MBESS (version 4.6.0)

ci.sm: Confidence Interval for the Standardized Mean

Description

Function to obtain the exact confidence interval for the standardized mean.

Usage

ci.sm(sm = NULL, Mean = NULL, SD = NULL, ncp = NULL, N = NULL, 
conf.level = 0.95, alpha.lower = NULL, alpha.upper = NULL, ...)

Arguments

sm

standardized mean

Mean

mean

SD

standard deviation

ncp

noncentral parameter

N

sample size

conf.level

confidence interval coverage (i.e., 1 - Type I error rate); default is .95

alpha.lower

Type I error for the lower confidence limit

alpha.upper

Type I error for the upper confidence limit

…

allows one to potentially include parameter values for inner functions

Value

Lower.Conf.Limit.Standardized.Mean

lower confidence limit of the standardized mean

Standardized.Mean

standardized mean

Upper.Conf.Limit.Standardized.Mean

upper confidence limit of the standardized mean

Details

The user must specify the standardized mean in one and only one of the three ways: a) mean and standard deviation (Mean and SD), b) standardized mean (sm), and c) noncentral parameter (ncp). The confidence level must be specified in one of following two ways: using confidence interval coverage (conf.level), or lower and upper confidence limits (alpha.lower and alpha.upper).

This function uses the exact confidence interval method based on noncentral t-distributions. The confidence interval for noncentral t-parameter can be obtained from the conf.limits.nct function in MBESS.

References

Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1--24.

Steiger, J. H., & Fouladi, R. T. (1997). Noncentrality interval estimation and the evaluation of statistical methods. In L. L. Harlow, S. A. Mulaik, & J.H. Steiger (Eds.), What if there were no significance tests? (pp. 221--257). Mahwah, NJ: Lawrence Erlbaum.

See Also

conf.limits.nct

Examples

# NOT RUN {
ci.sm(sm=2.037905, N=13, conf.level=.95)
ci.sm(Mean=30, SD=14.721, N=13, conf.level=.95)
ci.sm(ncp=7.347771, N=13, conf.level=.95)
ci.sm(sm=2.037905, N=13, alpha.lower=.05, alpha.upper=0)
ci.sm(Mean=50, SD=10, N=25, conf.level=.95)
# }