Nonparametric inference for network data

Published

Journal Article

Various theoretical concerns often require researchers to answer questions of the form does co-membership in a group predict other ties between those individuals. Data appropriate for answering such a question often is referred to as network data. Network data exhibits row-column dependencies that often invalidate traditional statistical methods for doing group comparisons such as analysis of variance and the Kruskal-Wallis Procedure. Because of the positive dependence within rows/columns the significance probabilities of such traditional methods may be exaggerated. This paper uses restricted-randomization to develop exact permutation tests for network data where co-membership in groups can be specified a priori. This enables the nonparametric estimation of the significance of standard statistics for group-difference tests and ordered-alternative tests where the group orderings have been prespecified. These methods are demonstrated by examining three different data sets: Sampson's Monastery data, Carley's Tutor Selection data, and Humana's Human Rights data. © 1993, Taylor & Francis Group, LLC. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Carley, K; Banks, D

Published Date

  • July 1, 1993

Published In

Volume / Issue

  • 18 / 1

Start / End Page

  • 1 - 26

Electronic International Standard Serial Number (EISSN)

  • 1545-5874

International Standard Serial Number (ISSN)

  • 0022-250X

Digital Object Identifier (DOI)

  • 10.1080/0022250X.1993.9990113

Citation Source

  • Scopus