A0584
Title: Unequally sampled networks: Biases and corrections
Authors: Chih-Sheng Hsieh - National Taiwan University (Taiwan) [presenting]
Abstract: Statistical issues arising from networks based on non-representative samples of the population are analyzed. First, the biases in both network statistics and estimates of network effects under unequal probability sampling analytically and numerically are characterized. Sampled network data systematically bias the properties of the population network and suffer from non-classical measurement-error problems when applied as regressors. Apart from the sampling rate and the elicitation procedure, these biases depend in a non-trivial way on which subpopulations are missing with higher probability. A methodology adapting post-stratification weighting approaches is proposed for networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The advantages of the proposed methodology are that it can be applied to network data collected via both designed and non-designed sampling procedures, does not require one to assume any network formation model, and is straightforward to implement. The approach is applied to two widely used network data sets, and it is shown that accounting for the non-representativeness of the sample dramatically changes the results of regression analysis.
