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Hypothesis Testing for Group Structure in Legislative Networks

Published online by Cambridge University Press:  25 January 2021

Justin H. Kirkland
Justin H. Kirkland*
Affiliation:
University of Houston, TX, USA
*
Justin H. Kirkland, Department of Political Science, The University of Houston, 447 Phillip Gutherie Hoffman Hall Houston, TX 77204-3011, TX, USA. Email: jhkirkla@central.uh.edu
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Abstract

Scholars of social networks often rely on summary statistics to measure and compare the structures of their networks of interest. However, measuring the uncertainty inherent in these summaries can be challenging, thus making hypothesis testing for network summaries difficult. Computational and nonparametric procedures can overcome these difficulties by allowing researchers to generate reference distributions for comparison directly from their data. In this research, I demonstrate the use of nonparametric hypothesis testing in networks using the popular network summary statistic network modularity. I provide a method based on permutation testing for assessing whether a particular network modularity score is larger than a researcher might expect due to random chance. I then create a simulation study of network modularity and its simulated reference distribution that I propose. Finally, I provide an empirical example of this technique using cosponsorship networks from U.S. state legislatures.

Keywords

legislative politicscomparative legislatureslegislative behaviormethodologynetwork analysisquantitative methodsmethodologysimulations

Information

Type
Research Article
Information
State Politics & Policy Quarterly , Volume 13 , Issue 2 , June 2013, pp. 225 - 243
Copyright
Copyright © The Author(s) 2012

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