Getting the best out of single case data
This article analyzes the value of single-case designs (SCDs) in the fields of personalized healthcare and education, highlighting their growing relevance in research with small samples. In these designs, the same subjects are repeatedly assessed based on the same outcomes, which offers methodologi...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Universidad de Murcia
2025-06-01
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| Series: | Revista Española de Educación Médica |
| Subjects: | |
| Online Access: | https://revistas.um.es/edumed/article/view/664271 |
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| Summary: | This article analyzes the value of single-case designs (SCDs) in the fields of personalized healthcare and education, highlighting their growing relevance in research with small samples. In these designs, the same subjects are repeatedly assessed based on the same outcomes, which offers methodological advantages but also poses statistical challenges.
The author compares three common statistical methods for analyzing SCD data: randomization tests, Bayesian percentage of non-overlapping data (PAND-B), and time series regression (TSR). While PAND-B allows for the incorporation of prior knowledge and avoids questionable assumptions required by TSR, it has limitations: it does not adequately consider ordinal or quantitative information and provides invalid estimates when samples are unequal.
To overcome these shortcomings, the use of the Generalized Relative Effect (GRE), based on the Brunner-Munzel relative effect statistic, is proposed. This approach retains applicability to all levels of measurement (nominal, ordinal, and quantitative) and allows for the integration of prior theoretical or empirical knowledge through Bayesian prior distributions.
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| ISSN: | 2660-8529 |