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

Stoneman, Paul, Sturgis, Patrick ORCID: 0000-0003-1180-3493 and Allum, Nick (2013) Exploring public discourses about emerging technologies through statistical clustering of open-ended survey questions. Public Understanding of Science, 22 (7). pp. 850-868. ISSN 0963-6625

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Identification Number: 10.1177/0963662512441569

Abstract

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.

Item Type: Article
Official URL: https://journals.sagepub.com/home/pus
Additional Information: © 2012 The Authors
Divisions: Methodology
Subjects: H Social Sciences
T Technology
Q Science
Date Deposited: 09 Oct 2019 11:00
Last Modified: 20 May 2020 04:06
URI: http://eprints.lse.ac.uk/id/eprint/101969

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