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.
eta.full.SS(dfm, dfe, ssm, sst, Fvalue, a = 0.05)degrees of freedom for the model/IV/between
degrees of freedom for the error/residual/within
sum of squares for the model/IV/between
sum of squares total
F statistic
significance level
Provides eta with associated confidence intervals and relevant statistics.
effect size
lower level confidence interval of eta
upper level confidence interval of eta
degrees of freedom for the model/IV/between
degrees of freedom for the error/resisual/within
F-statistic
p-value
the eta squared statistic and confidence interval in APA style for markdown printing
the F-statistic in APA style for markdown printing
Eta squared is calculated by dividing the sum of squares for the model by the sum of squares total.
eta^2 = ssm / sst
# NOT RUN {
#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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