A sample size heuristic for network scale-up studies

TL;DR

This paper introduces a sample size heuristic for network scale-up studies, providing a formula to determine the minimum sample size needed for accurate population estimates in surveys of hidden groups.

Contribution

It offers the first analytical and simulation-based guidelines for calculating the minimum sample size in NSUM surveys, ensuring desired precision and robustness.

Findings

Sample size formula controls error at the nominal rate.

Method is robust to some network model mis-specifications.

Application to published surveys demonstrates practical utility.

Abstract

The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individuals report how many others they know in the subpopulation of interest (e.g. "How many sex workers do you know?") and how many others they know in subpopulations of the general population (e.g. "How many bus drivers do you know?"). NSUM is widely used to estimate the size of important epidemiological risk groups, including men who have sex with men, sex workers, HIV+ individuals, and drug users. Unlike several other methods for population size estimation, NSUM requires only a single random sample and the estimator has a conveniently simple form. Despite its popularity, there are no published guidelines for the minimum sample size calculation to achieve a desired statistical precision. Here, we provide a sample size formula that can be employed in any NSUM survey. We show analytically and by simulation that the sample size controls error at the nominal rate and is robust to some forms of network model mis-specification. We apply this methodology to study the minimum sample size and relative error properties of several published NSUM surveys.