Inproceedings,
Mixed-Up Mixed Models: Things That Look Like They Should Work But Don't, and Things That Look Like They Shouldn't Work But Do
page Paper 20-25. (2000)3885<br/>Mixed models.
Abstract
In recent years, the use of mixed models in fitting data in the biomedical sciences, social sciences, economics, and business, has become more widespread. Part of the increase in the use of such models, apart from their inherent utility, is because software to fit such models has become increasingly available. SAS InstituteÆs contribution to the mixed model software is PROC MIXED. However, because mixed models are more complex and more flexible than the general linear model, the potential for confusion and errors is higher. This paper outlines some confusion that may occur when data analysts experienced in the use PROC GLM to analyze data for both the fixed-effects and mixed-effects models use PROC MIXED to analyze data. Littell, Milliken, Stroup, and Wolfinger (1996) is a very good reference on mixed models in the context of using PROC MIXED, and Milliken and Johnson (1992, 1989, and in press) are good general references on experimental design, including mixed models.
