How social network influences human behavior: An integrated latent space approach
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How human behavior is influenced by a social network that they belong has been an interested topic in applied research. Existing methods often utilized scale-level behavioral data to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence by using 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. We then measure social influence as the impact of the latent space configuration contributed by the social network data on the behavior data. The performance and properties of the proposed approach are evaluated via simulation studies. We apply the proposed model to an empirical dataset to explain how students' friendship network influences their participation in school activities.
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