Quality standards for single-case experimental designs (SCEDs) recommend inspecting six data aspects: level, trend, variability, overlap, immediacy, and consistency of data patterns. The data aspect consistency has long been neglected by visual and statistical analysts of SCEDs despite its importance for inferring a causal relationship. However, recently a first quantification has been proposed in the context of A-B-A-B designs, called CONsistency of DAta Patterns (CONDAP). In the current paper, we extend the existing CONDAP measure for assessing consistency in designs with more than two successive A-B elements (e.g., A-B-A-B-A-B), multiple baseline designs, and changing criterion designs. We illustrate each quantification with published research.

中文翻译：

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单例实验一致性评估：超越 ABAB 设计。

单案例实验设计 (SCED) 的质量标准建议检查六个数据方面：水平、趋势、可变性、重叠、即时性和数据模式的一致性。数据方面的一致性长期以来一直被 SCED 的视觉和统计分析人员忽视，尽管它对于推断因果关系很重要。然而，最近在 ABAB 设计的背景下提出了第一个量化，称为数据模式的一致性 (CONDAP)。在当前的论文中，我们扩展了现有的 CONDAP 措施，以评估具有两个以上连续 AB 元素（例如，ABABAB）、多个基线设计和不断变化的标准设计的设计的一致性。我们用已发表的研究来说明每个量化。