Correlated data arise frequently in statistical analyses. This may be due to grouping of subjects, e.g., students within classrooms, or to repeated measurements on each subject over time or space, or to multiple related outcome measures at one point in time. Mixed model analysis provides a general, flexible approach in these situations, because it allows a wide variety of correlation patterns (or variance-covariance structures) to be explicitly modeled.
Ernesto, this is hard to answer, because it is not completely clear what applications interest you most. Although Raudenbush and Bryk is a classic, even the second edition is somewhat old now, 2002. I believe Snijders and Bosker has a more recent edition. For longitudinal modeling, Singer and Willett is quite popular and they have a multilevel book in the works, which is sure to be a hit, but I wouldn't expect it too soon, maybe next year. Books in this field go back at least to 1989 or so and they are quite a few. The advantage of more recent books is that they often contain examples of software code or have links to web pages with the software examples. Bob
One of the best books for mixed models (especially if you are using R) is the one by Andrew Gelman:
http://www.stat.columbia.edu/~gelman/arm/
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