General methodology is developed here to deal with the association
between a a binary variable and network connections with or without
confounding covariates. Also the case when the network is observed
at several time periods is treated. As an application we consider
the diffusion of organic farming in the province of North Karelia
in Finland. It turns out that organic farms are more clustered than
would be expected under pure random allocation. The neighborhood
effect remains when adjusting for the production lines of the farms.
The spatio-temporal analysis shows that new adopters are more often
found within the neighborhoods of each others and of earlier adopters.
%0 Journal Article
%1 Nyblom2003/5
%A Nyblom, Jukka
%A Borgatti, Steve
%A Roslakka, Juha
%A Salo, Mikko A.
%D 2003/5
%J Social Networks
%K Logistic Neighborhood Organic Permutation Spatio-temporal association effect; farming; regression; tests;
%N 2
%P 175-195
%T Statistical analysis of network data—an application to diffusion
of innovation
%V 25
%X General methodology is developed here to deal with the association
between a a binary variable and network connections with or without
confounding covariates. Also the case when the network is observed
at several time periods is treated. As an application we consider
the diffusion of organic farming in the province of North Karelia
in Finland. It turns out that organic farms are more clustered than
would be expected under pure random allocation. The neighborhood
effect remains when adjusting for the production lines of the farms.
The spatio-temporal analysis shows that new adopters are more often
found within the neighborhoods of each others and of earlier adopters.
@article{Nyblom2003/5,
abstract = {General methodology is developed here to deal with the association
between a a binary variable and network connections with or without
confounding covariates. Also the case when the network is observed
at several time periods is treated. As an application we consider
the diffusion of organic farming in the province of North Karelia
in Finland. It turns out that organic farms are more clustered than
would be expected under pure random allocation. The neighborhood
effect remains when adjusting for the production lines of the farms.
The spatio-temporal analysis shows that new adopters are more often
found within the neighborhoods of each others and of earlier adopters.},
added-at = {2008-08-31T18:03:07.000+0200},
author = {Nyblom, Jukka and Borgatti, Steve and Roslakka, Juha and Salo, Mikko A.},
biburl = {https://www.bibsonomy.org/bibtex/253ef32fdf545606db23ee8d5b5a8d807/jomiralb},
description = {Old biblio},
interhash = {d4b051092f0fdbcb1f2eaa625c99297f},
intrahash = {53ef32fdf545606db23ee8d5b5a8d807},
journal = {Social Networks},
keywords = {Logistic Neighborhood Organic Permutation Spatio-temporal association effect; farming; regression; tests;},
number = 2,
owner = {oriol},
pages = {175-195},
timestamp = {2008-08-31T18:03:20.000+0200},
title = {Statistical analysis of network data—an application to diffusion
of innovation},
volume = 25,
year = {2003/5}
}