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2019 | OriginalPaper | Chapter

Designing an Interface for Sharing Quantitative Ethnographic Research Data

Authors : Zachari Swiecki, Cody Marquart, Arjun Sachar, Cesar Hinojosa, Andrew R. Ruis, David Williamson Shaffer

Published in: Advances in Quantitative Ethnography

Publisher: Springer International Publishing

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Abstract

Recently, there have been growing calls to make research data more widely available. While the potential benefits of sharing research data are many, there are also many challenges, including the interpretability, attendability, and complexity of the data. These challenges are particularly salient for research data associated with quantitative ethnographic analyses, which often use relatively novel and sophisticated techniques. In this paper, we explore design considerations for an interface for sharing research data that attempts to address these challenges for quantitative ethnographic analyses. These considerations include: (a) maintaining the consistency of the interpretive space, (b) simplifying model details, (c) including example results and interpretations, and (d) highlighting key affordances in the user interface. To explore these considerations, we describe the design of an interactive visualization of the thematic networks present in the HBO television series, Game of Thrones.

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Footnotes
1
Of course, there are many other important challenges associated with sharing research data, including: practical challenges, such as the establishment of sharing protocols, epistemological challenges, such as what it means to reproduce results or whether it is possible at all, and ethical challenges, such as maintaining the confidentiality of participants involved in the data collection. However, these challenges are beyond the scope of this paper.
 
Literature
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go back to reference Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017) Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017)
go back to reference Shen, H., Huang, J.Z.: Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 99(6), 1015–1034 (2008)MathSciNetCrossRef Shen, H., Huang, J.Z.: Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 99(6), 1015–1034 (2008)MathSciNetCrossRef
Metadata
Title
Designing an Interface for Sharing Quantitative Ethnographic Research Data
Authors
Zachari Swiecki
Cody Marquart
Arjun Sachar
Cesar Hinojosa
Andrew R. Ruis
David Williamson Shaffer
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33232-7_30

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