This article walks through steps to conduct a meta-analysis of Monte Carlo simulation studies. The selected Monte Carlo simulation studies focused on mixture modeling, which is becoming increasingly popular in the social and behavioral sciences. We provide details for the following steps in a meta-analysis: (a) formulating a research question; (b) identifying the relevant literature; (c) screening of the literature; (d) extracting data; (e) analyzing the data; and (f) interpreting and discussing the findings. Our goal was to investigate which simulation design factors (moderators) impact class enumeration accuracy in mixture modeling analyses. We analyzed the meta-analytic data using a generalized linear mixed model with a multilevel structure and examined the impact of the design moderators on the outcome of interest with a meta-regression model. For instance, the Bayesian information criterion was found to perform more accurately in conditions with larger sample sizes whereas entropy was found to perform more accurately with smaller sample sizes. It is hoped that this article can serve as a guide for others to follow in order to quantitatively synthesize results from Monte Carlo simulation studies. In turn, the findings from meta-analyzing Monte Carlo simulation studies can provide more details about factors that influence outcomes of interest as well as help methodologists when planning Monte Carlo simulation studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

蒙特卡洛模拟的荟萃分析，使用混合模型检查类枚举的准确性。


本文介绍了对 Monte Carlo 模拟研究进行荟萃分析的步骤。选定的 Monte Carlo 模拟研究侧重于混合物建模，这在社会和行为科学中越来越受欢迎。我们在荟萃分析中提供了以下步骤的详细信息：（a） 制定研究问题;（b） 识别相关文献;（c） 文献筛选;（d） 提取数据;（e） 分析数据;以及 （f） 解释和讨论调查结果。我们的目标是研究哪些仿真设计因素 （调节因子） 会影响混合物建模分析中的类别枚举准确性。我们使用具有多级结构的广义线性混合模型分析了荟萃分析数据，并使用荟萃回归模型检查了设计调节因子对感兴趣结果的影响。例如，发现贝叶斯信息准则在样本量较大的条件下表现得更准确，而发现熵在样本量较小的情况下表现得更准确。希望本文可以作为其他人遵循的指南，以便定量综合蒙特卡洛模拟研究的结果。反过来，荟萃分析蒙特卡洛模拟研究的结果可以提供有关影响感兴趣结果的因素的更多详细信息，并帮助方法学家规划蒙特卡洛模拟研究。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。