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Oct 23, 2009 7:59 AM
(914 views)

However, when I then go on and do LS means student's t-test to compare the different levels of my parameters in a pair wise way, I sometimes find that parameters which are not significant (higher than p=0.05) in the "main model" test give rise to significant effects in the students t-test and vice versa...(levels not connected by same letter)..? Moreover, there are no "pair wise" p-values just as for the 1-way ANOVA.

Shouldn't there be a way of getting these p-values just as for the 1-way ANOVA..

Is this because the students t-test uses a different algorithm, or am I mixing things up.

Cheers

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Oct 23, 2009 10:34 AM
(864 views)

> so on. As I understand it, the Prob>T given in the

> effect tests of the parameter estimates is identical

> to the p-value of the parameter test.

This is correct if your independent variable has 2 levels. It is not correct if the independent variable has more than 2 levels.

> However, when I then go on and do LS means student's

> t-test to compare the different levels of my

> parameters in a pair wise way, I sometimes find that

> parameters which are not significant (higher than

> p=0.05) in the "main model" test give rise to

> significant effects in the students t-test and vice

> versa...(levels not connected by same letter)..?

> Moreover, there are no "pair wise" p-values just as

> for the 1-way ANOVA.

> Shouldn't there be a way of getting these p-values

> just as for the 1-way ANOVA..

>

> Is this because the students t-test uses a different

> algorithm, or am I mixing things up.

Yes, t-tests use a different algorithm (and different measure of error, and different degrees of freedom) than the model test. Furthermore, the model test and the pairwise t-tests are not transitive ... that is, if you know the model test result, you cannot use this to determine the pairwise t-test result.

The technically correct method is to examine the tests in the ANOVA table, if one of those is statistically significant, only then could you look at predetermined t-tests. Of course, some people don't do it that way, and the world doesn't end.