| eta.F | R Documentation |
This function displays eta squared from ANOVA analyses and their non-central confidence interval based on the F distribution. These values are calculated directly from F statistics and can be used for between subjects and repeated measures designs. Remember if you have two or more IVs, these values are partial eta squared.
eta.F(dfm, dfe, Fvalue, a = 0.05)
dfm |
degrees of freedom for the model/IV/between |
dfe |
degrees of freedom for the error/residual/within |
Fvalue |
F statistic |
a |
significance level |
Eta is calculated by multiplying the degrees of freedom of the model by the F-statistic. This is divided by the product of degrees of freedom of the model, the F-statistic, and the degrees of freedom for the error or residual.
eta^2 = (dfm * Fvalue) / (dfm * Fvalue + dfe)
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 |
#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.F(dfm = 2, dfe = 8,
Fvalue = 5.134, a = .05)