[R-sig-ME] A question about multilevel models using the lmer package in R

Verena Hinze veren@@h|nze @end|ng |rom p@ych@ox@@c@uk
Tue Sep 13 16:46:59 CEST 2022


Dear mailing list at R-sig-mixed-models,

I am very interested in using multilevel growth analyses for my research.
I have come across a lot of very helpful tutorials on the internet, recommending the lmer package.
However, I have one question for which I didn't manage to find the answer yet, and I was wondering whether you might be able to point me in the right direction...

Specifically, I am interested in modelling three-level data (students nested within schools and within time). I am hoping to predict student outcomes by student-level and school-level predictors, and I have been using the lmer package in R to model the data.

However, in the model output, I have noticed that sometimes the variance (for the student- or school-level intercepts or slopes) increases instead of decreases, compared to the simpler model without the respective predictor. This is contrary to what we would expect from ordinary regression analyses, where we expect the variance to decrease, if we add predictors to the model that help us to explain such variance.

I was wondering what might be going on here? Have you encountered something similar before? And how could we evaluate the impact of a predictor on these student-/school-level intercepts and slopes in multi-level models instead?

Any advice would be highly appreciated.

With many thanks and kind regards,

Verena

Verena Hinze
Postdoctoral Research Fellow
Oxford Precision Psychiatry Lab
University of Oxford, Department of Psychiatry
Warneford Lane, Oxford, OX3 7JX ​
E: verena.hinze using psych.ox.ac.uk

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