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