In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta‐analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta‐analysis regression models (viz. “meta‐regression”) to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at moderator levels of interest as well as continuous confidence bands around the meta‐regression line itself. Such estimates, in turn, can be highly informative to interpret the nature of the phenomenon being studied in the meta‐analysis, especially when a comparison with an absolute or a practical criterion is the goal. Knowing the point at which effect size estimates reach statistical significance or other practical criteria of effect size magnitude can be quite important. Examples ranging from simple to complex models illustrate these principles. Limitations and extensions of the strategy are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

中文翻译：

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使用元回归模型中的截距描述估计：移动常数技术。

在任何科学学科中，以图形方式描绘研究模式的能力通常有助于解释现象。部分为了描述现象，元分析模型的统计数据和功能变得越来越复杂。因此，本文详细介绍了如何移动加权元分析回归模型（即“元回归”）中的常数，以阐明此类模型在一系列复杂性中的模式。尽管在实践中通常被忽略，但当它不降级为通常的静态角色时，此类模型中的常量（或截距）可能是不可或缺的。移动常数技术可以在感兴趣的调节水平以及元回归线本身周围的连续置信区间上进行估计和置信区间。这样的估计反过来，可以为解释元分析中研究的现象的性质提供大量信息，尤其是当目标是与绝对或实际标准进行比较时。了解效应量估计达到统计显着性的点或效应量大小的其他实用标准可能非常重要。从简单模型到复杂模型的示例说明了这些原则。讨论了该策略的限制和扩展。版权所有 © 2011 John Wiley & Sons, Ltd. 从简单模型到复杂模型的示例说明了这些原则。讨论了该策略的限制和扩展。版权所有 © 2011 John Wiley & Sons, Ltd. 从简单模型到复杂模型的示例说明了这些原则。讨论了该策略的限制和扩展。版权所有 © 2011 John Wiley & Sons, Ltd.