Statistical analysis of network data - An application to diffusion of innovation

Jukka Nyblom; Steve Borgatti; Juha Roslakka; Mikko A. Salo

doi:10.1016/S0378-8733(02)00050-3

Statistical analysis of network data - An application to diffusion of innovation

Jukka Nyblom, Steve Borgatti, Juha Roslakka, Mikko A. Salo

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52 Scopus citations

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.

Original language	English
Pages (from-to)	175-195
Number of pages	21
Journal	Social Networks
Volume	25
Issue number	2
DOIs	https://doi.org/10.1016/S0378-8733(02)00050-3
State	Published - May 2003

Bibliographical note

Funding Information:
The authors thank an anonymous referee whose suggestions led to improvements in the paper. The research was funded by Academy of Finland.

Keywords

Logistic regression
Neighborhood effect
Organic farming
Permutation tests
Spatio-temporal association

ASJC Scopus subject areas

Anthropology
Sociology and Political Science
General Social Sciences
General Psychology

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10.1016/S0378-8733(02)00050-3

Cite this

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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.",

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AU - Borgatti, Steve

AU - Roslakka, Juha

AU - Salo, Mikko A.

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N2 - 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.

AB - 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.

KW - Logistic regression

KW - Neighborhood effect

KW - Organic farming

KW - Permutation tests

KW - Spatio-temporal association

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Statistical analysis of network data - An application to diffusion of innovation

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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