University of Canterbury/Te Whare Wānanga o Waitaha.
Paediatric Feeding International.
J Appl Behav Anal. 2022 Jun;55(3):986-996. doi: 10.1002/jaba.921. Epub 2022 Apr 27.
Behavior analysts typically rely on visual inspection of single-case experimental designs to make treatment decisions. However, visual inspection is subjective, which has led to the development of supplemental objective methods such as the conservative dual-criteria method. To replicate and extend a study conducted by Wolfe et al. (2018) on the topic, we examined agreement between the visual inspection of five raters, the conservative dual-criteria method, and a machine-learning algorithm (i.e., the support vector classifier) on 198 AB graphs extracted from clinical data. The results indicated that average agreement between the 3 methods was generally consistent. Mean interrater agreement was 84%, whereas raters agreed with the conservative dual-criteria method and the support vector classifier on 84% and 85% of graphs, respectively. Our results indicate that both objective methods produce results consistent with visual inspection, which may support their future use.
行为分析师通常依赖于对个案实验设计的视觉检查来做出治疗决策。然而，视觉检查是主观的，这导致了补充客观方法的发展，如保守的双重标准方法。为了复制和扩展 Wolfe 等人（2018 年）在该主题上的研究，我们检查了 5 位评分者的视觉检查、保守的双重标准方法和机器学习算法（即支持向量分类器）对从临床数据中提取的 198 个 AB 图之间的一致性。结果表明，这 3 种方法之间的平均一致性通常是一致的。平均评分者间一致性为 84%，而评分者与保守的双重标准方法和支持向量分类器在 84%和 85%的图表上分别达成一致。我们的结果表明，这两种客观方法产生的结果与视觉检查一致，这可能支持它们的未来使用。
