单主题评价研究中的元分析

Charles Auerbach
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引用次数: 0

摘要

荟萃分析技术可用于汇总跨研究的评估结果。在单主题研究设计的情况下,我们可以结合对5个、10个或20个客户的评估结果来确定,平均而言,干预的效果如何。这是一种比定性报告这些变化或简单报告每项研究的个体效应大小更复杂、更复杂的理解研究差异的方法。在本章中,作者讨论了为什么在单主题研究中考虑元分析是重要的,特别是在建立研究证据的背景下。然后,他们演示了如何使用SSD来实现R函数。在第4章中介绍的效应量的基础上,作者说明了在哪些条件下使用传统效应量进行元分析是合适的,如何引入干预变量,以及如何评估统计输出。此外,作者讨论并说明了在传统效应量不能使用的情况下,所有对的平均非重叠的计算和解释。
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Meta-Analysis in Single-Subject Evaluation Research
Meta-analytic techniques can be used to aggregate evaluation results across studies. In the case of single-subject research designs, we could combine findings from evaluations with 5, 10 or 20 clients to determine, on average, how effective an intervention is. This is a more complex and sophisticated way of understanding differences across studies than reporting those changes qualitatively or simply reporting the individual effect sizes for each study. In this chapter, the authors discuss why meta-analysis is important to consider in single-subject research, particularly in the context of building research evidence. They then demonstrate how to do this using SSD for R functions. Building upon effect sizes, introduced in Chapter 4, the authors illustrate the conditions under which it is appropriate to use traditional effect sizes to conduct meta-analyses, how to introduce intervening variables, and how to evaluate statistical output. Additionally, the authors discuss and illustrate the computation and interpretation of a mean Non-Overlap of All Pairs in situations which traditional effect sizes cannot be used.
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Comparing Baseline and Intervention Phases Overview of SSDforR Functions Meta-Analysis in Single-Subject Evaluation Research Using RMarkdown to Present Your Findings Analyzing Baseline Phase Data
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