not_used@COMCAST.NET replied: >I don't understand why some of the answers to this question imply that OLS >is invalid if the dependent variable is skewed or not continuous. OLS is >based on the distribution of errors for the correct linear model, so >everything is relative to the independent variables. Can't say much until >we >know about these variables. > >Also-- just because most of the survey answers are 5 6 or 7 does not make >the variable skewed-- could still be symmetric around 6 or even 5.5

I see Peter has already jumped all over oyu, so let me point out a couple things.

[1] You're right that it is the form of the errors that matters, not the form of the dependent variable. BUT when the dependent variable is a categorical variable with a small number of values, and the independent variables are also restricted in values, then the behavior of the error terms is fairly predictable.

[2] When the survey values go from 1 to 7, and 75% of the values are placed on 6 and 7, then that *does* mathematically force the data to be skewed. Plus the large point masses on '6' and '7' will tend to force the model into fairly specific forms for the error terms.

It used to be that *everyone* did OLS with data like this. But that was because they had to given the state of computers and computer programs, not because they ought to do it. Even after code became available, people did *not* want to change, and sub-optimal recommendations are still being made to trusting researchers. That's why people like me get to be such enormous pains in the, umm, eye. :-)

David -- David L. Cassell mathematical statistician Design Pathways 3115 NW Norwood Pl. Corvallis OR 97330

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