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Testing theoretical network classes and HIV-related correlates with latent class analysis

Bibliographic Data

ID19442091
AuthorsRachel A Smith (0000-0003-0392-3735, a Communication Arts & Sciences , Pennsylvania State University , USA, corresponding author), Stephanie T Lanza (0000-0002-6101-8381, b The Methodology Center , Pennsylvania State University , University Park , PA , USA)
Year2011
Volume23
Issue10
Pages1274-1281
Publication date2011-10-01
Open AccessNo
TypeARTICLE
VenueAIDS Care (JOURNAL)
Journal identifiersISSN: 0954-0121 • E-ISSN: 1360-0451
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/09540121.2011.555747
PMID21939405
PMCIDPMC3181093
OpenAlexW1998565108
LanguageEN
Citations received3
References cited29

Scientists designing network-based interventions intending to improve the adoption or maintenance of healthy behaviors are well-advised to classify potential adopters into network roles, such as opinion leaders, brokers, members, and isolates, and to work closely with existing opinion leaders. In past studies focusing on HIV, opinion-leader interventions have had mixed results. This may be addressed, in part, by empirically validating these network roles. To this end, we used latent class analysis to test whether people's social connections fall into mutually exclusive and exhaustive subgroups of social capital that represent theorized network roles well with a dataset collected in Nyangana, Namibia (n = 400). A four-class model best fits the dataset, but the categories identified do not clearly represent the theorized roles. Rather, this study revealed the following four network classes: single-group members (59%), connectors (24%), single-group loyalists (15%), and selective connectors (2%). The implications of their findings for opinion-leader interventions focused on HIV are discussed

Latent class model · Machine learning · Opinion leadership · Political science · Popular opinion · Psychological intervention · Public relations · Social capital · Social media · Social network analysis · Social science · Sociology · World Wide Web · Complex Network Analysis Techniques · Computer Science · Medicine · Psychology · Social Capital and Networks · Social Psychology · Artificial Intelligence

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Citations per year0.25
Citation span2014 - 2020 (7)
Highly citedNo

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