  The usage of psychological networks that conceptualize psychological behavior
as a complex interplay of psychological and other components has gained
increasing popularity in various fields of psychology. 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 studies to assess the
performance of both methods. Finally, we developed the free R-package bootnet
that allows for estimating psychological networks in a generalized framework in
addition to the proposed bootstrap methods. We showcase bootnet in a tutorial,
accompanied by R syntax, in which we analyze a dataset of 359 women with
posttraumatic stress disorder available online.
