Collecting egocentric network data with visual tools: A comparative study. (26th June 2020)

• Record Type: Journal Article
• Authors: Hollstein, Betina; Töpfer, Tom; Pfeffer, Jürgen
• Editors: Perry, Brea L.; Pescosolido, Bernice A.; Small, Mario L.; McCranie, Ann
• Is Part Of: Network science. Volume 8:Number 2(2020)
• Page Start: 223
• Page End: 250
• Publication Date: 2020-06-26
• Subjects: egocentric networks, -- mixed methods, -- qualitative network analysis, -- data collection, -- network maps, -- data quality; Social networks -- Research -- Periodicals; System analysis -- Periodicals; System theory -- Periodicals; Computer science -- Periodicals; 003.72
• DOI: 10.1017/nws.2020.4
• Languages: English
• ISSNs: 2050-1242

Abstract

Abstract: When collecting egocentric network data, visual representations of networks can function as a cognitive aid for depicting relationships, helping to maintain an overview of the relationships, and keeping the attention of the interviewees. Additionally, network maps can serve as a narration generator in qualitative and in mixed-methods studies. While varying visual instruments are used for collecting egocentric network data, little is known about differences among visual tools concerning the influence on the resulting network data, the usability for interviewees, and data validity. The article provides an overview of existing visually oriented tools that are used to collect egocentric networks and discusses their functions, advantages, and limitations. Then, we present results of an experimental study where we compare four different visual tools with regard to networks elicited, manageability, and the impact of follow-up questions. In order to assess the manageability of the four tools, we used the thinking aloud method. The results provide evidence that the decision in favor of a specific visual tool (structured vs. unstructured) can affect the size and composition of the elicited networks. Follow-up questions greatly affect the elicited networks and follow-up cues can level out differences among tools. Respondents tend to prefer the concentric circles tool, with some differences in preferences and manageability of tools between participants with low and those with high socioeconomic status. Finally, assets and drawbacks of the four instruments are discussed with regard to data quality and crucial aspects of the data collection process when using visual tools.