Network sampling coverage III: Imputation of missing network data under different network and missing data conditions

Jeffrey A. Smith, Jonathan H. Morgan, James Moody

Research output: Contribution to journalArticlepeer-review

Abstract

Missing data is a common, difficult problem for network studies. Unfortunately, there are few clear guidelines about what a researcher should do when faced with incomplete information. We take up this problem in the third paper of a three-paper series on missing network data. Here, we compare the performance of different imputation methods across a wide range of circumstances characterized in terms of measures, networks and missing data types. We consider a number of imputation methods, going from simple imputation to more complex model-based approaches. Overall, we find that listwise deletion is almost always the worst option, while choosing the best strategy can be difficult, as it depends on the type of missing data, the type of network and the measure of interest. We end the paper by offering a set of practical outputs that researchers can use to identify the best imputation choice for their particular research setting.

Original languageEnglish (US)
Pages (from-to)148-178
Number of pages31
JournalSocial Networks
Volume68
DOIs
StatePublished - Jan 2022

Keywords

  • Imputation
  • Missing data
  • Network bias
  • Network sampling

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • Social Sciences(all)
  • Psychology(all)

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