ss.aipe.sc.sensitivity {MBESS}R Documentation

Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspective

Description

Performs a sensitivity analysis when planning sample size from the Accuracy in Parameter Estimation (AIPE) Perspective for the standardized ANOVA contrast.

Usage

ss.aipe.sc.sensitivity(true.psi = NULL, estimated.psi = NULL, c.weights, 
desired.width = NULL, selected.n = NULL, assurance = NULL, certainty=NULL, 
conf.level = 0.95, G = 10000, print.iter = TRUE, detail = TRUE, ...)

Arguments

true.psi population standardized contrast
estimated.psi estimated standardized contrast
c.weights the contrast weights
desired.width the desired full width of the obtained confidence interval
selected.n selected sample size to use in order to determine distributional properties of at a given value of sample size
assurance parameter to ensure that the obtained confidence interval width is narrower than the desired width with a specified degree of certainty (must be NULL or between zero and unity)
certainty an alias for assurance
conf.level the desired confidence interval coverage, (i.e., 1 - Type I error rate)
G number of generations (i.e., replications) of the simulation
print.iter to print the current value of the iterations
detail whether the user needs a detailed (TRUE) or brief (FALSE) report of the simulation results; the detailde report includes all the raw data in the simulations
... allows one to potentially include parameter values for inner functions

Value

psi.obs observed standardized contrast in each iteration
Full.Width vector of the full confidence interval width
Width.from.psi.obs.Lower vector of the lower confidence interval width
Width.from.psi.obs.Upper vector of the upper confidence interval width
Type.I.Error.Upper iterations where a Type I error occurred on the upper end of the confidence interval
Type.I.Error.Lower iterations where a Type I error occurred on the lower end of the confidence interval
Type.I.Error iterations where a Type I error happens
Lower.Limit the lower limit of the obtained confidence interval
Upper.Limit the upper limit of the obtained confidence interval
replications number of replications of the simulation
True.psi population standardized contrast
Estimated.psi estimated standardized contrast
Desired.Width the desired full width of the obtained confidence interval
assurance the value assigned to the argument assurance
Sample.Size.per.Group sample size per group
Number.of.Groups number of groups
mean.full.width mean width of the obtained full conficence intervals
median.full.width median width of the obtained full conficence intervals
sd.full.width standard deviation of the widths of the obtained full confidence intervals
Pct.Width.obs.NARROWER.than.desired percentage of the obtained full confidence interval widths that are narrower than the desired width
mean.Width.from.psi.obs.Lower mean lower width of the obtained confidence intervals
mean.Width.from.psi.obs.Upper mean upper width of the obtained confidence intervals
Type.I.Error.Upper Type I error rate from the upper side
Type.I.Error.Lower Type I error rate from the lower side

Author(s)

Ken Kelley (University of Notre Dame; KKelley@ND.Edu); Keke Lai <Lai.15@ND.Edu>

References

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., & Rausch, J. R. (2006). Sample size planning for the standardized mean difference: Accuracy in Parameter Estimation via narrow confidence intervals. Psychological Methods, 11 (4), 363-385.

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., & 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 where no significance tests? (pp. 221-257). Mahwah, NJ: Lawrence Erlbaum.

See Also

ss.aipe.sc, ss.aipe.c, conf.limits.nct


[Package MBESS version 2.0.0 Index]