g.ind.t: d-g Corrected for Independent t

g.ind.tR Documentation

d-g Corrected for Independent t

Description

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

Usage

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

Arguments

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

Details

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.

Value

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

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.

    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.


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