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Effect sizes of the differences between means without assuming variance equality and between a mean and a constant

Effect sizes of the difference, or standardized mean differences, are widely used for meta-analysis or power-analysis. However, common effect sizes of the difference such as Cohen's d or Hedges' d assume variance equality that is fragile and is often violated in practical applications. Bas...

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Detalles Bibliográficos
Autor principal: Aoki, Satoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002853/
https://www.ncbi.nlm.nih.gov/pubmed/32051873
http://dx.doi.org/10.1016/j.heliyon.2020.e03306
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author Aoki, Satoshi
author_facet Aoki, Satoshi
author_sort Aoki, Satoshi
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description Effect sizes of the difference, or standardized mean differences, are widely used for meta-analysis or power-analysis. However, common effect sizes of the difference such as Cohen's d or Hedges' d assume variance equality that is fragile and is often violated in practical applications. Based on Welch's t tests, we defined a new effect size of the difference between means, which did not assume variance equality, thereby providing a more accurate value for data with unequal variance. In addition, we presented the unbiased estimator of an effect size of the difference between a mean and a known constant. An R package is also provided to compute these effect sizes with their variance and confidence interval.
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spelling pubmed-70028532020-02-12 Effect sizes of the differences between means without assuming variance equality and between a mean and a constant Aoki, Satoshi Heliyon Article Effect sizes of the difference, or standardized mean differences, are widely used for meta-analysis or power-analysis. However, common effect sizes of the difference such as Cohen's d or Hedges' d assume variance equality that is fragile and is often violated in practical applications. Based on Welch's t tests, we defined a new effect size of the difference between means, which did not assume variance equality, thereby providing a more accurate value for data with unequal variance. In addition, we presented the unbiased estimator of an effect size of the difference between a mean and a known constant. An R package is also provided to compute these effect sizes with their variance and confidence interval. Elsevier 2020-01-27 /pmc/articles/PMC7002853/ /pubmed/32051873 http://dx.doi.org/10.1016/j.heliyon.2020.e03306 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aoki, Satoshi
Effect sizes of the differences between means without assuming variance equality and between a mean and a constant
title Effect sizes of the differences between means without assuming variance equality and between a mean and a constant
title_full Effect sizes of the differences between means without assuming variance equality and between a mean and a constant
title_fullStr Effect sizes of the differences between means without assuming variance equality and between a mean and a constant
title_full_unstemmed Effect sizes of the differences between means without assuming variance equality and between a mean and a constant
title_short Effect sizes of the differences between means without assuming variance equality and between a mean and a constant
title_sort effect sizes of the differences between means without assuming variance equality and between a mean and a constant
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002853/
https://www.ncbi.nlm.nih.gov/pubmed/32051873
http://dx.doi.org/10.1016/j.heliyon.2020.e03306
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