Multilevel Modeling: Applications in STATA (R), IBM (R) SPSS (R), SAS (R), R & HLM (TM) provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson's step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.

Hierarchical Linear Modeling provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original "how-to" application articles following a standardized instructional format. The Guide portion consists of five chapters that provide an overview of HLM, discussion of methodological assumptions, and parallel worked model examples in SPSS, SAS, and HLM software. The Applications portion consists of ten contributions in which authors provide step-by-step presentations of how HLM is implemented and reported for introductory to intermediate applications. "The book covers the three most widely accessible statistical programs for multilevel modeling rather than just focusing on one. . . . An excellent tool for researchers who are beginning to learn multilevel modeling, as well as a great resource for experienced researchers who want to learn a different statistical program for multilevel models." -Debbie L. Hahs-Vaughn, University of Central Florida "The intelligent use of the examples helps explain both the conceptual framework of HLM and its basic individual applications."-Luis L. Cabo, Mercyhurst College

Neural networks have influenced many areas of research but have only just started to be utilized in social science research. Neural Networks provides the first accessible introduction to this analysis as a powerful method for social scientists. It provides numerous studies and examples that illustrate the advantages of neural network analysis over other quantitative and modeling methods in wide-spread use among social scientists. The author, G. David Garson, presents the methods in an accessible style for the reader who does not have a background in computer science. Features include an introduction to the vocabulary and framework of neural networks, a concise history of neural network methods, a substantial review of the literature, detailed neural network applications in the social sciences, coverage of the most common alternative neural network models, methodological considerations in applying neural networks, examples using the two leading software packages for neural network analysis, and numerous illustrations and diagrams. This introductory guide to using neural networks in the social sciences will enable students, researchers, and professionals to utilize these important new methods in their research and analysis.

Neural networks have influenced many areas of research but have only just started to be utilized in social science research. Neural Networks provides the first accessible introduction to this analysis as a powerful method for social scientists. It provides numerous studies and examples that illustrate the advantages of neural network analysis over other quantitative and modeling methods in wide-spread use among social scientists. The author, G. David Garson, presents the methods in an accessible style for the reader who does not have a background in computer science. Features include an introduction to the vocabulary and framework of neural networks, a concise history of neural network methods, a substantial review of the literature, detailed neural network applications in the social sciences, coverage of the most common alternative neural network models, methodological considerations in applying neural networks, examples using the two leading software packages for neural network analysis, and numerous illustrations and diagrams. This introductory guide to using neural networks in the social sciences will enable students, researchers, and professionals to utilize these important new methods in their research and analysis.

Nearly 80% of the informational needs of local government policymakers are related to geographic location. As a result, the techniques of analytic mapping (the study of the dynamic diffusion and distribution of any variable across area and over time) and of geographic information systems (GIS) have become increasingly important tools for analyzing census, crime, environmental and consumer data. The authors of this significant little volume discuss data access, transformation and preparation issues, and how to select the appropriate analytic graphics techniques through a review of various GIS and common data sources: census products, TIGER files, and CD-ROM access. Garson and Biggs describe each procedure, review its assumptions and requirements, and provide illustrative output for sample data using selected software. Researchers and administrators who need to manage data of geographic locations will find Analytic Mapping and Geographic Databases a useful guide for systems storing, retrieving, analyzing, and displaying this information.