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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.


Predicted probability of optimism about genetic science by membership of narrative class 3 and science knowledge.
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fig2-0963662512441569: Predicted probability of optimism about genetic science by membership of narrative class 3 and science knowledge.

Mentions: To aid the interpretation of statistical interactions, it is useful to produce visual plots of the model fitted values. By way of illustration, Figure 2 shows the predicted probabilities from model 4 for the interaction between science knowledge and narrative class 3. Figure 2 shows that, at low levels of science knowledge, membership of class 3 is associated with low levels of optimism, lower indeed than members of all the remaining groups combined. However, as science knowledge increases, the level of optimism increases rapidly across all respondents but particularly those in class 3, until at the highest levels of knowledge those in class 3 are the most optimistic.


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

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

Predicted probability of optimism about genetic science by membership of narrative class 3 and science knowledge.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2-0963662512441569: Predicted probability of optimism about genetic science by membership of narrative class 3 and science knowledge.
Mentions: To aid the interpretation of statistical interactions, it is useful to produce visual plots of the model fitted values. By way of illustration, Figure 2 shows the predicted probabilities from model 4 for the interaction between science knowledge and narrative class 3. Figure 2 shows that, at low levels of science knowledge, membership of class 3 is associated with low levels of optimism, lower indeed than members of all the remaining groups combined. However, as science knowledge increases, the level of optimism increases rapidly across all respondents but particularly those in class 3, until at the highest levels of knowledge those in class 3 are the most optimistic.

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.