The Impact of Sampling and Network Topology on the Estimation of Social Intercorrelations.

With the growing popularity of online social networks, it is becoming more important for marketing researchers to understand and measure social intercorrelations among consumers. The authors show that the estimation of consumers' social intercorrelations can be significantly affected by the sampling method used in the study and the topology of the social network. Through a series of simulation studies using a spatial model, the authors find that the magnitude of social intercorrelations in consumer networks tends to be underestimated if samples of the networks are used (rather than using the entire population of the network). The authors further demonstrate that sampling methods that better preserve the network structure perform best in recovering the social intercorrelations. However, this advantage decreases in networks characterized by the scale-free power-law distribution for the number of connections of each member. The authors discuss the insights they glean from these findings and propose a method to obtain unbiased estimation of the magnitude of social intercorrelations. [ABSTRACT FROM AUTHOR]