Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Research exists comparing subsets of heterogeneity estimators over limited conditions. Additionally, heterogeneity is a parameter estimated with uncertainty. Various methods exist for heterogeneity interval estimation, and similar to heterogeneity estimators, these evaluations are limited. The current simulation study examined the performance of Bayesian (with 5 prior specifications) and non-Bayesian estimators over conditions found after a review of meta-analyses of the standardized mean difference in education and psychology research. Three simulation conditions were varied: (a) number of effect sizes per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Estimators were evaluated over average bias and means square error. Methods of interval estimation were then evaluated with the estimators found to operate optimally. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. Overall, the Paule and Mandel estimator, in conjunction with the Jackson method of interval estimation, is recommended if no knowledge exists with regards to the expected value of heterogeneity when synthesizing the standardized mean difference effect size. If heterogeneity is expected to be small (e.g., < .075), then REML with the profile likelihood interval estimator is recommended. Sensitivity analysis evaluating differences in substantive conclusions with a suite of heterogeneity estimators, such as Paule and Mandel, REML, and Hedges and Olkin, is recommended. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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在教育和心理学标准化均值差异的随机效应荟萃分析中评估异质性和异质性区间估计量。

进行荟萃分析以综合相关研究的定量结果。随机效应荟萃分析模型基于这样的假设，即总体中存在真实效应的分布。通常假定此分布为均值和方差的正态分布。人口方差也称为异质性，可以通过多种方法进行估算。已有研究比较有限条件下的异质性估计子集。另外，异质性是具有不确定性的估计参数。存在用于异质性间隔估计的各种方法，并且与异质性估计器相似，这些评估受到限制。当前的模拟研究在对教育和心理学研究的标准均值差异进行荟萃分析后，根据发现的条件检查了贝叶斯（具有5个先前的规范）和非贝叶斯估计量的性能。改变了三种模拟条件：（a）每个荟萃分析的效应量数量，（b）真正的异质性，以及（c）每个荟萃分析中的每个效应量样本量。在平均偏差和均方误差上评估估计量。然后用发现的最佳估计器评估区间估计的方法。基于覆盖概率，间隔宽度和估计值的覆盖范围来评估间隔估计量。总体而言，Paule和Mandel估算器结合间隔估算的Jackson方法，如果在合成标准化均值差效应大小时不了解异质性的预期值，建议使用。如果异质性较小（例如，<.075），则建议使用轮廓似然区间估计量的REML。建议使用敏感性分析来评估实体结论中的差异，并使用一组异质性估计量进行评估，例如Paule和Mandel，REML，Hedges和Olkin。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。建议使用敏感性分析来评估实体结论中的差异，并使用一组异质性估计量进行评估，例如Paule和Mandel，REML，Hedges和Olkin。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。建议使用敏感性分析来评估实体结论中的差异，并使用一组异质性估计量进行评估，例如Paule和Mandel，REML，Hedges和Olkin。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。