<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This research captures local networks of German political parties and welfare agencies in regards to poverty. The article explores whether there are differences in regards to homophily and brokerage between the two studied groups using a dataset of 33 egonetworks in two German cities. The computer assisted drawn networks were collected in an interactive participative way together with the interviewed egonetworks. To achieve the theoretical aim of analysing homophily and brokerage between politicians and welfare workers, two hypotheses are examined, resting upon social capital theory. The hypotheses were quantified and explicated with different variables. The first hypothesis states that heterophile networks imply more social capital, which referred to different measurements (size, density, homophily). This could be partially validated since the analysed networks of association representatives (n=12) were denser and slightly more heterophile than those of party representatives (n=21). Second, it was assumed that politicians, because of their function as elected representatives, would be more likely to take on an interface function within the communities than representatives of civil society institutions. Results based on calculated EI-indices, subgraphs and brokerage show that party representatives do indeed have larger networks, but these networks split into fewer subgraphs than association representatives’ networks.</p>
</abstract>

This research captures local networks of German political parties and welfare agencies in regards to poverty. The article explores whether there are differences in regards to homophily and brokerage between the two studied groups using a dataset of 33 egonetworks in two German cities. The computer assisted drawn networks were collected in an interactive participative way together with the interviewed egonetworks. To achieve the theoretical aim of analysing homophily and brokerage between politicians and welfare workers, two hypotheses are examined, resting upon social capital theory. The hypotheses were quantified and explicated with different variables. The first hypothesis states that heterophile networks imply more social capital, which referred to different measurements (size, density, homophily). This could be partially validated since the analysed networks of association representatives (n=12) were denser and slightly more heterophile than those of party representatives (n=21). Second, it was assumed that politicians, because of their function as elected representatives, would be more likely to take on an interface function within the communities than representatives of civil society institutions. Results based on calculated EI-indices, subgraphs and brokerage show that party representatives do indeed have larger networks, but these networks split into fewer subgraphs than association representatives’ networks.

A Visual Data Collection Method: German Local Parties and Associations

Connections

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Visual Data Collection Method: German Local Parties and Associations"}

This research captures local networks of German political parties and welfare agencies in regards to poverty. The article explores whether there are...

Function		Size	Density	EI-index via sector affiliation	Number of subgraphs
party (n=21)	mean	24.950	0.12870	−0.122520	21.380
party (n=21)	SD	9.897	0.12527	0.588792	13.261
association (n=12)	mean	23.670	0.25010	−0.245500	23.000
association (n=12)	SD	12.272	0.38182	0.696312	13.423
total (n=33)	mean	24.480	0.17280	−0.167240	21.970
total (n=33)	SD	10.648	0.25187	0.622052	13.133
significance (n=26)	t	0.599	0.08000	0.775000	0.489
squared eta (n=26)	ŋ2	0.012	0.00400	0.001000	0.210

A Visual Data Collection Method: German Local Parties and Associations

Publicado en línea: 04 jun 2018

Páginas: 45 - 52

DOI: https://doi.org/10.21307/connections-2017-003

© 2017 Isabelle Borucki published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Comparison of the mean value for size, density, EI-index and subgraphs