Supervised scaling of semi-structured interview transcripts to characterize the ideology of a social policy reform

Pierre-Marc Daigneault (), Dominic Duval () and Louis M. Imbeau ()
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Pierre-Marc Daigneault: Université Laval
Dominic Duval: Université Laval
Louis M. Imbeau: Université Laval

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 5, No 12, 2162 pages

Abstract: Abstract Automated content analysis methods treat “text as data” and can therefore analyze efficiently large qualitative databases. Yet, despite their potential, these methods are rarely used to supplement qualitative analysis in small-N designs. We address this gap by replicating the qualitative findings of a case study of a social policy reform using automated content analysis. To characterize the ideology of this reform, we reanalyze the same interview data with Wordscores, using academic publications as reference texts. As expected, the reform’s ideology is center/center-right, a result that we validate using content, convergent and discriminant strategies. The validation evidence suggests not only that the ideological positioning of the policy reform is credible, but also that Wordscores’ scope of application is greater than expected.

Keywords: Automated content analysis; Supervised scaling; Quantitative text analysis; Social policy; Mixed methods; Case study (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11135-017-0650-0

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