delta.ind.t: d-delta for Between Subjects with Control Group SD...

In doomlab/MOTE: Effect Size and Confidence Interval Calculator

d-delta for Between Subjects with Control Group SD Denominator

Description

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

Usage

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

Arguments

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

Details

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.

Value

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

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.

    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.

doomlab/MOTE documentation built on April 17, 2022, 2:08 a.m.