Homogeneity of Variance. Pooling the variances doesn’t make sense when we cannot assume all of the sample Variances are estimating the same value. For two groups : Levene (1960): replace all of the individual scores with either then run a t-test. or. F - test.
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Pooling the variances doesn’t make sense when we cannot assume all of the sample
Variances are estimating the same value.
For two groups:
Levene (1960): replace all of the individual scores with either
then run a t-test
F - test
Given: 1. Random and independent samples
2. Both samples approach normal distributions
Then: F is distributed with (n-large-1) and (n-small-1) df.
Hartley: If the two maximally different variances are NOT significantly different,
Then it is reasonable to assume that all k variances are estimating the population variance.
The average differences between pairs will be less than the difference between the smallest
And the largest variance.
A and B are randomly selected pairs.
will NOT be distributed as a normal F.
(k groups, n-1) df
Table to test
Looking at the correlation between the variances (or standard deviations)
And the means or the squared means.
b) Use square root transformation
c) Use logarithmic transformation
d) Use reciprocal transformation