PoweR Analysis in Review
Leveraging R to Conduct Power Analyses for a Range of Research Designs
Bibliographic Data
| ID | 21336767 |
|---|---|
| Authors | Mikael Rubin (0000-0002-8742-4951, Palo Alto University Palo Alto California USA, corresponding author) |
| Year | 2025 |
| Volume | 60 |
| Issue | 2 |
| Pages | e70017-e70017 |
| Publication date | 2025-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0020-7594 • E-ISSN: 1464-066X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ijop.70017 |
| PMID | 39972929 |
| OpenAlex | W4407798855 |
| Language | EN |
| References cited | 32 |
There are many helpful tools that exist for conducting power analyses. However, even the simplest tools require the researcher to make specific decisions about the research design and analytic methodology. This is not always a simple task, especially when the design and/or analysis are more sophisticated (e.g., in the case of longitudinal data with multiple levels). The aims of this paper are to (1) provide an overview of some existing tools in R that researchers can use and (2) introduce simulated power analyses in R. Existing tools are powerful, and it is important to consider their use, as they are often user‐friendly and may be more appropriate in some cases. The purpose of introducing simulations for power analysis is that it provides the most control for researchers in terms of how the sample size is determined. This paper will focus on the use of R for power analysis, which requires some background in coding, although some examples provided can be copy/pasted if the R environment is already available, as it is free and open‐source, and tools are continually evolving that facilitate advances in power analytic approaches
Coding (social sciences) · Data science · Focus (optics) · Management science · Power (physics) · Power analysis · Range (aeronautics) · Sample (material) · Simple (philosophy) · Statistics · Systems engineering · Task (project management) · Computer Science · Data Analysis with R · Engineering · Mathematics · Meta-analysis and systematic reviews · Statistical Methods and Bayesian Inference
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The Persistence of Underpowered Studies in Psychological Research
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An Introduction to Linear Mixed-Effects Modeling in R
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Mixed-effects modeling with crossed random effects for subjects and items
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| Highly cited | No |
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