Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. Standard self-report measures—e.g., as collected through the popular ‘name-generator’ method—however, do not provide an impartial representation of transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic—variable across members of any given group. Past research has high- lighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we introduce a latent network model that allows us to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact our ability to accurately infer fundamental network properties. These impacts are not adequately addressed using standard approaches to network reconstruction (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully-documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population.
How to make methodological decisions when inferring social networks
