It isn't actually that easy, in the sense that
most data humans make up has a low efficiency with
respect to design criteria -- the determinant of
the cross-product matrix tends to be small. The
simplest way is to use a computer program that
calculates algorithmic designs.
jim clark wrote:
>
> Hi
>
> I like to use small, artificially generated data sets with
> integer parameters to introduce analyses. Often, however, I find
> it difficult to avoid undesirable contingencies among the scores
> (e.g., linear dependencies in within-subject designs). Is there
> an algorithmic way to generate such scores and avoid such
> dependencies? Here is a small example with 4 scores for each of
> 5 subjects. The following analysis reveals the undesirable
> linear dependencies. I'm assuming the dependencies arise from
> the noise vectors that I used to generate the cell scores by
> adding them to the main effect of the factor and the subject
> effects. Is there a systematic way to create such noise vectors
> to avoid linear dependencies?
<snip>
--
Bob Wheeler --- (Reply to: [EMAIL PROTECTED])
ECHIP, Inc.
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