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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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