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The following article has just been published in the Journal of Social

Huisman, M. (2009). Imputation of missing network data: Some simple

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 imputation 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.

Dr Garry Robins
School of Behavioural Science
University of Melbourne
Victoria 3010

Journal of Social Structure

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