Constructing a valid confidence interval for newman's assortativity coefficient when cluster size is informative

Background: Population-level mixing patterns can be quantified using Newman's assortativity coefficient r. Suggested methods for estimating the standard error for r may lead to inappropriate statistical conclusions in situations where intra-cluster correlation is ignored and/or when cluster size is predictive of the response. Methods: We describe a computer-intensive within-cluster resampling approach for providing a valid large-sample estimated standard error for r and an associated 95% confidence interval. Network data and a simulation model were employed to compare within-cluster resampling to standard methods when cluster size is informative. Results: For the analysis of network data, when cluster size is informative, simulations studies demonstrate that within-cluster resampling produces valid statistical inferences about Newman's assortativity coefficient, a popular statistic used to quantify the strength of mixing patterns. In contrast, commonly used methods are biased with attendant extremely poor confidence interval coverage. Within-cluster resampling is recommended when cluster size is informative and/or when there is within-cluster response correlation. Conclusions: Within-cluster resampling is recommended for providing valid statistical inferences when applying Newman's assortativity coefficient r to network data.