Search

Due to repeated observations of an outcome behavior in N-of-1 or single-case design (SCD) intervention studies, the occurrence of missing scores is inevitable in such studies. Approximately 21% of SCD articles published in five reputable journals between 2015 and 2019 exhibited evidence of missing scores. Missing rates varied by designs, with the highest rate (24%) found in multiple baseline/probe designs. Missing scores cause difficulties in data analysis. And inappropriate treatments of missing scores lead to consequences that threaten internal validity and weaken generalizability of intervention effects reported in SCD research. In this paper, we comprehensively review nine methods for treating missing SCD data: the available data method, six single imputations, and two model-based methods. The strengths, weaknesses, assumptions, and examples of these methods are summarized. The available data method and three single imputation methods are further demonstrated in assessing an intervention effect at the class and students' levels. Assessment results are interpreted in terms of effect sizes, statistical significances, and visual analysis of data. Differences in results among the four methods are noted and discussed. The extensive review of problems caused by missing scores and possible treatments should empower researchers and practitioners to account for missing scores effectively and to support evidence-based interventions vigorously. The paper concludes with a discussion of contingencies for implementing the nine methods and practical strategies for managing missing scores in single-case intervention studies.

Single-case experimental studies are relevant and important to substantiate the effectiveness of behavioral, clinical, and educational interventions. When a baseline trend is present in intervention studies, it is challenging for researchers to determine, visually or statistically, if an effect is due to the intervention or to the naturally occurring trend. In this paper, we demonstrated and appraised four methods that can assess an intervention effect in the presence of baseline trends. These four methods quantified an intervention effect as a phase change effect size (i.e., mean phase difference and the slope and level change) or a nonoverlap effect size (i.e., Tau-UAB-A and Tauc). Empirical data from an intervention study were used in the demonstration. All methods were evaluated in terms of types of intervention effects assessed, assumptions, specialized computing tools, and application issues (e.g., missing scores). To empower researchers to use these four methods, we provide a summary of each method's appropriate inferences about an intervention effect and its computing tools' strengths and limitations. Based on the results, we recommend using multiple baseline trend-controlled methods to draw a conclusion about an intervention effect. [ABSTRACT FROM AUTHOR]

In the single-case design (SCD) literature, five sets of standards have been formulated and distinguished: design standards, assessment standards, analysis standards, reporting standards, and research synthesis standards. This article reviews computing tools that can assist researchers and practitioners in meeting the analysis standards recommended by the What Works Clearinghouse: Procedures and Standards Handbook—the WWC standards. These tools consist of specialized web-based calculators or downloadable software for SCD data, and algorithms or programs written in Excel, SAS procedures, SPSS commands/Macros, or the R programming language. We aligned these tools with the WWC standards and evaluated them for accuracy and treatment of missing data, using two published data sets. All tools were tested to be accurate. When missing data were present, most tools either gave an error message or conducted analysis based on the available data. Only one program used a single imputation method. This article concludes with suggestions for an inclusive computing tool or environment, additional research on the treatment of missing data, and reasonable and flexible interpretations of the WWC standards. [ABSTRACT FROM AUTHOR]