omega.full.SS {MOTE}

Omega Squared for One-Way and Multi-Way ANOVA from F

Description

This function displays omega 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 which error term you are using for the calculation.

Usage

omega.full.SS(dfm, dfe, msm, mse, sst, a = 0.05)

Arguments

dfm

degrees of freedom for the model/IV/between

dfe

degrees of freedom for the error/residual/within

msm

mean square for the model/IV/between

mse

mean square for the error/residual/within

sst

sum of squares total

a

significance level

Details

Omega squared is calculated by deducting the mean square of the error from the mean square of the model and multiplying by the degrees of freedom for the model. This is divided by the sum of the sum of squares total and the mean square of the error.

omega = (dfm * (msm - mse)) / (sst + mse)

Learn more on our example page.

Value

Provides omega squared with associated confidence intervals and relevant statistics.

omega

omega squared

omegalow

lower level confidence interval of omega

omegahigh

upper level confidence interval of omega

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 omega squared statistic and confidence interval in APA style for markdown printing

statistic

the F-statistic in APA style for markdown printing

Examples

#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)

omega.full.SS(dfm = 2, dfe = 8,
              msm = 12.621, mse = 2.548,
              sst = (25.54+19.67), a = .05)