Effect Sizes and Statistical Methods for Meta-Analysis in Higher Education
Effect Sizes and Statistical Methods for Meta-Analysis in Higher Education
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DOI：
10.1007/s11162-011-9232-5
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发表时间：
2012-05-01
影响因子：
2.1
通讯作者：
Bowman, Nicholas A.
中科院分区：
文献类型：
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作者：
Bowman, Nicholas A.
定量元分析是一种非常有用的，但未充分利用，在高等教育综合研究成果的技术。在高等教育中，元分析调查可能比其他研究领域更具挑战性，这是由于（a）对使用回归系数作为比较研究间效应大小的度量的关注，以及（B）当单个研究包含多个效应量时，观察结果的非独立性。本方法说明讨论了这两个重要问题，并提出了解决这些问题的具体建议。首先，荟萃分析学者得出的结论是，标准化回归系数，这往往是在高等教育手稿提供，构成了一个适当的度量效果大小。其次，分层线性模型（HLM）分析提供了一种有效的方法进行元分析研究，同时占观察的非独立性，和HLM通常是上级其他方法，试图解决这个问题。讨论了如何适当地实现这些技术。
Quantitative meta-analysis is a very useful, yet underutilized, technique for synthesizing research findings in higher education. Meta-analytic inquiry can be more challenging in higher education than in other fields of study as a result of (a) concerns about the use of regression coefficients as a metric for comparing the magnitude of effects across studies, and (b) the non-independence of observations that occurs when a single study contains multiple effect sizes. This methodological note discusses these two important issues and provides concrete suggestions for addressing them. First, meta-analysis scholars have concluded that standardized regression coefficients, which are often provided in higher education manuscripts, constitute an appropriate metric of effect size. Second, hierarchical linear modeling (HLM) analyses provide an effective method for conducting meta-analytic research while accounting for the non-independence of observations, and HLM is generally superior to other proposed methods that attempt to remedy this same problem. A discussion of how to implement these techniques appropriately is provided.