Aydin, Orhan

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies.

Aydin, Orhan

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.