<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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 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.

Imputation of missing network data: Some simple procedures

Journal of Social Structure

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Imputation of missing network data: Some simple procedures"}

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...

Imputation of missing network data: Some simple procedures

Pubblicato online: 10 gen 2020

Pagine: 1 - 29

DOI: https://doi.org/10.21307/joss-2019-050

Parole chiave
Missing data, Single imputation, Descriptive network analysis, Friendship network

© 2019 Mark Huisman et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Imputation of missing network data: Some simple procedures

Pubblicato online: 10 gen 2020

Pagine: 1 - 29

DOI: https://doi.org/10.21307/joss-2019-050

Parole chiaveMissing data, Single imputation, Descriptive network analysis, Friendship network

© 2019 Mark Huisman et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Missing data, Single imputation, Descriptive network analysis, Friendship network