g.ind.t: d-g Corrected for Independent t
In MOTE: Effect Size and Confidence Interval Calculator

Description Usage Arguments Details Value Examples

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

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

`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

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.

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

#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.