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Dear Mlwin users,

Have you ever had the following issue?

The variance (estimate) of Level 2 increases when new Level 1 predictors are added in a regression equation (all as predictors of a Level 1 outcome).
I usually have this when all predictors in the equation are at Level 1.

I'm quite puzzled by this because I have read that this is mathematically impossible. This has as a result than when I calculate the Rsquare of the new model, this is a negative percentage. To calculate the Rsquare per model I use the following formula:
(Level 2 variance of null model – Level 2 variance of subsequent model) / Level 2 variance null model.

Thank you in advance!

Best,
Paris

Hi Paris,
This is a quite common phenomenon and easy to explain. Imagine for example growth curves between individuals measured yearly for 10 years. In a model without age we would have lots of variation within an individual as their heights at age 4 say will be very different from their heights at age 13 and in fact this will dominate the between individual variation. Now add age (a level 1 variable in) and we explain lots of the within individual variation and suddenly the between individual variation (level 2) appears.
Hope this helps,
Bill.

Thank you so much Billb!!

This is what im looking for!!

Keep up the good work!