MOTE (version 1.0.2)

omega.F: Omega Squared for ANOVA from F

Description

This function displays omega squared from ANOVA analyses and its 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 omega squared.

Usage

omega.F(dfm, dfe, Fvalue, n, a = 0.05)

Arguments

dfm

degrees of freedom for the model/IV/between

dfe

degrees of freedom for the error/residual/within

Fvalue

F statistic

n

full sample size

a

significance level

Value

The effect size (Cohen's d) with associated confidence intervals and relevant statistics.

omega

omega statistic

omegalow

lower level confidence interval d value

omegahigh

upper level confidence interval d value

dfm

degrees of freedom for the model/IV/between

dfe

degrees of freedom for the error/residual/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

Details

Omega squared or partial omega squared is calculated by subtracting one from the F-statistic and multiplying it by degrees of freedom of the model. This is divided by the same value after adding the number of valid responses. This value will be omega squared for one-way ANOVA designs, and will be partial omega squared for multi-way ANOVA designs (i.e. with more than one IV).

omega^2 = (dfm * (Fvalue-1)) / ((dfm * (Fvalue-1)) + n)

Learn more on our example page.

Examples

# 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)

omega.F(dfm = 2, dfe = 8,
      Fvalue = 5.134, n = 11, a = .05)
# }