{"$schema":"https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json","name":"org.agenticjournal/paper-lakens-caldwell-2021-superpower","description":"Simulation-based power analysis for factorial ANOVA designs (Superpower).","version":"0.2.0","title":"Factorial ANOVA Power Agent","websiteUrl":"https://doi.org/10.1177/2515245920951503","remotes":[{"type":"streamable-http","url":"https://paper-agent-superpower-418969583050.europe-west1.run.app/superpower/mcp"}],"_meta":{"org.agenticjournal.registry/agent":{"longDescription":"Lakens and Caldwell's simulation approach to power for factorial ANOVA: specify the design, cell means, standard deviations and correlations, simulate data, and read off the power of every main effect, interaction and planned comparison.","availability":"hosted","capabilityKinds":["simulate","score"],"skills":[{"id":"anova_power_exact","name":"Anova power exact","description":"Power of every ANOVA effect and pairwise comparison, from one exact data set.","tags":[]},{"id":"anova_power_simulation","name":"Anova power simulation","description":"Power of every ANOVA effect and pairwise comparison, by Monte Carlo simulation.","tags":[]},{"id":"versions","name":"Versions","description":"Report the R and package versions this agent runs, its limits, and its paper.","tags":[]}]},"org.agenticjournal.registry/official":{"status":"active","isLatest":true},"org.agenticjournal.registry/paper":{"doi":"10.1177/2515245920951503","title":"Simulation-Based Power Analysis for Factorial Analysis of Variance Designs","year":2021,"venue":"Advances in Methods and Practices in Psychological Science","license":"CC-BY-4.0","openAccessUrl":"https://pure.tue.nl/ws/files/174397225/2515245920951503.pdf","authors":[{"family":"Lakens","given":"Daniël","orcid":"https://orcid.org/0000-0002-0247-239X"},{"family":"Caldwell","given":"Aaron R."}],"csl":{"DOI":"10.1177/2515245920951503","URL":"http://dx.doi.org/10.1177/2515245920951503","ISSN":["2515-2459","2515-2467"],"link":[{"URL":"https://journals.sagepub.com/doi/pdf/10.1177/2515245920951503","content-type":"application/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https://journals.sagepub.com/doi/full-xml/10.1177/2515245920951503","content-type":"application/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https://journals.sagepub.com/doi/pdf/10.1177/2515245920951503","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"type":"journal-article","issue":"1","score":1,"title":"Simulation-Based Power Analysis for Factorial Analysis of Variance Designs","author":[{"role":[{"role":"author","vocabulary":"crossref"}],"ORCID":"https://orcid.org/0000-0002-0247-239X","given":"Daniël","family":"Lakens","sequence":"first","affiliation":[{"name":"Human-Technology Interaction Group, Eindhoven University of Technology"}],"authenticated-orcid":false},{"role":[{"role":"author","vocabulary":"crossref"}],"given":"Aaron R.","family":"Caldwell","sequence":"additional","affiliation":[{"name":"Department of Health, Human Performance and Recreation, University of Arkansas"},{"name":"Thermal and Mountain Medicine Division, U.S. Army Research Institute of Environmental Medicine, Natick, Massachusetts"}]}],"funder":[{"id":[{"id":"10.13039/501100003246","id-type":"DOI","asserted-by":"publisher"}],"DOI":"10.13039/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","award":["VIDI Grant 452-17-013"],"award-info":[{"award-number":["VIDI Grant 452-17-013"]}],"doi-asserted-by":"publisher"}],"issued":{"date-parts":[[2021,1]]},"member":"179","prefix":"10.1177","source":"Crossref","volume":"4","created":{"date-time":"2021-03-23T04:01:35Z","timestamp":1616472095000,"date-parts":[[2021,3,23]]},"indexed":{"version":"4.1.0","date-time":"2026-09-28T20:55:23Z","timestamp":1790628923697,"date-parts":[[2026,9,28]]},"license":[{"URL":"https://creativecommons.org/licenses/by/4.0/","start":{"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000,"date-parts":[[2021,1,1]]},"delay-in-days":0,"content-version":"unspecified"}],"subject":[],"abstract":"<jats:p>Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental designs. We have created the R package Superpower and online Shiny apps to enable researchers without extensive programming experience to perform simulation-based power analysis for ANOVA designs of up to three within- or between-participants factors. Predicted effects are entered by specifying means, standard deviations, and, for within-participants factors, the correlations. The simulation provides the statistical power for all ANOVA main effects, interactions, and individual comparisons. The software can plot power across a range of sample sizes, can control for multiple comparisons, and can compute power when the homogeneity or sphericity assumption is violated. This Tutorial demonstrates how to perform a priori power analysis to design informative studies for main effects, interactions, and individual comparisons and highlights important factors that determine the statistical power for factorial ANOVA designs.</jats:p>","language":"en","relation":{"has-preprint":[{"id":"10.31234/osf.io/baxsf","id-type":"doi","asserted-by":"object"}]},"resource":{"primary":{"URL":"https://journals.sagepub.com/doi/10.1177/2515245920951503"}},"subtitle":[],"deposited":{"date-time":"2026-04-28T10:10:49Z","timestamp":1777371049000,"date-parts":[[2026,4,28]]},"published":{"date-parts":[[2021,1]]},"publisher":"SAGE Publications","reference":[{"DOI":"10.4324/9781315171500","key":"bibr1-2515245920951503","doi-asserted-by":"publisher"},{"DOI":"10.1037//1082-989X.2.2.208","key":"bibr2-2515245920951503","unstructured":"Algina J., Keselman H. J. (1997). Detecting repeated measures effects with univariate and multivariate statistics. Psychological Methods, 2(2), 208–218. https://doi.org/10.1037/1082-989X.2.2.208","doi-asserted-by":"crossref"},{"key":"bibr3-2515245920951503","year":"2011","author":"Bretz F.","volume-title":"Multiple comparisons using R"},{"DOI":"10.5334/joc.72","key":"bibr4-2515245920951503","unstructured":"Brysbaert M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition, 2(1), Article 16. https://doi.org/10.5334/joc.72","doi-asserted-by":"crossref"},{"key":"bibr5-2515245920951503","unstructured":"Caldwell A. R., Lakens D., Parlett-Pelleriti C. M. (2020). Power analysis with Superpower. http://arcaldwell49.github.io/SuperpowerBook"},{"DOI":"10.3758/s13428-012-0186-0","key":"bibr6-2515245920951503","unstructured":"Campbell J. I. D., Thompson V. A. (2012). MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behavior Research Methods, 44(4), 1255–1265. https://doi.org/10.3758/s13428-012-0186-0","doi-asserted-by":"crossref"},{"key":"bibr7-2515245920951503","unstructured":"Champely S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Computer software]. Comprehensive R Archive Network. https://CRAN.R-project.org/package=pwr"},{"key":"bibr8-2515245920951503","year":"1988","author":"Cohen J.","edition":"2","volume-title":"Statistical power analysis for the behavioral sciences"},{"DOI":"10.3758/s13423-015-0913-5","key":"bibr9-2515245920951503","unstructured":"Cramer A. O. J., van Ravenzwaaij D., Matzke D., Steingroever H., Wetzels R., Grasman R. P. P. P., Waldorp L. J., Wagenmakers E.J. (2016). Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies. Psychonomic Bulletin & Review, 23(2), 640–647. https://doi.org/10.3758/s13423-015-0913-5","doi-asserted-by":"crossref"},{"DOI":"10.32614/CRAN.package.faux","key":"bibr10-2515245920951503","unstructured":"DeBruine L. (2020). faux: Simulation for factorial designs (Version 0.0.1.2) [Computer software]. Zenodo. http://doi.org/10.5281/zenodo.2669586","doi-asserted-by":"crossref"},{"DOI":"10.5334/irsp.82","key":"bibr11-2515245920951503","unstructured":"Delacre M., Lakens D., Leys C. (2017). Why psychologists should by default use Welch’s\n                      t\n                      -test instead of Student’s\n                      t\n                      -test. International Review of Social Psychology, 30(1), 92–101. https://doi.org/10.5334/irsp.82","doi-asserted-by":"crossref"},{"DOI":"10.5334/irsp.198","key":"bibr12-2515245920951503","unstructured":"Delacre M., Leys C., Mora Y. L., Lakens D. (2019). Taking parametric assumptions seriously: Arguments for the use of Welch’s\n                      F\n                      -test instead of the classical\n                      F\n                      -test in one-way ANOVA. International Review of Social Psychology, 32(1), Article 13. https://doi.org/10.5334/irsp.198","doi-asserted-by":"crossref"},{"DOI":"10.3758/BF03193146","key":"bibr13-2515245920951503","unstructured":"Faul F., Erdfelder E., Lang A.G., Buchner A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146","doi-asserted-by":"crossref"},{"key":"bibr14-2515245920951503","unstructured":"Haans A. (2018). Contrast analysis: A tutorial. Practical Assessment, Research & Evaluation, 23, Article 9. https://doi.org/10.7275/7dey-zd62"},{"DOI":"10.1177/2515245918770963","key":"bibr15-2515245920951503","unstructured":"Lakens D., Scheel A. M., Isager P. M. (2018). Equivalence testing for psychological research: A Tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963","doi-asserted-by":"crossref"},{"DOI":"10.20982/tqmp.13.2.p127","key":"bibr16-2515245920951503","unstructured":"Lang A.G. (2017). Is intermediately inspecting statistical data necessarily a bad research practice? The Quantitative Methods for Psychology, 13(2), 127–140. https://doi.org/10.20982/tqmp.13.2.p127","doi-asserted-by":"crossref"},{"key":"bibr17-2515245920951503","unstructured":"Lenth R. (2019). emmeans: Estimated marginal means, aka least-squares means (Version 1.4.8) [Computer software]. Comprehensive R Archive Network. https://CRAN.R-project.org/package=emmeans"},{"DOI":"10.4324/9781410609243","key":"bibr18-2515245920951503","unstructured":"Maxwell S. E., Delaney H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Erlbaum.","doi-asserted-by":"crossref"},{"DOI":"10.4324/9781315642956","key":"bibr19-2515245920951503","doi-asserted-by":"publisher"},{"DOI":"10.1146/annurev.psych.59.103006.093735","key":"bibr20-2515245920951503","unstructured":"Maxwell S. E., Kelley K., Rausch J. R. (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annual Review of Psychology, 59, 537–563. https://doi.org/10.1146/annurev.psych.59.103006.093735","doi-asserted-by":"crossref"},{"DOI":"10.1037/1082-989X.8.4.434","key":"bibr21-2515245920951503","unstructured":"Olejnik S., Algina J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8(4), 434–447. https://doi.org/10.1037/1082-989X.8.4.434","doi-asserted-by":"crossref"},{"DOI":"10.5334/irsp.181","key":"bibr22-2515245920951503","unstructured":"Perugini M., Gallucci M., Costantini G. (2018). A practical primer to power analysis for simple experimental designs. International Review of Social Psychology, 31(1), Article 20. https://doi.org/10.5334/irsp.181","doi-asserted-by":"crossref"},{"key":"bibr23-2515245920951503","unstructured":"Westfall J. (2015a). PANGEA: Power analysis for general anova designs. Retrieved from at http://jakewestfall.org/publications/pangea.pdfwork?"},{"key":"bibr24-2515245920951503","unstructured":"Westfall J. (2015b, May 26). Think about total N, not n per cell. Cookie Scientist. http://jakewestfall.org/blog/index.php/2015/05/26/think-about-total-n-not-n-per-cell/"}],"short-title":[],"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"update-policy":"https://doi.org/10.1177/sage-journals-update-policy","alternative-id":["10.1177/2515245920951503"],"article-number":"2515245920951503","content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"original-title":[],"container-title":"Advances in Methods and Practices in Psychological Science","published-print":{"date-parts":[[2021,1]]},"reference-count":24,"published-online":{"date-parts":[[2021,3,23]]},"references-count":24,"container-title-short":"Advances in Methods and Practices in Psychological Science","is-referenced-by-count":422},"relation":"is_derived_from"},"org.agenticjournal.registry/classification":{"contributionType":"methodological","contributionSubtype":"new_method","secondaryContributionTypes":[],"dataBasis":"synthetic","documentType":"full_article","reviewStage":"peer_reviewed","disciplines":["statistics","psychology"]},"org.agenticjournal.registry/verification":{"doi_resolved":{"result":"pass","checkedAt":null},"endpoint_verified":{"result":"pass","checkedAt":"2026-09-29T13:41:01+00:00"}},"org.agenticjournal.registry/runtime":{"mcpRevisions":["2026-07-28"]},"org.agenticjournal.registry/links":{"self":"https://api.ajournal.xyz/works/10-1177-2515245920951503","agentCard":"https://api.ajournal.xyz/works/10-1177-2515245920951503/agent-card.json","jsonLd":"https://api.ajournal.xyz/works/10-1177-2515245920951503/paper.jsonld"}}}