ci.sc {MBESS}R Documentation

Confidence Interval for a Standardized Contrast in a Fixed Effets ANOVA

Description

Function to obtain the confidence interval for a standardized contrast in a fixed effets analysis of variance context.

Usage

ci.sc(means = NULL, error.variance = NULL, c.weights = NULL, n = NULL, 
N = NULL, Psi = NULL, ncp = NULL, conf.level = 0.95, 
alpha.lower = NULL, alpha.upper = NULL, df.error = NULL, ...)

Arguments

means a vector of the group means or the means of the particular level of the effect (for fixed effect designs)
error.variance the common variance of the error (i.e., the mean square error)
c.weights the contrast weights (the sum of the contrast weights should be zero)
n sample sizes per group or level of the particular factor (if length 1 it is assumed that the per group/level sample sizes are equal)
N total sample size
Psi the (unstandardized) contrast effect, obtained by multiplying the jth mean by the jth contrast weight (this is the unstandardized effect)
ncp the noncentrality paramter from the t-distribution
conf.level desired level of confidence for the computed interval (i.e., 1 - the Type I error rate)
alpha.lower the Type I error rate for the lower confidence interval limit
alpha.upper the Type I error rate for the upper confidence interval limit
df.error the degrees of freedom for the error. In one-way designs, this is simply N-length (means) and need not be specified; it must be specified if the design has multiple factors.
... optional additional specifications for nested functions

Value

Lower.Conf.Limit.Standardized.Contrast the lower confidence limit for the standardized contrast
Standardized.contrast standardized contrast
Upper.Conf.Limit.Standardized.Contrast the upper confidence limit for the standardized contrast

Note

Be sure to use the error varaince and not its square root (i.e., the standard deviation of the errors).

Author(s)

Ken Kelley (University of Notre Dame; KKelley@ND.Edu)

References

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

Lai, K., & Kelley, K. (2007). Sample size planning for standardized ANCOVA and ANOVA contrasts: Obtaining narrow confidence intervals. Manuscript submitted for publication.

Steiger, J. H. (2004). Beyond the F Test: Effect size confidence intervals and tests of close fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9, 164–182.

See Also

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

Examples

ci.sc(means=c(2, 4, 9, 13), error.variance=1, c.weights=c(1, -1, -1, 1), 
n=c(3, 3, 3, 3), N=12, conf.level=.95)

ci.sc(means=c(2, 4, 9, 13), error.variance=1, c.weights=c(1, -1, -1, 1), 
n=c(3, 3, 3, 3), N=12, conf.level=.95)

ci.sc(means=c(1.6, 0), error.variance=1, c.weights=c(1, -1), n=c(10, 10), 
N=20, conf.level=.95)

[Package MBESS version 2.0.0 Index]