R: d-g Corrected for Independent t

g.ind.t

R Documentation

d-g Corrected for Independent t

Description

This function displays d-g corrected and the non-central confidence interval for independent t.

Usage

g.ind.t(m1, m2, sd1, sd2, n1, n2, a = 0.05)

Arguments

`m1`	mean group one
`m2`	mean group two
`sd1`	standard deviation group one
`sd2`	standard deviation group two
`n1`	sample size group one
`n2`	sample size group two
`a`	significance level

Details

The correction is calculated by dividing three by the sum of both sample sizes after multiplying by four and subtracting nine. This amount is deducted from one.

correction = 1 - (3 / (4 * (n1 + n2) - 9))

D-g corrected is calculated by substracting mean two from mean one, dividing by the pooled standard deviation which is multiplied by the correction above.

d_g corrected = ((m1 - m2) / spooled) * correction

Learn more on our example page.

Value

D-g corrected with associated confidence intervals, the confidence intervals associated with the means of each group, standard deviations of the means for each group, relevant statistics.

`d`	d-g corrected effect size
`dlow`	lower level confidence interval d-g corrected
`dhigh`	upper level confidence interval d-g corrected
`M1`	mean group one
`sd1`	standard deviation of group one
`se1`	standard error of group one
`M1low`	lower level confidence interval of mean one
`M1high`	upper level confidence interval of mean one
`M2`	mean two
`sd2`	standard deviation of mean two
`se1`	standard error of mean two
`M2low`	lower level confidence interval of mean two
`M2high`	upper level confidence interval of mean two
`spooled`	pooled standard deviation
`sepooled`	pooled standard error
`correction`	g corrected
`n1`	size of sample one
`n2`	size of sample two
`df`	degrees of freedom
`t`	t-statistic
`p`	p-value
`estimate`	the d statistic and confidence interval in APA style for markdown printing
`statistic`	the t-statistic in APA style for markdown printing

Examples


#The following example is derived from the "indt_data" dataset, included
#in the MOTE library.

#A forensic psychologist conducted a study to examine whether
#being hypnotized during recall affects how well a witness
#can remember facts about an event. Eight participants
#watched a short film of a mock robbery, after which
#each participant was questioned about what he or she had
#seen. The four participants in the experimental group
#were questioned while they were hypnotized. The four
#participants in the control group recieved the same
#questioning without hypnosis.

    t.test(correctq ~ group, data = indt_data)

#You can type in the numbers directly, or refer to the dataset,
#as shown below.

    g.ind.t(m1 = 17.75, m2 = 23, sd1 = 3.30,
           sd2 = 2.16, n1 = 4, n2 = 4, a = .05)

    g.ind.t(17.75, 23, 3.30, 2.16, 4, 4, .05)

    g.ind.t(mean(indt_data$correctq[indt_data$group == 1]),
            mean(indt_data$correctq[indt_data$group == 2]),
            sd(indt_data$correctq[indt_data$group == 1]),
            sd(indt_data$correctq[indt_data$group == 2]),
            length(indt_data$correctq[indt_data$group == 1]),
            length(indt_data$correctq[indt_data$group == 2]),
            .05)

#Contrary to the hypothesized result, the group that underwent hypnosis were
#significantly less accurate while reporting facts than the control group
#with a large effect size, t(6) = -2.66, p = .038, d_g = 1.64.