Skip navigation

Eliciting personal network data in web surveys through participant-generated sociograms

Eliciting personal network data in web surveys through participant-generated sociograms

Tubaro, Paola, Casilli, Antonio A. and Mounier, Lise (2013) Eliciting personal network data in web surveys through participant-generated sociograms. Field Methods, 26 (2). pp. 107-125. ISSN 1525-822X (Print), 1552-3969 (Online) (doi:10.1177/1525822X13491861)

[img] PDF (Author's accepted manuscript)
9195_Tubaro_Eliciting personal network data (AAM) 2014.pdf - Accepted Version
Restricted to Registered users only

Download (378kB)

Abstract

The paper presents a method to elicit personal network data in Internet surveys, exploiting the renowned appeal of network visualizations to reduce respondent burden and risk of drop-out. It is a participant-generated computer-based sociogram, an interactive graphical interface enabling participants to draw their own personal networks with simple and intuitive tools. In a study of users of websites on eating disorders, we have embedded the sociogram within a two-step approach aiming to first elicit the broad ego network of an individual, and then to extract subsets of issue-specific support ties. We find this to be a promising tool to facilitate survey experience and adaptable to a wider range of network studies.

Item Type: Article
Additional Information: [1] Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The French Agence Nationale de la Recherche, ANR (grant number ANR-09-ALIA-001).
Uncontrolled Keywords: social networks, personal networks, name generators, network visualizations, internet surveys
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HF Commerce
Faculty / Department / Research Group: Faculty of Business > Centre for Business Network Analysis
Faculty of Business > Department of International Business & Economics
Related URLs:
Last Modified: 14 Oct 2016 09:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/9195

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics