Shadish William R, Hedges Larry V, Pustejovsky James E, Boyajian Jonathan G, Sullivan Kristynn J, Andrade Alma, Barrientos Jeannette L
a School of Social Sciences, Humanities and Arts , University of California , Merced , CA , USA.
Neuropsychol Rehabil. 2014;24(3-4):528-53. doi: 10.1080/09602011.2013.819021. Epub 2013 Jul 18.
We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.
我们描述了一种用于单病例设计的标准化均值差统计量（d），它等同于组间实验中常用的d。我们展示了如何使用它来总结研究中各病例的治疗效果，在规划新研究和撰写资助申请时进行功效分析，以及对同一问题的多项研究结果进行元分析。我们讨论了这种d统计量的局限性以及可能的补救措施。即便如此，这种d统计量在统计学上比单病例设计的其他效应量测量方法更有依据，并且与许多通用线性模型方法（如多层建模或广义相加模型）不同，它产生的标准化效应量可以整合不同结果测量的研究。现已有用于效应量计算和功效分析的SPSS宏程序。
