ind.oneway.second: A one-way design with independent samples using published... In rpsychi: Statistics for psychiatric research

Description

ind.oneway.second conducts a one-way design with independent samples, namely one-way randomized-group analysis of variance, using published work.

Usage

 1 2 ind.oneway.second(m, sd, n, unbiased = TRUE, contr = NULL, sig.level = 0.05, digits = 3)

Arguments

 m a numeric vector contains the means (length(m) >= 2) sd a numeric vector contains the sample/unbiased standard deviations (length(sd) >= 2) n a numeric contains the sample size (length(n) >= 2) unbiased sd contains unbiased standard deviations (unbiased = TRUE, default) or sample standard deviations (unbiased = FALSE) contr a matrix or vector contains the contrast weights sig.level a numeric contains the significance level (default 0.05) digits the specified number of decimal places (default 3)

Details

This function conducts a one-way design with independent samples, namely one-way randomized-group analysis of variance, using published work. If you do not specify contr, all possible pairwise contrasts will be calculated. Statistical power is calculated using the following specifications:

(a) small (η^2 = 0.01), medium (η^2 = 0.06), and large (η^2 = 0.14) population effect sizes, according to the interpretive guideline for effect sizes by Cohen (1992)

(b) sample size specified by n

(c) significance level specified by sig.level

Value

The returned object of ind.oneway.second contains the following components:

 anova.table returns a ANOVA table containing sums of squares, degrees of freedom, mean squares, F values omnibus.es returns a omnibus effect size which is a η^2, and its' confidence interval raw.contrasts returns raw mean differences, their confidence intervals, and standard errors standardized.contrasts returns standardized mean differences for the contrasts (Hedges's g), their approximate confidence intervals for population standardized mean differences, and standard errors power returns statistical power for detecting small (η^2 = 0.01), medium (η^2 = 0.06), and large (η^2 = 0.14) population effect sizes

Author(s)

Yasuyuki Okumura
Department of Social Psychiatry,
National Institute of Mental Health,
National Center of Neurology and Psychiatry
yokumura@blue.zero.jp

References

Cohen B (2000) Calculating a factorial ANOVA from means and standard deviations. Understanding Statistics, 1, 191-203.

Cohen J (1992) A power primer. Psychological Bulletin, 112, 155-159.

Kline RB (2004) Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association.