Dietrichson Jens, Klokker Rasmus, Filges Trine, Bengtsen Elizabeth, Pigott Therese D
Quantitative Methods, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Administration, VIVE-The Danish Center for Social Science Research Copenhagen Denmark.
Campbell Syst Rev. 2024 Dec 10;20(4):e70009. doi: 10.1002/cl2.70009. eCollection 2024 Dec.
Objectives This is the protocol for a Campbell systematic review. The objectives are as follows: The first objective is to find and describe machine and statistical learning (ML) methods designed for moderator meta-analysis. The second objective is to find and describe applications of such ML methods in moderator meta-analyses of health, medical, and social science interventions. These two parts of the meta-review will primarily involve a systematic review and will be conducted according to guidelines specified by the Campbell Collaboration (MECCIR guidelines). The outcomes will be a list of ML methods that are designed for moderator meta-analysis (first objective), and a description of how (some of) these methods have been applied in the health, medical, and social sciences (second objective). The third objective is to examine how the ML methods identified in the meta-review can help researchers formulate new hypotheses or select among existing ones, and compare the identified methods to one another and to regular meta-regression methods for moderator analysis. To compare the performance of different moderator meta-analysis methods, we will apply the methods to data on tutoring interventions from two systematic reviews of interventions to improve academic achievement for students with or at risk-of academic difficulties, and to an independent test sample of tutoring studies published after the search period in the two reviews.
目标 这是一项坎贝尔系统评价的方案。目标如下:第一个目标是查找并描述为调节变量元分析设计的机器学习和统计学习(ML)方法。第二个目标是查找并描述此类ML方法在健康、医学和社会科学干预措施调节变量元分析中的应用。元评价的这两个部分将主要涉及系统评价,并将根据坎贝尔合作组织指定的指南(MECCIR指南)进行。结果将是一份为调节变量元分析设计的ML方法列表(第一个目标),以及对这些方法(其中一些)如何应用于健康、医学和社会科学的描述(第二个目标)。第三个目标是研究元评价中确定的ML方法如何帮助研究人员提出新假设或在现有假设中进行选择,并将确定的方法相互比较,以及与用于调节变量分析的常规元回归方法进行比较。为了比较不同调节变量元分析方法的性能,我们将把这些方法应用于两项关于改善学业困难或有学业困难风险学生学业成绩干预措施的系统评价中的辅导干预数据,以及应用于在两项评价搜索期之后发表的辅导研究的独立测试样本。