Estimating and Correcting Degree Ratio Bias in the Network Scale-up Method

TL;DR

This paper investigates the degree ratio bias in the Network Scale-up Method, proposing a new adjustment technique to improve size estimates of marginalized populations by accounting for social network size differences.

Contribution

It introduces a method to estimate degree ratios without extra data and demonstrates improved accuracy of NSUM estimates through simulations and real data.

Findings

Degree ratio bias significantly affects size estimates.

The proposed adjustment improves estimation accuracy.

Validation with simulations and real data confirms effectiveness.

Abstract

The Network Scale-up Method (NSUM) uses social networks and answers to "How many X's do you know?" questions to estimate sizes of groups excluded by standard surveys. This paper addresses the bias caused by varying average social network sizes across populations, commonly referred to as the degree ratio bias. This bias is especially important for marginalized populations like sex workers and drug users, where members tend to have smaller social networks than the average person. We show how the degree ratio affects size estimates and provide a method to estimate degree ratios without collecting additional data. We demonstrate that our adjustment procedure improves the accuracy of NSUM size estimates using simulations and data from two data sources.