Description Usage Arguments Details Value Author(s) References See Also Examples
ind.oneway.second
conducts a oneway design with independent samples, namely oneway randomizedgroup analysis of variance, using published work.
1 2  ind.oneway.second(m, sd, n,
unbiased = TRUE, contr = NULL, sig.level = 0.05, digits = 3)

m 
a numeric vector contains the means (length( 
sd 
a numeric vector contains the sample/unbiased standard deviations (length( 
n 
a numeric contains the sample size (length( 
unbiased 

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) 
This function conducts a oneway design with independent samples, namely oneway randomizedgroup 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
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 
Yasuyuki Okumura
Department of Social Psychiatry,
National Institute of Mental Health,
National Center of Neurology and Psychiatry
yokumura@blue.zero.jp
Cohen B (2000) Calculating a factorial ANOVA from means and standard deviations. Understanding Statistics, 1, 191203.
Cohen J (1992) A power primer. Psychological Bulletin, 112, 155159.
Kline RB (2004) Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ##Kline (2004) Table 6.3
dat < data.frame(y = c(9,12,13,15,16,
8,12,11,10,14,
10,11,13,11,15),
x = rep(factor(c("a","b","c")), each=5)
)
##contrast 1: a  c, contrast 2: 1/2(a + c)  b
my.cont < matrix(c(1,0,1,1/2,1,1/2), ncol=3, nrow=2, byrow=TRUE)
ind.oneway.second(m = tapply(dat$y, dat$x, mean),
sd = tapply(dat$y, dat$x, sd),
n= tapply(dat$y, dat$x, length))
ind.oneway.second(m = tapply(dat$y, dat$x, mean),
sd = tapply(dat$y, dat$x, sd),
n= tapply(dat$y, dat$x, length),
contr = my.cont)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.