ind.t.test.second: A t-test with independent samples using published work:...
In rpsychi: Statistics for psychiatric research

Description Usage Arguments Details Value Author(s) References See Also Examples

ind.t.test.second conducts a t-test with independent samples using published work.

1 2	ind.t.test.second(m, sd, n, unbiased = TRUE, correct=TRUE, sig.level = 0.05, digits = 3)

`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)
`correct`	a logical indicating whether to compute an unbiased standardized mean difference (delta) or not (`correct` = TRUE)
`sig.level`	a numeric contains the significance level (default 0.05)
`digits`	the specified number of decimal places (default 3)

This function conducts a t-test with independent samples using published work. Statistical power is calculated using the following specifications:

(a) small (d = 0.20), medium (d = 0.50), and large (d = 0.80) population effect sizes, according to the interpretive guideline for effect sizes by Cohen (1992)

(b) sample size specified by n

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

`samp.stat`	returns the means, standard deviations, and sample sizes
`raw.difference`	returns a raw mean difference, its' confidence interval, and standard error
`standardized.difference`	returns a standardized mean difference (Hedges's g), its' approximate confidence interval for population standardized mean difference, and standard error
`power`	returns statistical power for detecting small (d = 0.20), medium (d = 0.50), and large (d = 0.80) 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 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.

ind.t.test, samplesize.d

##Kline (2004) Table 4.4
dat <- data.frame(y = c(9,12,13,15,16,8,12,11,10,14),
                  x =  rep(factor(c("a","b")), each=5)
                  )
ind.t.test.second(m = tapply(dat$y, dat$x, mean),
                  sd = tapply(dat$y, dat$x, sd),
                  n = tapply(dat$y, dat$x, length), correct=FALSE
                  )
ind.t.test.second(m = tapply(dat$y, dat$x, mean),
                  sd = tapply(dat$y, dat$x, sd),
                  n = tapply(dat$y, dat$x, length), correct=TRUE
                  )     #approximate unbiased estimator of delta