Intermediate Statistics: A Modern Approach - Tapa blanda

Reseña del editor

James Stevens’ best-selling text, Intermediate Statistics, is written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results.  SAS and SPSS are an integral part of each chapter.  Definitional formulas are used on small data sets to provide conceptual insight into what is being measured.

The assumptions underlying each analysis are emphasized and the reader is shown how to test the critical assumptions using SPSS or SAS.  Printouts with annotations from SAS or SPSS show how to process the data for each analysis.  The annotations highlight what the numbers mean and how to interpret the results.  Numerical, conceptual, and computer exercises enhance understanding.  Answers are provided for half of the exercises.

The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts.  Highlights of the Third Edition include:

• A new chapter on hierarchical linear modeling using HLM6
• A CD containing all of the book’s data sets
• New coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Chapter 6)
• More exercises in each chapter.

Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required.  An Instructor's Resource is available upon adoption.  See www.researchmethodsarena.com .

Reseña del editor

Following the format of his "Applied Multivariate Statistics for the Social Sciences", the author integrates two of the major statistical packages, SAS and SPSSX, in this text. The chapter on factorial ANOVA features discussions of the unequal cell size case and interpreting effects in three-way designs, and an extensive computer example of real data which integrates many of the concepts. In addition, there are chapters on covariance and repeated measures analysis. Features include: complete annotated SAS and SPSSX control lines for every analysis; selected annotated printout from at least one of the packages for every analysis; thorough discussions of assumptions, outliers, power analysis (including the use of SPSS MANOVA), and planned comparisons; and seven large real data sets (available on 3.5" and 5.25" disks).