MOTE (version 1.0.2)

d.dep.t.rm: d for Repeated Measures with Average SD Denominator

Description

This function displays d and the non-central confidence interval for repeated measures data, using the average standard deviation of each level as the denominator, but controlling for r.

Usage

d.dep.t.rm(m1, m2, sd1, sd2, r, n, a = 0.05)

Arguments

m1

mean from first level

m2

mean from second level

sd1

standard deviation from first level

sd2

standard deviation from second level

r

correlation between first and second level

n

sample size

a

significance level

Value

Controls for correlation and provides the effect size (Cohen's d) with associated confidence intervals,m the confidence intervals associated with the means of each group,mstandard deviations and standard errors of the means for each group.

d

effect size

dlow

lower level confidence interval d value

dhigh

upper level confidence interval d value

M1

mean one

sd1

standard deviation of mean one

se1

standard error of mean 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

se2

standard error of mean two

M2low

lower level confidence interval of mean two

M2high

upper level confidence interval of mean two

r

correlation

n

sample size

df

degrees of freedom (sample size - 1)

estimate

the d statistic and confidence interval in APA style for markdown printing

Details

To calculate d, mean two is subtracted from mean one, which is divided by the average standard deviation, while mathematically controlling for the correlation coefficient (r).

d_rm = ((m1 - m2) / sqrt(( sd1^2 + sd2^2 ) - (2 x r x sd1 x sd2))) x sqrt(2 x (1-r))

Learn more on our example page.

Examples

# NOT RUN {
#The following example is derived from the "dept_data" dataset included
#in the MOTE library.

#In a study to test the effects of science fiction movies on people's
#belief in the supernatural, seven people completed a measure of belief
#in the supernatural before and after watching a popular science fiction
#movie. Higher scores indicated higher levels of belief.

    t.test(dept_data$before, dept_data$after, paired = TRUE)

    scifi_cor = cor(dept_data$before, dept_data$after, method = "pearson",
                use = "pairwise.complete.obs")

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

    d.dep.t.rm(m1 = 5.57, m2 = 4.43, sd1 = 1.99,
                sd2 = 2.88, r = .68, n = 7, a = .05)

    d.dep.t.rm(5.57, 4.43, 1.99, 2.88, .68, 7, .05)

    d.dep.t.rm(mean(dept_data$before), mean(dept_data$after),
                sd(dept_data$before), sd(dept_data$after),
                scifi_cor, length(dept_data$before), .05)

#The mean measure of belief on the pretest was 5.57, with a standard
#deviation of 1.99. The posttest scores appeared lower (M = 4.43, SD = 2.88)
#but the dependent t-test was not significant using alpha = .05,
#t(7) = 1.43, p = .203, d_rm = 0.43. The effect size was a medium effect suggesting
#that the movie may have influenced belief in the supernatural.

# }