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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Network sampling coverage II: The effect of non-random missing data on network measurement.

Jeffrey A Smith1, James Moody2, Jonathan Morgan2

  • 1University of Nebraska-Lincoln United States.

Social Networks
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Summary

Missing data in network studies can bias results, especially when central nodes are lost. Researchers can use a new Java application to assess and mitigate this measurement bias in their network data.

Keywords:
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Area of Science:

  • Network Science
  • Data Analysis
  • Sociology

Background:

  • Missing data is a critical issue in network studies, impacting measurement validity.
  • Previous work (Smith and Moody, 2013) examined bias from randomly missing nodes.
  • This study addresses non-random missing data, focusing on the impact of central node loss.

Purpose of the Study:

  • To investigate the effect of non-randomly missing data on network measurement.
  • To determine how the likelihood of missing central nodes influences network statistics.
  • To provide tools for researchers to gauge bias in their network data.

Main Methods:

  • Analysis of bias in network measurement across diverse empirical networks.
  • Evaluation of various network measures under conditions of missing central nodes.
  • Development of a Java application for practical bias assessment.

Main Results:

  • Bias in network measurement is exacerbated when more central nodes are missing.
  • Bonacich centrality is highly sensitive to central node loss; closeness centrality is not.
  • Larger, directed networks show weak robustness; distance and bicomponent size are more affected than triad measures; behavioral homophily is more robust than degree-homophily.

Conclusions:

  • Non-random missing data, particularly the loss of central nodes, significantly biases network measurements.
  • Different network measures exhibit varying sensitivity to missing data.
  • A practical tool is provided to help researchers quantify and manage bias in network studies.