Center for Behavioral Education and Research, Neag School of Education, University of Connecticut, Storrs 06269, USA.
J Sch Psychol. 2011 Jun;49(3):301-21. doi: 10.1016/j.jsp.2011.03.004. Epub 2011 May 6.
A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice.
提出了一种从单案例设计中推导出效应量的新方法。该策略适用于具有自回归误差的小样本时间序列数据。该方法使用广义最小二乘法（GLS）来模拟数据的自相关性，并估计回归参数，以产生以标准差为单位的治疗效果，代表从基线到治疗阶段的治疗效果的大小。在本文中，该方法应用于两个使用常见单案例设计（即退出和多基线）的已发表的示例。描述了这些研究的结果，并将该方法与十个单案例效应量的理想标准进行了比较。基于该应用的结果，我们对使用 GLS 作为视觉分析的支持进行了观察，为未来的研究提供了建议，并描述了对实践的影响。
