Description Usage Arguments Details Value Examples

This function displays d-delta for between subjects data and the non-central confidence interval using the control group standard deviation as the denominator.

1 | ```
delta.ind.t(m1, m2, sd1, sd2, n1, n2, a = 0.05)
``` |

`m1` |
mean from control group |

`m2` |
mean from experimental group |

`sd1` |
standard deviation from control group |

`sd2` |
standard deviation from experimental group |

`n1` |
sample size from control group |

`n2` |
sample size from experimental group |

`a` |
significance level |

To calculate d-delta, the mean of the experimental group is subtracted from the mean of the control group, which is divided by the standard deviation of the control group.

d_delta = (m1 - m2) / sd1

Learn more on our example page.

Provides the effect size (Cohen's d) with associated confidence intervals, the t-statistic, the confidence intervals associated with the means of each group, as well as the standard deviations and standard errors of the means for each group.

`d` |
d-delta effect size |

`dlow` |
lower level confidence interval of d-delta value |

`dhigh` |
upper level confidence interval of d-delta value |

`M1` |
mean of group one |

`sd1` |
standard deviation of group one mean |

`se1` |
standard error of group one mean |

`M1low` |
lower level confidence interval of group one mean |

`M1high` |
upper level confidence interval of group one mean |

`M2` |
mean of group two |

`sd2` |
standard deviation of group two mean |

`se2` |
standard error of group two mean |

`M2low` |
lower level confidence interval of group two mean |

`M2high` |
upper level confidence interval of group two mean |

`spooled` |
pooled standard deviation |

`sepooled` |
pooled standard error |

`n1` |
sample size of group one |

`n2` |
sample size of group two |

`df` |
degrees of freedom (n1 - 1 + n2 - 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 |

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 35 | ```
#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.
hyp = t.test(correctq ~ group, data = indt_data)
#You can type in the numbers directly, or refer to the dataset,
#as shown below.
delta.ind.t(m1 = 17.75, m2 = 23,
sd1 = 3.30, sd2 = 2.16,
n1 = 4, n2 = 4, a = .05)
delta.ind.t(17.75, 23, 3.30, 2.16, 4, 4, .05)
delta.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_delta = 1.59.
``` |

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