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High Performance School-Age Athletes at Australian Schools: A Study of Conflicting Demands
Dataset

High Performance School-Age Athletes at Australian Schools: A Study of Conflicting Demands

Maureen O'Neill
The Qualitative Data Repository
Center for Qualitative and Multi-Method Inquiry
2017
DOI: https://doi.org/10.5064/F6ZP448B
url
https://doi.org/10.5064/F6ZP448BView
Published Version Open

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Abstract

Human Movement and Sports Science Specialist Studies in Education athlete bullying high performance NVivo parent school age schools student-athlete teacher
All interviews were originally audio-recorded and then transcribed verbatim. A Livescribe pen was used to record and then converted to .mp4 for transcription in NVivoâ„¢. Observations were noted about each participant only during the interview. For the purposes of sharing the underlying data, all transcripts have been anonymized using pseudonyms, used throughout the text and as part of the files naming convention. Additionally, a letter code preceding the pseudonym in the file titles stands for A (athlete), P (parent), T (teacher). In the few cases where an interviewee had two or three of these designations, the primary category was selected (ex: a parent who was also a former athlete was labeled "P", while a teacher who happened to also be a parent of one of the students, was labeled as "T"). Pattern codes which were converted from excel sheets are included to demonstrate the thematic pattern coding for the group known as athletes. Additionally, there are two examples of individual word queries in NVivo, including the word tags "bullying", as well as "tired and sore". As individual word queries, these depict pieces of conversational analysis. A document depicting word frequency query is also present, as an example of cluster analysis outputs that can be created in NVivo, which serves to visualize a number of themes across all interviews. Finally, there is an example of a manually abstracted transcript, as an image. This document provides an example of how to interrogate data prior to their input in NVivo software. The hand-written notes on the left list concerns about tensions and feelings. The notes on the right indicate broad initial themes that the researcher identified. With that, this process reflects a first step in providing greater transparency to the study prior to the second step of aggregating and entering it into the software.

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