# g.ind.t: d-g Corrected for Independent t In MOTE: Effect Size and Confidence Interval Calculator

## Description

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

## Usage

 `1` ```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

## 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

 ``` 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 28 29 30 31 32 33 34``` ```#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. ```

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