Quantifying emotionally grounded discursive knowledge with cognitive-affective maps

Jasmin Luthardt (), Jonathan Howard Morgan (), Inka Bormann and Tobias Schröder
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Jasmin Luthardt: Freie Universität Berlin
Jonathan Howard Morgan: University of Applied Sciences
Inka Bormann: Freie Universität Berlin
Tobias Schröder: University of Applied Sciences

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 3, No 32, 1557-1595

Abstract: Abstract Belief systems matter for all kinds of human social interaction. People have individual cognitions and feelings concerning processes in their environment, which is why they may evaluate them differently. Belief systems can be visualized with cognitive-affective maps (CAMs; as reported by Thagard (in: McGregor (ed) EMPATHICA: A computer support system with visual representations for cognitive-affective mapping, AAAI Press, CA, 2010)). However, it is unclear whether CAMs can be constructed in an intersubjective way by different researchers attempting to map the beliefs of a third party based on qualitative text data. To scrutinize this question, we combined qualitative strategies and quantitative methods of text and network analysis in a case study examining belief networks about participation. Our data set consists of 10 sets of two empirical CAMs: the first CAM was created based on participants’ freely associating concepts related to participation in education (N = 10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (N = 10). Both CAM-types were compared along three dimensions of similarity (network similarity, concept association similarity, affective similarity). On all dimensions of similarity, there was substantially higher intersubjective agreement in the text-based CAMs than in the free CAMs, supporting the viability of cognitive affective mapping as an intersubjective research method for studying the emotional coherence of belief systems and discursive knowledge. In addition, this study highlights the potential for identifying group-level differences based on how participants associate concepts.

Keywords: Cognitive-affective mapping; Similarity; Networks; Discourse; Knowledge; Triangulation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11135-021-01195-7

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