delta.ind.t: d-delta for Between Subjects with Control Group SD...
In MOTE: Effect Size and Confidence Interval Calculator

Description Usage Arguments Details Value Examples

This function displays d-delta for between subjects data and the non-central confidence interval using the control group standard deviation as the denominator.

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

`m1`	mean from control group
`m2`	mean from experimental group
`sd1`	standard deviation from control group
`sd2`	standard deviation from experimental group
`n1`	sample size from control group
`n2`	sample size from experimental group
`a`	significance level

To calculate d-delta, the mean of the experimental group is subtracted from the mean of the control group, which is divided by the standard deviation of the control group.

d_delta = (m1 - m2) / sd1

Learn more on our example page.

Provides the effect size (Cohen's d) with associated confidence intervals, the t-statistic, the confidence intervals associated with the means of each group, as well as the standard deviations and standard errors of the means for each group.

`d`	d-delta effect size
`dlow`	lower level confidence interval of d-delta value
`dhigh`	upper level confidence interval of d-delta value
`M1`	mean of group one
`sd1`	standard deviation of group one mean
`se1`	standard error of group one mean
`M1low`	lower level confidence interval of group one mean
`M1high`	upper level confidence interval of group one mean
`M2`	mean of group two
`sd2`	standard deviation of group two mean
`se2`	standard error of group two mean
`M2low`	lower level confidence interval of group two mean
`M2high`	upper level confidence interval of group two mean
`spooled`	pooled standard deviation
`sepooled`	pooled standard error
`n1`	sample size of group one
`n2`	sample size of group two
`df`	degrees of freedom (n1 - 1 + n2 - 1)
`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.

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

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

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

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

    delta.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_delta = 1.59.