eta.partial.SS {MOTE}

Partial Eta Squared for ANOVA from F and Sum of Squares

Description

This function displays partial 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.

Usage

eta.partial.SS(dfm, dfe, ssm, sse, Fvalue, a = 0.05)

Arguments

dfm

degrees of freedom for the model/IV/between

dfe

degrees of freedom for the error/residual/within

ssm

sum of squares for the model/IV/between

sse

sum of squares for the error/residual/within

Fvalue

F statistic

a

significance level

Details

Partial eta squared is calculated by dividing the sum of squares of the model by the sum of the sum of squares of the model and sum of squares of the error.

partial eta^2 = ssm / (ssm + sse)

Learn more on our example page.

Value

Provides partial eta squared with associated confidence intervals and relevant statistics.

eta

partial eta squared effect size

etalow

lower level confidence interval of partial eta squared

etahigh

upper level confidence interval of partial eta squared

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

Examples

#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.
eta.partial.SS(dfm = 4, dfe = 990,
               ssm = 338057.9, sse = 32833499,
               Fvalue = 2.548, a = .05)

#Here's an example for the interaction with code.
eta.partial.SS(dfm = anova_model$ANOVA$DFn[4],
               dfe = anova_model$ANOVA$DFd[4],
               ssm = anova_model$ANOVA$SSn[4],
               sse = anova_model$ANOVA$SSd[4],
               Fvalue =  anova_model$ANOVA$F[4],
               a = .05)