I don't have an answer, but I'm very glad this question was asked because
I'm having a similar problem. I have 14 grids, values from which are to be
used as the dependent variable in a regression. Each 6x6 grid consists of
36 observation points. Their are some fairly strong spatial correlations
among the values at each grid, so I certainly can't treat them as if they
were independent, yet reducing each grid to a single mean value (the other
extreme) seems like a foolish waste of power. I'm trying to figure out how
to use all of the observations, but also use the estimated spatial
autocorrelations to weight them in the regression. (The design was
originally created to answer a very different question, which is how I got
into this mess.)
I hope that there's a single answer to both of our questions.
Rich Strauss
At 10:54 AM 2/28/01 -0600, Michael M. Granaas wrote:
>
>I have a student coming in later to talk about a regression problem.
>Based on what he's told me so far he is going to be using predicting
>inter-response intervals to predict inter-stimulus intervals (or vice
>versa).
>
>What bothers me is that he will be collecting data from multiple trials
>for each subject and then treating the trials as independent replicates.
>That is, assuming 10 tials/S and 10 S he will act as if he has 100
>independent data points for calculating a bivariate regression.
>
>Obviously these are not independent data points.
>
>Is the non-independence likely to be severe enough to warrant concern?
>
>If yes, is there some method that will allow him to get the prediction
>equation he wants?
>
>Thanks
>
>Michael
========================
Dr Richard E Strauss
Biological Sciences
Texas Tech University
Lubbock TX 79409-3131
Email: [EMAIL PROTECTED] (formerly [EMAIL PROTECTED])
Phone: 806-742-2719
Fax: 806-742-2963
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