Week 12 Chapter 12 Flashcards
ΣSSinside each treatment (inside each treatment is a subscript)
formula for within-treatments sum of squares
SStotal - SSwithin
formula for between-treatments sum of squares
ΣX^2 - (G^2/N)
formula for total sum of squares
N - 1
formula for total degrees of freedom
Σ(n - 1) = Σdfin each treatment
formula for within-treatments degrees of freedom
k - 1
formula for between-treatments degrees of freedom
analysis of variance (ANOVA)
hypothesis-testing procedure used to evaluate mean differences between two or more treatments or populations
factor
variable that designates the groups being compared
level
individual condition or value that makes up a variable
two-factor design or a factorial design
study that combines two variables
single-factor design
study that has only one independent variable
testwise alpha level
risk of a Type I error for an individual hypothesis test
experimentwise alpha level
total probability of a Type I error accumulated from all individual tests in the experiment
between-treatments variance
measure of how much difference exists between treatment conditions
treatment effect
cause of differences between treatments
within-treatments variance
measure of how much difference exists inside each treatment condition
F-ratio
comparison between how much difference exists versus how big the differences are between treatment conditions
error term
measure of the variance caused by random, unsystematic differences
total
entire set of scores
within-treatments
term referring to differences that exist inside the individual conditions
between-treatments
term referring to differences from one condition to another
mean square
average of the squared deviations
distribution of F-ratios
all possible F values that can be obtained when the null hypothesis is true
ANOVA summary table
diagram showing the source of variability
eta squared
percentage of variance accounted for by the treatment effect in published reports of ANOVA results
post hoc test
additional hypothesis test done after an ANOVA to determine whether mean differences are significant
pairwise comparison
comparison of individual treatments two at a time
Tukey’s HSD test
computation of single value that determines the minimum difference between treatment means necessary for significance
Scheffé test
method using an F-ratio to evaluate the significance of the difference between two treatment conditions
