Brossart Daniel F, Parker Richard I, Olson Elizabeth A, Mahadevan Lakshmi
Texas A&M University, USA.
Behav Modif. 2006 Sep;30(5):531-63. doi: 10.1177/0145445503261167.
This study explored some practical issues for single-case researchers who rely on visual analysis of graphed data, but who also may consider supplemental use of promising statistical analysis techniques. The study sought to answer three major questions: (a) What is a typical range of effect sizes from these analytic techniques for data from "effective interventions"? (b) How closely do results from these same analytic techniques concur with visual-analysis-based judgments of effective interventions? and (c) What role does autocorrelation play in interpretation of these analytic results? To answer these questions, five analytic techniques were compared with the judgments of 45 doctoral students and faculty, who rated intervention effectiveness from visual analysis of 35 fabricated AB design graphs. Implications for researchers and practitioners using single-case designs are discussed.
本研究探讨了一些单案例研究者面临的实际问题，这些研究者依赖对图表数据的视觉分析，但也可能考虑补充使用有前景的统计分析技术。该研究试图回答三个主要问题：(a) 对于 “有效干预” 的数据，这些分析技术的典型效应大小范围是多少？(b) 这些相同分析技术的结果与基于视觉分析的有效干预判断的契合程度如何？以及 (c) 自相关在这些分析结果的解释中起什么作用？为了回答这些问题，将五种分析技术与45名博士生和教师的判断进行了比较，这些人通过对35个虚构的AB设计图表进行视觉分析来评定干预效果。文中还讨论了对使用单案例设计的研究者和从业者的启示。
