MOTE (version 1.0.2)

delta.ind.t: 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

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

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.

Examples

# NOT RUN {
#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.

# }