rpsychi (version 0.3)

dep.t.test.second: A t-test with dependent samples using published work

Description

dep.t.test.second conducts a t-test with dependent samples using published work.

Usage

dep.t.test.second(m, sd, n, corr, 
            unbiased = TRUE, 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)

corr

a numeric contains the correlation

unbiased

sd contains unbiased standard deviations (unbiased = TRUE, default) or sample standard deviations (unbiased = FALSE)

sig.level

a numeric contains the significance level (default 0.05)

digits

the specified number of decimal places (default 3)

Value

• The returned object of dep.t.test.second contains the following components:
• samp.stat
  returns the means, standard deviations, sample size, and correlation
• raw.difference
  returns a raw mean difference, its' confidence interval, and standard error
• standardized.difference
  returns a standardized mean difference (Hedges's $g$) and its' approximate confidence interval for a population standardized mean difference

encoding

UTF-8

Details

This function conducts a t-test with dependent samples using published work.

References

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

See Also

dep.t.test

Examples

##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),
                  subj = rep(paste("s", 1:5, sep=""), times=2)
                  )
datwide <- reshape(dat, direction="wide", idvar="subj", timevar="x")

dep.t.test.second(m = tapply(dat$y, dat$x, mean),
                  sd = tapply(dat$y, dat$x, sd),
                  n = nlevels(dat$subj),
                  corr = cor(datwide[,2:3])[1,2]
                  )

dep.t.test.second(m = tapply(dat$y, dat$x, mean),
                  sd = tapply(dat$y, dat$x, sd),
                  n = 30,
                  corr = cor(datwide[,2:3])[1,2]
                  )