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

Dados Bibliográficos

ID2331230
AutoresIan Laga (0000-0002-5164-4856, Mathematical Sciences, Montana State University, Bozeman, MT, USA, autor correspondente), Jessica P Kunke (0009-0006-1475-3734, Statistics and Sociology, University of Washington, Seattle, WA, USA), Tyler H Mccormick (0000-0002-6490-1129, Statistics and Sociology, University of Washington, Seattle, WA, USA), Xiaoyue Niu (0000-0003-3384-7446, Statistics, Pennsylvania State University, University Park, PA, USA)
Ano2025
Data de publicação2025-08-07
Open AccessSim
TipoARTICLE
PeriódicoSociological Methods & Research (JOURNAL)
Identificadores do periódicoISSN: 0049-1241 • E-ISSN: 1552-8294
EditoraSAGE Publications Inc (PUBLISHER)
DOI10.1177/00491241251364233
OpenAlexW4413119012
IdiomaEN
Citações recebidas2
Referências citadas13

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 likely exists for all populations, resulting in either positively or negatively biased population size estimates. 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

Degree (music) · Econometrics · Physics · Scale (ratio) · Statistics · Advanced Causal Inference Techniques · Complex Network Analysis Techniques · Computer Science · Mathematics · Mental Health Research Topics