首页> 外文期刊>Journal of sports sciences. >Difference-based meta-analytic procedures for between-participant and/or within-participant designs: a tutorial review for sports and exercise scientists.
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Difference-based meta-analytic procedures for between-participant and/or within-participant designs: a tutorial review for sports and exercise scientists.

机译:参与者与参与者和/或参与者内部设计之间的基于差异的META分析程序:体育和运动科学家的教程审查。

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The aim of this paper is to provide a contemporary summary of statistical and non-statistical meta-analytic procedures that have relevance to the type of experimental designs often used by sport scientists when examining differences/change in dependent measure(s) as a result of one or more independent manipulation(s). Using worked examples from studies on observational learning in the motor behaviour literature, we adopt a random effects model and give a detailed explanation of the statistical procedures for the three types of raw score difference-based analyses applicable to between-participant, within-participant, and mixed-participant designs. Major merits and concerns associated with these quantitative procedures are identified and agreed methods are reported for minimizing biased outcomes, such as those for dealing with multiple dependent measures from single studies, design variation across studies, different metrics (i.e., raw scores and difference scores), and variations in sample size. To complement the worked examples, we summarize the general considerations required when conducting and reporting a meta-analysis, including how to deal with publication bias, what information to present regarding the primary studies, and approaches for dealing with outliers. By bringing together these statistical and non-statistical meta-analytic procedures, we provide the tools required to clarify understanding of key concepts and principles.
机译:本文的目的是提供当代统计和非统计元分析程序的概要,这些程序与体育科学家经常使用的实验设计类型相关,当由于依赖性措施的差异/变化而言一个或多个独立的操纵。使用从事学习在运动行为文献中的研究中的研究,我们采用了随机效应模型,并详细解释了适用于参与者内部的参与者之间的基于原始分数差异分析的三种类型的基于原始分数分析的统计程序。和混合参与者设计。鉴定了与这些定量手术相关的主要优点和疑虑,并据报告了可偏见的结果,例如用于处理来自单一研究的多种依赖措施的偏见结果,设计变异,不同度量(即原始评分和差异分数)和样本大小的变化。为了补充所作的例子,我们总结了在进行和报告元分析时所需的一般考虑因素,包括如何处理出版物偏见,有关初级研究的信息,以及处理异常值的方法。通过汇集这些统计和非统计元分析程序,我们提供了澄清对关键概念和原则的理解所需的工具。

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