The topic of replicability has recently captivated the emerging field of network psychometrics. Although methodological practice (e.g., p-hacking) has been identified as a root cause of unreliable research findings in psychological science, the statistical model itself has come under attack in the partial correlation network literature. In a motivating example, I first describe how sampling variability inherent to partial correlations can merely give the appearance of unreliability. For example, when going from zero-order to partial correlations there is necessarily more sampling variability that translates into reduced statistical power. I then introduce novel methodology for deriving expected network replicability (ENR), wherein replication is modeled with the Poisson-binomial distribution. This analytic solution can be used with the Pearson, Spearman, Kendall, and polychoric partial correlation coefficient. I first employed the method to estimate ENR for a variety of data sets from the network literature. Here it was determined that partial correlation networks do not have inherent limitations, given current estimates of replicability were consistent with ENR. I then highlighted sources that can reduce replicability, that is, when going from continuous to ordinal data with few categories and employing a multiple comparisons correction. To address these challenges, I described a strategy for using the proposed method to plan for network replication. I end with recommendations that include the importance of the network literature repositioning itself with gold-standard approaches for assessing replication, including explicit consideration of Type I and Type II error rates. The method for computing ENR is implemented in the R package GGMnonreg.

中文翻译：

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学习适应抽样可变性：部分相关网络中的预期可复制性。

可复制性的话题最近吸引了新兴的网络心理测量学领域。尽管方法论实践（例如，p -hacking）已被确定为心理科学研究结果不可靠的根本原因，但统计模型本身已在偏相关网络文献中受到攻击。在一个鼓舞人心的例子中，我首先描述了偏相关所固有的抽样可变性如何仅能给人一种不可靠的感觉。例如，当从零阶到偏相关时，必然会有更多的抽样可变性转化为降低的统计功效。然后，我介绍了用于推导预期网络可复制性 (ENR) 的新方法，其中复制是用泊松二项分布建模的。此解析解可与 Pearson、Spearman、Kendall 和多变量偏相关系数一起使用。我首先采用该方法来估计来自网络文献的各种数据集的 ENR。鉴于目前对可复制性的估计与 ENR 一致，在这里确定偏相关网络没有固有的局限性。然后我强调了可以降低可复制性的来源，即 当从具有少数类别的连续数据到有序数据并采用多重比较校正时。为了应对这些挑战，我描述了一种使用建议的方法来规划网络复制的策略。最后，我提出了一些建议，其中包括使用评估复制的黄金标准方法重新定位网络文献的重要性，包括明确考虑 I 类和 II 类错误率。计算ENR的方法在R包中实现 包括明确考虑 I 类和 II 类错误率。计算ENR的方法在R包中实现 包括明确考虑 I 类和 II 类错误率。计算ENR的方法在R包中实现GGMnonreg .