There is a claim that PROC MIXED does not handle weighting correctly in multilevel models: Using TIMSS to Analyze Correlates of Performance Variation in Mathematics available at: http://nces.ed.gov/pubs2001/200105.pdf (See Appendix D in particular for a comparison of results using their estimator and the PROC MIXED estimates.)

In the paper, the authors claim that PROC MIXED produces biased estimates of the fixed effects. They then go on to use a correction attributed to Pfeffermann et. al Pfeffermann, D., Skinner, C. J., Holmes D. J,and H. Goldstein, Rasbash, J., (1998). Weighting for Unequal Selection Probabilities in Multilevel Models. JRSS, Series B, 60, 123-40.

There is also some discussion about this on: http://www.cpc.unc.edu/restools/data_analysis/ml_sampling_weights

This seems to be the usual argument that ordinary statistical procedures are not suitable for survey data and possibly a need for a SURVEYMIXED procedure. However, survey procedures and non-survey procedures produce unbiased estimates for fixed effects (just the standard errors are at issue). Here, the claim is that PROC MIXED produces biased estimates with sampling weights. Any thoughts or any other evaluations using own data?