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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Bibliographic Details
Published in:Neuropsychological rehabilitation 2014-07, Vol.24 (3-4), p.572-589
Main Author: Rindskopf, David
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian
Bayesian analysis
Hierarchical models
Humans
Models, Statistical
Multilevel models
Parameters
Research Design - statistics & numerical data
Single case
Single subject
Software
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Summary: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.
ISSN:0960-2011
1464-0694
DOI:10.1080/09602011.2013.866903