Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results.

中文翻译：

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单例实验设计中缺失数据的特征：对已发表数据的调查。

单案例实验设计（SCED）在教育、心理学、医学和康复等领域越来越受欢迎。尽管 SCED 是确定循证实践的有效实验设计，但它们在数据分析方面遇到了一些挑战。这些挑战之一，即数据缺失，由于随着时间的推移不断重复测量，在 SCED 研究中很可能经常发生。由于缺失数据是削弱研究有效性和普遍性的关键因素，因此确定 SCED 中缺失数据的特征非常重要，尤其是在少数参与者中进行的。在这方面，本研究旨在详细描述 SCEDs 研究中缺失的数据特征。为了实现这一目标，调查纳入了近 5 年内在 6 种期刊上发表的 465 篇 SCED 研究。总体结果显示，SCED 文章中至少一个阶段（作为至少一个数据点）的数据缺失发生率约为 30%。此外，结果表明，在大多数发生数据缺失的研究中，数据缺失率都在10%以上。尽管缺失数据在 SCED 研究中非常常见，但只有少数研究 (5%) 处理了缺失数据；然而，他们的方法是传统的。在分析 SCED 数据时，文献中提出了几种考虑缺失数据比率的方法。因此，本研究结果确定的缺失数据率可以通过提高研究结果的有效性和普遍性，为采用适当方法分析 SCED 数据提供启示。