"Preface Multilevel modeling has become a mainstream data analysis tool over the past decade, now figuring prominently in a range of social and behavioral science disciplines. Where it originally required specialized software, mainstream statistics packages such as IBM SPSS, SAS, and Stata all have included routines for multilevel modeling in their programs. Although some devotees of these statistical packages have been making good use of the relatively new multilevel modeling functionality, progress has been slower in carefully documenting these routines to facilitate meaningful access to the average user. Two years ago we developed Multilevel and Longitudinal Modeling with IBM SPSS to demonstrate how to use these techniques in IBM SPSS Version 18. Our focus was on developing a set of concepts and programming skills within the IBM SPSS environment that could be used to develop, specify, and test a variety of multilevel models with continuous outcomes, since IBM SPSS is a standard analytic tool used in many graduate programs and organizations globally. Our intent was to help readers gain facility in using the IBM SPSS linear-mixed models routine for continuous outcomes. We offered multiple examples of several different types of multilevel models, focusing on how to set up each model and how to interpret the output. At the time, mixed modeling for categorical outcomes was not available in the IBM SPSS software program. Over the past year or so, however, the generalized linear mixed model (GLMM) has been added to the mixed modeling analytic routine in IBM SPSS starting with Version 19. This addition prompted us to create this companion workbook that would focus on introducing readers to the multilevel approach to modeling with categorical outcomes"--