ci.smd: Confidence limits for the standardized mean difference.

In MBESS: The MBESS R Package

Confidence limits for the standardized mean difference.

Description

Function to calculate the confidence limits for the population standardized mean difference using the square root of the pooled variance as the divisor. This function is thus used to determine the confidence bounds for the population quantity of what is generally referred to as Cohen's d (delta being that population quantity).

Usage

ci.smd(ncp=NULL, smd=NULL, n.1=NULL, n.2=NULL, conf.level=.95, 
alpha.lower=NULL, alpha.upper=NULL, tol=1e-9, ...)

Arguments

ncp	is the estimated noncentrality parameter, this is generally the observed t-statistic from comparing the two groups and assumes homogeneity of variance
smd	is the standardized mean difference (using the pooled standard deviation in the denominator)
n.1	is the sample size for Group 1
n.2	is the sample size for Group 2
conf.level	is the confidence level (1-Type I error rate)
alpha.lower	is the Type I error rate for the lower tail
alpha.upper	is the Type I error rate for the upper tail
tol	is the tolerance of the iterative method for determining the critical values
...	allows one to potentially include parameter values for inner functions

Value

Lower.Conf.Limit.smd	The lower bound of the computed confidence interval
smd	The standardized mean difference
Upper.Conf.Limit.smd	The upper bound of the computed confidence interval

Warning

This function uses conf.limits.nct, which has as one of its arguments tol (and can be modified with tol of the present function). If the present function fails to converge (i.e., if it runs but does not report a solution), it is likely that the tol value is too restrictive and should be increased by a factor of 10, but probably by no more than 100. Running the function conf.limits.nct directly will report the actual probability values of the limits found. This should be done if any modification to tol is necessary in order to ensure acceptable confidence limits for the noncentral-t parameter have been achieved.

Author(s)

Ken Kelley (University of Notre Dame; [e-mail])

References

Cohen, J. (1988) Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Cumming, G. & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions, Educational and Psychological Measurement, 61, 532–574.

Hedges, L. V. (1981). Distribution theory for Glass's Estimator of effect size and related estimators. Journal of Educational Statistics, 2, 107–128.

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

Kelley, K., Maxwell, S. E., & Rausch, J. R. (2003). Obtaining Power or Obtaining Precision: Delineating Methods of Sample-Size Planning, Evaluation and the Health Professions, 26, 258–287.

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

smd, smd.c, ci.smd.c, conf.limits.nct

Examples

# Steiger and Fouladi (1997) example values.
ci.smd(ncp=2.6, n.1=10, n.2=10, conf.level=1-.05)
ci.smd(ncp=2.4, n.1=300, n.2=300, conf.level=1-.05)

MBESS documentation built on Oct. 26, 2023, 9:07 a.m.