A set of functions that
ss.aipe.smd calls upon to calculate the appropriate sample size
for the standardized mean difference such that the expected value of the confidence interval
is sufficiently narrow.
ss.aipe.smd.full(delta, conf.level, width, ...) ss.aipe.smd.lower(delta, conf.level, width, ...) ss.aipe.smd.upper(delta, conf.level, width, ...)
the population value of the standardized mean difference
the desired degree of confidence (i.e., 1-Type I error rate)
desired width of the specified (i.e.,
specify additional parameters in functions these functions call upon
The necessary sample size per group in order to satisfy the specified goals.
The returned value is the sample size per group. Currently only
ss.aipe.smd.full returns the exact value. However,
provide approximate sample size values.
ss.aipe.smd is the function users should generally use. The function
ss.aipe.smd calls upon these functions as needed. They can be thought of loosely
as internal MBESS functions.
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
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 were no significance tests? (pp. 221–257). Mahwah, NJ: Lawrence Erlbaum.
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