Non-response in social networks: The impact of different non-response treatments on the stability of blockmodels

Žnidaršič, Anja; Ferligoj, Anuška; Doreian, Patrick

doi:10.1016/j.socnet.2012.02.002

Cited by 43 publications

(24 citation statements)

References 23 publications

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“…In contrast, analysis of the chi-square test revealed some significant differences in the level of interaction among scientists, service providers, and decision makers (Figure 2). We report on the analysis of the chi-square test below, as we believe missing (sampled) data from the permutation test are more likely to account for the detected distribution of ties, rather than an actual presence of no significant differences (Znidarsic et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

“…Differences in the expected frequency of network ties across and within each of the mutually exclusive roles were investigated based on interpretation of a block model permutation test using UCINET. We also run a chi-squared test to investigate differences in network ties, given the limitations of incomplete data and our sampling procedures for analyzing network-level data (Znidarsic et al, 2012). We acknowledge that chi-square tests are more likely to provide false positive claims of significance when assumptions of independence are not met (Kramer and Schmidhammer, 1992).…”

Section: Participants Were Provided a Description Of Climate Service mentioning

confidence: 99%

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Conceptualizing the Science-Practice Interface: Lessons from a Collaborative Network on the Front-Line of Climate Change

Kettle

Trainor

Loring

2017

Front. Environ. Sci.

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The gap between science and practice is widely recognized as a major concern in the production and application of decision-relevant science. This research analyzed the roles and network connections of scientists, service providers, and decision makers engaged in climate science and adaptation practice in Alaska, where rapid climate change is already apparent. Our findings identify key actors as well as significant differences in the level of bonding ties between network members who perceive similarity in their social identities, bridging ties between network members across different social groups, and control of information across roles-all of which inform recommendations for adaptive capacity and the co-production of usable knowledge. We also find that some individuals engage in multiple roles in the network suggesting that conceptualizing science policy interactions with the traditional categories of science producers and consumers oversimplifies how experts engage with climate science, services, and decision making. Our research reinforces the notion that the development and application of knowledge is a networked phenomenon and highlights the importance of centralized individuals capable of playing multiple roles in their networks for effective translation of knowledge into action.

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Section: Resultsmentioning

confidence: 99%

Section: Participants Were Provided a Description Of Climate Service mentioning

confidence: 99%

Conceptualizing the Science-Practice Interface: Lessons from a Collaborative Network on the Front-Line of Climate Change

Kettle

Trainor

Loring

2017

Front. Environ. Sci.

View full text Add to dashboard Cite

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“…18 Our blockmodel measure only deals with the partitioning of nodes and ignores the larger macro picture encapsulated in an image matrix. We have thus run additional analyses where the measure of interest is based on the partitioning of nodes and the image matrix (see also Žnidaršič, Ferligoj, and Doreian 2012). The results are included in the Appendix and suggest, generally, that a measure based on the image matrix and node partitioning is less robust to missing data.…”

Section: Resultsmentioning

confidence: 99%

Structural effects of network sampling coverage I: Nodes missing at random

2013

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Network measures assume a census of a well-bounded population. This level of coverage is rarely achieved in practice, however, and we have only limited information on the robustness of network measures to incomplete coverage. This paper examines the effect of node-level missingness on 4 classes of network measures: centrality, centralization, topology and homophily across a diverse sample of 12 empirical networks. We use a Monte Carlo simulation process to generate data with known levels of missingness and compare the resulting network scores to their known starting values. As with past studies (Borgatti et al 2006; Kossinets 2006), we find that measurement bias generally increases with more missing data. The exact rate and nature of this increase, however, varies systematically across network measures. For example, betweenness and Bonacich centralization are quite sensitive to missing data while closeness and in-degree are robust. Similarly, while the tau statistic and distance are difficult to capture with missing data, transitivity shows little bias even with very high levels of missingness. The results are also clearly dependent on the features of the network. Larger, more centralized networks are generally more robust to missing data, but this is especially true for centrality and centralization measures. More cohesive networks are robust to missing data when measuring topological features but not when measuring centralization. Overall, the results suggest that missing data may have quite large or quite small effects on network measurement, depending on the type of network and the question being posed.

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“…Part I of this study offers one of the more comprehensive analysis of nodes missing at random (see also Kossinets, 2006; Huisman, 2009; Wang et al, 2012; Žnidaršič et al, 2012; González-Bailón et al, 2014). Most topological measures decreased in accuracy with more missing data, but the rate of deterioration varies widely across measures and networks (see Kossinets, 2006 as well).…”

Section: Prior Workmentioning

confidence: 99%

Network sampling coverage II: The effect of non-random missing data on network measurement

2017

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Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.

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Non-response in social networks: The impact of different non-response treatments on the stability of blockmodels

Cited by 43 publications

References 23 publications

Conceptualizing the Science-Practice Interface: Lessons from a Collaborative Network on the Front-Line of Climate Change

Conceptualizing the Science-Practice Interface: Lessons from a Collaborative Network on the Front-Line of Climate Change

Structural effects of network sampling coverage I: Nodes missing at random

Network sampling coverage II: The effect of non-random missing data on network measurement

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