Analysis of Networks with Missing Data with Application to the National Longitudinal Study of Adolescent Health
Year: 2016
Type: article
Abstract: It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this paper we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health.
DOI: 10.1111/rssc.12184
Authors: Krista J. Gile, Mark S. Handcock
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G2998923865, G4154686922, Innovations in Network Modeling for HIV Prevention Studies, Scalable Model-Based Inference for Social Networks from Complex Sampling Designs, BIGDATA: Collaborative Research: IA: Population Reproduction of Poverty at Birth from Surveys, Censuses, and Birth Registrations, G5577462602, Diagnosis and Reduction of Bias in Respondent-driven Sampling, California Center for Population Research (CCPR) at UCLA, G8876996369, Valid Inference for Respondent Driven Sampling of Hidden Networked Populations
OpenAlex ID: W2525527392