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

Stoneman P, Sturgis P, Allum N - Public Underst Sci (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: University of Southampton, UK.

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.

No MeSH data available.


Alceste correspondence analysis: DNA.
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fig1-0963662512441569: Alceste correspondence analysis: DNA.

Mentions: The Alceste-generated contingency table which outlines the clusters and associated key words can be presented graphically as a correspondence plot which will identify similarities and/or differences between the classes. From Figure 1, we can see that all of the descriptive discourse is on the right hand side of the chart, while the more function-based classes appear on the left. Thus, we can think of members of the public being positioned on a dimension from description to functionality in relation to DNA. The accurate description in class 3 is the most distinctive class, in terms of its distance to the middle of both horizontal and vertical axes, and in terms of the distance between it and the other classes. Respondents in class 3 appear, then, to be the most distinct group.


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

Stoneman P, Sturgis P, Allum N - Public Underst Sci (2012)

Alceste correspondence analysis: DNA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2 - License 3
Show All Figures
getmorefigures.php?uid=PMC4400270&req=5

fig1-0963662512441569: Alceste correspondence analysis: DNA.
Mentions: The Alceste-generated contingency table which outlines the clusters and associated key words can be presented graphically as a correspondence plot which will identify similarities and/or differences between the classes. From Figure 1, we can see that all of the descriptive discourse is on the right hand side of the chart, while the more function-based classes appear on the left. Thus, we can think of members of the public being positioned on a dimension from description to functionality in relation to DNA. The accurate description in class 3 is the most distinctive class, in terms of its distance to the middle of both horizontal and vertical axes, and in terms of the distance between it and the other classes. Respondents in class 3 appear, then, to be the most distinct group.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: University of Southampton, UK.

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.

No MeSH data available.