Manolov Rumen, Moeyaert Mariola
University of Barcelona.
State University of New York.
Behav Ther. 2017 Jan;48(1):97-114. doi: 10.1016/j.beth.2016.04.008. Epub 2016 May 16.
The current paper responds to the need to provide guidance to applied single-case researchers regarding the possibilities of data analysis. The amount of available single-case data analytical techniques has been growing during recent years and a general overview, comparing the possibilities of these techniques, is missing. Such an overview is provided that refers to techniques that yield results in terms of a raw or standardized difference and procedures related to regression analysis, as well as nonoverlap and percentage change indices. The comparison is provided in terms of the type of quantification provided, data features taken into account, conditions in which the techniques are appropriate, possibilities for meta-analysis, and evidence available on their performance. Moreover, we provide a set of recommendations for choosing appropriate analysis techniques, pointing at specific situations (aims, types of data, researchers' resources) and the data analytical techniques that are most appropriate in these situations. The recommendations are contextualized using a variety of published single-case data sets in order to illustrate a range of realistic situations that researchers have faced and may face in their investigations.
本文旨在满足为应用单病例研究人员提供数据分析可能性指导的需求。近年来，可用的单病例数据分析技术数量不断增加，但缺乏对这些技术可能性进行比较的总体概述。本文提供了这样一个概述，涉及产生原始或标准化差异结果的技术、与回归分析相关的程序，以及非重叠和百分比变化指数。比较内容包括所提供的量化类型、考虑的数据特征、技术适用的条件、元分析的可能性以及关于其性能的现有证据。此外，我们针对选择合适的分析技术提供了一组建议，指出了特定情况（目标、数据类型、研究人员资源）以及在这些情况下最适用的数据分析技术。通过使用各种已发表的单病例数据集对这些建议进行情境化，以说明研究人员在调查中已经面临和可能面临的一系列现实情况。
