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首页> 外文期刊>Behavior Research Methods, Instruments & Computers >Estimating the mean effect size in meta-analysis: Bias, precision, and mean squared error of different weighting methods
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Estimating the mean effect size in meta-analysis: Bias, precision, and mean squared error of different weighting methods

机译：估算荟萃分析的平均效应大小：不同权重方法的偏差，精度和均方误差

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摘要

Although use of the standardized mean difference in meta-analysis is appealing for several reasons, there are some drawbacks. In this article, we focus on the following problem: that a precision-weighted mean of the observed effect sizes results in a biased estimate of the mean standardized mean difference. This bias is due to the fact that the weight given to an observed effect size depends on this observed effect size. In order to eliminate the bias, Hedges and Olkin (1985) proposed using the mean effect size estimate to calculate the weights. In the article, we propose a third alternative for calculating the weights: using empirical Bayes estimates of the effect sizes. In a simulation study, these three approaches are compared. The mean squared error (MSN) is used as the criterion by which to evaluate the resulting estimates of the mean effect size. For a meta-analytic dataset with a small number of studies, the MSE is usually smallest when the ordinary procedure is used, whereas for a moderate or large number of studies, the procedures yielding the best results are the empirical Bayes procedure and the procedure of Hedges and Olkin, respectively.
机译：尽管由于多种原因，在荟萃分析中使用标准化均值差异很有吸引力，但仍存在一些缺点。在本文中，我们关注以下问题：观察到的效应大小的精确加权均值导致均值标准化均值差的偏差估计。该偏差是由于以下事实：赋予观察到的效果大小的权重取决于该观察到的效果大小。为了消除偏差，Hedges和Olkin（1985）提出使用平均效应大小估计来计算权重。在本文中，我们提出了计算权重的第三种方法：使用效果大小的经验贝叶斯估计。在仿真研究中，比较了这三种方法。均方误差（MSN）用作评估平均效果大小的最终估计值的标准。对于具有少量研究的元分析数据集，当使用普通程序时，MSE通常最小；而对于中等或大量研究，产生最佳结果的程序是经验贝叶斯程序和贝叶斯程序。对冲和奥尔金分别。

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