Performs a sensitivity analysis when planning sample size from the Accuracy in Parameter Estimation (AIPE) Perspective for the standardized ANOVA contrast.
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, ...)observed standardized contrast in each iteration
vector of the full confidence interval width
vector of the lower confidence interval width
vector of the upper confidence interval width
iterations where a Type I error occurred on the upper end of the confidence interval
iterations where a Type I error occurred on the lower end of the confidence interval
iterations where a Type I error happens
the lower limit of the obtained confidence interval
the upper limit of the obtained confidence interval
number of replications of the simulation
population standardized contrast
estimated standardized contrast
the desired full width of the obtained confidence interval
the value assigned to the argument assurance
sample size per group
number of groups
mean width of the obtained full conficence intervals
median width of the obtained full confidence intervals
standard deviation of the widths of the obtained full confidence intervals
percentage of the obtained full confidence interval widths that are narrower than the desired width
mean lower width of the obtained confidence intervals
mean upper width of the obtained confidence intervals
Type I error rate from the upper side
Type I error rate from the lower side
population standardized contrast
estimated standardized contrast
the contrast weights
the desired full width of the obtained confidence interval
selected sample size to use in order to determine distributional properties of at a given value of sample size
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)
an alias for assurance
the desired confidence interval coverage, (i.e., 1 - Type I error rate)
number of generations (i.e., replications) of the simulation
to print the current value of the iterations
whether the user needs a detailed (TRUE) or brief (FALSE) report of the simulation results; the
detailed report includes all the raw data in the simulations
allows one to potentially include parameter values for inner functions
Ken Kelley (University of Notre Dame; KKelley@ND.Edu); Keke Lai (University of California -- Merced)
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.
ss.aipe.sc, ss.aipe.c, conf.limits.nct