Cargando…
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs
Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be use...
Autor principal: | Lakens, Daniël |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3840331/ https://www.ncbi.nlm.nih.gov/pubmed/24324449 http://dx.doi.org/10.3389/fpsyg.2013.00863 |
Ejemplares similares
-
Sample size determination for Bayesian ANOVAs with informative hypotheses
por: Fu, Qianrao, et al.
Publicado: (2022) -
Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses
por: Lakens, Daniël
Publicado: (2017) -
Constrained statistical inference: sample-size tables for ANOVA and regression
por: Vanbrabant, Leonard, et al.
Publicado: (2015) -
An implementation of N-way repeated measures ANOVA: Effect coding, automated unpacking of interactions, and randomization testing
por: Gladwin, Thomas Edward
Publicado: (2020) -
Repeated measures ANOVA and adjusted F-tests when sphericity is violated: which procedure is best?
por: Blanca, María J., et al.
Publicado: (2023)