Cross-Classified Multilevel Models for Personal Networks: Detecting and Accounting for Overlapping Actors

Autor/inn/en	Vacca, Raffaele; Stacciarini, Jeanne-Marie R.; Tranmer, Mark
Titel	Cross-Classified Multilevel Models for Personal Networks: Detecting and Accounting for Overlapping Actors
Quelle	In: Sociological Methods & Research, 51 (2022) 3, S.1128-1163 (36 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Vacca, Raffaele)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124119882450
Schlagwörter	Hierarchical Linear Modeling; Sociology; Social Science Research; Self Concept; Immigrants; Rural Areas; Hispanic Americans; Social Support Groups; Social Networks; Classification + Suchen Sie Ihr Suchwort? Soziologie; Social scientific research; Sozialwissenschaftliche Forschung; Selbstkonzept; Immigrant; Immigrantin; Immigranten; Rural area; Ländlicher Raum; Hispanic; Hispanoamerikaner; Social support; Soziale Unterstützung; Social network; Soziales Netzwerk; Classification system; Klassifikation; Klassifikationssystem
Abstract	Multilevel models are increasingly used in sociology and other social sciences to analyze variation of tie outcomes in egocentrically sampled network data, particularly in studies of social support. Existing research assumes that the personal networks in the data do not overlap (i.e., they do not have actors in common), which makes standard hierarchical models suitable for analysis. This assumption is unrealistic in certain sampling designs, including the case of egos sampled from higher level groups or via link-tracing methods. We describe different types of ego-network overlap and propose a method to detect overlapping actors and analyze the resulting data with cross-classified multilevel models. The method is demonstrated with an application to research on personal networks and social support among Hispanic immigrants in rural U.S. destinations. Overlap detection and modeling result in better model fit, more correct partition of tie variation among different sources, and the ability to test new substantive hypotheses. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01