Social influence effects have been extensively studied in various empirical network research. However, many challenges remain in estimating social influence effects in networks, as influence effects are often entangled with other factors, such as homophily in the selection process and the common social-environmental factors that individuals are embedded in. Methods currently available either do not solve these problems or require stringent assumptions. Recent works by Xu (2018) and others have shown that a latent space adjusted approach based on the latent space model has the potential to disentangle the influence effects from other processes, and the simulation evidence has shown that this approach outperforms other state-of-the-art approaches in terms of recovering the true social influence effect when there is an unobserved trait co-determining influence and selection. In this paper, I will further illustrate how the latent space adjusted approach can account for bias in the estimation of social influence effects and how this approach can be easily implemented in R.
lavaan, plm, latentnet, statnet, RSiena
SocialSciences, Econometrics, Cluster, HighPerformanceComputing, MissingData, OfficialStatistics, Psychometrics, SpatioTemporal
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For attribution, please cite this work as
Xu, "The R Journal: Estimating Social Influence Effects in Networks Using A Latent Space Adjusted Approach in R ", The R Journal, 2021
BibTeX citation
@article{RJ-2021-069,
author = {Xu, Ran},
title = {The R Journal: Estimating Social Influence Effects in Networks Using A Latent Space Adjusted Approach in R },
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-069},
doi = {10.32614/RJ-2021-069},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {57-69}
}