Random sampling of alters from networks: A promising direction in egocentric network research.

ABSTRACT: The social network perspective has great potential for advancing knowledge of social mechanisms in many fields. However, collecting egocentric (i.e., personal) network data is costly and places a heavy burden on respondents. This is especially true of the task used to elicit information on ties between network members (i.e., alter-alter ties or density matrix), which grows exponentially in length as network size increases. While most existing national surveys circumvent this problem by capping the number of network members that can be named, this strategy has major limitations. Here, we apply random sampling of network members to reduce cost, respondent burden, and error in network studies. We examine the effectiveness and reliability of random sampling in simulated and real-world egocentr

SUBMITTER: Peng S 

PROVIDER: S-EPMC10022747 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature