MBESS (version 4.3.0)

ci.sc.ancova: Confidence interval for a standardized contrast in ANCOVA with one covariate

Description

Calculate the confidence interval for a standardized contrast in ANCOVA with one covariate. The standardizer (i.e., the divisor) can be either the error standard deviation of the ANOVA model (i.e., the model excluding the covariate) or of the ANCOVA model.

Usage

ci.sc.ancova(Psi=NULL, adj.means=NULL, s.anova = NULL, s.ancova, 
standardizer = "s.ancova", c.weights, n, cov.means, SSwithin.x, 
conf.level = 0.95)

Arguments

Psi

unstandardized contrast of adjusted means

adj.means

the vector that contains the adjusted mean of each group on the dependent variable

s.anova

the standard deviation of the errors from the ANOVA model (i.e., the square root of the mean square error from ANOVA)

s.ancova

the standard deviation of the errors from the ANCOVA model (i.e., the square root of the mean square error from ANCOVA)

standardizer

which error standard deviation the user wants to use, the value of which can be either "s.ancova" or "s.anova"

c.weights

the contrast weights (chose weights so that the positive c-weights sum to 1 and the negative c-weights sum to -1; i.e., use fractional values not integers).

n

either a single number that indicates the sample size per group, or a vector that contains the sample size of each group

cov.means

a vector that contains the group means of the covariate

SSwithin.x

the sum of squares within groups obtained from the summary table for ANOVA on the covariate

conf.level

the desired confidence interval coverage, (i.e., 1 - Type I error rate)

Value

standardizer

the divisor used in the standardization

psi.limit.lower

the lower confidence limit of the standardized contrast

psi

the estimated contrast

psi.limit.upper

the upper confidence limit of the standardized contrast

References

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

Kelley, K., & Rausch, J. R. (2006). Sample size planning for the standardized mean difference: Accuracy in Parameter Estimation via narrow confidence intervals. Psychological Methods, 11, 363--385.

Lai, K., & Kelley, K. (2012). Accuracy in parameter estimation for ANCOVA and ANOVA contrasts: Sample size planning via narrow confidence intervals. British Journal of Mathematical and Statistical Psychology, 65, 350--370.

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

ci.c.ancova, ci.sc

Examples

Run this code
# Maxwell & Delaney (2004, pp. 428--468) offer an example that 30 depressive 
# individuals are randomly assigned to three groups, 10 in each, and ANCOVA 
# is performed on the posttest scores using the participants' pretest 
# scores as the covariate. The means of pretest scores of group 1, 2, and 3 are 
# 17, 17.7, and 17.4, respectively, whereas the adjusted means of groups 1, 2, and 3 
# are 7.5, 12, and 14, respectively. The error variance in ANCOVA is 29 and thus 
# 5.385165 is the error standard deviation, with the sum of squares within groups 
# from an ANOVA on the covariate is 752.5. 

# To obtained the confidence interval for the standardized adjusted
# mean difference between group 1 and 2, using the ANCOVA error standard
# deviation:
ci.sc.ancova(adj.means=c(7.5, 12, 14), s.ancova=5.385165, c.weights=c(1,-1,0), 
n=10, cov.means=c(17, 17.7, 17.4), SSwithin.x=752.5)

# Or, with less error in rounding:
ci.sc.ancova(adj.means=c(7.54, 11.98, 13.98), s.ancova=5.393, c.weights=c(-1,0,1), 
n=10, cov.means=c(17, 17.7, 17.4), SSwithin.x=752.5)

# Now, using the standard deviation from ANOVA (and not ANCOVA as above), we have:
ci.sc.ancova(adj.means=c(7.54, 11.98, 13.98), s.anova=6.294, s.ancova=5.393, c.weights=c(-1,0,1),
n=10, cov.means=c(17, 17.7, 17.4), SSwithin.x=752.5, standardizer= "s.anova", conf.level=.95)

Run the code above in your browser using DataCamp Workspace