An Examination of Measurement Procedures and Characteristics of Baseline Outcome Data in Single-Case Research.
Baseline data in single-case studies are usually messy—pick stats and visual rules that expect the mess.
01Research in Context
What this study did
Emerson et al. (2023) looked at over 1,800 single-case graphs. They wanted to see what baseline data really look like.
They coded how each baseline was measured and the shape of the data points. No kids were treated; this was a map of what we already have.
What they found
Most baselines are not nice, straight, normal lines. They skew, jump, or stay flat.
Knowing these real shapes matters when you pick a stats test or eyeball a graph.
How this fits with other research
Wolfe et al. (2019) give you a step-by-step visual checklist. Their rules now make more sense because E et al. show the messy data you will actually rate.
Verboon et al. (2018) push the generalized logistic model. That model needs curvy baselines; E et al. confirm those curves are common, so the model fits better than we thought.
Davis et al. (2018) warn that different quality tools spit out different verdicts. E et al. add that the tools also assume baseline shapes that rarely happen—so some "low quality" calls may be unfair.
Why it matters
Next time you run an A-B-A-B or multiple baseline, peek at your baseline first. If it drifts or skews, choose stats or visual rules that welcome that shape instead of forcing normal. You will make safer go-or-no-go calls and avoid false negatives for your kids.
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02At a glance
03Original abstract
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.
Behavior modification, 2023 · doi:10.1177/0145445519864264