Response Item Network (ResIN): A network-based approach to explore attitude systems

Dino Carpentras (), Adrian Lueders and Michael Quayle
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Dino Carpentras: ETH Zürich
Adrian Lueders: University of Hohenheim
Michael Quayle: University of Limerick

Palgrave Communications, 2024, vol. 11, issue 1, 1-14

Abstract: Abstract Belief network analysis (BNA) refers to a class of methods designed to detect and outline structural organizations of complex attitude systems. BNA can be used to analyze attitude-structures of abstract concepts such as ideologies, worldviews, and norm systems that inform how people perceive and navigate the world. The present manuscript presents a formal specification of the Response-Item Network (or ResIN), a new methodological approach that advances BNA in at least two important ways. First, ResIN allows for the detection of attitude asymmetries between different groups, improving the applicability and validity of BNA in research contexts that focus on intergroup differences and/or relationships. Second, ResIN’s networks include a spatial component that is directly connected to item response theory (IRT). This allows for access to latent space information in which each attitude (i.e. each response option across items in a survey) is positioned in relation to the core dimension(s) of group structure, revealing non-linearities and allowing for a more contextual and holistic interpretation of the attitudes network. To validate the effectiveness of ResIN, we develop a mathematical model and apply ResIN to both simulated and real data. Furthermore, we compare these results to existing methods of BNA and IRT. When used to analyze partisan belief-networks in the US-American political context, ResIN was able to reliably distinguish Democrat and Republican attitudes, even in highly asymmetrical attitude systems. These results demonstrate the utility of ResIN as a powerful tool for the analysis of complex attitude systems and contribute to the advancement of BNA.

Date: 2024
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DOI: 10.1057/s41599-024-03037-x

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