calculate_d | R Documentation |

`d_s`

for Between Subjects with Pooled SD DenominatorThis function displays d for two between subjects groups and gives the central and non-central confidence interval using the pooled standard deviation as the denominator.

```
calculate_d(
m1 = NULL,
m2 = NULL,
sd1 = NULL,
sd2 = NULL,
n1 = NULL,
n2 = NULL,
t = NULL,
model = NULL,
df = NULL,
x_col = NULL,
y_col = NULL,
d = NULL,
a = 0.05,
lower = TRUE
)
```

`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 |

`t` |
optional, calculate d from independent t, you must include n1 and n2 for degrees of freedom |

`model` |
optional, calculate d from t.test for independent t, you must still include n1 and n2 |

`df` |
optional dataframe that includes the x_col and y_col |

`x_col` |
name of the column that contains the factor levels OR a numeric vector of group 1 scores |

`y_col` |
name of the column that contains the dependent score OR a numeric vector of group 2 scores |

`d` |
a previously calculated d value from a study |

`a` |
significance level |

`lower` |
Use this to indicate if you want the lower or upper bound
of d for one sided confidence intervals. If d is positive, you generally
want |

To calculate `d_s`

, mean two is subtracted from mean one and divided
by the pooled standard deviation.

`d_s = \frac{M_1 - M_2}{S_{pooled}}`

You should provide one combination of the following:

1: m1 through n2

2: t, n1, n2

3: model, n1, n2

4: df, "x_col", "y_col"

5: x_col, y_col as numeric vectors

6: d, n1, n2

You must provide alpha and lower to ensure the right confidence interval is provided for you.

Provides the effect size (Cohen's *d*) with associated central and non-central 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. The one-tailed confidence interval is also included for sensitivity analyses.

`d` |
effect size |

`dlow` |
noncentral lower level confidence interval of d value |

`dhigh` |
noncentral upper level confidence interval of d value |

`dlow_central` |
central lower level confidence interval of d value |

`dhigh_central` |
central upper level confidence interval of d value |

`done_low` |
noncentral lower bound of one tailed confidence interval |

`done_low_central` |
central lower bound of one tailed confidence interval |

`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 |

```
calculate_d(m1 = 14.37, # neglect mean
sd1 = 10.716, # neglect sd
n1 = 71, # neglect n
m2 = 10.69, # none mean
sd2 = 8.219, # none sd
n2 = 3653, # none n
a = .05, # alpha/confidence interval
lower = TRUE) # lower or upper bound
```

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