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| David Silverman |
| Chapter 20
Writing Your Data Chapters
Based on her fieldwork in Bali, Pam Nilan examines the problems involved in writing up research from messy and chaotic datasets.
Qualitative Research, 2 (3), 363-86 (2002)
http://qrj.sagepub.com/cgi/reprint/2/3/363
'"Dangerous fieldwork" re-examined: the question of researcher subject position'
Pamela M. Nilan, University of Newcastle, Australia
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| In this fascinating paper, based on her study of music education, Kathryn Roulson reviews the problems she discovered in her first research report and shows how she revised her data analysis with striking results.
Qualitative Research, 1 (3), 279-302 (2001)
http://qrj.sagepub.com/cgi/reprint/1/3/279
'Data analysis and "theorizing as ideology"'
Kathryn Roulson, University of Georgia, USA
EXERCISE
Kathryn Roulson reveals the importance of analytical models in shaping your data analysis.
- What model informs your research and why are you using it?
- Try redoing your analysis of a small piece of data using a different model.
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| In this article, Marvasti and Faircloth show how 'romantic' assumptions may unintentionally influence how we do ethnography.
Qualitative Inquiry, 8 (6), 760-84 (2002)
DOI 10.1177/1077800402238078
http://qix.sagepub.com/cgi/reprint/8/6/760
'Writing the exotic, the authentic, and the moral: romanticism as discursive resource for the ethnographic text'
Amir Marvasti, Penn State Altoona, USA
Christopher Faircloth, Boston University Gerontology Center, USA
TIP
Romanticism is an approach in which 'authenticity' is attached to 'personal' experiences. Although this approach originated in the early nineteenth century, it underlies much contemporary popular culture.
EXERCISE
- What 'romantic' elements are present in your data analysis?
- How would your analysis differ/be improved/worsened if you removed them?
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