Hello!
Our data is about sociolinguistic relations of several teenage
students (12 years old) in sociocentric networks of multiple
schools. The sociolinguistic relations have two types of
information: relations between adolescents and language used in
this relation. Not all dyads have relations, and if relations
exist, can be in language A or language B. Our information can
be more complex, because language use is in language A, language
B, language A and B, o other languages. But we can dichotomize
the data.
We have problems with the analysis of our data, because edges
have attributes, and and I couldn’t find any reference about how
to handle whit it, neither with R nor any other software.
Then, I have three questions:
How can we analyse valued networks (qualitative links)?
We are interested in multiple analysis at the descriptive level:
language reciprocity, centrality in different languages, triad
analysis with multiple languages relations, and so on... We are
working it combining UCINET results of full network, and network
in language A and network in language B, and comparing it with
Excel. Is it possible analyse better how reciprocity dyads have
language reciprocity too? For exemple, “90% of reciprocity
relations are in the same language”. Or 20% of transitive triads
use A language in X -> Y and Y -> Z relations, and B
language in X -> Z relation. We are also interested in more
complex analysis like UCINET> Tools> Testing
Hypotheses> Mixed Dyadic / Nodal> Categorical
attributes> Relational Contingency Table analysis. And triad
analysis with qualitative links (and node attributes).
How can we analyse edge attributes with ERGM? Is it
possible? A possible way is work with multiple networks, like
“relations in language A” and “relations in language B”. It is
possible compare networks with ERG Models with XPnet. But our
data is (theorically) negative correspondence, and we can’t
interpret results of multiple networks analysis like it. Do you
know bibliography that can help us?
Maybe a solution is working with signed networks.
Origins of this analysis is other, about positive relations
(friends) and negative relations (enemies). And balanced
networks theory don’t adjust to language networks. But is not
far that language use in networks can be understanded like
relations in A language (+), in B language (-) or in both
languages (0). Wich software can work better with signed
networks?
Last but not least, my presentation: I am Natxo Sorolla, and I
am working in sociology of language at the University of
Barcelona.
Natxo Sorolla
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