Imputation of missing network data: Some simple procedures

M. Huisman

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Abstract

Analysis of social network data is often hampered by non-response and missing
data. Recent studies show the negative effects of missing actors and ties on the
structural properties of social networks. This means that the results of social
network analyses can be severely biased if missing ties were ignored and only
complete cases were analyzed. To overcome the problems created by missing
data, several treatment methods are proposed in the literature: model-based
methods within the framework of exponential random graph models, and im-
putation methods. In this paper we focus on the latter group of methods, and
investigate the use of some simple imputation procedures to handle missing
network data. The results of a simulation study show that ignoring the missing
data can have large negative effects on structural properties of the network.
Missing data treatment based on simple imputation procedures, however, does
also have large negative effects and simple imputations can only successfully
correct for non-response in a few specific situations.
Original languageEnglish
Number of pages29
JournalJournal of Social Structure
Volume10
Issue number1
Publication statusPublished - 2009

Keywords

  • Missing data
  • Single imputation
  • Descriptive network analysis
  • Friendship network

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