Generalized least squares can overcome the critical threshold in respondent-driven sampling

TL;DR

This paper demonstrates that generalized least squares (GLS) can significantly reduce the variance in respondent-driven sampling estimates, overcoming the critical threshold where variance typically fails to decay properly, thus improving estimation accuracy.

Contribution

The authors introduce feasible GLS estimators for RDS that achieve $O(n^{-1})$ variance decay, even under complex social network models and model misspecification.

Findings

Feasible GLS estimators drastically reduce estimation error.

Spectral properties of social networks can be estimated from sampled data.

GLS outperforms standard estimators under various conditions.

Abstract

In order to sample marginalized and/or hard-to-reach populations, respondent-driven sampling (RDS) and similar techniques reach their participants via peer referral. Under a Markov model for RDS, previous research has shown that if the typical participant refers too many contacts, then the variance of common estimators does not decay like $O(n^{-1})$, where $n$ is the sample size. This implies that confidence intervals will be far wider than under a typical sampling design. Here we show that generalized least squares (GLS) can effectively reduce the variance of RDS estimates. In particular, a theoretical analysis indicates that the variance of the GLS estimator is $O(n^{-1})$. We then derive two classes of feasible GLS estimators. The first class is based upon a Degree Corrected Stochastic Blockmodel for the underlying social network. The second class is based upon a rank-two model. It might be of independent interest that in both model classes, the theoretical results show that it is possible to estimate the spectral properties of the population network from the sampled observations. Simulations on empirical social networks show that the feasible GLS (fGLS) estimators can have drastically smaller error and rarely increase the error. A diagnostic plot helps to identify where fGLS will aid estimation. The fGLS estimators continue to outperform standard estimators even when they are built from a misspecified model and when there is preferential recruitment.