The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields.While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are.In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation.We first introduce the current state-of-the-art of network estimation.Second, we provide a rationale why researchers should investigate the accuracy of psychological networks.Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other.We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices.We conducted and present simulation
Stored only when both papers are in the corpus — 5,658 citations exist upstream.