Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data

American Economic Review, Journal Year: 2020, Volume and Issue: 110(8), P. 2454 - 2484

Published: July 28, 2020

Social network data are often prohibitively expensive to collect, limiting empirical research. We propose an inexpensive and feasible strategy for elicitation using Aggregated Relational Data (ARD): responses questions of the form "how many your links have trait k ?" Our method uses ARD recover parameters a formation model, which permits sampling from distribution over node- or graph-level statistics. replicate results two field experiments that used draw similar conclusions with alone.

Language: Английский

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Thirty Years of The Network Scale-up Method

Journal of the American Statistical Association, Journal Year: 2021, Volume and Issue: 116(535), P. 1548 - 1559

Published: May 26, 2021

Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) a relatively new approach to estimate these by asking respondents question, "How X's do you know," where X population interest (e.g. female sex workers know?"). answers questions form Aggregated Relational Data (ARD). NSUM has been used variety subpopulations, including workers, drug users, and even children who have hospitalized choking. Within methodology, there are multitude estimators hidden population, direct estimators, maximum likelihood Bayesian estimators. In this article, we first provide in-depth analysis ARD properties techniques collect data. Then, comprehensively review different estimation methods in terms assumptions behind each model, relationships between practical considerations implementing methods. We apply models discussed one canonical data set compare their performance unique features, presented supplementary materials. Finally, summary dominant extensive list applications, discuss open problems potential research directions area.

Language: Английский

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Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph β-Model

IEEE Transactions on Information Theory, Journal Year: 2024, Volume and Issue: 70(8), P. 6000 - 6024

Published: June 11, 2024

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New Data Sources for Demographic Research

Population and Development Review, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 30, 2024

Abstract We are in the early stages of a new era demographic research that offers exciting opportunities to quantify phenomena at scale and resolution once unimaginable. These scientific possibilities opened up by sources data, such as digital traces arise from ubiquitous social computing, massive longitudinal datasets produced digitization historical records, information about previously inaccessible populations reached through innovations classic modes data collection. In this commentary, we describe five promising their potential appeal. identify cross‐cutting challenges shared these argue realizing full will demand both innovative methodological developments continued investment high‐quality, traditional surveys censuses. Despite considerable challenges, future is bright: lead demographers develop theories revisit sharpen old ones.

Language: Английский

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