Generalizing the Regression Model

"This comprehensive text introduces regression, the general linear model, structural equation modeling, the hierarchical linear model, growth curve models, panel data, and event history models, and includes discussion of published implementations of eachtechnique showing how it was used to address substantive and interesting research questions. The text takes a step-by-step approach in the presentation of each topic, using mathematical derivations where necessary, but primarily emphasizing how the methods involved can be implemented, are used in addressing representative substantive problems than span a number of disciplines, and can be interpreted in words. Students often face the "What do I do now?" question. This book contains liberal advice about what to do in many situations. A recurring theme in the book is to emphasize the importance of using the "right" (better) model rather than simpler models that may often produce misleading results. Many examples start with the simple version, and proceed toshow how results change when a more appropriate approach is used. In summary, this text occupies a unique niche: multiple and very popular topics, with an emphasis on the choices, strengths and limits, and unique perspectives offered by each technique, relative to others. The book demonstrates the analyses in STATA and SAS"--
This comprehensive text introduces regression, the general linear model, structural equation modeling, the hierarchical linear model, growth curve models, panel data, and event history models, and includes discussion of published implementations of each technique showing how it was used to address substantive and interesting research questions. It takes a step-by-step approach in the presentation of each topic, using mathematical derivations where necessary, but primarily emphasizing how the methods involved can be implemented, are used in addressing representative substantive problems than span a number of disciplines, and can be interpreted in words. The book demonstrates the analyses in STATA and SAS. Generalizing the Regression Model provides students with a bridge from the classroom to actual research practice and application.