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Published on January 7, 2008

Author: Connor

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Strengthening interpretative research through the use of complementary data sources in corpus-based spoken discourse analysis :  Strengthening interpretative research through the use of complementary data sources in corpus-based spoken discourse analysis Fiona Farr University of Limerick Outline:  Outline Background Methodological concerns with qualitative discourse analysis Data description Analysis and results Discussion Background:  Background Examination of feedback interactions in post TP dyadic interactions on an MA in ELT programme. Focus on perceptions of the participants and deconstruction of the communication Pragmatically sensitive encounter – introspection and criticism being prime aims Action research project to examine professional practice and change when/if appropriate How best to uncover and interpret the complexity of the situation? What data? What procedures? What methodology? Methodological Issues:  Methodological Issues Paradigmatic differentiation between quantitative and qualitative (Nunan 1992, Gall, Borg, and Gall 1996, Denzin and Lincoln 2000, Holliday 2002).   Qualitative researchers see themselves as,  ‘ participants in the situations they investigate, and assert that their values and beliefs are multiply involved in choosing what to research, how to research it, and how to represent and to use their findings. They maintain that there are differing versions of truth and reality, depending on perspective, that experience cannot be understood by breaking it up into pieces […] An in-depth, inter-subjective understanding of a particular situation is the desired outcome of their work.’ (Edge and Richards 1998:336)   Issues with Qualitative DA Research:  Issues with Qualitative DA Research Localised ► danger of overgeneralisation or missing significant happenings Partial solution: discourse based corpus approach ‘Broadly, corpus linguistics may be performed in two ways: quantitative and qualitative. The quantitative approach usually looks for the largest corpus possible […..] from as wide a range of sources as possible. These data are then analysed computationally and […] tell the discourse analyst about the frequency of occurrence of words, phrases, collocations or structures. […] But for the discourse analyst, statistical facts raise the question “Why?”, and the answers can only be found by looking at the contexts of the texts in the corpus. Discourse analysts, therefore, work with corpora in a qualitative way.’ (McCarthy, Matthiessen, and Slade 2002:70)  Slide6:  2. Descriptive Issues a. ‘Descriptive validity’ (Maxwell 1992), ‘credibility’ (Edge and Richards 1998) b. Observer’s Paradox (Labov 1972) c. Recapturing context in transcription (Cook 1990, Maxwell 1992, Widdowson 1995, McCarthy 1998) Slide7:  3. Interpretative Issues Credible interpretation involves the cognitive uncovering of the conscious and subconscious intentions, beliefs, concepts, and values of the participants. Problems: - human-related concepts difficult to interpret objectively or consistently (Yule 1996, Jaworski and Coupland 1999) - underlying enigma and inevitable ambiguities of interpreting motives for human actions (Wertsch 1998) Slide8:  Schegloff (1992:128) poses the following questions:   1.   How can we show that what is so loomingly relevant for us (as competent members of a society or as professional social scientists) was relevant for the parties to the interaction we are examining, and thereby arguably implicated in their production of the details of that interaction? 2.   How can we show that what seems inescapably relevant, both to us and the participants, about the “context” of the interaction is demonstrably consequential for some specifiable aspect of the interaction? Slide9:  Possible solutions: Analyst is also member of the community under examination (Labov and Fanshal 1977) Emic perspective, insider’s view through elicitation from the participants, e.g. questionnaires, notes, think aloud protocol (Patton 1990, Lazaraton 2003) Participant researcher paradigm (Morrow and Schocker 1993, Freeman 1996) Data Description:  Data Description Trainee reflective diaries – undirected accounts of TP and feedback experiences Pre-TP feedback questionnaires to establish any predispositions and preconceptions about TP feedback Audio-recordings transcribed into the POTTI corpus Post-feedback questionnaires (trainer and trainee) The POTTI Corpus:  The POTTI Corpus Trainer and Trainee Questionnaires:  Trainer and Trainee Questionnaires Background information How they felt during and after feedback Rate the feedback positively or negatively (scale) Particular aspects that informed these ratings Trainees react to a number of statements Trainers detail their approach, aims, and how they feel the feedback was received The Analysis :  The Analysis 1. General analysis: frequency, keywords and clusters. Participation and interactivity.   Detailed analysis of personal pronouns, vocatives, validating and cathartic words, listenership (response tokens, overlaps and interruptions), modality and hypotheticality   2. Analysis of questionnaire results to establish ratings and perceptions of the feedback sessions   3. Combining corpus and questionnaire data (triangulation) Combining Corpus and Questionnaires:  Combining Corpus and Questionnaires Creation of a Positive Corpus and a Negative Corpus based on the ratings of participants. Relative analysis of the PC and the NC to determine convergence and divergence in the specific areas examined in the general analysis (participation and interactivity, word frequency, response tokens, modality and hypotheticality). Results: Participation and Interactivity :  Results: Participation and Interactivity Very little difference in the quantity or distribution of talk in both corpora: NC = 64.27% : 35.73% PC = 62.49% : 37.51% Very little difference in the relative mean utt length: Trainers: NC = 15.52, PC = 18.85 Trainees: NC = 8.73, PC = 11.39 Relative margin between trainer and trainee utt length: NC = 6.79 words, PC = 7.46 words Results: Frequency:  Results: Frequency Common core of 41 of top 50 most frequent words Of those 41, 11 show more than 10% difference in frequency use in one corpus: More frequent words in PC: that, of, were, at, it’s, be, know, think, would More frequent words in the NC: they, so, this, because 9 words exclusive to each corpus in the top 50 frequency list: See Table 3. Results: Response Tokens:  Results: Response Tokens Minimal and non-minimal response tokens: - overall divergent frequencies - specific differences in frequencies of right, oh, ah Results: Modality and Hypotheticality:  Results: Modality and Hypotheticality NC: significantly higher frequencies of should and need PC: significantly higher frequencies of would and could No significant differences in hypothetical if structure frequencies. More adjectives, adverbs, and nouns with hypothetical meaning occur in the PC e.g. probably, perhaps, possibly.

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