MOTE (version 1.0.2)

omega.partial.SS.bn: Partial Omega Squared for Between Subjects 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 is appropriate for multi-way between subjects designs.

Usage

omega.partial.SS.bn(dfm, dfe, msm, mse, ssm, n, 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

ssm

sum of squares for the model/IV/between

n

total sample size

a

significance level

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

Details

Partial omega squared is calculated by subtracting the mean square for the error from the mean square of the model, which is multiplied by degrees of freedom of the model. This is divided by the product of the degrees of freedom for the model are deducted from the sample size, multiplied by the mean square of the error, plus the sum of squares for the model.

omega^2 <- (dfm * (msm - mse)) / (ssm + (n-dfm)*mse)

Learn more on our example page.

Examples

# NOT RUN {
#The following example is derived from the "bn2_data" dataset, included
#in the MOTE library.

#Is there a difference in atheletic spending budget for different sports?
#Does that spending interact with the change in coaching staff? This data includes
#(fake) atheletic budgets for baseball, basketball, football, soccer, and volleyball teams
#with new and old coaches to determine if there are differences in
#spending across coaches and sports.

library(ez)
bn2_data$partno = 1:nrow(bn2_data)
anova_model = ezANOVA(data = bn2_data,
                      dv = money,
                      wid = partno,
                      between = .(coach, type),
                      detailed = TRUE,
                      type = 3)

#You would calculate one eta for each F-statistic.
#Here's an example for the interaction with typing in numbers.
omega.partial.SS.bn(dfm = 4, dfe = 990,
                    msm = 338057.9 / 4,
                    mse = 32833499 / 990,
                    ssm = 338057.9,
                    n = 1000, a = .05)

#Here's an example for the interaction with code.
omega.partial.SS.bn(dfm = anova_model$ANOVA$DFn[4],
                    dfe = anova_model$ANOVA$DFd[4],
                    msm = anova_model$ANOVA$SSn[4] / anova_model$ANOVA$DFn[4],
                    mse = anova_model$ANOVA$SSd[4] / anova_model$ANOVA$DFd[4],
                    ssm = anova_model$ANOVA$SSn[4],
                    n = nrow(bn2_data),
                    a = .05)
# }