Correlations are ubiquitous in scientometric research. The present work illustrates a formula to quantify the predicted correlation between a composite indicator and a primary indicator (i.e., the composite indicator can be expressed as a weighted sum of the primary indicator), for example. Total citations received and number of self-citations or total publications and number of first-authorship publications, for example, represent such variable pairs. However, predicted correlation has a far wider range of potential applications in scientometrics. It is demonstrated that the predicted correlation provides a useful reference that allows a more conclusive interpretation of the data. Ignoring the outlined approach can result in overlooking of robust correlational patterns in the data. This is illustrated by a small simulation and two illustrations based on re-analyses of previous work. The approach can be used in new studies to understand the complete correlational pattern. In addition, the outlined approach can be used to revisit past findings reported in journal articles.

相关性在科学计量研究中无处不在。例如，目前的工作说明了一个公式来量化综合指标和主要指标之间的预测相关性（即，综合指标可以表示为主要指标的加权和）。例如，收到的总引用次数和自引次数或总发表次数和第一作者发表次数表示这样的变量对。然而，预测的相关性在科学计量学中具有更广泛的潜在应用。结果表明，预测的相关性提供了有用的参考，可以对数据进行更确凿的解释。忽略概述的方法可能会导致忽略数据中的稳健相关模式。这可以通过一个小型模拟和两个基于对先前工作的重新分析的插图来说明。该方法可用于新研究以了解完整的相关模式。此外，概述的方法可用于重新审视期刊文章中报告的过去发现。