ss.aipe.smd | R Documentation |

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`

.

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

`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., |

`which.width` |
the width that the |

`degree.of.certainty` |
parameter to ensure confidence interval width with a specified degree of certainty |

`assurance` |
an alias for |

`certainty` |
an alias for |

`...` |
for modifying parameters of functions this function calls upon |

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

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.

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.

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

`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`

# 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)

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