Description Usage Arguments Details Value Author(s) References See Also Examples
ind.twoway.second
conducts a two-way design with independent samples, namely two-way randomized-group analysis of variance, using published work.
1 2 | ind.twoway.second(m, sd, n,
unbiased = TRUE, sig.level = 0.05, digits = 3)
|
m |
a matrix contains the means |
sd |
a matrix contains the sample/unbiased standard deviations |
n |
a matrix contains the sample size |
unbiased |
|
sig.level |
a numeric contains the significance level (default 0.05) |
digits |
the specified number of decimal places (default 3) |
This function conducts a two-way design with independent samples, namely two-way randomized-group analysis of variance, using published work. 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 sizes which is a partial η^2, and its' confidence interval for each main and interaction effect |
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, 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.
Tabachnick BG, Fidell LS (2007) Experimental designs using ANOVA. Belmont, CA: Thomson.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ##Cohen (2000) Table 1
m.mat <- matrix(c(37.13, 39.31, 39.22, 32.71), ncol=2) #2 * 2
sd.mat <- matrix(c(13.82, 9.42, 9.43, 9.62), ncol=2)
n.mat <- matrix(c(9, 13, 8, 14), ncol=2)
ind.twoway.second(m = m.mat, sd = sd.mat, n = n.mat)
##Tabachnick and Fidell (2007)
#5.7 Complete example of two-way randomized-groups ANOVA (p.221-236)
m.mat <- matrix(c(837.9, 573.6, 354.9, 699.0, 112.0,
852.2, 781.6, 683.3, 1193.9, 130.0), ncol=2) #5 * 2
sd.mat <- matrix(c(189.87449, 61.31195, 147.93351, 128.51891, 43.36922,
227.17042, 104.81221, 116.25934, 198.36692, 37.64158), ncol=2) #5 * 2
n.mat <- matrix(rep(10, 10), ncol=2)
ind.twoway.second(m = m.mat, sd = sd.mat, n = n.mat)
##Kline (2004) Table 7.5
dat <- data.frame(
y = c(2,3,4,1,3,1,3,4,5,5,6,6,6,7),
A = factor(c(rep("A1",5), rep("A2", 9))),
B = factor(c(rep("B1",3), rep("B2",2), rep("B1",2), rep("B2",7)))
)
ind.twoway.second(m = tapply(dat$y, list(dat$A,dat$B), mean),
sd = tapply(dat$y, list(dat$A,dat$B), sd),
n = tapply(dat$y, list(dat$A,dat$B), length)
)
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