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Exploring public discourses about emerging technologies through statistical clustering of open-ended survey questions

Record type: Article

The primary method by which social scientists describe public opinion about science and technology is to present frequencies from fixed response survey questions and to use multivariate statistical models to predict where different groups stand with regard to perceptions of risk and benefit. Such an approach requires measures of individual preference which can be aligned numerically in an ordinal or, preferably, a continuous manner along an underlying evaluative dimension – generally the standard 5- or 7-point attitude question. The key concern motivating the present paper is that, due to the low salience and “difficult” nature of science for members of the general public, it may not be sensible to require respondents to choose from amongst a small and predefined set of evaluative response categories. Here, we pursue a different methodological approach: the analysis of textual responses to “open-ended” questions, in which respondents are asked to state, in their own words, what they understand by the term “DNA.” To this textual data we apply the statistical clustering procedures encoded in the Alceste software package to detect and classify underlying discourse and narrative structures. We then examine the extent to which the classifications, thus derived, can aid our understanding of how the public develop and use “everyday” images of, and talk about, biomedicine to structure their evaluations of emerging technologies.

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Citation

Stoneman, Paul, Sturgis, Patrick and Allum, Nick (2012) Exploring public discourses about emerging technologies through statistical clustering of open-ended survey questions Public Understanding of Science (doi:10.1177/0963662512441569).

More information

e-pub ahead of print date: April 2012
Published date: 2012
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 339653
URI: http://eprints.soton.ac.uk/id/eprint/339653
ISSN: 0963-6625
PURE UUID: b5f10170-a2a6-4b3e-93c3-16bb9cc9850c

Catalogue record

Date deposited: 28 May 2012 15:39
Last modified: 18 Jul 2017 05:53

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Contributors

Author: Paul Stoneman
Author: Patrick Sturgis
Author: Nick Allum

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