Estimating psychological networks and their accuracy: A tutorial paper.
Estimating psychological networks and their accuracy: A tutorial paper.
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DOI：
10.3758/s13428-017-0862-1
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发表时间：
2018-03
影响因子：
5.4
通讯作者：
Fried EI
中科院分区：
文献类型：
--
作者：
Epskamp S;Borsboom D;Fried EI
心理网络的使用，概念化的行为作为一个复杂的相互作用的心理和其他组成部分，已获得越来越多的流行在各个研究领域。虽然先前的出版物已经解决了估计和解释这种网络的主题，但是很少有工作来检查如何准确（即，易于采样变化）网络，以及网络的稳定性（即，解释保持相似，观察较少）来自网络结构的推断（如中心性指数）。在本教程论文中，我们旨在向读者介绍该领域并解决抽样变化下的准确性问题。我们首先介绍了目前的国家的最先进的网络估计。其次，我们提供了一个理由，为什么研究人员应该调查的准确性心理网络。第三，我们描述了如何引导例程可以用来（A）评估估计的网络连接的准确性，（B）调查中心性指标的稳定性，以及（C）测试是否网络连接和中心性估计不同的变量彼此不同。我们介绍了两种新的统计方法：（B）的相关稳定系数，（C）的边权重和中心性指标的自举差异检验。我们进行了模拟研究，以评估这两种方法的性能。最后，我们开发了免费的R-包bootnet，它允许在一个广义的框架中估计心理网络，除了建议的bootstrap方法。我们在一个教程中展示了bootnet，伴随着R语法，在这个教程中，我们分析了359名患有创伤后应激障碍的女性的数据集。本文的在线版本（doi：10.3758/s13428-017-0862-1）包含补充材料，可供授权用户使用。
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 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. The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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影响因子：
3.1
作者：
Costenbader, E;Valente, TW
通讯作者：
Valente, TW
影响因子：
5.9
作者：
Costantini, Giulio;Richetin, Juliette;Perugini, Marco
通讯作者：
Perugini, Marco
DOI：
10.1146/annurev-clinpsy-050212-185608
发表时间：
2013-01-01
期刊：
ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL 9
影响因子：
--
作者：
Borsboom, Denny;Cramer, Angelique O. J.
通讯作者：
Cramer, Angelique O. J.
影响因子：
2.7
作者：
Chen, Jiahua;Chen, Zehua
通讯作者：
Chen, Zehua
影响因子：
2.4
作者：
Barzel, Baruch;Biham, Ofer
通讯作者：
Biham, Ofer