Applications in STATA (R), IBM (R) SPSS (R), SAS (R), R, & HLM (TM)
Specifically designed for instructors teaching multilevel modeling courses where students use a variety of software packages, this text offers detailed guidance on the software and uniquely focuses on introductory multilevel modeling. The authors take a hands-on, applications-focused approach, allowing students to learn multilevel modeling using the software of their choice and highlighting the different assumptions implicit in each software package.
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
This work presents neural network analysis to the social scientist without a background in computer science. Studies and examples illustrate the advantages of neural network analysis over other procedures in use amongst social scientists. Other features include: an introduction to the vocabulary and framework of neural networks; a history of ......
The techniques of analytic mapping and of geographic information systems (GIS) have become increasingly important tools for analyzing census, crime, environmental and consumer data. The authors of this significant 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, such as census products, TIGER files, and CD-ROM access. They 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 this book a useful guide to systems for storing, retrieving, analyzing, and displaying this information.
This work presents neural network analysis to the social scientist without a background in computer science. Studies and examples illustrate the advantages of neural network analysis over other procedures in use amongst social scientists. Other features include: an introduction to the vocabulary and framework of neural networks; a history of ......
