Homogeneity of Variance

1
Homogeneity of Variance
Pooling the variances doesnt 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
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.
Null Hypothesis Alternate Hypothesis
2
K independent groups
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.
Thus
will NOT be distributed as a normal F.
(k groups, n-1) df
Then, use
Table to test
Null Hypothesis Alternate Hypothesis
3
Data Transformation When Homogeneity of Variance
is violated
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