ss.aipe.smd: Sample size planning for the standardized mean difference...

In MBESS: The MBESS R Package

Sample size planning for the standardized mean difference from the Accuracy in Parameter Estimation (AIPE) perspective

Description

A function to calculate the appropriate sample size for the standardized mean difference such that the expected value of the confidence interval is sufficiently narrow, optionally with a degree.of.certainty.

Usage

ss.aipe.smd(delta, conf.level, width, which.width="Full", 
degree.of.certainty=NULL, assurance=NULL, certainty=NULL, ...)

Arguments

delta	the population value of the standardized mean difference
conf.level	the desired degree of confidence (i.e., 1-Type I error rate)
width	desired width of the specified (i.e., Full, Lower, and Upper widths) region of the confidence interval
which.width	the width that the width argument refers identifies the width of interest (i.e., Full, Lower, and Upper widths)
degree.of.certainty	parameter to ensure confidence interval width with a specified degree of certainty
assurance	an alias for degree.of.certainty
certainty	an alias for degree.of.certainty
...	for modifying parameters of functions this function calls upon

Value

Returns the necessary sample size per group in order to achieve the desired degree of accuracy (i.e., the sufficiently narrow confidence interval).

Warning

Finding sample size for lower and uppper confidence limits is approximate, but very close to being exact. The pt() function is limited to accurate values when the the noncentral parameter is less than 37.62.

Note

The function ss.aipe.smd is the preferred function, and is the one that is recommended for widespread use. The functions ss.aipe.smd.lower, ss.aipe.smd.upper and ss.aipe.smd.full are called from the ss.aipe.smd function.

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. (2005). The effects of nonnormal distributions on confidence intervals around the standardized mean difference: Bootstrap and parametric confidence intervals, Educational and Psychological Measurement, 65, 51–69.

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.

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.

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

smd, smd.c, ci.smd, ci.smd.c, conf.limits.nct, power.t.test, ss.aipe.smd.lower, ss.aipe.smd.upper, ss.aipe.smd.full

Examples

# ss.aipe.smd(delta=.5, conf.level=.95, width=.30)
# ss.aipe.smd(delta=.5, conf.level=.95, width=.30, degree.of.certainty=.8)
# ss.aipe.smd(delta=.5, conf.level=.95, width=.30, degree.of.certainty=.95)

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