A t-test with dependent samples using individual data: Reporting effect size

Share:

Description

dep.t.test conducts a t-test with dependent samples using individual data.

Usage

1
2
    dep.t.test(formula, data, block, 
                    sig.level=.05, digits=3)

Arguments

formula

two-sided formula; the left-hand-side of which gives one dependent variable containing a numeric variable, and the right-hand-side of one independent variable containing a factor with two levels

data

a data frame contains the variables in the fomrmula

block

a character string specify the blocking variable

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 t-test with dependent samples using individual data.

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

Author(s)

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

References

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

See Also

dep.t.test.second

Examples

1
2
3
4
5
6
7
 
##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)
                  )
dep.t.test(y~x, block="subj", data=dat)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.