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How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence

Jina Park, Ick Hoon Jin () and Minjeong Jeon
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Jina Park: Department of Applied Statistics
Ick Hoon Jin: Department of Applied Statistics
Minjeong Jeon: University of California

Psychometrika, 2023, vol. 88, issue 4, No 16, 1529-1555

Abstract: Abstract How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g., total number of positive responses) to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence that utilizes item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents’ social network data and item-level behavior measures into a single space we call ‘interaction map’. The interaction map visualizes the association between the latent homophily among respondents and their item-level behaviors, revealing differential social influence effects across item-level behaviors. We also measure overall social influence by assessing the impact of the interaction map. We evaluate the properties of the proposed approach via extensive simulation studies and demonstrate the proposed approach with a real data in the context of studying how students’ friendship network influences their participation in school activities.

Keywords: differential social influence; network analysis; item response model; latent space model; latent space item response model; peer Network (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-023-09934-5

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