week 9 Flashcards
independent group anova
ANOVA tests the difference between
more than two groups
what is multiple comparison bias
every time you carry out a t-test within the same set of data you increase chance of making type 1 error (inflating alpha)
f-ratio allows us to
determine if two variances are equal
if treatment and error are equal f-ratio will be
1
as treatment variance gets larger in relation to error variance what happens to f-ratio
it exceeds 1 (gets larger)
t/f: the larger the f-ratio, the more statistically significant effect
true
the f-ratio is an
omnibus test
characteristics of single-factor independent group ANOVA
- one independent variable
- one dependent variable
- subjects are randomly assigned
treatment variance is variability due to action of our
independent variable
assumptions of independent ANOVA
- independent random sampling
- normality
- homogenity of variance
anova is based on the assumption that
errors will be normally distributed
when is the grand mean equal to the average of group means
when group sizes are equal
what does no variability within groups suggest
no measurement error within the study
total df formula
N-1 (participants - 1)
treatment effect df formula
k-1 (number of groups -1)
error df formula
N-K or df total - df treatment
f distribution in an anova is
never split
eta square (n^2)
proportion of variance accounted for
how to conduct a pairwise comparison
post hoc test
tukeys HSD
post hoc test that computes multiple t-tests while controlling for the inflation of type 1 error that results from multiple comparissons
what do the letters in q(kv) represent
k= number of treatment groups
v= error DF
look up on table
what does anova allow for
Allows us to partition the total variability of our data into “explained variance” and “unexplained variance”
what kind of ratio is the test statistic anova
f-ratio
bonferroni correction
It is the idea that the inflation of type 1 error is only a problem if the experiment-wise alpha is larger than our tolerance for error
- adjusts the per-comparison alpha so that the “inflates alpha” is not acceptably high
- if you know how many comparisons your making you can just adjust the per-comparison alpha to a value that when multiplied by your number of calculations is not acceptably high
- this is not something we will use
