MOTE (version 1.0.2)

d.dep.t.diff: d for Dependent t with SD Difference Scores Denominator

Description

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.

Usage

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

Arguments

mdiff

mean difference score

sddiff

standard deviation of the difference scores

n

sample size

a

significance level

Value

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

Details

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.

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. 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.

# }