DOI: 10.5061/dryad.v3s0k
title: Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics

description: Background: Connected individuals (or nodes) in a network are more likely
 to be similar than two randomly selected nodes due to homophily and/or
 network influence. Distinguishing between these two influences is an
 important goal in network analysis, and generalized estimating equation
 (GEE) analyses of longitudinal dyadic network data are an attractive
 approach. It is not known to what extent such regressions can accurately
 extract underlying data generating processes. Therefore our primary
 objective is to determine to what extent, and under what conditions, does
 the GEE-approach recreate the actual dynamics in an agent-based model.
 Methods: We generated simulated cohorts with pre-specified network
 characteristics and attachments in both static and dynamic networks, and
 we varied the presence of homophily and network influence. We then used
 statistical regression and examined the GEE model performance in each
 cohort to determine whether the model was able to detect the presence of
 homophily and network influence. Results: In cohorts with both static and
 dynamic networks, we find that the GEE models have excellent sensitivity
 and reasonable specificity for determining the presence or absence of
 network influence, but little ability to distinguish whether or not
 homophily is present. Conclusions: The GEE models are a valuable tool to
 examine for the presence of network influence in longitudinal data, but
 are quite limited with respect to homophily.
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