Week 3: ANOVAs Flashcards
what type of numbers do ANOVAs use?
ANOVAs use the F statistic
if variance between samples is small, F will be ______
small
if variance within samples is small, F will be ______
large
ANOVAs will compare
3 or more groups (= levels of 1 IV)
One-way ANOVA definition
one IV with 3+ levels: dry needling vs massage vs sham dry needling; separate groups for each intervention
One-way repeated measures ANOVA
one IV with 3+ levels: dry needling vs massage vs sham dry needling; everyone gets all interventions
Two-way ANOVA
two IVs: Dry needling vs sham dry needling AND stretching vs no stretching; separate groups for each intervention
Two-way repeated measures ANOVA
two IVs: Dry needling vs sham dry needling AND stretching vs no stretching; everyone gets ALL combination of IVs
Mixed Model ANOVA
Two IVs: dry needling vs sham dry needling AND time (pretest, 4-weeks post, 8-weeks post); two intervention groups, but all participants are measured at all points in time
Total Variance in a One-Way ANOVA is what two groups
between groups and within groups
between groups variance is
differences between means
within groups variance is
between subjects error variance
comparison of group means
ANOVA looks at distance of each group mean from the grand mean (total group)
Interpreting F statistic
“omnibus test”; overall; nonspecific; tell you a difference exists, but will not specify WHERE
Multiple comparison tests will tell you ________ the difference exists
WHERE the difference exists; if null is not rejected, no multiple comparison tests are needed
effect size =
how much the IV affected the DV
effect size indices for the ANOVA
eta squared (n^2) and Cohen’s f
small effect size: n^2
.01
medium effect size: n^2
.06
large effect size: n^2
.14
small effect size: f
.10
medium effect size: f
.25
large effect size: f
.40
designs for repeated measures has what characteristic
the same people in each level of the IV
simplest example of a repeated measures design is
one-way repeated measures design
true/false: subjects act as their own controls in designs for repeated measures
true
multiple comparison tests are used to
determine “where” difference is after using ANOVA; also called “pairwise comparisons”
what are the 2 different strategies for multiple comparison tests
post-hoc and planned comparisons
post-hoc info
- performed after ANOVA (only if significant)
- most common
- test every difference, therefore are “exploratory”
planned comparisons info
- performed instead of ANOVA (a priori)
- focused only on specific comparisons
- you won’t see this used very often
types of multiple comparison tests for independent groups
- Fisher’s least significant difference
- Duncan multiple range test
- Newman-Keuls Method
- Tukey’s honestly significant difference
- Bonferroni t-test
- Scheffé’s comparison
Fisher’s Least Significant Difference information
essentially unadjusted t-tests (LSD)
Tukey’s Honestly Significant Difference info
“middle of the road” in terms of risk and most commonly used
Bonferroni t-test info
simply divides alpha by # of comparisons (also called Bonferroni adjustment or correction
multiple comparison test for Repeated Measures
LSD, Sidak, Bonferroni correction
LSD definition
unadjusted paired t-tests
Sidak definition
adjusted, but good balance of type 1 and type 2 error protection; MOST COMMON
Bonferroni correction definition
divides alpha by # of comparison
We only need Multiple Comparisons IF there is a _______________ in the omnibus test
significant difference
