d.dep.t.diff: d for Dependent t with SD Difference Scores Denominator
In MOTE: Effect Size and Confidence Interval Calculator

Description Usage Arguments Details Value Examples

This function displays d and the non-central confidence interval for repeated measures data, using the standard deviation of the difference score as the denominator.

1	d.dep.t.diff(mdiff, sddiff, n, a = 0.05)

`mdiff`	mean difference score
`sddiff`	standard deviation of the difference scores
`n`	sample size
`a`	significance level

To calculate d, the mean difference score is divided by divided by the standard deviation of the difference scores.

d_z = mdiff / sddiff

Learn more on our example page.

The effect size (Cohen's d) with associated confidence intervals, mean differences with associated confidence intervals, standard deviation of the differences, standard error, sample size, degrees of freedom, the t-statistic, and the p-value.

`d`	effect size
`dlow`	lower level confidence interval d value
`dhigh`	upper level confidence interval d value
`mdiff`	mean difference score
`Mlow`	lower level of confidence interval of the mean
`Mhigh`	upper level of confidence interval of the mean
`sddiff`	standard deviation of the difference scores
`n`	sample size
`df`	degrees of freedom (sample size - 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 "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. The mean difference score was 1.14,
#while the standard deviation of the difference scores was 2.12.

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

    d.dep.t.diff(mdiff = 1.14, sddiff = 2.12, n = 7, a = .05)

    d.dep.t.diff(1.14, 2.12, 7, .05)

    d.dep.t.diff(mdiff = mean(dept_data$before - dept_data$after),
                 sddiff = sd(dept_data$before - dept_data$after),
                 n = length(dept_data$before),
                 a = .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_z = 0.54. The effect size was a medium
#effect suggesting that the movie may have influenced belief
#in the supernatural.