MOTE (version 1.0.2)

ges.partial.SS.rm: Partial Generalized Eta-Squared for ANOVA from F

Description

This function displays partial ges squared from ANOVA analyses and its non-central confidence interval based on the F distribution. This formula works for multi-way repeated measures designs.

Usage

ges.partial.SS.rm(dfm, dfe, ssm, sss, sse1, sse2, sse3, 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

sss

sum of squares subject variance

sse1

sum of squares for the error/residual/within for the first IV

sse2

sum of squares for the error/residual/within for the second IV

sse3

sum of squares for the error/residual/within for the interaction

Fvalue

F statistic

a

significance level

Value

Partial generalized eta-squared (GES) with associated confidence intervals and relevant statistics.

ges

effect size

geslow

lower level confidence interval for ges

geshigh

upper level confidence interval for ges

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 generalized eta squared statistic and confidence interval in APA style for markdown printing

statistic

the F-statistic in APA style for markdown printing

Details

To calculate partial generalized eta squared, first, the sum of squares of the model, sum of squares of the subject variance, sum of squares for the first and second independent variables, and the sum of squares for the interaction are added together. The sum of squares of the model is divided by this value.

partial ges <- ssm / (ssm + sss + sse1 + sse2 + sse3)

Learn more on our example page.

Examples

# NOT RUN {
#The following example is derived from the "rm2_data" dataset, included
#in the MOTE library.

#In this experiment people were given word pairs to rate based on
#their "relatedness". How many people out of a 100 would put LOST-FOUND
#together? Participants were given pairs of words and asked to rate them
#on how often they thought 100 people would give the second word if shown
#the first word.  The strength of the word pairs was manipulated through
#the actual rating (forward strength: FSG) and the strength of the reverse
#rating (backward strength: BSG). Is there an interaction between FSG and
#BSG when participants are estimating the relation between word pairs?

library(ez)
library(reshape)
long_mix = melt(rm2_data, id = c("subject", "group"))
long_mix$FSG = c(rep("Low-FSG", nrow(rm2_data)),
                 rep("High-FSG", nrow(rm2_data)),
                 rep("Low-FSG", nrow(rm2_data)),
                 rep("High-FSG", nrow(rm2_data)))
long_mix$BSG = c(rep("Low-BSG", nrow(rm2_data)*2),
                 rep("High-BSG", nrow(rm2_data)*2))

anova_model = ezANOVA(data = long_mix,
                      dv = value,
                      wid = subject,
                      within = .(FSG, BSG),
                      detailed = TRUE,
                      type = 3)

#You would calculate one partial GES value for each F-statistic.
#Here's an example for the interaction with typing in numbers.
ges.partial.SS.rm(dfm = 1, dfe = 157,
                  ssm = 2442.948, sss = 76988.13,
                  sse1 = 5402.567, sse2 = 8318.75, sse3 = 6074.417,
                  Fvalue = 70.9927, a = .05)

#Here's an example for the interaction with code.
ges.partial.SS.rm(dfm = anova_model$ANOVA$DFn[4],
                  dfe = anova_model$ANOVA$DFd[4],
                  ssm = anova_model$ANOVA$SSn[4],
                  sss = anova_model$ANOVA$SSd[1],
                  sse1 = anova_model$ANOVA$SSd[4],
                  sse2 = anova_model$ANOVA$SSd[2],
                  sse3 = anova_model$ANOVA$SSd[3],
                  Fvalue = anova_model$ANOVA$F[4],
                  a = .05)
# }