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Books on longitudinal analysis
Here are six books on longitudinal data, all of which I like; the order is not significant. I've added a few comments
- Hedeker and Gibbons (2006). Longitudinal Data Analysis. A very practical book, and, to my mind, one of the clearest on the topic. It starts with very simple methods (t-tests, ANOVA) and builds to the models available in PROC MIXED and NLMIXED.
- Singer and Willet (2003). Applied Longitudinal Data Analysis. Another very clear and practical book
- Verbeke and Molenberghs (2001). Linear Mixed Models for Longitudinal Data.
- Molenberghs and Verbeke (2005). Models for Discrete Longitudinal Data.
These two are more technical than the others in the list - for those who want more of the math behind the methods. Both use SAS, and have some relatively clear discussion of REPEATED vs RANDOM (however, I've nowhere seen a REALLY clear explanation of this).
- Collins and Sayer (2001). New methods for the analysis of change. Covers a much wider range of techniques than the other books on this list. Broad, rather than deep, but if you need something outside the usual models, this is a place to start; it emphasizes psychology and related fields.
- Littell, Milliken and Stroup (2006). SAS for Mixed Models (2nd edition). Only a portion of this book is on longitudinal data, but it is unsurpassed in its coverage of SAS, including chapters on NLMIXED and GLIMMIX, the latter written by Oliver Schabenberger, who developed GLIMMIX at SAS. If you started from learning these models as 'mixed' models rather than 'multilevel' models, this book may be a good fit.