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Bayesian analysis of data from single case designs
Bayesian statistical methods have great potential advantages for the analysis of data from single case designs. Bayesian methods combine prior information with data from a study to form a posterior distribution of information about their parameters and functions. The interpretation of results from a...
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| Published in: | Neuropsychological rehabilitation 2014-07, Vol.24 (3-4), p.572-589 |
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| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c527t-148ccb667d55f198d95536eac47059fd900386af7fbfa358adc102daa0f72c3f3 |
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| cites | cdi_FETCH-LOGICAL-c527t-148ccb667d55f198d95536eac47059fd900386af7fbfa358adc102daa0f72c3f3 |
| container_end_page | 589 |
| container_issue | 3-4 |
| container_start_page | 572 |
| container_title | Neuropsychological rehabilitation |
| container_volume | 24 |
| creator | Rindskopf, David |
| description | Bayesian statistical methods have great potential advantages for the analysis of data from single case designs. Bayesian methods combine prior information with data from a study to form a posterior distribution of information about their parameters and functions. The interpretation of results from a Bayesian analysis is more natural than those from classical methods, and there are interpretations of useful quantities that are not possible in classical statistics, such as the probability that an effect size is small, or is greater than zero, or is large enough to be considered important. They are not based on asymptotic theory, so small sample size is not a problem for inference. These methods are implemented on free software, and are similar to non-Bayesian software, so analysts familiar with frequentist methods for multilevel data should find the transition relatively painless. |
| doi_str_mv | 10.1080/09602011.2013.866903 |
| format | article |
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| ispartof | Neuropsychological rehabilitation, 2014-07, Vol.24 (3-4), p.572-589 |
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| source | Applied Social Sciences Index & Abstracts (ASSIA); EBSCOhost MLA International Bibliography With Full Text; EBSCOhost SPORTDiscus with Full Text; Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Social Sciences and Humanities Collection (Reading list) |
| subjects | Bayes Theorem Bayesian Bayesian analysis Hierarchical models Humans Models, Statistical Multilevel models Parameters Research Design - statistics & numerical data Single case Single subject Software |
| title | Bayesian analysis of data from single case designs |
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