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UID:pretalx-2026-RVJTKR@conferences.acspri.org.au
DTSTART;TZID=AEST:20261126T114500
DTEND;TZID=AEST:20261126T120000
DESCRIPTION:There is a significant body of egonet (ego-centric) network res
 earch using the signature methodology of name generators and follow-up (na
 me interpreter) questions to elicit alter (others) and ego-alter relations
 hip characteristics sometimes supplemented by the collection of a roster o
 f alter-alter relations (as perceived by the ego). Egonet network structur
 es only emerge with the alter-alter relations. Such a ‘structural’ ego
 net dataset is amenable to the array of analytic procedures used in ‘who
 le network’ (WN)\, sociocentric studies. A practical question for social
  researchers is what these WN measures mean in an egonet setting and wheth
 er they are useful enough to warrant collection. The methodological issue 
 is how to use the WN output for comparative analysis of multiple egonets.\
 nThere is a significant body of personal network/ egonet literature\, both
  substantive research (e.g. Bidart et al. Living in Networks\, 2020) and m
 ethodological issues (e.g. Bellotti\, Qualitative Networks\, 2015\; McCart
 y et al. Conducting Personal Network Research\, 2019\; Perry et al. Egocen
 tric Network Analysis\, 2018). Vacca (“Structure in personal networks”
 \, Network Science\, June 2020) directly addresses issues around structura
 l egonet data\, defining a typology of egonet structures.\nWe approached t
 hese questions by exploring WN measures on a databank of 15 egonet dataset
 s from a purposive sampling of emerging adults collected in Brisbane in 20
 10-2013. We carried out our analyses on datasets with the ego node\, and e
 go-alter relations included (EAA) and the ego node and ego-alter relations
  excluded leaving only alter-alter ties (AA). The WN analytics were done w
 ith UCINET and we used NetDraw to produce standardised visualisations of t
 he 15 egonets.\nWith average path length in the AA datasets we found 10 ca
 ses where average distance was 1.00\, i.e. all the clusters were complete 
 components. We suggest such cluster closure can be an important feature of
  egonets. To work around this aspect of our data we took the average path 
 length in the ego included (EAA) datasets normalized as the proportion of 
 the average path length of ego’s star formation. We suggest this provide
 s a meaningful\, standardized metric of cohesion controlling for egonet si
 ze.\nComponent analysis is crucial for egonet analyses. The few component-
 based procedures in UCINET are highly influenced by the number of singleto
 ns. We recommend that the singletons in the egonets be a measure indicatin
 g the amount of interactive social activity\, as does Vacca.\nThere are ma
 ny possible configurations of different sized components within an egonet 
 and this diversity will be evident in the degree distribution of the AA eg
 onets. As with average path length we propose a metric that places the obs
 erved average degree between the extreme variation for the number of nodes
  in the data set and the minimum. This again produces standardised metric 
 controlling for size.\nWe discuss how these metrics may enrich qualitative
  insights in personal network research. We also draw attention to the poss
 ibilities for regression analyses\, QCA and Vacca’s typology.
DTSTAMP:20261102T131111Z
LOCATION:MacCallum Room
SUMMARY:Egonet structure: Closure\, cohesion and diversity in personal netw
 orks - Malcolm Alexander
URL:https://conferences.acspri.org.au/2026/talk/RVJTKR/
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