Sunbelt XXXI International Network for Social Network ... - INSNA

How Far Our Network Perceptions Are From Reality: The Role Of Structural Positions And Self‐monitoringVecchi, PatriziaKnowledge NetworksCognitive Social Structures, Personality, Interpersonal NetworksTHURS.AM1Cognition of social networks is an extremely young field of study in the broader area of social network research. The few studies conducted so far on this topichave shown that accuracy in social network perception (perceiving the structure of social relations in the social environment) plays an important role inexplaining social and organizational behavior, and that accuracy in network perception can be enhanced by occupying more central positions in the socialgroup. In addition, accuracy was found to be affected by some motivational traits, that are presumably out of individuals’ control. This study adds to researchon the antecedents of accuracy in social network perception by verifying the impact of individuals’ centrality in the advice and friendship networks at work ontheir ability to accurately perceive the overall structure of the two networks. Also, the study analyzes the effects, on accuracy, excised by a personality trait(i.e., self‐monitoring) which implies an acute sensitivity to the social context and to individual behavior. The network data for this study were collected in alarge multinational company and are represented by the individual perceptions of the people constituting the top management team of the company (n = 45).The results indicate that the position that an individual occupies in the informal structure of the network is the only determinant of his or her degree ofaccuracy in perceiving the network itself.Identifying Bias And Its Effects On Interpreting Behavioral Social Network DataBienenstock, Elisa J.; Singh, Lisa; Samuel, Nayyara; Bansal, Srividya .; Stanton, Margaret; Mann, JanetCollecting Network DataData Collection, Animal Networks, Validation Methods, Bias Correction, Sensitivity AnalysisTHURS.PM1Exponential growth in computational power has extended the uses of SNA beyond description to inference about context from network structure. Here, westudy how observer and sampling biases can affect these inferences using a 25 year dolphin behavioral database from the Shark Bay Dolphin Research Project.Bias occurs when the collection of behavioral data is correlated in some way with a variable of explicit or implicit interest to the researcher. Additionally, somesubjects (e.g., mangled dorsal fins) or behaviors (e.g., socializing) are more obvious or easier to observe or classify than others. We examine how social networkmetrics vary by observer by comparing each researcher’s observed network with the representative (aggregated) network. Doing this allows us to identify thebiases of different researchers and determine how sensitive network metrics are to observational differences. We also investigate whether these differencesare related to dolphin attributes (age, sex, distinctiveness) and environmental and/or social conditions (season, group size, location). We find that networkmetrics differ by observer and interact with dolphin attributes and external factors (e.g. some observers provide more reliable records on dolphins of one sexor age class than others and/or tend to bias observations towards larger groups).