyond a simple search for the use of assessment terms. Additional cues (e.g.

prosodic contours, facial display) as well as the recipients‘ responses which potentially indicate the turns as negative assessments were taken into account and come to have a role here in identifying the instances of assessment sequences.

3.5.4.2 Transcription

A transcript, in conjunction with the recordings, is the basis for doing the analysis (ten Have, 1999). The salience of the transcript was described by ten Have (2007) as ―the timeline

59

of the interactional stream‖ (p. 32) that researchers can present to the audience about the contribution of each participant in relation to the others in interaction, noting interaction phenomena such as pauses, laughter, overlapping, speech rate and intonation. In this study, a version of transcription convention originally developed by Gail Jefferson (2004) will be followed (see Appendix D). This transcription convention is commonly used by conversation analysts, and it allows the delivery of the talk details to be presented to an audience, delivering many details of the talk by incorporating not only what is said but also how the utterance is said.

It is useful to note that the transcript is not a replacement, but a representation of the actual interaction data (Heritage, 1984; Psathas & Anderson, 1990), which is used as a tool for presentation of the analysis, allowing the analyst as well as the audience to see the details of live talk that are captured in a written static format which is more convenient to follow (Liddicoat, 2007; ten Have 2007). Two digital transcribing software tools - Audacity and CLAN - were utilized in doing the transcription. These software tools allowed the researcher to complete the transcription process faster and in a more valid manner. Audacity is used mainly to listen to the sound files at lower rates of speed when needed, while CLAN allows transcripts to be digitally aligned to the video files. This helped the researcher to easily access the video data immediately from the transcript at a later point after transcription.

The first step in developing the transcript is to capture the verbal interaction that is produced by the participants, but it is not only words that are transcribed. Details of how words and utterances are produced are also put in the transcript, including other phenomena in talk (e.g. stress, intonation, lengths of sounds, speed of delivery, overlap) as these features can be interactionally important for the participants as well as for the analysts (Liddicoat, 2007).

For instance, intonation can distinguish between questions and declaratives while stress can be used to communicate contrast and emphasis. Similarly, other characteristics of speech production, for example elongation of sounds, pauses, contiguous talk, and overlap have implications in understanding the turn production. Thus, it is crucial to represent these communicative features in the transcripts. Information, such as the date and places of the recording and nationalities of the participants is also provided in the transcripts. Additionally, non-verbal elements of talk that are relevant to the actions are also included in the transcripts.

The embodiments produced by speakers (e.g. mutual gaze and body movement) which seem to make meaning for the speakers and recipients were included in the transcripts, illustrating

60

what are produced by the speakers and when they are produced. To be specific, description and/or screen shots of the actions were provided another line along the verbal production.

It needs to be emphasised that the recordings were re-visited and repeatedly transcribed in order to add granular details and obtain a good informative transcript. Later, the transcripts were cross-checked by a colleague who was also doing conversation analysis research.

Moreover, presentation in data sessions in the Micro-Analysis Research Group (MARG), in which some of the transcripts were commented on by CA colleagues, is believed to have helped improve the quality and validity of the transcription.