Comparing membership interest group networks across space and time, size, issue and industry

2015 ◽  
Vol 3 (1) ◽  
pp. 78-97 ◽  
Author(s):  
JANET M. BOX-STEFFENSMEIER ◽  
DINO P. CHRISTENSON

AbstractWe compare and contrast the network formation of interest groups across industry and issue area. We focus on membership interest groups, which by virtue of representing the interests of voluntary members face particular organizational and maintenance constraints. To reveal their cooperative behavior we build a network dataset based on cosigner status to United States Supreme Court amicus curiae briefs and analyze it with exponential random graph models and multidimensional scaling. Our methodological approach culminates in a clear and compact spatial representation of network similarities and differences. We find that while many of the same factors shape membership networks, religious, labor, and political organizations do not share the same structure as each other or as the business, civic and professional groups.

2017 ◽  
Vol 7 (3) ◽  
pp. 505-522 ◽  
Author(s):  
Stefan Wojcik

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.


2015 ◽  
Vol 37 (1) ◽  
pp. 22-44 ◽  
Author(s):  
Ji Youn Rose Kim ◽  
Michael Howard ◽  
Emily Cox Pahnke ◽  
Warren Boeker

2018 ◽  
Vol 26 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Dino P. Christenson ◽  
Jason W. Morgan

In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the restrictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to account for unobserved heterogeneity can have a significant impact on inferences about network formation. The proposed frailty extension to the ERGM (FERGM) generally outperforms the ERGM in these cases, and does so by relatively large margins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding the problem of degeneration that plagues the standard MCMC-MLE approach.


2021 ◽  
Vol 64 ◽  
pp. 225-238
Author(s):  
George G. Vega Yon ◽  
Andrew Slaughter ◽  
Kayla de la Haye

2019 ◽  
Vol 121 (10) ◽  
pp. 1-32
Author(s):  
David Diehl ◽  
Robert A. Marx

Background/Context Research on the patterns of philanthropic funding of charter schools has largely focused on the behavior of major foundations. This work has documented how the once diffuse giving by these major foundations has become increasingly concentrated on a small number of jurisdictional challengers in the form of charter schools, charter management organizations, and intermediary organizations. Purpose The current study examines whether this convergence in giving has spread to the entire network of foundations giving to charter-school-related organizations. We do so by extending current work and focus on the broader institutional field that includes the interactions between major foundations, smaller foundations, and grantees over time. Moreover, we look to see, if such a field-wide convergence is present, whether there is evidence consistent with the institutional process of isomorphism in which low-status foundations match the giving strategy of higher status ones. Research Design We test for these dynamics using exponential random graph models (ERGMs), a hypothesis-testing framework for network analysis. More specifically, we analyze the funding ties among 809 foundations that gave grants to California charter schools and charter-school-related organizations between 2003 and 2014, as available through the Foundation Directory Online. We constructed multiyear windows to examine funding ties between foundations and recipients, using organizational characteristics, such as foundation type, foundation year, professionalization, foundation size, organizational type, and location, and endogenous features of the network as independent variables. Findings Results indicate centralization of giving over time, as larger and newer foundations began practicing more targeted giving and the most connected recipients were involved in a disproportionate number of funding ties. We also found evidence consistent with institutionalization, as foundations with professional staffs played a larger role in giving, and smaller foundations increasingly engaged in behavior similar to their larger peers over time. Finally, we found evidence for the consistent effect of propinquity: We observe co-funding and co-receiving ties between foundations and grantees in geographical proximity to each other. Conclusions This work examines the network dynamics of charter school philanthropic giving and provides evidence for the centralization and institutionalization of the field. In turn, this may create inequity in funding for charter schools because it may be more difficult for smaller or less ideologically popular organizations to penetrate the field. Policy makers should be aware of these forces and should take them into account when making budgetary and funding decisions.


2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.


2016 ◽  
Vol 46 ◽  
pp. 11-28 ◽  
Author(s):  
S. Thiemichen ◽  
N. Friel ◽  
A. Caimo ◽  
G. Kauermann

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