The network autocorrelation model using two-mode data: Affiliation exposure and potential bias in the autocorrelation parameter

The network autocorrelation model has been a workhorse for modeling network influences on individual behavior. The standard network approaches to mapping social influence using network measures, however, are limited to specifying an influence weight matrix (W) based on a single mode network. Additionally, it has been demonstrated that the estimate of the autocorrelation parameter ρ of the network effect tends to be negatively biased as the density in W matrix increases. The current study introduces a two-mode version of the network autocorrelation model. We then conduct simulations to examine conditions under which bias might exist. We show that the estimate for the affiliation autocorrelation parameter (ρ) tends to be negatively biased as density increases, as in the one-mode case. Inclusion of the diagonal of W, the count of the number of events participated in, as one of the variables in the regression model helps to attenuate such bias, however. We discuss the implications of these results.

• Introduces a two-mode version of the network autocorrelation model.
• Conduct simulation analysis to explore biasness in autocorrelation parameter.
• Affiliation exposure helps attenuating negative bias in autocorrelation parameter.