An Examination of Measurement Procedures and Characteristics of Baseline Outcome Data in Single-Case Research

• Published in: Behavior modification Vol. 47; no. 6; pp. 1423 - 1454
• Main Authors: Pustejovsky, James E., Swan, Daniel M., English, Kyle W.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.11.2023; SAGE PUBLICATIONS, INC
• Subjects: Monte Carlo Methods; Monte Carlo simulation; Outcome Measures; Statistical Analysis; Statistical methods; alternating renewal process; systematic review; single-case research; behavioral observation
• ISSN: 0145-4455; 1552-4167; 1552-4167
• DOI: 10.1177/0145445519864264

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions—particularly regarding normality of error distributions—may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.