Research Library A-E

Research Library: A-E

Papers by first-author surname, A to E. Return to the Research Library index.

Network Canvas: Key decisions in the design of an interviewer assisted network data collection software suite (2021)

Birkett, M; Melville, J; Janulis, P; Phillips II, G; Contractor, N; Hogan, B. Social Networks, 66, 114-124. DOI 10.1016/j.socnet.2021.02.003

This paper documents the design rationale behind the Network Canvas software suite, a free, open-source, three-application toolkit for collecting complex egocentric and personal network data. It offers a detailed account of why visual, touch-based, interviewer-assisted sociogram interfaces reduce participant burden and improve data quality. [draft]

Population: n=181; Social network and public health researchers plus research participants in validation studies.

Keywords: social-networks, ego-networks, network-data-collection, software-design, participant-aided-sociogram.

Evidence: low, methods, design-report. Risk of bias: high. Flags: high risk of bias.

Network of Categories: A Method to Aggregate Egocentric Network Survey Data into a Whole Network Structure (2025)

Bolíbar, Mireia; Martínez-Ariño, Julia; Schiller, Maria. Field Methods, 37(2), 110-127. DOI 10.1177/1525822X241262678

This paper introduces the network of categories, a method for converting collections of egocentric network survey data into a whole-network structure by aggregating ego and alter actors into attribute-based categories. It bridges the gap between egocentric and sociocentric network analysis without requiring costly link-tracing. [draft]

Population: n=476; Local urban organisations in France and Germany, including political parties, trade unions, welfare organisations, business associations,....

Keywords: egocentric-networks, whole-network-analysis, inter-organisational-networks, governance-networks, network-methodology.

Evidence: moderate, methodological, comparative-cross-sectional. Risk of bias: medium.

Inferring global network properties from egocentric data with applications to epidemics (2021)

Britton, Tom; Trapman, Pieter

This paper formalises the limits of what egocentric data can tell us about a social network as a whole, showing that knowing only local neighbourhood structure leaves the size of the giant component and epidemic outbreak almost unconstrained, but that richer egocentric data progressively tightens the upper bound on outbreak size. [draft]

Population: Theoretical population of large abstract social networks; no empirical human sample..

Keywords: network-structure, egocentric-data, giant-component, epidemic-spreading, degree-distribution.

Evidence: theoretical, mathematical-modelling. Risk of bias: low. Flags: no DOI recorded; venue unverified.