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This book provides a clear explanation of mixed modeling techniques, introducing their application through the analyses of real datasets and presenting each example using the most commonly used software packages - R, SAS, SPSS, HLM, and STATA. The authors describe common research designs and corresponding data structures for which mixed models analysis is an appropriate statistical tool, and they include detailed descriptions on how to set up datasets for these types of analyses. The treatment also includes real-world datasets that contain common problems, such as missing data, that must be addressed using mixed models. A supporting Web site provides software code and the datasets discussed in the book.

Linear models (Statistics) --- Data processing. --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 519.2 --- 681.3*G3 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Data processing --- Methoden en technieken --- statistiek. --- Linear models (Statistics) - Data processing --- Statistiek.