ES.ired: Calculating Effect Size (Cohen's D) Of Paired Two-sample T...

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powerAnalysis Power Analysis in Experimental Design

Package index Search the powerAnalysis package Functions 21 Source code 12 Man pages 12

• ES.anova.oneway: Calculating effect size (Cohen's f) of one-way anova for...
• ES.chisq.assoc: Compute effect size of chi-squared test of association
• ES.chisq.gof: Compute effect size of chi-squared test of goodness of fit
• ES.proportions: Compute effect size for a difference in proportions
• ES.t.one: Calculating effect size (Cohen's d) of one-sample t test
• ES.t.paired: Calculating effect size (Cohen's d) of paired two-sample t...
• ES.t.two: Calculating effect size (Cohen's d) of independent two-sample...
• power.anova.oneway: Power calculations for balanced one-way analysis of variance...
• power.chisq: Power calculations for chi-squared test
• power.plot.chisq: Power analysis plot of chi-squared test
• power.proportions: Power calculations for proportion tests (two-sided)
• power.t: Power calculations for t-test
• Browse all...

Home / CRAN / powerAnalysis / ES.t.paired: Calculating effect size (Cohen's d) of paired two-sample t... ES.t.paired: Calculating effect size (Cohen's d) of paired two-sample t... In powerAnalysis: Power Analysis in Experimental Design

Description Usage Arguments See Also Examples

View source: R/ES.t.paired.R

Description

Calculating effect size (Cohen's d) of paired two-sample t test

Usage

1 2	ES.t.paired(md = NULL, sd = NULL, n = NULL, t = NULL, se = NULL, df = NULL, alternative = c("two.sided", "one.sided"))

Arguments

md	mean difference (e.g., mean(x-y))
sd	standard deviation of mean differences (e.g., sd(x-y))
n	number of paires
t	t statistic
se	standard error of mean differences
df	degree of freedom
alternative	The test is two sided or one sided

See Also

ES.t.one

ES.t.two

Examples

1 2 3 4 5 6 7 8 9 10 11

## md, sd -> d ES.t.paired(md=-0.08062384,sd=1.401886) ## md,se -> d ES.t.paired(md=-0.08062384,se=0.1982566,n=50) ## t, df -> d ES.t.paired(t=-0.4067,df=49) ## t, n -> d ES.t.paired(t=-0.4067,n=50)

Example output

effect size (Cohen's d) of paired two-sample t test d = 0.05751098 alternative = two.sided NOTE: The alternative hypothesis is md != 0 small effect size: d = 0.2 medium effect size: d = 0.5 large effect size: d = 0.8 effect size (Cohen's d) of paired two-sample t test d = 0.05751099 alternative = two.sided NOTE: The alternative hypothesis is md != 0 small effect size: d = 0.2 medium effect size: d = 0.5 large effect size: d = 0.8 effect size (Cohen's d) of paired two-sample t test d = 0.0581 alternative = two.sided NOTE: The alternative hypothesis is md != 0 small effect size: d = 0.2 medium effect size: d = 0.5 large effect size: d = 0.8 effect size (Cohen's d) of paired two-sample t test d = 0.0581 alternative = two.sided NOTE: The alternative hypothesis is md != 0 small effect size: d = 0.2 medium effect size: d = 0.5 large effect size: d = 0.8 powerAnalysis documentation built on May 2, 2019, 12:40 p.m.

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