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. ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================