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i have this following situation. I have a dataset of four measurement occasions. In the first three occasions the same test was introduced. This three timepoints are equidistant. The fourth data aqcuisition was some years later. The time distance to the fourth measurement occasion is much longer, and another test, though a similiar psychological construct, was conducted.

At first i tried to fit a model on the data of the first three measurement occasions. On theese, a linear model fits perfect to the data. To take this special fourth measure into account, i thought about using the information of the slope and intercept mean and variation (of the first three measurements) as a predictor for the fourth measurement occasion in a regression.

So i wrote a model: i s | y1@0 y2@.1 y3@.2;

4th_var ON i s;

But the model fit decreased drastically.

So i was wondering, if this is the false solution for this problem? Are there other, elegant solutions for this problem?

Thank you very much!

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  • $\begingroup$ Can you please clarify the question a bit? Do you want to predict the fourth measurement using the three first tests? Or do you want to use a repeated measures design and test the effect of some independent variable on the test scores at all four time points? $\endgroup$ – JonB Sep 22 '15 at 10:50
  • $\begingroup$ What do you mean by fitst perfectly? Do you mean that chi-square = 0? $\endgroup$ – Jeremy Miles Nov 14 '15 at 1:17
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I thinnk your syntax is correct, but I would change the intercept of your growth model to the third measurement:

i s | y1@-2 y2@-1 y3@0 ;

In this way the intercept at this 3th wave predicts the 4th measurement, which seems theoretically more sounds.

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