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Simulation-Based Power Analysis for Factorial Analysis of Variance Designs
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...
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Published in: | Advances in methods and practices in psychological science 2021-01, Vol.4 (1) |
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description | 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
η
p
2
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. |
doi_str_mv | 10.1177/2515245920951503 |
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η
p
2
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.</description><identifier>ISSN: 2515-2459</identifier><identifier>EISSN: 2515-2467</identifier><identifier>DOI: 10.1177/2515245920951503</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><ispartof>Advances in methods and practices in psychological science, 2021-01, Vol.4 (1)</ispartof><rights>The Author(s) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-4272d34e1c8256b8d649bb28be423c84080fa0bce13afa1b2164800ca5cd159a3</citedby><cites>FETCH-LOGICAL-c389t-4272d34e1c8256b8d649bb28be423c84080fa0bce13afa1b2164800ca5cd159a3</cites><orcidid>0000-0002-0247-239X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/2515245920951503$$EPDF$$P50$$Gsage$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/2515245920951503$$EHTML$$P50$$Gsage$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,21966,27853,27924,27925,44945,45333</link.rule.ids></links><search><creatorcontrib>Lakens, Daniël</creatorcontrib><creatorcontrib>Caldwell, Aaron R.</creatorcontrib><title>Simulation-Based Power Analysis for Factorial Analysis of Variance Designs</title><title>Advances in methods and practices in psychological science</title><description>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
η
p
2
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.</description><issn>2515-2459</issn><issn>2515-2467</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><recordid>eNp1kEtLAzEUhYMoWGr3LucPRG9eM8myVmuVgoKP7XCTyZSU6USSFum_d0pFQXB1Dh_3XDiHkEsGV4xV1TVXTHGpDAczOBAnZHRAlMuyOv3xypyTSc5rAOAgTCnEiDy-hM2uw22IPb3B7JviOX76VEx77PY55KKNqZij28YUsPvFsS3ecUC988Wtz2HV5wty1mKX_eRbx-Rtfvc6W9Dl0_3DbLqkTmizpZJXvBHSM6e5Kq1uSmms5dp6yYXTEjS0CNZ5JrBFZjkrpQZwqFzDlEExJnD861LMOfm2_khhg2lfM6gPc9R_5xgi9BjJuPL1Ou7S0CP_f_8FeWhelw</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Lakens, Daniël</creator><creator>Caldwell, Aaron R.</creator><general>SAGE Publications</general><scope>AFRWT</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-0247-239X</orcidid></search><sort><creationdate>20210101</creationdate><title>Simulation-Based Power Analysis for Factorial Analysis of Variance Designs</title><author>Lakens, Daniël ; Caldwell, Aaron R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-4272d34e1c8256b8d649bb28be423c84080fa0bce13afa1b2164800ca5cd159a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lakens, Daniël</creatorcontrib><creatorcontrib>Caldwell, Aaron R.</creatorcontrib><collection>Sage Journals GOLD Open Access 2024</collection><collection>CrossRef</collection><jtitle>Advances in methods and practices in psychological science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lakens, Daniël</au><au>Caldwell, Aaron R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Simulation-Based Power Analysis for Factorial Analysis of Variance Designs</atitle><jtitle>Advances in methods and practices in psychological science</jtitle><date>2021-01-01</date><risdate>2021</risdate><volume>4</volume><issue>1</issue><issn>2515-2459</issn><eissn>2515-2467</eissn><abstract>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
η
p
2
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.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><doi>10.1177/2515245920951503</doi><orcidid>https://orcid.org/0000-0002-0247-239X</orcidid><oa>free_for_read</oa></addata></record> |
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title | Simulation-Based Power Analysis for Factorial Analysis of Variance Designs |
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