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

## 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

 `1` ```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

## Details

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

d_z = mdiff / sddiff

## 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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```#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. ```

MOTE documentation built on May 2, 2019, 5:51 a.m.