Haaf Julia M, Rouder Jeffrey N
Psychological Methods Unit, University of Amsterdam.
Department of Cognitive Sciences, University of California, Irvine.
Psychol Methods. 2023 Apr;28(2):472-487. doi: 10.1037/met0000428. Epub 2021 Nov 22.
The most prominent goal when conducting a meta-analysis is to estimate the true effect size across a set of studies. This approach is problematic whenever the analyzed studies have qualitatively different results; that is, some studies show an effect in the predicted direction while others show no effect and still others show an effect in the opposite direction. In case of such qualitative differences, the average effect may be a product of different mechanisms, and therefore uninterpretable. The first question in any meta-analysis should therefore be whether all studies show an effect in the same, expected direction. To tackle this question a model with ordinal constraints is proposed where the ordinal constraint holds each study in the set. This "every study" model is compared with a set of alternative models, such as an unconstrained model that predicts effects in both directions. If the ordinal constraints hold, one underlying mechanism may suffice to explain the results from all studies, and this result could be supported by reduced between-study heterogeneity. A major implication is then that average effects become interpretable. We illustrate the model comparison approach using Carbajal et al.'s (2021) meta-analysis on the familiar-word-recognition effect, show how predictor analyses can be incorporated in the approach, and provide R-code for interested researchers. As common in meta-analysis, only surface statistics (such as effect size and sample size) are provided from each study, and the modeling approach can be adapted to suit these conditions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
进行元分析时最突出的目标是估计一系列研究中的真实效应量。只要所分析的研究结果在性质上存在差异，这种方法就会出现问题；也就是说，一些研究显示出预期方向的效应，而另一些研究则没有效应，还有一些研究显示出相反方向的效应。在存在这种性质差异的情况下，平均效应可能是不同机制的产物，因此无法解释。因此，任何元分析中的第一个问题都应该是所有研究是否都显示出相同的、预期方向的效应。为了解决这个问题，提出了一种具有顺序约束的模型，其中顺序约束适用于集合中的每一项研究。将这个“每项研究”模型与一组替代模型进行比较，比如一个预测两个方向效应的无约束模型。如果顺序约束成立，一个潜在机制可能足以解释所有研究的结果，并且这种结果可以通过研究间异质性的降低得到支持。一个主要的含义是平均效应变得可以解释了。我们使用卡瓦哈尔等人（2021年）关于熟悉单词识别效应的元分析来说明模型比较方法，展示如何将预测变量分析纳入该方法，并为感兴趣的研究人员提供R代码。与元分析中的常见情况一样，每项研究只提供表面统计数据（如效应量和样本量），并且建模方法可以进行调整以适应这些条件。（PsycInfo数据库记录（c）2023美国心理学会，保留所有权利）
