Conducting Bayesian-Classical Hybrid Power Analysis with R Package Hybridpower
Bibliographic Data
| ID | 19290652 |
|---|---|
| Authors | Joonsuk Park (0000-0003-0227-3283, independent scholar, corresponding author), Jolynn Pek (0000-0002-9694-4967, Psychology, The Ohio State University) |
| Year | 2023 |
| Volume | 58 |
| Issue | 3 |
| Pages | 543-559 |
| Publication date | 2023-05-04 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2022.2038056 |
| PMID | 35263213 |
| OpenAlex | W4220991391 |
| Language | EN |
| References cited | 34 |
There are several approaches to incorporating uncertainty in power analysis. We review these approaches and highlight the Bayesian-classical hybrid approach that has been implemented in the R package hybridpower. Calculating Bayesian-classical hybrid power circumvents the problem of local optimality in which calculated power is valid if and only if the specified inputs are perfectly correct. hybridpower can compute classical and Bayesian-classical hybrid power for popular testing procedures including the t-test, correlation, simple linear regression, one-way ANOVA (with equal or unequal variances), and the sign test. Using several examples, we demonstrate features of hybridpower and illustrate how to elicit subjective priors, how to determine sample size from the Bayesian-classical approach, and how this approach is distinct from related methods. hybridpower can conduct power analysis for the classical approach, and more importantly, the novel Bayesian-classical hybrid approach that returns more realistic calculations by taking into account local optimality that the classical approach ignores. For users unfamiliar with R, we provide a limited number of RShiny applications based on hybridpower to promote the accessibility of this novel approach to power analysis. We end with a discussion on future developments in hybridpower
Algorithm · Bayesian probability · Power (physics) · Prior probability · Artificial Intelligence · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Statistical Methods in Clinical Trials
Design sensitivity
Uncertain Judgements
Sample-Size Planning for More Accurate Statistical Power
Improving transparency and replication in Bayesian statistics
Bayes factor design analysis
Measurement of health status
Science and Statistics
Bayes Factors
How Many Subjects
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Presidential Address
Assurance in Intervention Research
Analyzing data
| Citation velocity | historical |
|---|---|
| Highly cited | No |