Description Usage Arguments Details Value Examples

This function displays eta squared from ANOVA analyses and its non-central confidence interval based on the F distribution. This formula works for one way and multi way designs with careful focus on the sum of squares total.

1 | ```
eta.full.SS(dfm, dfe, ssm, sst, Fvalue, a = 0.05)
``` |

`dfm` |
degrees of freedom for the model/IV/between |

`dfe` |
degrees of freedom for the error/residual/within |

`ssm` |
sum of squares for the model/IV/between |

`sst` |
sum of squares total |

`Fvalue` |
F statistic |

`a` |
significance level |

Eta squared is calculated by dividing the sum of squares for the model by the sum of squares total.

eta^2 = ssm / sst

Learn more on our example page.

Provides eta with associated confidence intervals and relevant statistics.

`eta` |
effect size |

`etalow` |
lower level confidence interval of eta |

`etahigh` |
upper level confidence interval of eta |

`dfm` |
degrees of freedom for the model/IV/between |

`dfe` |
degrees of freedom for the error/resisual/within |

`F` |
F-statistic |

`p` |
p-value |

`estimate` |
the eta squared statistic and confidence interval in APA style for markdown printing |

`statistic` |
the F-statistic in APA style for markdown printing |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
#The following example is derived from the "bn1_data" dataset, included
#in the MOTE library.
#A health psychologist recorded the number of close inter-personal
#attachments of 45-year-olds who were in excellent, fair, or poor
#health. People in the Excellent Health group had 4, 3, 2, and 3
#close attachments; people in the Fair Health group had 3, 5,
#and 8 close attachments; and people in the Poor Health group
#had 3, 1, 0, and 2 close attachments.
anova_model = lm(formula = friends ~ group, data = bn1_data)
summary.aov(anova_model)
eta.full.SS(dfm = 2, dfe = 8, ssm = 25.24,
sst = (25.24+19.67), Fvalue = 5.134, a = .05)
``` |

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