Andy suggested the simplest thing to implement. I've done a couple of other

things, once or twice.

By the way, for the sake of generality, I want to mention that your example

does NOT show any strong trend of linearity across time, which is often an

artifact that confuses interpretations of "time-series" data. (Hmm, a program

designed for time series might offer desirable options for handling these data.)

You can use Aggregate by ID to collect sums, sums of the squares and cross-products;

and in another step, compute statistics by person from the formulas for r or b.

I would also look briefly at whether the variances vary a lot across person, since a

tiny variance for x or y for a person would yield an r or b with a huge error band.

(The number of scores above 90, using a 0-100 range, makes me wonder if you

have a "ceiling effect" which might be deserve some compensatory transformation.

But that wasn't your question.)

Also, for a single Within-Subject correlation matrix across all subjects --

If the number of IDs is not too many, you can use Discriminant Function, where

ID is used as "groups"; that matrix is among the options for output.

--

Rich Ulrich

Hi everyone,

I conducted a longitudinal study in which X and Y were measured once a day

for 10 consecutive days. As can be seen attached, my dataset is organized in

a "stacked" way so that I can conduct multilevel analyses. I was wondering

whether a syntax could be used to generate the "within-person" coefficients

for the association between X and Y ? For instance, for each participant

(ID), I would like to know the magnitude of the association between X and Y.

Is there a way to generate either a correlation coefficient (or any other

statistical estimate quantifying the strength of the X-Y association),

separately for each ID ? I'm not sure if this would be a correlation

coefficient or a beta.

Thanks in advance for your support.

O.

<

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