docs/stdmean.ps.md

In cwendorf/DGB: DGB

Standardized Mean Difference from Paired Samples

ci.stdmean.ps

Computes confidence interval for a population standardized mean difference in a paired-samples design.

Arguments: - alpha: alpha level for 1-alpha confidence - mj: sample mean in condition j - sdj: sample standard deviation in condition j - n: sample size - cor: sample correlation

Values: - estimate, SE, lower limit, and upper limit for equal variance and unequal variance methods plus single condition standardizer

alpha = .05
m1 = 110.4
m2 = 102.1
sd1 = 15.3
sd2 = 14.6
n = 25
cor = .75
ci.stdmean.ps(alpha, m1, m2, sd1, sd2, n, cor)

##                               Estimate        SE        LL        UL
## Equal Variances Not Assumed: 0.5550319 0.1609934 0.2394905 0.8705732
## Equal Variances Assumed:     0.5550319 0.1581582 0.2450476 0.8650162
## Condition 1 Standardizer:    0.5424837 0.1615500 0.2258515 0.8591158
## Condition 2 Standardizer:    0.5684932 0.1692955 0.2366800 0.9003063

size.ci.stdmean.ps

Computes sample size required to estimate a population standardized mean difference with desired precision in a paired-samples design.

Arguments: - alpha: alpha level for 1-alpha confidence - d: planning value of standardized mean difference - cor: planning value of correlation - w: desired confidence interval width

Values: - required sample size

alpha = .05
d = 1
cor = .65
w = .6
size.ci.stdmean.ps(alpha, d, cor, w)

## [1] 46

cwendorf/DGB documentation built on May 3, 2022, 9:34 p.m.