Assessing the performance of the bootstrap in simulated assemblage networks

John M. Roberts, Yi Yin, Emily Dorshorst, Matthew A. Peeples, Barbara J. Mills

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Archaeologists are increasingly interested in networks constructed from site assemblage data, in which weighted network ties reflect sites’ assemblage similarity. Equivalent networks would arise in other scientific fields where actors’ similarity is assessed by comparing distributions of observed counts, so the assemblages studied here can represent other kinds of distributions in other domains. One concern with such work is that sampling variability in the assemblage network and, in turn, sampling variability in measures calculated from the network must be recognized in any comprehensive analysis. In this study, we investigated the use of the bootstrap as a means of estimating sampling variability in measures of assemblage networks. We evaluated the performance of the bootstrap in simulated assemblage networks, using a probability structure based on the actual distribution of sherds of ceramic wares in a region with 25 archaeological sites. Results indicated that the bootstrap was successful in estimating the true sampling variability of eigenvector centrality for the 25 sites. This held both for centrality scores and for centrality ranks, as well as the ratio of first to second eigenvalues of the network (similarity) matrix. Findings encourage the use of the bootstrap as a tool in analyses of network data derived from counts.

Original languageEnglish (US)
Pages (from-to)98-109
Number of pages12
JournalSocial Networks
StatePublished - May 2021


  • Archaeological networks
  • Assemblage data
  • Bootstrap
  • Centrality
  • Sampling variability
  • Simulation

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • General Social Sciences
  • General Psychology


Dive into the research topics of 'Assessing the performance of the bootstrap in simulated assemblage networks'. Together they form a unique fingerprint.

Cite this